Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 33) Show all publications
Kleyko, D., Osipov, E. & Wiklund, U. (2019). A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing. IEEE Access, 7, 34403-34415
Open this publication in new window or tab >>A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 34403-34415Article in journal (Refereed) Published
Abstract [en]

Objective: Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB where autonomic function cannot be evaluated. This study presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using principles of hyperdimensional computing. Methods: Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via classification of the similarity indices into three groups. Results: The distance-based methods achieved good separation of signals into classes with different degree of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Conclusions: Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. Significance: The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of autonomic function is problematic or even impossible, thus, increasing the correctness of the conventional DB scores.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Deep breathing test, Deep breathing index, similarity analysis, heart rate variability, hyperdimensional computing
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-73196 (URN)10.1109/ACCESS.2019.2904311 (DOI)000463487400001 ()
Note

Validerad;Nivå 2;2019-04-17 (oliekm)

Available from: 2019-03-14 Created: 2019-03-14 Last updated: 2019-04-17Bibliographically approved
Lyamin, N., Kleyko, D., Quentin, D. & Vinel, A. (2019). Real-time jamming DoS detection in safety-critical V2V C-ITS using data mining. IEEE Communications Letters, 23(3), 442-445
Open this publication in new window or tab >>Real-time jamming DoS detection in safety-critical V2V C-ITS using data mining
2019 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 23, no 3, p. 442-445Article in journal (Refereed) Published
Abstract [en]

A data mining-based method for real-time detection of radio jamming Denial-of-Service (DoS) attacks in IEEE 802.11p vehicle-to-vehicle (V2V) communications is proposed. The method aims at understanding the reasons for losses of periodic cooperative awareness messages (CAM) exchanged by vehicles in a platoon. Detection relies on a knowledge of IEEE 802.11p protocols rules as well as on historical observation of events in the V2V channel. In comparison to the state-of-theart method, the proposed method allows operating under the realistic assumption of random jitter accompanying every CAM transmission. The method is evaluated for two jamming models: random and ON-OFF.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
C-ITS, VANET, jamming, Denial-of-Service attack, security, platooning, data mining
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-73069 (URN)10.1109/LCOMM.2019.2894767 (DOI)000461240300014 ()2-s2.0-85062972118 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-04-04 (svasva)

Available from: 2019-02-28 Created: 2019-02-28 Last updated: 2019-04-12Bibliographically approved
Frady, E. P., Kleyko, D. & Sommer, F. T. (2018). A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks. Neural Computation, 30(6), 1449-1513
Open this publication in new window or tab >>A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks
2018 (English)In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 30, no 6, p. 1449-1513Article in journal (Refereed) Published
Abstract [en]

To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA) and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy, and cross-talk noise limits the retrieval accuracy and information capacity. A novel theory to optimize memory performance in such networks is presented and compared with simulation experiments. The theory describes linear readout of analog data and readout with winner-take-all error correction of symbolic data as proposed in VSA models. We find that diverse VSA models from the literature have universal performance properties, which are superior to what previous analyses predicted. Further, we propose novel VSA models with the statistically optimal Wiener filter in the readout that exhibit much higher information capacity, in particular for storing analog data. The theory we present also applies to memory buffers, networks with gradual forgetting, which can operate on infinite data streams without memory overflow. Interestingly, we find that different forgetting mechanisms, such as attenuating recurrent weights or neural nonlinearities, produce very similar behavior if the forgetting time constants are aligned. Such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size. These results enable the design of new types of VSA models for the online processing of data streams.

Place, publisher, year, edition, pages
MIT Press, 2018
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-68365 (URN)10.1162/neco_a_01084 (DOI)000432863200001 ()29652585 (PubMedID)2-s2.0-85047470315 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-06-07 (andbra)

Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-06-08Bibliographically approved
Lyamin, N., Kleyko, D., Delooz, Q. & Vinel, A. (2018). AI-Based Malicious Network Traffic Detection in VANETs. IEEE Network, 32(6), 15-21
Open this publication in new window or tab >>AI-Based Malicious Network Traffic Detection in VANETs
2018 (English)In: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 32, no 6, p. 15-21Article in journal (Refereed) Published
Abstract [en]

Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Vehicle safety, Telecommunication traffic, Road traffic, Wireless communication, Networked control systems, Real-time systems, Vehicular ad hoc networks, Intelligent vehicles, Artificial intelligence, Cams, Jamming
National Category
Communication Systems Telecommunications Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-71965 (URN)10.1109/MNET.2018.1800074 (DOI)000451962400004 ()2-s2.0-85057959135 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-01-09 (marisr)

Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2019-01-09Bibliographically approved
Kleyko, D., Rahimi, A., Rachkovskij, D. A., Osipov, E. & Rabaey, J. M. (2018). Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 5880-5898
Open this publication in new window or tab >>Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics
Show others...
2018 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 12, p. 5880-5898Article in journal (Refereed) Published
Abstract [en]

Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HD representations when applied to pattern recognition tasks. Particular design choices include density of representations and strategies for mapping data from the original representation. It is demonstrated that for the considered pattern recognition tasks (using synthetic and real-world data) both sparse and dense representations behave nearly identically. This paper also discusses implementation peculiarities which may favor one type of representations over the other. Finally, the capacity of representations of various densities is discussed.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-68400 (URN)10.1109/TNNLS.2018.2814400 (DOI)000451230100008 ()29993669 (PubMedID)2-s2.0-85045214003 (Scopus ID)
Funder
Swedish Research Council, 2015- 04677
Note

Validerad;2018;Nivå 2;2018-12-05 (svasva)

Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2019-03-04Bibliographically approved
Kleyko, D., Osipov, E., Papakonstantinou, N. & Vyatkin, V. (2018). Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant. IEEE Access, 6, 30766-30777
Open this publication in new window or tab >>Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 30766-30777Article in journal (Refereed) Published
Abstract [en]

This paper presents an approach for distributed fault isolation in a generic system of systems. The proposed approach is based on the principles of hyperdimensional computing. In particular, the recently proposed method called Holographic Graph Neuron is used. We present a distributed version of Holographic Graph Neuron and evaluate its performance on the problem of fault isolation in a complex power plant model. Compared to conventional machine learning methods applied in the context of the same scenario the proposed approach shows comparable performance while being distributed and requiring simple binary operations, which allow for a fast and efficient implementation in hardware.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Computer Systems Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-69034 (URN)10.1109/ACCESS.2018.2840128 (DOI)000437220700001 ()2-s2.0-85047613488 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-08-06 (rokbeg)

Available from: 2018-06-01 Created: 2018-06-01 Last updated: 2018-08-06Bibliographically approved
Kleyko, D. (2018). Vector Symbolic Architectures and their applications: Computing with random vectors in a hyperdimensional space. (Doctoral dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Vector Symbolic Architectures and their applications: Computing with random vectors in a hyperdimensional space
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Vektor symboliska Arkitekturer och deras tillämpningar : Beräkning med slumpmässiga vektorer i ett hyperdimensionellt utrymme
Abstract [en]

The main focus of this thesis lies in a rather narrow subfield of Artificial Intelligence. As any beloved child, it has many names. The most common ones are Vector Symbolic Architectures and Hyperdimensional Computing. Vector Symbolic Architectures are a family of bio-inspired methods of representing and manipulating concepts and their meanings in a high-dimensional space (hence Hyperdimensional Computing). Information in Vector Symbolic Architectures is evenly distributed across representational units, therefore, it is said that they operate with distributed representations. Representational units can be of different nature, however, the thesis concentrates on the case when units have either binary or integer values. 

This thesis includes eleven scientific papers and extends the research area in three directions: theory of Vector Symbolic Architectures, their applications for pattern recognition, and unification of Vector Symbolic Architectures with other neural-like computational approaches. 

Previously, Vector Symbolic Architectures have been used mainly in the area of cognitive computing for representing and reasoning upon semantically bound information, for example, for analogy-based reasoning. This thesis significantly extends the applicability of Vector Symbolic Architectures to an area of pattern recognition. Pattern recognition is the area constantly enlarging its theoretical and practical horizons. Applications of pattern recognition and machine learning can be found in many areas of the present day world including health-care, robotics, manufacturing, economics, automation, transportation, etc. Despite the success in many domains pattern recognition algorithms are still far from being close to their biological vis-a-vis – the brain. In particular, one of the challenges is a large amount of training data required by conventional machine learning algorithms. Therefore, it is important to look for new possibilities in the area via exploring biologically inspired approaches.

All application scenarios, which are considered in the thesis, contribute to the development of the global strategy of creating an information society. Specifically, such important applications as biomedical signal processing, automation systems, and text processing were considered. All applications scenarios used novel methods of mapping data to Vector Symbolic Architectures proposed in the thesis.

In the domain of biomedical signal processing, Vector Symbolic Architectures were applied for three tasks: classification of a modality of medical images, gesture recognition, and assessment of synchronization of cardiovascular signals. In the domain of automation systems, Vector Symbolic Architectures were used for a data-driven fault isolation. In the domain of text processing, Vector Symbolic Architectures were used to search for the longest common substring and to recognize permuted words.

The theoretical contributions of the thesis come in four aspects. First, the thesis proposes several methods for mapping data from its original representation into a distributed representation suitable for further manipulations by Vector Symbolic Architectures. These methods can be used for one-shot learning of patterns of generic sensor stimuli. Second, the thesis presents the analysis of an informational capacity of Vector Symbolic Architectures in the case of binary distributed representations. Third, it is shown how to represent finite state automata using Vector Symbolic Architectures. Fourth, the thesis describes the approach of combining Vector Symbolic Architectures and a cellular automaton.

Finally, the thesis presents the results of unification of two computational approaches with Vector Symbolic Architectures. This is one of the most interesting cross-disciplinary contributions of the thesis. First, it is shown that Bloom Filters – an important data structure for an approximate membership query task – can be treated in terms of Vector Symbolic Architectures. It allows generalizing the process of building the filter. Second, Vector Symbolic Architectures and Echo State Networks (a special kind of recurrent neural networks) were combined together. It is possible to implement Echo State Networks using only integer values in network’s units and much simpler operation for a recurrency operation while preserving the entire dynamics of the network. It results in a simpler architecture with lower requirements on memory and operations. 

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-68338 (URN)978-91-7790-110-5 (ISBN)978-91-7790-111-2 (ISBN)
Public defence
2018-06-11, A109, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2015-04677
Available from: 2018-04-16 Created: 2018-04-13 Last updated: 2018-05-31Bibliographically approved
Osipov, E., Kleyko, D. & Papakonstantinou, N. (2017). Approximate Sensing with Vector Symbolic Architectures: The case of fault isolation in distributed automation systems. In: Proceedings: EWSN ’17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks. Paper presented at 2017 International Conference on Embedded Wireless Systems and Networks Uppsala, Sweden, February 20 - 22, 2017 (pp. 224-225). New york: ACM Digital Library
Open this publication in new window or tab >>Approximate Sensing with Vector Symbolic Architectures: The case of fault isolation in distributed automation systems
2017 (English)In: Proceedings: EWSN ’17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, New york: ACM Digital Library, 2017, p. 224-225Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Due to the stochastic and imprecise nature of sensory data, the current (exact computational) algorithms for their processing introduce unnecessary computational overhead. One of the major trends in the development of computation al elements for processing of sensory data is low-power imprecise electronics and accompanying algorithmic solutions for approximate computing. This poster introduces the usage of hyper-dimensional computing and vector-symbolic architectures in the context of wireless embedded systems. A problem of fault isolation in a distributed automation system is considered as a showcase. The poster presents the performance of the associative sensing approach as well as challenges associated with the design of communication techniques and network protocols for exchanging of VSA information.

Place, publisher, year, edition, pages
New york: ACM Digital Library, 2017
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-64649 (URN)978-0-9949886-1-4 (ISBN)
Conference
2017 International Conference on Embedded Wireless Systems and Networks Uppsala, Sweden, February 20 - 22, 2017
Available from: 2017-06-29 Created: 2017-06-29 Last updated: 2018-03-28Bibliographically approved
Osipov, E., Kleyko, D. & Legalov, A. (2017). Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing. In: Proceedings IECON 2017: 43rd Annual Conference of the IEEE Industrial Electronics Society. Paper presented at 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Bejing, China, 29 October - 1 November 2017 (pp. 3276-3281). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing
2017 (English)In: Proceedings IECON 2017: 43rd Annual Conference of the IEEE Industrial Electronics Society, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3276-3281Conference paper, Published paper (Refereed)
Abstract [en]

The main contribution of this paper is a study of the applicability of hyperdimensional computing and learning with an associative memory for modeling the dynamics of complex automation systems. Specifically, the problem of learning an evidence-based model of a plant in a distributed automation and control system is considered. The model is learned in the form a finite state automata. 

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE Industrial Electronics Society, ISSN 1553-572X
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-66594 (URN)10.1109/IECON.2017.8216554 (DOI)000427164803042 ()2-s2.0-85046676612 (Scopus ID)9781538611272 (ISBN)
Conference
43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Bejing, China, 29 October - 1 November 2017
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2018-05-21Bibliographically approved
Rahimi, A., Datta, S., Kleyko, D., Frady, E. P., Olshausen, B., Kanerva, P. & Rabaey, J. M. (2017). High-Dimensional Computing as a Nanoscalable Paradigm. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 64(9), 2508-2521
Open this publication in new window or tab >>High-Dimensional Computing as a Nanoscalable Paradigm
Show others...
2017 (English)In: IEEE Transactions on Circuits and Systems Part 1: Regular Papers, ISSN 1549-8328, E-ISSN 1558-0806, Vol. 64, no 9, p. 2508-2521Article in journal (Refereed) Published
Abstract [en]

We outline a model of computing with high-dimensional (HD) vectors—where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neural nets/deep learning, and complements them with ideas from probability theory, statistics, and abstract algebra. Key properties of HD computing include a well-defined set of arithmetic operations on vectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operation, making it possible to develop efficient algorithms for large-scale real-world tasks. We present a 2-D architecture and demonstrate its functionality with examples from text analysis, pattern recognition, and biosignal processing, while achieving high levels of classification accuracy (close to or above conventional machine-learning methods), energy efficiency, and robustness with simple algorithms that learn fast. HD computing is ideally suited for 3-D nanometer circuit technology, vastly increasing circuit density and energy efficiency, and paving a way to systems capable of advanced cognitive tasks.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Alternative computing, bio-inspired computing, hyperdimensional computing, vector symbolic architectures, in-memory computing
National Category
Computer Systems Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-63783 (URN)10.1109/TCSI.2017.2705051 (DOI)000409058000026 ()2-s2.0-85020453052 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-31 (rokbeg)

Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6032-6155

Search in DiVA

Show all publications