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  • 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.
    Balasubramaniam, Sasitharan
    et al.
    Tampere University of Technology.
    Lyamin, Nikita
    Halmstad University.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Skurnik, Mikael
    University of Helsinki.
    Vinel, Alexey
    Halmstad University.
    Koucheryavy, Yevgeni
    Tampere University of Technology.
    Exploiting bacterial properties for multi-­‐hop nanonetworks2014In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 52, no 7, p. 184-191Article in journal (Refereed)
    Abstract [en]

    Molecular communication is a relatively new communication paradigm for nanomachines where the communication is realized by utilizing existing biological components found in nature. In recent years, researchers have proposed using bacteria to realize molecular communication because the bacteria have, (i) the ability to swim and migrate between locations, (ii) the ability to carry DNA contents (i.e. plasmids), which could be utilized for information storage, and (iii) the ability to interact and transfer plasmids to other bacteria (one of this process is known as bacterial conjugation). However, current proposals for bacterial nanonetworks have not considered the internal structures of the nanomachines that can facilitate the use of bacteria as an information carrier. This article presents the types and functionalities of nanomachines that can be utilized in bacterial nanonetworks. A particular focus is placed on the bacterial conjugation and its support for multi-hop communication between nanomachines. Simulations of the communication process have also been evaluated, to analyze the quantity of bit received as well as the delay performances. Wet lab experiments have also been conducted to validate the bacterial conjugation process. The article also discusses potential applications of bacterial nanonetworks for cancer monitoring and therapy.

  • 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.
    Frady, E. Paxon
    et al.
    Redwood Center for Theoretical Neuroscience, University of California, Berkeley.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sommer, Friedrich T.
    Redwood Center for Theoretical Neuroscience, University of California, Berkeley.
    A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks2018In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 30, no 6, p. 1449-1513Article in journal (Refereed)
    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.

  • 5.
    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. 

  • 6.
    Karvonen, Niklas
    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.
    A Domain Knowledge-Based Solution for Human Activity Recognition: The UJA Dataset Analysis2018In: The 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018): Punta Cana, Dominican Republic | 4–7 December 2018 / [ed] José Bravo,Oresti Baños, MDPI, 2018, article id 1261Conference paper (Refereed)
    Abstract [en]

    Detecting activities of daily living (ADL) allows for rich inference about user behavior, which can be of use in the care of for example, elderly people, chronic diseases, and psychological conditions. This paper proposes a domain knowledge-based solution for detecting 24 different ADLs in the UJA dataset. The solution is inspired by a Finite State Machine and performs activity recognition unobtrusively using only binary sensors. Each day in the dataset is segmented into: morning, day, evening in order to facilitate the inference from the sensors. The model performs the ADL recognition in two steps. The first step is to detect the sequence of activities in a given event stream of binary sensors, and the second step is to assign a starting and ending times for each of detected activities. Our proposed model achieved an accuracy of 81.3% using only a very small amount of operations, making it an interesting approach for resource-constrained devices that are common in smart environments. It should be noted, however, that the model can end up in faulty states which could cause a series of mis-classifications before the model is returned to the true state.

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  • 7.
    Karvonen, Niklas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Nilsson, Joakim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jimenez, Lara Lorna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Low-Power Classification using FPGA: An Approach based on Cellular Automata, Neural Networks, and Hyperdimensional Computing2019In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) / [ed] M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem (Jim) Seliya, IEEE, 2019, p. 370-375Conference paper (Other academic)
    Abstract [en]

    Field-Programmable Gate Arrays (FPGA) are hardware components that hold several desirable properties for wearable and Internet of Things (IoT) devices. They offer hardware implementations of algorithms using parallel computing, which can be used to increase battery life or achieve short response-times. Further, they are re-programmable and can be made small, power-efficient and inexpensive. In this paper we propose a classifier targeted specifically for implementation on FPGAs by using principles from hyperdimensional computing and cellular automata. The proposed algorithm is shown to perform on par with Naive Bayes for two benchmark datasets while also being robust to noise. It is also synthesized to a commercially available off-the-shelf FPGA reaching over 57.1 million classifications per second for a 3-class problem using 40 input features of 8 bits each. The results in this paper show that the proposed classifier could be a viable option for applications demanding low power-consumption, fast real-time responses, or a robustness against post-training noise.

  • 8.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Pattern Recognition with Vector Symbolic Architectures2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Pattern recognition is an 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 some success in many domains pattern recognition algorithms are still far from being close to their biological vis-a-vis – human brain. New possibilities in the area of pattern recognition may be achieved by application of biologically inspired approaches. This thesis presents the usage of a bio-inspired method of representing concepts and their meaning – Vector Symbolic Architectures – in the context of pattern recognition with possible applications in intelligent transportation systems, automation systems, and language processing. Vector Symbolic Architectures is an approach for encoding and manipulating distributed representations of information. They have previously been used mainly in the area of cognitive computing for representing and reasoning upon semantically bound information. First, it is shown that Vector Symbolic Architectures are capable of pattern classification of temporal patterns. With this approach, it is possible to represent, learn and subsequently classify vehicles using measurements from vibration sensors.Next, an architecture called Holographic Graph Neuron for one-shot learning of patterns of generic sensor stimuli is proposed. The architecture is based on implementing the Hierarchical Graph Neuron approach using Vector Symbolic Architectures. Holographic Graph Neuron shows the previously reported performance characteristics of Hierarchical Graph Neuron while maintaining the simplicity of its design. The Holographic Graph Neuron architecture is applied in two domains: fault detection and longest common substrings search. In the area of fault detection the architecture showed superior performance compared to classical methods of artificial intelligence while featuring zero configuration and simple operations. The application of the architecture for longest common substrings search showed its ability to robustly solve the task given that the length of a common substring is longer than 4% of the longest pattern. Furthermore, the required number of operations on binary vectors is equal to the suffix trees approach, which is the fastest traditional algorithm for this problem. In summary, the work presented in this thesis extends understanding of the performance proprieties of distributed representations and opens the way for new applications.

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  • 9.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vector Symbolic Architectures and their applications: Computing with random vectors in a hyperdimensional space2018Doctoral thesis, comprehensive summary (Other academic)
    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. 

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  • 10.
    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.

  • 11.
    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%.

  • 12.
    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.

  • 13.
    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.

  • 14.
    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.

  • 15.
    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.

  • 16.
    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.

  • 17.
    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.

  • 18.
    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.

  • 19.
    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.

  • 20.
    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.

  • 21.
    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.

  • 22.
    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. 

  • 23.
    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.
    Gayler, Ross W.
    Independent Researcher, Melbourne, VIC, Australia.
    Recognizing Permuted Words with Vector Symbolic Architectures: A Cambridge Test for Machines2016In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 88, p. 169-175Article in journal (Refereed)
    Abstract [en]

    This paper proposes a simple encoding scheme for words using principles of Vector Symbolic Architectures. The proposed encoding allows finding a valid word in the dictionary for a given permuted word (represented using the proposed approach) using only a single operation - calculation of Hamming distance to the distributed representations of valid words in the dictionary. The proposed encoding scheme can be used as an additional processing mechanism for models of word embedding, which also form vectors to represent the meanings of words, in order to match the distorted words in the text to the valid words in the dictionary.

  • 24.
    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.
    Gayler, Ross W.
    La Trobe University.
    Khan, Asad I.
    Monash University, Melbourne, VIC, Clayton School of Information Technology, CSIT, Monash University.
    Dyer, Adrian G.
    Media and Communication School, Royal Melbourne Institute of Technology.
    Imitation of honey bees’ concept learning processes using Vector Symbolic Architectures2015In: Biologically Inspired Cognitive Architectures, ISSN 2212-683X, E-ISSN 2212-6848, Vol. 14, p. 57-72Article in journal (Refereed)
    Abstract [en]

    This article presents a proof-of-concept validation of the use of Vector Symbolic Architectures as central component of an online learning architectures. It is demonstrated that Vector Symbolic Architectures enable the structured combination of features/relations that have been detected by a perceptual circuitry and allow such relations to be applied to novel structures without requiring the massive training needed for classical neural networks that depend on trainable connections.The system is showcased through the functional imitation of concept learning in honey bees. Data from real-world experiments with honey bees (Avarguès-Weber et al., 2012) are used for benchmarking. It is demonstrated that the proposed pipeline features a similar learning curve and accuracy of generalization to that observed for the living bees. The main claim of this article is that there is a class of simple artificial systems that reproduce the learning behaviors of certain living organisms without requiring the implementation of computationally intensive cognitive architectures. Consequently, it is possible in some cases to implement rather advanced cognitive behavior using simple techniques.

  • 25.
    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.
    Papakonstantinou, Nikolaos
    VTT Technical Research Center of Finland.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Finland.
    Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 30766-30777Article in journal (Refereed)
    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.

  • 26.
    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.
    Papakonstantinou, Nikolaos
    VTT Technical Research Centre of Finland, Espoo.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mousavi, Arash
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Fault Detection in the Hyperspace: Towards Intelligent Automation Systems2015In: IEEE International Conference on Industrial Informatics: INDIN 2015, Cambridge, UK, July 22-24, 2015. Proceedings, Piscataway, NJ: IEEE Communications Society, 2015, p. 1219-1224, article id 7281909Conference paper (Refereed)
    Abstract [en]

    This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection the proposed methodology shows an unprecedented performance, while featuring zero configuration and simple operations.

  • 27.
    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.
    Patil, Sandeep
    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.
    Pang, Zhibo
    ABB AB, Corporate research.
    On Methodology of Implementing Distributed Function Block Applications using TinyOS WSN nodes2014In: Proceedings of 2014 IEEE 19th International Conference on Emerging Technologies & Factory Automation (ETFA 2014): Barcelona, Spain, 16-19 Sept. 2014, Piscataway, NJ: IEEE Communications Society, 2014, article id 7005107Conference paper (Refereed)
    Abstract [en]

    This paper presents a feasibility study of implementing parts of a distributed function block application as TinyOS modules running on Wireless Sensors as a part of Wireless Sensor Network. The paper first briefly describes underlying technologies and gives motivation for implementation of function blocks in TinyOS. The paper then presents implementation details about TinyOS realization of the one of the function block, which is a part of bigger distributed control application with the help of distributed function block application.

  • 28.
    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.
    Rachkovskij, Dmitri A.
    International Research and Training Center for Information Technologies and Systems.
    Modification of Holographic Graph Neuron using Sparse Distributed Representations2016In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 88, p. 39-45Article in journal (Refereed)
    Abstract [en]

    This article presents a modification of the recently proposed Holographic Graph Neuron approach for memorizing patterns of generic sensor stimuli. The original approach represents patterns as dense binary vectors, where zeros and ones are equiprobable. The presented modification employs sparse binary distributed representations where the number of ones is less than zeros. Sparse representations are more biologically plausible because activities of real neuronsare sparse. Performance was studied comparing approaches for different sizes of dimensionality.

  • 29.
    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.
    Senior, Alexander
    Monash University, Melbourne, VIC.
    Khan, Asad
    Monash University, Melbourne, VIC.
    Sekercioglu, Ahmet
    Monash University, Melbourne, VIC.
    Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing2017In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 28, no 6, p. 1250-1262Article in journal (Refereed)
    Abstract [en]

    This article proposes the use of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The adoption of a Vector Symbolic representation ensures a one-layered design for the approach, while maintaining the previously reported properties and performance characteristics of Hierarchical Graph Neuron, and also improving the noise resistance of the architecture. The proposed architecture enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern.

  • 30.
    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.
    Wiklund, Urban
    Department of Radiation Sciences, Biomedical Engineering, Umeå University, Umeå, Sweden.
    A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 20172020In: Biomedical Engineering & Physics Express, E-ISSN 2057-1976, Vol. 6, no 2, article id 025010Article in journal (Refereed)
    Abstract [en]

    Objective: The 2017 PhysioNet/CinC Challenge focused on automatic classification of atrial fibrillation (AF) in short ECGs. This study aimed to evaluate the use of the data and results from the challenge for detection of AF in longer ECGs, taken from three other PhysioNet datasets. Approach: The used data-driven models were based on features extracted from ECG recordings, calculated according to three solutions from the challenge. A Random Forest classifier was trained with the data from the challenge. The performance was evaluated on all non-overlapping 30 s segments in all recordings from three MIT-BIH datasets. Fifty-six models were trained using different feature sets, both before and after applying three feature reduction techniques. Main Results: Based on rhythm annotations, the AF proportion was 0.00 in the MIT-BIH Normal Sinus Rhythm (N = 46083 segments), 0.10 in the MIT-BIH Arrhythmia (N = 2880), and 0.41 in the MIT-BIH Atrial Fibrillation (N = 28104) dataset. For the best performing model, the corresponding detected proportions of AF were 0.00, 0.11 and 0.36 using all features, and 0.01, 0.10 and 0.38 when using the 15 best performing features. Significance: The results obtained on the MIT-BIH datasets indicate that the training data and solutions from the 2017 Physionet/Cinc Challenge can be useful tools for developing robust AF detectors also in longer ECG recordings, even when using a low number of carefully selected features. The use of feature selection allows significantly reducing the number of features while preserving the classification performance, which can be important when building low-complexity AF classifiers on ECG devices with constrained computational and energy resources.

  • 31.
    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.
    Wiklund, Urban
    Department of Radiation Sciences, Biomedical Engineering, Umeå University, Umeå, Sweden.
    A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 34403-34415Article in journal (Refereed)
    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.

  • 32.
    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.
    Wiklund, Urban
    Umeå University, Umeå, Sweden.
    Vector-Based Analysis of the Similarity Between Breathing and Heart Rate During Paced Deep Breathing2018In: Computing in Cardiology 2018: Proceedings / [ed] Christine Pickett; Cristiana Corsi; Pablo Laguna; Rob MacLeod, IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    The heart rate (HR) response to paced deep breathing (DB) is a common test of autonomic function, where the scoring is based on indices reflecting the overall heart rate variability (HRV), where high scores are considered as normal findings but can also reflect arrhythmias. This study presents a method based on hyperdimensional computing for assessment of the similarity between feature vectors derived from the HR and breathing signals. The proposed method was used to identify subjects where HR did not follow the paced breathing pattern in recordings from DB tests in 174 healthy subjects and 135 patients with cardiac autonomic neuropathy. Subjects were classified in 4 similarity classes, where the lowest similiarity class included 35 patients and 3 controls. In general, the autonomic function cannot be evaluated in subjects in the lowest similarity class if they also present with high HRV scores, since this combination is a strong indicator of the presence of arrhythmias. Thus, the proposed vector-based similarity analysis is one tool to identify subjects with high HRV but low cardiorespiratory synchronization during the DB test, which falsely can be interpreted as normal autonomic function.

  • 33.
    Kleyko, Denis
    et al.
    University of California at Berkeley Berkeley CA; Research Institutes of Sweden.
    Rachkovskij, Dmitri
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. International Research and Training Center for Information Technologies, Ukraine.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rahimi, Abbas
    IBM Research Zurich, Zurich, Switzerland.
    A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges2023In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 55, no 9, article id 175Article in journal (Refereed)
    Abstract [en]

    This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [321, 326] is an influential HDC/VSA model that is well known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field.Part I of this survey [222] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain; however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.

  • 34.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rahimi, Abbas
    ETH Zurich, Zurich, Switzerland.
    Gayler, Ross W.
    Melbourne, Australia.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Autoscaling Bloom filter: controlling trade-off between true and false positives2020In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 32, no 8, p. 3675-3684Article in journal (Refereed)
    Abstract [en]

    A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called “autoscaling Bloom filters”, which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate.

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  • 35.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rahimi, Abbas
    University of California at Berkeley, Berkeley.
    Rachkovskij, Dmitri A.
    International Research and Training, Center for Information Technologies and Systems, Kiev, Ukraine.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rabaey, Jan M.
    University of California at Berkeley, Berkeley.
    Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics2018In: 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)
    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.

  • 36.
    Krasheninnikov, Pavel V.
    et al.
    Siberian State University of Telecommunications and Information Sciences, Novosibirsk, Russia.
    Melent’ev, Oleg G.
    Siberian State University of Telecommunications and Information Sciences, Novosibirsk, Russia.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Shapin, Alexey
    Ericsson Research, Luleå, Sweden.
    Parameter Estimation for the Resulting Logical Channel Formed by Minimizing Channel Switching2019In: Automation and remote control, ISSN 0005-1179, E-ISSN 1608-3032, Vol. 80, no 2, p. 278-285Article in journal (Refereed)
    Abstract [en]

    We propose a method for calculating the parameters of the resulting discrete channel for secondary users in cognitive radio systems formed by the algorithm for minimizing channel switchings. The availability of channel slots is defined by a simple Markov chain. We obtain mathematical expressions for determining the transition probabilities of a graph reduced to two states for any number of primary channels.

  • 37.
    Lyamin, Nikita
    et al.
    Halmstad University, Sweden.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Delooz, Quentin
    University of Liège, Belgium.
    Vinel, Alexey
    Halmstad University, Sweden.
    AI-Based Malicious Network Traffic Detection in VANETs2018In: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 32, no 6, p. 15-21Article in journal (Refereed)
    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.

  • 38.
    Lyamin, Nikita
    et al.
    Halmstad University, Sweden.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Quentin, Delooz
    Technische Hochschule Ingolstadt, Germany and Halmstad University, Sweden.
    Vinel, Alexey
    Halmstad University, Sweden.
    Real-time jamming DoS detection in safety-critical V2V C-ITS using data mining2019In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 23, no 3, p. 442-445Article in journal (Refereed)
    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.

  • 39.
    Melentyev, Oleg
    et al.
    Siberian State University of Telecommunications and Information Sciences.
    Kleyko, Denis
    Siberian State University of Telecommunications and Information Sciences.
    Computing the parameters of the discrete channel resulting under frequency hopping in the general case2013In: Automation and remote control, ISSN 0005-1179, E-ISSN 1608-3032, Vol. 74, no 7, p. 1128-1131Article in journal (Refereed)
    Abstract [en]

    We derive expressions for computing the parameters of the resulting discrete channel formed by frequency hopping between an arbitrary number of original channels defined by simple Markov chains for any hopping slot lengths. In obtaining the expressions, we aggregate the graph that described the hopping process. The expressions define transition probabilities of the graph defined by a Markov chain reduced to two states.

  • 40.
    Osipov, Evgeny
    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.
    Legalov, Alexander
    Siberian Federal University.
    Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing2017In: 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 (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. 

  • 41.
    Osipov, Evgeny
    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.
    Papakonstantinou, Nikolaos
    VTT Technical Research Centre of Finland.
    Approximate Sensing with Vector Symbolic Architectures: The case of fault isolation in distributed automation systems2017In: 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 (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.

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  • 42.
    Osipov, Evgeny
    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.
    Shapin, Alexey
    Siberian State University of Telecommunications and Information Sciences.
    An Approach for Self-Adaptive Path Loss Modelling for Positioning in Underground Environments2016In: International Journal of Antennas and Propagation, ISSN 1687-5869, E-ISSN 1687-5877, Vol. 2016, article id 3424768Article in journal (Refereed)
    Abstract [en]

    This paper proposes a real-time self-adaptive approach for accurate path loss estimation in underground mines or tunnels based on signal strength measurements from heterogeneous radio communication technologies. The proposed model features simplicity of implementation. The methodology is validated in simulations and verified by measurements taken in real environments. The proposed method leverages accuracy of positioning matching the existing approaches while requiring smaller engineering efforts.

  • 43.
    Osipov, Evgeny
    et al.
    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.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lyamin, Nikita
    Siberian State University of Telecommunications and Information Sciences.
    Packet-less medium access approach for dependable wireless event passing in highly noisy environments2012Report (Other academic)
    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 constant delay in deployments exposing below 0 dB signal-to-noise ratio figures.

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    FULLTEXT01
  • 44.
    Rahimi, Abbas
    et al.
    Department of Electrical Engineering and Computer Sciences, University of California at Berkeley.
    Datta, Sohum
    Department of Electrical Engineering and Computer Sciences, University of California at Berkeley .
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Frady, Edward Paxon
    Helen Wills Neuroscience Institute, University of California at Berkeley.
    Olshausen, Bruno
    Helen Wills Neuroscience Institute, University of California at Berkeley.
    Kanerva, Pentti
    Helen Wills Neuroscience Institute, University of California at Berkeley.
    Rabaey, Jan M.
    Department of Electrical Engineering and Computer Sciences, University of California at Berkeley.
    High-Dimensional Computing as a Nanoscalable Paradigm2017In: 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)
    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.

  • 45.
    Rutqvist, David
    et al.
    BnearlIT AB.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Blomstedt, Fredrik
    BnearlIT AB.
    An Automated Machine Learning Approach for Smart Waste Management Systems2019In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 1, p. 384-392Article in journal (Refereed)
    Abstract [en]

    This paper presents the use of automated machine learning for solving a practical problem of a real-life Smart Waste Management system. In particular, the focus of the paper is on the problem of detection (i.e., binary classification) of emptying of a recycling container using sensor measurements. Numerous data-driven methods for solving the problem are investigated in a realistic setting where most of the events are not actual emptying. The investigated methods include the existing manually engineered model and its modification as well as conventional machines learning algorithms. The use of machine learning allows improving the classification accuracy and recall of the existing manually engineered model from $86.8\%$ and $47.9\%$ to $99.1\%$ and $98.2\%$ , respectively, when using the best performing solution. This solution uses a Random Forest classifier on a set of features based on the filling level at different given time spans. Finally, compared to the baseline existing manually engineered model, the best performing solution also improves the quality of forecasts for emptying time of recycling containers.

  • 46.
    Shapin, Alexey G.
    et al.
    Ericsson Research, Luleå, Sweden.
    Kleyko, Denis V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Krasheninnikov, Pavel V.
    SibSUTIS, Novosibirsk, Russia .
    Melentyev, Oleg G.
    SibSUTIS, Novosibirsk, Russia.
    An Algorithm for the Exact Packet Error Probability Calculation for Viterbi Decoding2018In: 2018 14th International Scientific Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE – 2018) – Proceedings: [Труды XIV международной научнотехнической конференцииактуальные проблемыэлектронного приборостроения (АПЭП – 2018)], IEEE, 2018, p. 282-287Conference paper (Refereed)
    Abstract [en]

    The performance of the Viterbi decoding algorithm in a binary symmetric channel is usually estimated by a lower or an upper bounds of a bit error probability. Nowadays, there are expressions for the exact bit error probability estimation for several trivial convolutional codes. However, traffic in modern systems is packetized. Thus, packet error probability is also an important performance metric. Besides, the existed methods could not be used for the task of packet error probability estimation. Therefore this paper presents an algorithm for the calculation of the exact packet error probability for the Viterbi decoding. The algorithm is based on a recurrent search of the cases, which lead to an error free-decoding and then cumulative probabilities of these cases are calculated.

  • 47.
    Shapin, Alexey G.
    et al.
    Ericsson Research, Luleå, Sweden.
    Kleyko, Denis V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Melentyev, Oleg G.
    SibSUTIS, Novosibirsk, Russia .
    Performance Peculiarities of Viterbi Decoder in Mathworks Simulink, GNU Radio and Other Systems with Likewise Implementation2018In: 2018 14th International Scientific Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE – 2018) – Proceedings: [Труды XIV международной научнотехнической конференцииактуальные проблемыэлектронного приборостроения (АПЭП – 2018)], IEEE, 2018, p. 288-292Conference paper (Refereed)
    Abstract [en]

    The performance of convolutional codes decoding by the Viterbi algorithm should not depend on the particular distribution of zeros and ones in the input messages, as they are linear. However, it was identified that specific implementations of Add-Compare-Select unit for the Viterbi Algorithm demonstrate the decoding performance that depends on proportion of elements in the input message. It is conjectured that the modern commercial hard- and software defined communication equipment may also feature similar implementation and as such their decoding performance could also vary.

  • 48.
    Shapin, Alexey
    et al.
    Siberian State University of Telecommunications and Information Sciences.
    Kleyko, Denis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lyamin, Nikita
    Halmstad University, Siberian State University of Telecommunications and Information Sciences.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Melentyev, Oleg
    Siberian State University of Telecommunications and Information Sciences.
    Performance Peculiarities of Viterbi Decoder in Mathworks Simulink, GNU Radio and Other Systems with Likewise Implementation2016Report (Other academic)
    Abstract [en]

    The performance of convolutional codes decoding by the Viterbi algorithm should not depend on the particular distribution of zeros and ones in the input messages, as they are linear. However, it was identified that specific implementations of Add-Compare-Select unit for the Viterbi Algorithm demonstrate the decoding performance that depends on proportion of elements in the input message. It is conjectured that the modern commercial hard- and software defined communication equipment may also feature similar implementation and as such their decoding performance could also vary.

  • 49.
    Wedekind, Daniel
    et al.
    Institute of Biomedical Engineering, TU Dresden, Dresden Germany .
    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.
    Malberg, Hagen
    Institute of Biomedical Engineering, TU Dresden, Dresden Germany .
    Zaunseder, Sebastian
    Institute of Biomedical Engineering, TU Dresden, Dresden Germany .
    Wiklund, Urban
    Department of Biomedical Engineering and Informatics, Umea Universitet.
    Robust Methods for Automated Selection of Cardiac Signals after Blind Source Separation2018In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 65, no 10, p. 2248-2258Article in journal (Refereed)
    Abstract [en]

    Objective: Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal Blind Source Separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e. the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. Methods: Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat component. Results: The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. Conclusions: Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia. Significance: The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.

  • 50.
    Wedekind, Daniel
    et al.
    Institute of Biomedical Engineering, TU Dresden.
    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.
    Malberg, Hagen
    Institute of Biomedical Engineering, TU Dresden.
    Zaunseder, Sebastian
    Institute of Biomedical Engineering, TU Dresden.
    Wiklund, Urban
    Department of Biomedical Engineering & Informatics, Umea University, Umea, Sweden.
    Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation2016In: Computing in cardiology, ISSN 2325-8861, E-ISSN 2325-887X, Vol. 43, p. 785-788, article id 7868860Article in journal (Refereed)
    Abstract [en]

    Wearable sensor technology like textile electrodes provides novel ambulatory health monitoring solutions but most often goes along with low signal quality. Blind Source Separation (BSS) is capable of extracting the Electrocardiogram (ECG) out of heavily distorted multichannel recordings. However, permutation indeterminacy has to be solved, i.e. the automated selection of the desired BSS output. Accordingly, we exploit the sparsity of the ECG modeled as a spike train of successive heartbeats. A binary code derived from a two-item dictionary fpeak, no peakg and physiological a-priori information temporally represents every BSS output component. The (best) ECG component is automatically selected based on a modified Hamming distance comparing the components’ code with the expected code behavior. Non-standard ECG recordings from ten healthy subjects performing common motions while wearing a sensor garment were subsequently processed in 10 s segments with spatio-temporal BSS. Our sparsity-based selection RCODE achieved 98.1% heart beat detection accuracy (ACC) by selecting a single component each after BSS. Traditional component selection based on higher-order statistics (e.g. skewness) achieved only 67.6% ACC.

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