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  • 1.
    Eriksson, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Change Point Detection with Applications to Wireless Sensor Networks2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis work we develop a new algorithm for detecting joint changes in statistical behavior of multiple, simultaneously recorded, signals. Such signal analysis is commonly known as multivariate change point (CP) detection (CPD) and is of interest in many scientific and engineering applications.

    First we review some of the existing CPD algorithms, where special attention is given to the Bayesian methods. Traditionally, many of the previous works on Bayesian CPD have focused on sampling based methods using Markov Chain Monte Carlo (MCMC). More recent work has shown that it is possible to avoid the computationally expensive MCMC methods by using a technique that is reminiscent of the forward-backward algorithm used for hidden Markov models. We revisit that technique and extend it to a multivariate CPD scenario where subsets of the monitored signals are affected at each CP. The extended algorithm has excellent CPD accuracy, but unfortunately, this fully Bayesian approach quickly becomes intractable when the size of the data set increases.

    For large data sets, we propose a two-stage algorithm which, instead of considering all possible combinations of joint CPs as in the fully Bayesian approach, only computes an approximate solution to the most likely combination. In the first stage, the time series are processed in parallel with a univariate CPD algorithm. In the second stage, a dynamic program (DP) is used to search for the combination of joint CPs that best explains the CPs detected by the first stage. The computational efficiency of the second stage is improved by incorporating a pruning condition which reduces the search space of the DP. 

    To motivate the algorithm, we apply it to measurements of radio channels in factory environments. The analysis shows that certain subsets of radio channels often experiences simultaneous changes in channel gain.

    In addition, a detailed statistical study of the radio channel measurements is presented, including empirical evidence that radio channels exhibit statistical dependencies over long time horizons which implies that it is possible to design predictors of future channel conditions.

    List of papers
    1. On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories
    Open this publication in new window or tab >>On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories
    2016 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 3078-3091Article in journal (Refereed) Published
    Abstract [en]

    The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments.

    Place, publisher, year, edition, pages
    Uppsala: , 2016
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-366290 (URN)10.1109/TCOMM.2016.2563431 (DOI)
    Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-28Bibliographically approved
    2. Computationally Efficient Off-Line Joint Change Point Detection in Multiple Time Series
    Open this publication in new window or tab >>Computationally Efficient Off-Line Joint Change Point Detection in Multiple Time Series
    2019 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 1, p. 149-163Article in journal (Refereed) Published
    Abstract [en]

    In this paper, a computationally efficient algorithm for Bayesian joint change point (CP) detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of CPs in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the Maximization-Maximization algorithm which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov Chain Monte Carlo algorithm.

    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-366291 (URN)10.1109/TSP.2018.2880669 (DOI)000451940300002 ()
    Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-23Bibliographically approved
    3. Multivariate Change Point Detection with Optimality Preserving Pruning
    Open this publication in new window or tab >>Multivariate Change Point Detection with Optimality Preserving Pruning
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    In this paper we present a new algorithm for joint change point (CP) detection (CPD) in multiple time series. The algorithm is a computationally more efficient version of the previously proposed JCPD algorithm which is based on a dynamic program (DP). Here we show how to reduce the computational cost of the DP by introducing a pruning step which removes unnecessary computations. The algorithm uses a Bayesian data generation model and the CPs are estimated in a two stage procedure: First, a univariate CPD algorithm is applied separately to each time series. The outputs from this stage are univariate posterior distributions of CPs which are then combined to approximate the joint, multivariate, CP posterior. The second stage of the algorithm uses a DP to find the maxima of the joint CP posterior. In this work we show that the computational cost of the second stage can be reduced by using pruning techniques which preserve the optimality of the DP. We demonstrate the computational savings on sets of synthetic data, and for one of the sets, the pruning step reduced the processing time by an order of magnitude. Finally, we demonstrate the practical applicability of the algorithm by applying it to measurements of channel gain from radio links inside a paper mill. Supplementary material is given in the following chapter, where we compare the algorithm with similar methods and also present an alternative pruning condition which leads to a faster, but approximate, algorithm.

    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-366292 (URN)
    Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-03-03
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  • 2.
    Eriksson, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Olofsson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Computationally Efficient Off-Line Joint Change Point Detection in Multiple Time Series2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 1, p. 149-163Article in journal (Refereed)
    Abstract [en]

    In this paper, a computationally efficient algorithm for Bayesian joint change point (CP) detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of CPs in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the Maximization-Maximization algorithm which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov Chain Monte Carlo algorithm.

  • 3.
    Eriksson, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Olofsson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories2016In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 3078-3091Article in journal (Refereed)
    Abstract [en]

    The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments.

  • 4.
    Eriksson, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Olofsson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories2016In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 3078-3091Article in journal (Refereed)
    Abstract [en]

    The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments.

1 - 4 of 4
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