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In this letter, we discuss the problem of unsupervised clustering of sensor signals based on their information content. In the past, the problem has been formulated as a matrix factorization problem and has been solved with different variants of gradient descent. We reformulate the nonconvex cost function as a mixed integer linear programing problem with explicit clustering constraints and solve it with branch and bound, while introducing a scalable variant to reduce the computational time. The proposed method is applied to clustering problems in hyperspectral imaging and multiview image clustering and extensive results have been presented demonstrating the superiority of the novel framework over existing alternatives. [ABSTRACT FROM AUTHOR]

Segmentation of sequential sensor data streams and classification of each segment are common steps in tasks dealing with the detection of events of interest in such data. In this paper, we introduce two correlation analysis-based methods for classifying time series data generated by sensors. Our first method is a lightweight supervised approach utilizing principal component analysis to jointly segment data and classify each segment into a class corresponding to an event of interest. The second method relies on unsupervised canonical correlation analysis to segment time series by clustering together consecutive data points that belong to the same event. Both methods operate without the need for prior feature extraction from the data. The theoretical model of the two methods and the solution to the resulting optimization problem are presented in detail. Classification of human activity from inertial measurement unit sensor data is used as a case study to demonstrate the applicability and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper focuses on unsupervised clustering of hyperspectral pixels whose intensity may not be available across certain spectral bands. The presence of statistical correlations among pixels that contain data originating from the same material is exploited here to develop a novel regularized correlation analysis framework to perform clustering. Kernel learning is integrated in a regularized correlations framework to exploit nonlinear dependencies of pixels acquiring information about similar materials. An effective technique is proposed to select the kernel mapping parameters and form pertinent kernel covariance matrices by proper weighted-averaging that takes into account the information content across the available spectral bands. The novel correlations framework will return proper sparse clustering matrices whose nonzero entries point to the correlated pixels. These matrices will be obtained via a minimization formulation that will be solved via computationally efficient subgradient descent iterations. The novel correlations formulation is applied in small-size pixel patches of the original hyperspectral image, while recursively bigger size patches are built that contain information about the same materials. Extensive numerical tests on real hyperspectral images reveal that the proposed approach, in spite of being unsupervised, can outperform existing supervised and unsupervised techniques especially in the presence of missing pixels. [ABSTRACT FROM AUTHOR]

Coordinated charging of plug-in electric vehicles (PEVs) can effectively mitigate the negative effects imposed on the power distribution grid by uncoordinated charging. Simultaneously, coordinated charging algorithms can accommodate the PEV user’s needs in terms of desired state-of-charge and charging time. In this paper, the problem of tracking an arbitrary power profile by coordinated charging of PEVs is formulated as a discrete scheduling process, while accounting for the heterogeneity in charging rates and restricting the charging to only the maximum rated power. Then, a novel distributed algorithm is proposed to coordinate the PEV charging and eliminate the need for a central aggregator. It is guaranteed to track, and not exceed, the power profile imposed by the utility, while maximizing the user convenience. A formal optimality analysis is provided to show that the algorithm is asymptotically optimal in case of Homogeneous charging, while it has a very small optimality gap for the heterogeneous case. Numerical simulations considering realistic charging scenarios with different penetration levels and tracking of a valley-filing profile are presented to validate the proposed charging algorithm. [ABSTRACT FROM PUBLISHER]