Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image...

Digital modeling of the microstructure is important for studying physical and transport properties porous media. Multiscale media can accurately characterize large-pores fine-pores in a large-FoV (field view) high-resolution three-dimensional pore structure model. This paper proposes multiscale reconstruction algorithm based on multiple dictionaries learning, which edge patterns fine-pore from ...

In this paper we bring to bear some new tools from statistical learning on the analysis of roll call data. We present a new data-driven model for roll call voting that is geometric in nature. We construct the model by adapting the “Partition Decoupling Method,” an unsupervised learning technique originally developed for the analysis of families of time series, to produce a multiscale geometric ...

Article history: Received 29 July 2015 Received in revised form 5 February 2016 Accepted 8 February 2016 Available online 29 March 2016

This paper introduces a multiscale analysis based on optimal piecewise linear approximations of time series. An optimality criterion is formulated and on its base a computationally effective algorithm is constructed for decomposition of a time series into a hierarchy of trends (local linear approximations) at different scales. The top of the hierarchy is the global linear approximation over the...

This paper introduces a new multiscale framework for estimating the tail probability of a queue fed by an arbitrary traffic process. Using traffic statistics at a small number of time scales, our analysis extends the theoretical concept of the critical time scale and provides practical approximations for the tail queue probability. These approximations are non-asymptotic; that is they apply to ...

Multiscale entropy (MSE) analysis [1, 2] is a new method of measuring the complexity of finite length time series. This computational tool can be applied both to physical and physiologic data sets, and can be used with a variety of measures of entropy. We have developed and applied MSE for the analysis of physiologic time series, for which we prefer to estimate entropy using the sample entropy ...