The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples...
A scheme to sample bandlimited graph signals in the presence of noise is analyzed. Samples are aggregated at a single node by successive applications of the so-called graph-shift operator that encodes the local structure of the underlying graph. In contrast to the noiseless case, when noise is present the choice of the sampling node and the local sample-selection scheme plays a major role in determining...
We study networked unconstrained convex optimization problems where the objective function changes continuously in time. We propose a decentralized algorithm (DePCoT) with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and gradient-based correction steps, while sampling the problem data at a constant sampling period h. Under suitable conditions and for...
We develop a framework for trajectory tracking in dynamic settings, where an autonomous system is charged with the task of remaining close to an object of interest whose position varies continuously in time. We model this scenario as a convex optimization problem with a time-varying objective function and propose an adaptive discrete-time sampling prediction-correction scheme to find and track the...
Schemes to reconstruct signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. The schemes, which are designed within the framework of linear shift-invariant graph filters, consider that the signal is injected at a single seeding...
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers,...
In this paper, we present a duality between two problems: the reconstruction of the angular periodogram from spatial-domain signals received at different time indices and that of the frequency periodogram from time-domain signals received at different wireless sensors. We assume the existence of a multiband structure in either the angular or frequency domain representation of the received spatial...
Localization is a fundamental challenge for any wireless network of nodes, in particular when the nodes are mobile. For an anchorless network of mobile nodes, we present a relative velocity estimation algorithm based on multidimensional scaling. We propose a generalized two-way ranging model, where the time-varying pairwise distances between the nodes are expressed as a Taylor series for a small observation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.