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We introduce a new methodology for noninvasive post-silicon characterization of the unique static power profile (tomogram) of each manufactured chip. The total chip leakage is measured for multiple input vectors in a linear optimization framework where the unknowns are the gate leakage variations. We propose compressive sensing for fast extraction of the unknowns since the leakage tomogram contains correlations and can be sparsely represented. A key advantage of our approach is that it provides leakage variation estimates even for inaccessible gates. Experiments show that the methodology enables fast and accurate noninvasive extraction of leakage power characteristics.

A Semidefinite Programming (SDP) relaxation is an effective computational method to solve a Sensor Network Localization problem, which attempts to determine the locations of a group of sensors given the distances between some of them. In this paper, we analyze and determine new sufficient conditions and formulations that guarantee that the SDP relaxation is exact, i.e., gives the correct solution. These conditions can be useful for designing sensor networks and managing connectivities in practice.Our main contribution is threefold: First, we present the first non-asymptotic bound on the connectivity (or radio) range requirement of randomly distributed sensors in order to ensure the network is uniquely localizable with high probability. Determining this range is a key component in the design of sensor networks, and we provide a result that leads to a correct localization of each sensor, for any number of sensors. Second, we introduce a new class of graphs that can always be correctly localized by an SDP relaxation. Specifically, we show that adding a simple objective function to the SDP relaxation model will ensure that the solution is correct when applied to a triangulation graph. Since triangulation graphs are very sparse, this is informationally efficient, requiring an almost minimal amount of distance information. Finally, we analyze a number of objective functions for the SDP relaxation to solve the localization problem for a general graph.

We propose a new scheme for limited feedback in MIMO systems. We consider transmit beamforming and receiver maximal ratio combining as a base for our work, and propose a novel beamforming codebook to exploit the inherent correlation of the channel. This novel beamforming codebook, unlike the conventional beamforming codebooks, adaptively changes with the channel matrix. Moreover, the adaptive approach is independent of the channel model and can be applied to any general MIMO channel with temporal and spatial correlations. Simulation results show that compared to previously known beamforming schemes, this technique significantly improves the BER performance in spatio-temporally correlated channels.

In this paper, we study online impression allocation in display advertising with budgeted advertisers. That is, upon arrival of each impression, cost and revenue vectors are revealed and the impression should be assigned to an advertiser almost immediately. Without any assumption on the distribution/arrival of impressions, we propose a framework to capture the risk to the ad network for each possible allocation; impressions are allocated to advertisers such that the risk of ad network is minimized. In practice, this translates to starting with an initial estimate of dual prices and updating them according to the belief of the ad network toward the future demand and remaining budgets. We apply our algorithms to a real data set, and we empirically show that Kullback-Leibler divergence risk measure has the best performance in terms of revenue and balanced budget delivery.