Decode-and-forward relay beamforming for secrecy with imperfect CSI and multiple eavesdroppers

In this paper, we evaluate secrecy rates in cooperative relay beamforming in the presence of imperfect channel state information (CSI) and multiple eavesdroppers. A source-destination pair aided by.. out of.. relays, 1 <= k <= M, using decode-and-forward relay beamforming is considered. We compute the worst case secrecy rate with imperfect CSI in the presence of multiple eavesdroppers, where the number of eavesdroppers can be more than the number of relays. We solve the optimization problem for all possible relay combinations to find the secrecy rate and optimum source and relay weights subject to a total power constraint. We relax the rank-1 constraint on the complex semi-definite relay weight matrix and use S-procedure to reformulate the optimization problem that can be solved using convex semi-definite programming.

Reversible Aptamer-Au Plasmon Rulers for Secreted Single Molecules

Plasmon rulers, consisting of pairs of gold nanoparticles, allow single-molecule analysis without photobleaching or blinking; however, current plasmon rulers are irreversible, restricting detection to only single events. Here, we present a reversible plasmon ruler, comprised of coupled gold nanoparticles linked by a single aptamer, capable of binding individual secreted molecules with high specificity. We show that the binding of target secreted molecules to the reversible plasmon ruler is characterized by single-molecule sensitivity, high specificity, and reversibility. Such reversible plasmon rulers should enable dynamic and adaptive live-cell measurement of secreted single molecules in their local microenvironment.

Prototype Selection on Large and Streaming Data

Since streaming data keeps coming continuously as an ordered sequence, massive amounts of data is created. A big challenge in handling data streams is the limitation of time and space. Prototype selection on streaming data requires the prototypes to be updated in an incremental manner as new data comes in. We propose an incremental algorithm for prototype selection. This algorithm can also be used to handle very large datasets. Results have been presented on a number of large datasets and our method is compared to an existing algorithm for streaming data. Our algorithm saves time and the prototypes selected gives good classification accuracy.