TSP Volume 67 Issue 9

The IEEE Signal Processing Society congratulates the following recipients who will receive the 2018 IEEE Signal Processing Society paper awards for their paper published in the IEEE Transactions on Signal Processing. Presentation of the paper awards will take place at ICASSP 2019 in Brighton, U.K.

A task of major practical importance in network science is inferring the graph structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations.

This paper discusses greedy methods for sensor placement in linear inverse problems. We comprehensively review the greedy methods in the sense of optimizing the mean squared error (MSE), the volume of the confidence ellipsoid, and the worst-case error variance. We show that the greedy method of optimizing an MSE related cost function can find a near-optimal solution.

Linear canonical transforms (LCTs) are of importance in many areas of science and engineering with many applications. Therefore, a satisfactory discrete implementation is of considerable interest. Although there are methods that link the samples of the input signal to the samples of the linear canonical transformed output signal, no widely-accepted definition of the discrete LCT has been established.