Passive acoustic methods are increasingly used in biodiversity research and monitoring programs because they are cost-effective and permit the collection of large datasets. However, the accuracy of the results depends on the bioacoustic characteristics of the focal taxa and their habitat use. In particular, this applies to bats which exhibit distinct activity patterns in three-dimensionally str...

Compressed sensing and magnetic resonance imaging are hot topics in the field of signal processing. In this study we introduced in Lustig’s variable density sampling method, integrated it to compressed sensing, and applied it to brain MRI acquisition. The realistic experiment shows the variable density sampling recovery better than traditional random sampling method on a 256x256 brain magnetic ...

Monitoring widespread environmental fields is undoubtedly a practically important area of research with many complex and challenging tasks. It involves the building of models of the fields or natural phenomena to be monitored, the estimation of the spatio-temporal distribution of a variety of environmental parameters of interest, such as moisture or salinity in a crop field, or the spatial dist...

Introduction Adaptive imaging allows multiple image sets, each having a different spatial-temporal balance, to be retrospectively reconstructed from the same dataset. High temporal resolution image sets from radial sampling schemes are typically undersampled, and suffer from streak artifacts that degrade image quality. It has been shown that a compressed sensing (CS) L1-penalized reconstruction...

We propose a new direct-sequence code division multiple access (CDMA) strategy for combining voice and variable-rate data. Our admission control policy uses a reservation policy for voice calls and different variable-rate schemes for data to maximize throughput on the residual bandwidth unused by voice. The data schemes we consider include variable bit rate (VBR), multicode (MC), and variable c...

There are various importance sampling schemes to estimate rare event probabilities in Markovian systems such as Markovian reliability models and Jackson networks. In this work, we present a general state dependent importance sampling method which partitions the state space and applies the cross-entropy method to each partition. We investigate two versions of our algorithm and apply them to seve...

Born from empirical observations in nonuniformly sampled multidimensional NMR data relating to gaps between sampled points, the Poisson-gap sampling method has enjoyed widespread use in biomolecular NMR. While the majority of nonuniform sampling schemes are fully randomly drawn from probability densities that vary over a Nyquist grid, the Poisson-gap scheme employs constrained random deviates t...