Diffuse ultrasound computed tomography.

An alternative approach to acquire transmission travel time data is proposed, exploiting the geometry of devices commonly used in ultrasound computed tomography for medical imaging or non-destructive testing with ultrasonic waves. The intent is to (i) shorten acquisition time for devices with a large number of emitters, (ii) to eliminate the calibration step, and (iii) to suppress instrument noise. Inspired by seismic ambient field interferometry, the method rests on the active excitation of diffuse ultrasonic wavefields and the extraction of deterministic travel time information by inter-station correlation.

Guided Zoom: Zooming into Network Evidence to Refine Fine-Grained Model Decisions.

In state-of-the-art deep single-label classification models, the top- k (k=2,3,4, ...

Hamiltonian Nullspace Shuttles.

We present a method to explore the effective nullspace of nonlinear inverse problems without Monte Carlo sampling. This is based on the construction of an artificial Hamiltonian system where a model is treated as a high-dimensional particle. Depending on its initial momentum and mass matrix, the particle evolves along a trajectory that traverses the effective nullspace, thereby producing a series of alternative models that are consistent with observations and their uncertainties.

Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature.

A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed.