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The relatively low resolution of Time-of-Flight (ToF) cameras, together with high power consumption and motion artifacts due to long exposure times, have kept ToF sensors away from classical lidar application fields, such as mobile robotics and autonomous driving. In this paper we note that while attempting to address the last two issues, e. g., via burst mode, the lateral resolution can be effectively increased. Differently from prior approaches, we propose a stripped-down modular super-resolution framework that operates in the raw data domain.

The relatively low resolution of Time-of-Flight (ToF) cameras, together with high power consumption and motion artifacts due to long exposure times, have kept ToF sensors away from classical lidar application fields, such as mobile robotics and autonomous driving. In this paper we note that while attempting to address the last two issues, e. g., via burst mode, the lateral resolution can be effectively increased. Differently from prior approaches, we propose a stripped-down modular super-resolution framework that operates in the raw data domain.

To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our key finding is to learn such representations by separating the target signal from contrastive interfering signals.

In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. Zero-shot learning in audio classification refers to classification problems that aim at recognizing audio instances of sound classes, which have no available training data but only semantic side information. In this paper, we address zero-shot learning by employing factored linear and nonlinear acoustic-semantic projections.

In this paper, the problem of classifying radar clutter returns into statistically homogeneous subsets is addressed. To this end, latent variables, which represent the classes to which the tested range cells belong, in conjunction with the expectation-maximization method are jointly exploited to devise the classification architecture. Moreover, two different models for the structure of the clutter covariance matrix are considered.