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Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements.

The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values.

Human cognition is supported by the combination of multi- modal information from different sources of perception. The two most important modalities are visual and audio. Cross- modal visual-audio generation enables the synthesis of da- ta from one modality following the acquisition of data from another. This brings about the full experience that can only be achieved through the combination of the two. In this pa- per, the Self-Attention mechanism is applied to cross-modal visual-audio generation for the first time.

Deep learning based methods have become dominant solutions to many image processing problems. A natural question would be “Is there any space for conventional methods on these problems?” In this paper, exposure interpolation is taken as an example to answer this question and the answer is “Yes”. A new hybrid learning framework is introduced to interpolate a medium exposure image for two large-exposure-ratio images from an emerging high dynamic range (HDR) video capturing device. The framework is set up by fusing conventional and deep learning methods.

Various spearheads countermeasure methods for automatic speaker verification (ASV) with considerable performance for anti-spoofing are proposed in ASVspoof 2019 challenge. However, previous work has shown that countermeasure models are subject to adversarial examples indistinguishable from natural data. A good countermeasure model should not only be robust to spoofing audio, including synthetic, converted, and replayed audios, but counter deliberately generated examples by malicious adversaries.

A wireless sensing system with n sensors, observing independent and identically distributed continuous random variables with a symmetric probability density function, and one non-collocated estimator acting as a fusion center is considered. The sensors transmit information to the fusion center via a limited capacity communication medium modeled by a collision channel. It is assumed that there is no communication among the sensors prior to transmission, and the collision channel allows at most k<n simultaneous transmissions.