This paper addresses the problem of target detection against a background of Gaussian clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector and detection performance under the Neyman-Pearson criterion for general frequency snapshot selection with arbitrary DOF. When the clutter statistics are unknown, we use a uniformly random frequency snapshot selection method and show how the DOF employed affects the detection performance. 

Target source extractionis significant for improving human speech intelligibility and the speech recognition performance of computers. This study describes a method for target source extraction, called the similarity-and-independence-awarebeamformer (SIBF). The SIBF extracts the target source using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain a more accurate signal than the spectrogram generated by target-enhancing methods such as speech enhancement based on deep neural networks. 

Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance.

This paper considers the problem of decentralized consensus optimization over a network, where each node holds a strongly convex and twice-differentiable local objective function. Our goal is to minimize the sum of the local objective functions and find the exact optimal solution using only local computation and neighboring communication.

Novel Monte Carlo estimators are proposed to solve both the Tikhonov regularization (TR) and the interpolation problems on graphs. These estimators are based on random spanning forests (RSF), the theoretical properties of which enable to analyze the estimators’ theoretical mean and variance.

The video captioning task aims to describe video content using several natural-language sentences. Although one-step encoder-decoder models have achieved promising progress, the generations always involve many errors, which are mainly caused by the large semantic gap between the visual domain and the language domain and by the difficulty in long-sequence generation.

The prevailing use of both images and text to express opinions on the web leads to the need for multimodal sentiment recognition. Some commonly used social media data containing short text and few images, such as tweets and product reviews, have been well studied. However, it is still challenging to predict the readers’ sentiment after reading online news articles, since news articles often have more complicated structures, e.g., longer text and more images.