In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. Furthermore, higher-fidelity evaluations of ...

In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal ...

This paper addresses the multi-object localization problem by using a hybrid of differential time delay (DTD) and angle-of-arrival (AOA) measurements collected by a single receiver in an unsynchronized multistatic localization system, where two kinds of ...

We consider a least absolute deviation (LAD) approach to the robust phase retrieval problem that aims to recover a signal from its absolute measurements corrupted with sparse noise. To solve the resulting non-convex optimization problem, we propose a ...

Graph Neural Networks (GNNs) have exhibited exceptional performance across diverse application domains by harnessing the inherent interconnectedness of data. Recent findings point towards instability of GNN under both feature and structure perturbations. ...

Steganography is the art of covert communication that pursues the secrecy of concealment. In adaptive steganography, the most commonly used framework of steganography, the sender embeds a “secret message” signal within another “cover&#...

Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often ...

Generalized bilinear factorization (GBF), in which two matrices are recovered from noisy and typically compressed measurements of their product, arises in various applications such as blind channel-and-signal estimation, image completion, and compressed ...

Dealing with time series with missing values, including those afflicted by low quality or over-saturation, presents a significant signal processing challenge. The task of recovering these missing values, known as imputation, has led to the development of ...

Non-line-of-sight (NLOS) mmWave imaging technology reconstructs the contour features of hidden targets by analyzing the indirect reflected signals of the relay surface, which has been a hot topic in disaster reserve and autonomous driving. However, due to ...

The vector autoregressive (VAR) model is extensively employed for modelling dynamic processes, yet its scalability is challenged by an overwhelming growth in parameters when dealing with several hundred time series. To overcome this issue, data relations ...

Despite the great potential of artificial intelligence (AI), which promotes machines to mimic human intelligence in performing tasks, it requires a deep/extensive model with a sufficient number of parameters to enhance the expressive ability. This aspect ...

The decentralized baseband processing (DBP) architecture is recently proposed for massive MIMO systems to reduce the interconnection cost of fronthaul links and baseband (BB) computational complexity. This paper studies the uplink multiuser equalization (...

Strongly Adaptive meta-algorithms (SA-meta) are popular in online portfolio selection due to their resilience in adversarial environments and adaptability to market changes. However, their application is often limited by high variance in errors, stemming ...

This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice ...

Distributed sensing is a key process for acquiring system state information in the network environments of industrial cyber-physical system (ICPS). Considering the unknown complex industrial system models, the intelligent methods for distributed sensing ...

The nonlinear behavior of loudspeakers is of great interest in a number of audio processing algorithms, as it may have a detrimental effect on their performance. These algorithms may be further enhanced when an accurate model of the loudspeaker's ...

Change-point analysis plays a significant role in various fields to reveal discrepancies in distribution in a sequence of observations. While a number of algorithms have been proposed for high-dimensional data, kernel-based methods have not been well ...

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the ...

In this paper we propose a novel framework that successfully combines random projection or compression to weighted Nonnegative Matrix Factorization (NMF). Indeed a large body of NMF research has focused on the unweighted case&#x2014;<italic>i.e.,</italic> ...