Signal Processing

This work provides a comprehensive overview of adaptive diffusion networks, from the first papers published on the subject to state-of-the-art solutions and current challenges. These networks consist of a collection of agents that can measure and ...

The primary objective of this research paper is to examine the challenges associated with optimization and tracking control in discrete-time multiagent systems. This study begins by defining the optimal tracking control issue within the framework ...

• Policy gradient and Actor-Critic reinforcement learning for multiagent systems.
• Our data-driven techniques boosts adaptability and agent consistency.
• The ways improves coordination adaptability of agents, and minimizes overhead.

In recent decades, many tools have been developed and applications have been made possible thanks to graph signal processing (GSP). In this scenario, relationships between elements always occur in pairs. Recently, this theory has been extended to ...

• A novel hypergraph Fourier transform that deals directly with one-dimensional signals is introduced.
• A methodology to perform vertex-frequency analysis on hypergraphs is presented.
• Experiments with path, cycle, squid, and random ...

Medical diagnosing systems generate sensitive information involving the patient’s privacy. Steganography supports securely embedding secrecy, guaranteeing that an adversary cannot distinguish cover images and stego images. However, directly ...

• We propose MedSteGAN to learn embedding distortions for practical optimal embedding.
• MedSteGAN provides an advanced framework to improve security and image quality.
• MedSteGAN’s generator has a large receptive field to extract high-...

We consider the problem of target detection with a constant false alarm rate (CFAR). This constraint is crucial in many practical applications and is a standard requirement in classical composite hypothesis testing. In settings where classical ...

• Definition of a general framework for Bayesian and learning-based CFAR detectors.
• Proving asymptotic equivalence to GLRT.
• Introducing CFARnet - a practical CFAR deep learning approach.
• Demonstration of the advantages of CFARnet ...

A reasonable resource management strategy can maximize the sensing performance of radar network system via adaptively controlling the working parameters of interest. In this paper, a collaborative trajectory planning and transmit resource ...

• Trajectory planning and resource scheduling for radar network with GTAR.
• Transmit power with bandwidth and trajectory can be controlled jointly.
• A solution technique integrated with the SDP, LHS and IPSOTS is proposed.

• More variates or coefficients are better for EIV/TLS problem.
• Quantity properties confirmed by analytical derivation of CRB tool.
• Additional variates and coefficients are encouraged for practical EIV system.
• Simulated by CRB ...

Total least-squares (TLS) aims to estimate the unknown parameters of an errors-in-variables (EIV) model from noisy observations when the coefficients are also perturbed by errors. It is helpful to know whether more variates and coefficients lead ...

Human motion prediction, the task of forecasting future poses from a given observed sequence, is crucial for advancing human-centric computer vision. Significant progress has been made by employing fixed or adaptively learned spatiotemporal Graph ...

• Our approach leverages the physical and kinematic regularities of the motion system.
• An attention mechanism learns weights for articulated, coordinated embeddings.
• A multi-head temporal self-attention captures dependencies in ...

The problem of sparse adaptive system identification such as acoustic echo cancellation (AEC) needs robust adaptive filtering algorithms in the situation where the system is often corrupted by impulsive noise. To solve this problem this work ...

• This work proposes a proportionate affine projection tanh algorithm robust against impulsive noise.
• The proportionate can speed convergence rate.
• The steady-state performance and stability conditions are analyzed.
• The step-size ...

Non-negative constraints arise in certain system identification problems when the systems to be identified have only positive coefficients. This paper studies the stochastic behavior of modified LMS and NLMS algorithms, modified so as to only ...

• Convergence of LMS and NLMS algorithms under non-negative weight constraints.
• At each iteration, the weight vector is projected onto the feasible space.
• The new algorithm optimally projects the weight vector onto the feasible set.

• A joint non-convex low-rank constrained model with unsupervised networks is proposed for image decomposition.
• The unsupervised network is first used for the image decomposition task to extract image information.
• The non-convex ...

Image decomposition is the separation of a given image into two parts with different features, i.e., structure and texture. In order to extract the image information comprehensively, a non-convex unsupervised image decomposition model is proposed ...

• This paper proposes a new nonlinear system model for signal processing and develops its corresponding adaptive algorithm.
• This paper also gives a variant version of the proposed algorithm to reduce its complexity.
• The performance ...

In the field of nonlinear signal processing, Volterra filter is generally used as an effective tool. The utilization of the augmented model can make the filter maintain its merit in both circular and non-circular signals. From this point of view, ...

Sparse representation-based methods have shown promising prospects in synthetic aperture radar (SAR) target recognition due to their robustness under complex application conditions. However, these methods are still susceptible to the feature ...

• Enhancing SAR target image description with local contrast perception.
• Local contrast features promote kernel sparse representation classification.
• Learning feature distribution to optimize parameters of sparse representation.
• ...

Joint channel estimation and signal detection (JCESD) is crucial in orthogonal frequency division multiplexing (OFDM) systems, but traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have ...

• We create a benchmark for the task of joint channel estimation and signal detection.
• Deep learning methods perform better in the more challenging low-SNR setting.
• The iterative algorithm outperforms in the high-SNR setting with ...

This article deals with the joint design of inter-pulse amplitude, phase and frequency to detect coexisting strong and weak targets in the presence of interference for cognitive radar. First, an amplitude–frequency-phase agile (AFPA) signal model ...

• Inter-pulse amplitude, frequency and phase agile waveform.
• Inter-pulse agile design via CD-FTECA algorithm for minimizing WISL.
• Developing a CD-ADMM framework to track the non-convex problem of minimizing WPSL.

In the context of channel estimation amid non-Gaussian impulse noise, traditional non-kernel-space methods face challenges of divergence, while many kernel-space methods fail to fully exploit the a priori information embedded in the channel. To ...

• We introduce a recursive variant of the KRSL method, denoted as RKRSL. The recursive approach achieves faster convergence and robustness against non-Gaussian noise. We incorporate convex regularization constraints into the recursive ...

In this paper we introduce a new approach for solving image restoration problems by using the infimal postcomposition of a convex function by a linear operator. We derive this formulation for general linear composite convex problems in Hilbert ...

• Use of infimal postcomposition of a convex function by a linear operator for solving inverse problems.
• Globally weakly convergent algorithms based on the Douglas–Rachford splitting.
• Explicit closed expression for the proximity ...

Millimeter-wave (MMW) 3-D imaging based on multiple-input-multiple-output (MIMO) radar has been widely studied due to its unique advantages in human security screening. However, more research has been done on the use of scanning 1-D MIMO array ...

• A fast imaging method based on boundary MIMO array is proposed.
• The computational complexity of the proposed method is comprehensively analyzed.
• The focusing error resulting from the proposed imaging method is analyzed.
• ...

Benefiting from the low latency and high dynamic range, event cameras have recently been adopted for Optical Flow (OF) prediction under harsh environments with high-speed motion or extreme lighting conditions. However, the emitted events only ...

• Alleviating the imperfection of events by utilizing the spatiotemporal attention-based module.
• Fully exploiting the cross-modal characteristics between frames and events.
• Designing long-term motion information for global temporal ...

In recent decades, numerous perceptual authentication hashing schemes have been proposed for image content authentication. However, most of these schemes are based on a single spatial or transform domain, and they fail to provide satisfactory ...

• A new robust perceptual hashing scheme for image authentication is proposed.
• Multi-domain feature fusion strategy is exploited for hash sequence generation.
• The channel filter and attention module are designed in frequency domain.

The new classes of radar signals are increasingly difficult to acquire under non-cooperative environments, which makes it difficult to support convolutional neural network training with limited labeled samples. The few-shot learning (FSL) methods ...

• A CDPSC method is proposed for few-shot radar modulation signal recognition.
• A domain prototype similarity mapping strategy improves the accuracy of prototypes.
• The method improves feature extraction ability by extracting time-...

In this paper, the mathematical derivation of the underlying probability distribution function for the normalized least-squares wavelet spectrogram is presented. The impact of empirical and statistical weights on the estimation of the spectral ...

• The Least-Squares Wavelet Analysis (LSWA) of GNSS time series in Rome is presented.
• The mathematical derivation of stochastic surfaces for spectrograms in LSWA is shown.
• Considering statistical weights improved the accuracy of ...

Underwater images often suffer from local distortions during the imaging and transmission process, which can negatively impact their quality. Fortunately, it is possible to improve image quality by removing local distortion without making any ...

• Establishing a dataset containing common local distortions in underwater images.
• Proposing a distortion localization and restoration framework for underwater images.
• Creating a network for extracting global, distortion-free, and ...

This paper studies the problem of resolvable group target track initiation (RGTI) and proposes a novel method to obtain the discrete association vectors and continuous trajectory parameters of individual targets within the group, which benefits ...

Echo cancellation has been effectively achieved through the successful application of subband adaptive filtering (SAF) algorithms. However, the performance of these algorithms may significantly deteriorate when the system exhibits nonlinear ...

Secure beamforming with constant modulus has been regarded as a promising solution in enhancing the physical-layer security by intelligently designing the antenna phase of massive multiple-input multiple-output (MIMO) devices. The resulting ...

• Provide an efficient and low-complexity method for massive MIMO secure beamforming.
• Project the problem onto a manifold to solve it without relaxation.
• Combine momentum and adaptive step size to accelerate convergence.
• ...