Showing papers in "Signal Processing in 2022"

TL;DR: This paper focused on wildfires detection at their early stages in forest and wildland areas, using deep learning-based computer vision algorithms to prevent and then reduce disastrous losses in terms of human lives and forest resources.

TL;DR: A height measurement signal model for meter wave polarimetric MIMO radar along with its effective solution is provided and the simulation results prove the accuracy of this approach.

TL;DR: In this article , the authors derived the height measurement signal model for flat ground reflection combined with the two diversity techniques and modified it to make it suitable for the classical super-resolution height measurement algorithms, including the generalized multiple signal classification(MUSIC) algorithm and steering vector synthesis (SVS) MUSIC algorithm.

TL;DR: In this paper , the authors focused on wildfire detection at early stages in forest and wildland areas, using deep learning-based computer vision algorithms to prevent and then reduce disastrous losses in terms of human lives and forest resources.

TL;DR: In this paper , three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector crossproduct technique and the LS method.

TL;DR: In this paper, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector crossproduct technique and the LS method.

TL;DR: Wang et al. as discussed by the authors proposed a stable image encryption scheme to create visually secure cipher image by using new fractional-order chaotic map, Bayesian compressive sensing and DVT embedding.

TL;DR: Gao et al. as discussed by the authors proposed a 3D model encryption method based on a 2D chaotic system constructed via the coupling of the logistic map and infinite collapse (2D-LAIC) and on semi-tensor product (STP) theory.

TL;DR: This work proposes a selective fixed-filter ANC method based on a simplified two-dimensional convolution neural network (2D CNN), which is implemented on a co-processor (e.g., in a mobile phone), to derive the most suitable control filter for different noise types.

TL;DR: Deep learning architectures are now pervasive and filled almost all applications under image processing, computer vision, and biometrics as discussed by the authors, and they have solved a lot of conventional image processing problems with much improved performance and efficiency.

TL;DR: A sensitive fault detection methodology combining a specifically tuned Local Mahalanobis Distance algorithm and an Empirical Probability Density (EPD) estimation technique is proposed, effective to non-Gaussian data and sensitive for incipient fault detection.

TL;DR: Different from most existing multichannel signal decomposition approaches, the proposed SMVMD does not need to predefine the mode number and is able to extract the joint or common modes successively and establish the successive variation optimization function.

TL;DR: Wang et al. as discussed by the authors proposed a selective fixed-filter active noise control (ANC) method based on a simplified two-dimensional convolution neural network (2D CNN), which is implemented on a co-processor (e.g., in a mobile phone), to derive the most suitable control filter for different noise types.

TL;DR: In this paper , a novel Successive Multivariate Variational Mode Decomposition (SMVMD) is presented, which does not need to predefine the mode number and is able to extract the joint or common modes successively.

TL;DR: A new GAN-based spatial steganographic scheme that builds multiple cross feedback channels between the contraction path and the expansion path of the generator, which allows that the down-sampling information is directly sent to the expansion layer through these feedback channels.

TL;DR: This work proposes a general framework for learning node representations in a self supervised manner called Graph Constrastive Learning (GraphCL), which learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph.

TL;DR: A distributed filter based on statistical characteristics of fading measurements of sensors, where an optimal Kalman filter gain for each sensor node and different optimal consensus filter gains for state estimates of neighbor nodes are solved to minimize locally an upper bound of filtering error covariance matrix under given parameters.

TL;DR: A review of state-of-the-art speckle filtering algorithms for various methods available in the literature is presented in this paper , where different multiplicative noise models in SAR images along with their characteristics are explained.

TL;DR: Wang et al. as mentioned in this paper proposed a new GAN-based spatial steganographic scheme, which builds multiple cross feedback channels between the contraction path and the expansion path of the generator, allowing that the down-sampling information is directly sent to the expansion layer through these feedback channels.

TL;DR: In this paper , a sensitive fault detection methodology combining a specifically tuned Local Mahalanobis Distance (LMD) algorithm and an empirical probability density (EPD) estimation technique is proposed.

TL;DR: Zhang et al. as mentioned in this paper proposed a novel image encryption algorithm based on deep neural network (DNN), which can effectively resist brute force attack, statistical attack, differential attack and cutting attack.

TL;DR: Deep learning architectures are now pervasive and filled almost all applications under image processing, computer vision, and biometrics as mentioned in this paper and they have solved a lot of conventional image processing problems with much-improved performance and efficiency.

TL;DR: An image tampering localization algorithm using semi-fragile watermarking and Error-Locating codes in the DWT domain and comparisons of image quality and tampering localization performances using error-detection, error-localization and error-correction approaches with different error control codes are provided.

TL;DR: Zhang et al. as mentioned in this paper proposed a target-attentional convolutional neural network (TACNN) which combines the CNN and attention mechanism for radar high-resolution range profile (HRRP) target recognition.

TL;DR: A new approach based on a Riemannian manifold, which is the product of complex circles and a Euclidean space is proposed to improve SINR (signal-to-interference-plus-noise ratio), which can achieve higher SINr via different input SNR while consumes a lower computation time.

TL;DR: In this paper , a feed-forward neural network-based one-time-scale distributed principal component analysis (PCA) algorithm is proposed for dimensionality reduction and decorrelation of features in a distributed setting.

TL;DR: In this paper , the authors classify image steganography research based on its goals and its assessment tools, and review the use of assessment tools in depth because it is closely related to the goal of steganographic research, including challenges or issues, methods, developments, and popular datasets.

TL;DR: In this paper , a new form of the Bayesian Information Criterion (BIC) was proposed for order selection in linear regression models where the parameter vector dimension is small compared to the sample size, which eliminates the scaling problem and at the same time is consistent for both large sample sizes and high-SNR scenarios.

TL;DR: GraphCL as discussed by the authors learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph and leverage a contrastive learning loss to maximize agreement between them.

TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale weighted group sparse coding model (MS-WGSC) for image denoising, wherein the patch groups are constructed using multiscale NSS priors.