Recently, quasi-complementary sequence sets (QCSSs) including low correlation zone complementary sequence sets (LCZ-CSSs), and low correlation complementary sequence sets (LC-CSSs) have raised lots of concerns. According to the theoretical bounds, the set size of a QCSS is much larger than that of a traditional perfect complementary sequence set (PCSS). As a result, it is believed that QCSSs have potential

Non-negative matrix factorization with transform learning (TL-NMF) aims at estimating a short-time orthogonal transform that projects temporal data into a domain that is more amenable to NMF than off-the-shelf time-frequency transforms. In this work, we study the identifiability of TL-NMF under the Gaussian composite model. We prove that one can uniquely identify row-spaces of the orthogonal transform

As a multi-label classification task, audio tagging aims to predict the presence or absence of certain sound events in an audio recording. Existing works in audio tagging do not explicitly consider the probabilities of the co-occurrences between sound events, which is termed as the label dependencies in this study. To address this issue, we propose to model the label dependencies via a graph-based

Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations

Particle Filters (PFs) are Sequential Monte Carlo methods which are widely used to solve filtering problems of dynamic models under Non-Linear Non-Gaussian noise. Modern PF applications have demanding accuracy and run-time constraints that can be addressed through parallel computing. However, an efficient parallelization of PFs can only be achieved by effectively parallelizing the bottleneck: resampling

We introduce a fast algorithm for parameter estimation in multidimensional Hawkes processes, a widely used class of temporal point processes for mutually exciting discrete event data. Our approach assumes a low-rank structure on the infectivity parameter of the multidimensional Hawkes process, and relies on a method of moments estimator. Notably, it requires only a single scan of the data, and consistently

Linear regression without correspondences concerns the recovery of a signal in the linear regression setting, where the correspondences between the observations and the linear functionals are unknown. The associated maximum likelihood function is NP-hard to compute when the signal has dimension larger than one. To optimize this objective function we reformulate it as a concave minimization problem

Saliency detection is a significant research topic in the field of image processing and computer vision. Currently, most saliency detection methods are applied to RGB images, so that they may encounter adverse scenarios characterized by complex background, inclement weather, and low illumination. Fusing complementary advantages of RGB and thermal infrared (RGB-T) images can effectively boost saliency

This letter proposes a prefiltered observation-based affine projection algorithm (APA) to achieve robustness against outliers. The conventional robust algorithm for correlated input signal, which is called the affine projection sign algorithm (APSA), was developed by using the $\mathcal {L}_1$ -norm of the error signal. However, it suffers from slow tracking speed for a system change suddenly, because

Robust adaptive signal processing algorithms based on a generalized maximum correntropy criterion (GMCC) suffers from high steady state misalignment. In an endeavour to achieve lower steady state misalignment, in this letter we propose a generalized hyperbolic secant function (GHSF) as a robust norm and derive the generalized hyperbolic secant adaptive filter (GHSAF). The new algorithm is seen to offer

The kernel adaptive filters (KAFs) based on the minimum mean square error (MMSE) criterion in reproducing kernel Hilbert space (RKHS) improve the performance of linear adaptive filters but result in instability issues and large burdens of computation and memory in impulsive noises. To this end, a novel Nyström kernel recursive generalized maximum correntropy (NKRGMC) with probability density rank-based

In interactive segmentation, the role of seed information provided by the user is significant. A seed is a clue to ease the ambiguity of the problem by making the object segmentation task interactive. However, in most deep network-based works, seed information has been used as an additional channel for input images. In this paper, we propose a novel bi-directional attention module for more actively

We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into

The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing video SR methods since spatial feature extraction and temporal motion compensation are usually performed sequentially. In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information

We consider that existing nonlinear state estimators cannot always yield a satisfactory performance when the distribution of the measurement noise deviates from the assumed Gaussian distribution. This letter investigates the non-linear robust fixed-interval smoothing problem where the measurement vectors are partially disturbed by outliers. The proposed smoother employs a binary indicator variable

We address the problem of 3-D reconstruction using neuromorphic cameras (also known as event-driven cameras), which are a new class of vision-inspired imaging devices. Neuromorphic cameras are becoming increasingly popular for solving image processing and computer vision problems as they have significantly lower data rates than conventional frame-based cameras. We develop a neuromorphic-camera-based

Chebyshev center computation problem, i.e. finding the point which is at minimum distance to a set of given points, on the probability simplex with $\alpha$ -divergence distance measure is studied. The proposed solution generalizes the Arimoto-Blahut (AB) algorithm utilizing Kullback-Leibler divergence to $\alpha$ -divergence, and reduces to the AB method as $\alpha \rightarrow 1$ . Similar to the

Weakly supervised temporal action localization (WSTAL) is a practical but challenging issue in video understanding. However, most existing methods have to activate background snippets or deactivate action snippets in cases of no boundary annotations, which inevitably affects the localization of action instances. In this letter, we propose a spatial enhancement and temporal constraint (SETC) model to

Shearlet Transform (ST) has been instrumental for the Densely-Sampled Light Field (DSLF) reconstruction, as it sparsifies the underlying Epipolar-Plane Images (EPIs). The sought sparsification is implemented through an iterative regularization, which tends to be slow because of the time spent on domain transformations for dozens of iterations. To overcome this limitation, this letter proposes a novel

Convolutional neural network (CNN) is a popular choice for visual object detection where two sub-nets are often used to achieve object classification and localization separately. However, the intrinsic relation between the localization and classification sub-nets was not exploited explicitly for object detection. In this letter, we propose a novel association loss, namely, the proxy squared error (PSE)

A previously described adaptive-gain tracking filter with the position only measurements is extended to models with additionally measured velocity or acceleration. The devised filter adapts the full matrix gain, copes with sensors cross-correlation, and improves the position-only prototype. By using a prediction error method, the adaptive gain always follows the Kalman gain. The filter proves efficient

As constituent parts of image objects, superpixels can improve several higher-level operations. However, image segmentation methods might have their accuracy severely compromised for reduced numbers of superpixels. To mitigate the problem, we introduce Dynamic Iterative Spanning Forest (DISF), a seed-based method that improves all components in the Iterative Spanning Forest (ISF) framework for superpixel

In this letter, the application of ultra-high frequency (UHF) radar for ship target detection over river is investigated for the first time. Due to the wide beam width of antenna, the ship detection of the UHF radar suffers from the broaden river clutters scattering from water waves. In addition, due to the high resolution, the ship echoes are seriously extended in both range and Doppler dimensions

We study a fundamental generalization of principal component analysis (PCA) that looks for a small set of common time series, i.e., principal components (PCs), whose filtered versions can explain most of the variances of multiple observed time series. This problem boils down to PCA in the frequency domain, in principle. But, frequency domain processing suffers from aliasing, and brings inconveniences

Person re-identification (Re-ID) has achieved great improvement with the development of deep learning. However, domain adaptation in unsupervised Re-ID has always been a challenging task. Most existing works based on clustering only cluster once, which may lead to pseudo labels of poor quality. In this letter, we propose a Pseudo Label based on Multiple Clustering (PLMC) approach, which makes full

Periocular biometric covering the immediate vicinity of human eye is a synergistic alternative to face particularly when the face is masked or occluded. Most present work for periocular recognition in the wild are mainly convolutional neural networks learned based on cross-entropy loss. However, periocular images only capture the least salient face features, and thus suffering from severe intra-class

This letter introduces a unified framework for accurate segmentation of five different types of pulmonary nodules, namely, solid, juxtapleural, juxtavascular, part solid and ground glass by designing a contrast-adaptive shape-driven level set algorithm. Most of the existing methods have targeted segmenting few specific types of nodules. Variability of shapes along with poor contrast make pulmonary

Tacotron-based end-to-end speech synthesis has shown remarkable voice quality. However, the rendering of prosody in the synthesized speech remains to be improved, especially for long sentences, where prosodic phrasing errors can occur frequently. In this letter, we extend the Tacotron-based speech synthesis framework to explicitly model the prosodic phrase breaks. We propose a multi-task learning scheme

Image corruption is a common phenomenon which occurs due to electromagnetic interference, and electric signal instabilities in a system. In this letter, a novel multi procedure Min-Max Average Pooling based Filter is proposed for removal of salt, and pepper noise that betide during transmission. The first procedure functions as a pre-processing step that activates for images with low noise corruption

We present a novel technique for estimating disk parameters (the center and the radius) from its 2D image. It is based on the maximal likelihood approach utilizing both edge pixels coordinates and the image intensity gradients. We emphasize the following advantages of our likelihood model. It has closed-form formulae for estimating the parameters, therefore, requiring less computational resources than

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression

The capability of associating an image to its geographical location is a significant concern in journalism and digital forensics. Given the availability of geo-tagged satellite imagery for most of the Earth's surface, retrieving the location of a generic picture can be addressed as a cross-view image matching between aerial and ground views. In this paper, we outline some initial steps toward the development

We develop a new tensor model for slow-time multiple-input multiple-output (MIMO) radar, and apply it for joint direction-of-departure (DOD), and direction-of-arrival (DOA) estimation. This tensor model aims to exploit the independence of phase modulation matrix, and receive array in the received signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors of different ranks, one

To achieve a reliable communication with short data blocks, we propose a novel decoding strategy for Kronecker-structured constant modulus signals that provides low bit error ratios (BERs) especially in the low energy per bit to noise power spectral density ratio $(E_b/N_o)$ . The encoder exploits the fact that any $M$ -PSK constellation can be factorized as Kronecker products of lower or equal order

Radars use coherence to increase the signal-processing gain. In electronic support, that same coherence can be exploited to accurately estimate carrier frequency when measured modulo the pulse repetition frequency. Such estimates can be used for passive emitter geolocation, for example. In the pioneering work of Howard & Lavoie on this topic [1], [2], results are demonstrated only where the pulse repetition

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a nonlinear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training

This letter provides a Cramér-Rao Bound (CRB) based analysis of compressive arrays with applications in mmWave channel sensing, where the measurements at the output of an antenna array are further compressed using a complex-valued compression matrix, in order to reduce the system complexity and power consumption. While necessary conditions for the existence of CRB for compressed arrays have been recently

Non-orthogonal multiple access (NOMA) has been proposed to raise the spectral efficiency, which also brought new security threats. This letter studies the channel gain feedback falsification (CGFF) attacks of malicious users in NOMA. Since a slight falsification on channel gain feedback can still seriously damage the efficiency and security of NOMA, the detection of malicious users in NOMA is an important

Low-rank matrix recovery is a fundamental problem in signal processing and machine learning. A recent very popular approach to recovering a low-rank matrix $\mathbf {X}$ is to factorize it as a product of two smaller matrices, i.e., $\mathbf {X}= \mathbf {U}\mathbf {V}^\top$ , and then optimize over $\mathbf {U}, \mathbf {V}$ instead of $\mathbf {X}$ . Despite the resulting non-convexity, recent results

In the task of haze scene restoration, dark channel prior is the most convincing theory. Its biggest contribution is to introduce the concept of dark channel map, which is convenient for acquiring initial transmission. Minimum filter is the basic operation for dark channel map. Nevertheless, the disadvantage of minimum filter is to cause edge information jump, which further brings Halo effect. Besides

We consider the joint design of transmit and receive beamforming for multiple-input multiple-output (MIMO) systems in the presence of multiple targets and interferences. The objective is to maximize the minimum receiver output signal-to-interference-plus-noise-ratio (SINR) of the targets with constraints on the total and, respectively, per-antenna transmission power. The problem can be solved by an

Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality

The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time

In this letter, we focus on the problem of direction-of-arrival (DOA) estimation with a nested array in the presence of unknown mutual coupling. Firstly, the measurement model of the nested array with mutual coupling is established, and the properties of the corresponding array covariance matrix are derived. Based on the obtained properties, the coarray output signal is then reconstructed with reduced

We present an improved kernel recursive least squares (KRLS) algorithm for the online prediction of nonstationary time series. In order to adaptively sparsify a selected kernel dictionary for the KRLS algorithm, the approximate linear dependency (ALD) criterion based KRLS algorithm is combined with the quantized kernel recursive least squares algorithm to provide an initial framework. In order to sufficiently

This letter proposes a correlation detection algorithm based on multi-channelization. The existing method has poor performance for detecting a time-varying signal with a low signal-to-noise ratio(SNR). To overcome this problem, we use the method of correlation summation after multi-channelization. According to the difference between the correlation between signals and noise in the received data, the

In this letter, a random sampling strategy is proposed for the non-cooperative spectrum sensing to improve its performance and efficiency in cognitive radio (CR) networks. The proposed refined Metropolis-Hastings (RMH) algorithm generates the desired channel sequence for fine sensing by sampling from the approximated channel availability distributions in an Markov chain Monte Carlo (MCMC) way. The

The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms also have the ability to adapt to drifts in problem parameters, such as data or model properties, and track the optimal solution with reasonable

Linear discriminant analysis technique is an effective strategy to solve the long-standing issue, i.e., the “curse of dimensionality” that brings many obstacles on high-dimensional data storage and analysis. However, the projections are prone to be affected, especially when the training set contains outlier samples whose distribution deviates from the globality. In many real-world applications, the

Video-based egocentric activity recognition involves fine-grained spatio-temporal human-object interactions. State-of-the-art methods, based on the two-branch-based architecture, rely on pre-calculated optical flows to provide motion information. However, this two-stage strategy is computationally intensive, storage demanding, and not task-oriented, which hampers it from being deployed in real-world

Leveraging the consensus and complementary principle to find a common representation for different views is an essential problem of multi-view clustering. To address the problem, many Low-Rank Representation (LRR) based methods have been proposed. However, existing LRR based methods have two common limitations: 1) they adopt graph regularization that only considers simple pairwise similarities among

Canonical correlation analysis (CCA) is a cost insensitive method. It assumes the same loss for different classification errors and aims to attain a low error rate by maximizing the cross-view correlation. However, in some real-world applications, different classification errors will lead to unequal misclassification losses. In addition, in practice, only limited cost label information is available

The problem of geolocating an unknown high-frequency emitter based on the quasi-parabolic ionosphere model with time-difference of arrival measurements of the refracted radio rays is of fundamental importance in various military and civilian applications. Such a problem admits a maximum-likelihood (ML) formulation, which is nonlinear and non-convex. By elucidating the geometry of the feasible set of

The letter deals with the problem known as robust principal component analysis (RPCA), that is, the decomposition of a data matrix as the sum of a low-rank matrix component and a sparse matrix component. After expressing the low-rank matrix component in factorized form, we develop a novel online RPCA algorithm that is based entirely on reweighted least squares recursions and is appropriate for sequential

This study develops a pseudo-energy-conservation relation to analyze the complementary mean-square performance of a complex-valued normalized least mean-square (CNLMS) algorithm. Based on this relation, we derive a recursion describing the transient complementary mean-square deviation (CMSD) behavior of the CNLMS. Closed-form expressions to predict the steady-state CMSD and complementary excess mean-square

In this letter, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems

Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for denoisers performing maximum a posteriori probability (MAP) estimation, they have not been analyzed for the minimum mean squared error (MMSE) denoisers. This letter addresses this gap by establishing the first theoretical convergence

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this letter we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks

In this letter, we study the interacting effects of the exclusion zone and eavesdropper colluding strategy on the secrecy performance of multiple-antenna transmission system. In particular, a base station (BS) applies random beamforming and user scheduling to enhance the legitimate user channel while multiple eavesdroppers try, either individually or jointly, to overhear the transmitted signal. We

This letter considers a joint user activity, channel and data estimation problem in an uplink grant-free massive MIMO systems with low-precision analog-to-digital converters (ADCs). This joint estimation is firstly formalized as a non-overlapping group sparse problem, in which the components of compound channel follow independent conditional distribution. To address this problem, a new algorithm consists