With increased degrees of freedom of the transmitter, coherent waveform-agile radar system can change its transmission on-the-fly in response to target detection’s requirement. This approach can provide better performance than a single waveform. In this paper, we consider unimodular sequence set design (USSD) problem based on summed range-Doppler ambiguity function (SRDAF) for coherent waveform-agile

This paper considers the problem of signal decomposition and filtering by extending its scope to various signals that cannot be effectively dealt with existing methods. For the core of our methodology, we introduce a new approach, termed “ensemble patch transformation” that provides a framework for decomposition and filtering of signals; thus, as a result, it enhances identification of local characteristics

This paper presents a novel type of a filter bank for image decomposition that incorporates “singular spectrum analysis” (SSA). SSA is based on “singular value decomposition” (SVD).The presented filter banks provide good directional selectivity. Sub-band images highlight exposed directional features such as 0∘,90∘,+ 45∘, and − 45∘ of the input image. The proposed method can be seen as a tool to separate

This paper focuses on a low-complexity one-dimensional (1D) direction-of-arrival (DOA) algorithm with an arbitrary cross-linear array. This algorithm is highly accurate without the performance error usually caused by the uncertainty factor of the wave velocity in the underwater environment. The geometric relationship between two crossed linear arrays is employed to derive the expression of DOA estimation

In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This proposed system is composed of three parts: pre-processing, candidate region detection, and digit recognition. To consider the efficiency of the proposed scheme, image normalization is performed

This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of convolutive features owes to the convolutional neural network (CNN). However, CNN cannot perform learning tasks in an unsupervised fashion. In a recent work, we show that such shortcoming can be

Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators

Thanks to massive antenna arrays in millimeter wave communications, a much higher resolution can be achieved for the estimation of angle of arrival. By taking full advantage of beamforming capability and high angular resolution in millimeter wave systems, a single anchor is sufficient to obtain a good position estimation for agents by combining angle of arrival and time of arrival. In this paper, a

This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise partly from the assumption that the models have a block-diagonal structure, with each block corresponding to an ARMA process, that allows the reconstruction of independent source components from

Recently, compressed sensing (CS) has aroused much attention for that sparse signals can be retrieved from a small set of linear samples. Algorithms for CS reconstruction can be roughly classified into two categories: (1) optimization-based algorithms and (2) greedy search ones. In this paper, we propose an algorithm called the preconditioned generalized orthogonal matching pursuit (Pre-gOMP) to promote

Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed

This paper develops a data collection and processing framework that achieves individual users’ data privacy and the operator’s information accuracy simultaneously. Data privacy is enhanced by adding noise and applying quantization to the data before transmission, and the privacy of an individual user is measured by information-theoretic analysis. This paper develops a data recovery and clustering method

This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art

The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) algorithm. One of the advantages

The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information divergence, as a criterion, is to choose the best match model that has the minimum divergence between candidate models and true mode. Subsequently, the model-conditioned estimation based on variational

Neural networks based on the attentional encoder-decoder model have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads to a lack of key information. And some key information, such as time, place, and people, is indispensable for humans to understand the main content. In this paper, we propose a key information

Image deconvolution consists in restoring a blurred and noisy image knowing its point spread function (PSF). This inverse problem is ill-posed and needs prior information to obtain a satisfactory solution. Bayesian inference approach with appropriate prior on the image, in particular with a Gaussian prior, has been used successfully. Supervised Bayesian approach with maximum a posteriori (MAP) estimation

This paper deals with cell identifier (ID) estimation for narrowband-Internet of things (NB-IoT) system. It is suggested to transform the usual maximum likelihood (ML) estimator expression in order to highlight a discrete Fourier transform (DFT), which can be computed with fast algorithms. Therefore, the proposed method is a DFT-based low-complexity cell ID estimator that can be qualified as optimal

The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both

A new variable step-size strategy for the least mean square (LMS) algorithm is presented for distributed estimation in adaptive networks using the diffusion scheme. This approach utilizes the ratio of filtered and windowed versions of the squared instantaneous error for iteratively updating the step-size. The result is that the dependence of the update on the power of the error is reduced. The performance

In millimeter wave (mmWave) massive MIMO (multiple-input multiple-output) systems, it is difficult to apply conventional digital precoding techniques due to hardware constraints. Fortunately, the hybrid precoding can be utilized to reduce power consumption and high costs. In this paper, a codebook-based hybrid precoding scheme for downlink multiuser mmWave massive MIMO systems is proposed. Our main

A measurement matrix and sensing dictionary are the basic tools for signal compression sampling and reconstruction, respectively, which are important aspects in the field of compression sensing. Previous studies which have divided the measurement matrix and sensing dictionary into two separate processes did not make full use of their inherent intercorrelations. In case of which could be fully utilized

We present a novel approach to non-rigid object tracking in this paper by deriving an adaptive data-driven kernel. In contrast with conventional kernel-based trackers which suffer from the constancy of kernel shape as well as scale and orientation selection problem when the tracking targets are changing in size, the adaptive kernel can robustly achieve the adaptation to target variation and act toward

Global navigation satellite systems (GNSS) are being the target of various jamming, spoofing, and meaconing attacks. This paper proposes a new statistical test for the presence of multiple spoofers based on range measurements observed by a plurality of receivers located on a rigid body platform. The relative positions of the receivers are known, but the location and orientation of the platform are

The brain dynamics in the electroencephalogram (EEG) data are often challenging to interpret, specially when the signal is a combination of desired brain dynamics and noise. Thus, in an EEG signal, anything other than the desired electrical activity, which is produced due to coordinated electrochemical process, can be considered as unwanted or noise. To make brain dynamics more analyzable, it is necessary

Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics of the signal under consideration. In this work, time-frequency analysis is achieved by tracking the roots of time-varying processes using autoregressive moving average (ARMA) models in cascade

Continuous miniaturization of circuitry has open the door for various novel application scenarios of millimeter-sized wireless agents such as for the exploration of difficult-to-access fluid environments. In this context, agents are envisioned to be employed, e.g., for pipeline inspection or groundwater analysis. In either case, the demand for miniature sensors is incompatible with propulsion capabilities

The existence of walls surrounding targets leads to multi-path return at the radar receiver, which provides additional information about the target, and thus can be exploited to strengthen the quality of through-wall radar imaging (TWRI). Based on this, a multi-path exploitation method is proposed to identify the location of the multi-path ghost. An algorithm that combining the modified Green’s function

An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology

In this paper, we present a non-convex ℓ2/ℓq(0

In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the power ratio of the received siackgnal to the jamming-plus-noise. By exploiting the different correlation characteristics between the target echo and MLJ on distributed antennas, the designed filter

Signal design is an important component for good performance of radar systems. Here, the problem of determining a good radar signal with the objective of minimizing autocorrelation sidelobes is addressed, and the first comprehensive comparison of a range of signals proposed in the literature is conducted. The search is restricted to a set of nonlinear, frequency-modulated signals whose frequency function

This work proposes an analog implementation of gradient-based algorithm for compressive sensing signal reconstruction. Compressive sensing has appeared as a promising technique for efficient acquisition and reconstruction of sparse signals in many real-world applications. It starts from the assumption that sparse signals can be exactly reconstructed using far less samples than in standard signal processing

Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks and thus gain more insights into the complicated networks. Strategic nodes belonging to different communities interact with each other

We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using

Traditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signals. However, the DOA estimation performance may degrade substantially when multiple LFM signals are spectrally overlapped in time-frequency (TF) domain. In order to solve this problem, this paper

State estimation in middle- (MV) and low-voltage (LV) electrical grids poses a number of challenges for the estimation method employed. A significant difference to high-voltage grids is the lack of measurements as the instrumentation with measurement equipment in MV and LV grids is very sparse due to economical reasons. Typically, pseudo-measurements are used as a replacement for actual measurements

In this paper, the problem of detection of small signal-to-noise ratio (SNR) variations in noisy signals is addressed in order to provide an efficient and fast method for detection of faulty electroencephalogram (EEG) electrodes which can improve the interpretation of medical data. The method for slight SNR variation assessment, based on the estimation of the longest useful information cluster, is

In this paper, we introduce the q-ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive signals and present an algorithm for computing this new measure. Both the mixed ℓ2/ℓq and the mixed ℓ2/ℓ1 norms of the reconstruction errors for stable and robust recovery using block basis pursuit (BBP), the block Dantzig

Pursuing better imaging quality and miniaturizing imaging devices are two trends in the current development of ultrasound imaging. While the first one leads to more complex and expensive imaging equipment, poor image quality is a common problem of portable ultrasound imaging systems. In this paper, an image reconstruction method was proposed to break through the imaging quality limitation of portable

Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena

The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired researchers to investigate the application of deep learning to the person re-identification. However, the huge amount of research on this problem considers classical settings, where pedestrians

The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy criterion (MCCC) employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the method. However, determining the optimal value for

The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity

This paper reports the investigation on the micromotion characteristics of chaff dipoles and a chaff cloud. Micromotion models of chaff dipoles and steady-state chaff clouds are discussed. Fourier transform method and time-frequency method are used to study the micromotion features of chaff dipoles, in the energy domain and time-frequency domain, respectively. Based on the characteristic parameters

Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its widespread applicability to human systems, including the evaluation of dietary and physical activity and the monitoring of patients and older adults. In this paper, we present a knowledge-driven multisource fusion framework for the recognition

In this paper, we propose a massive MIMO (multiple-input-multiple-output) architecture with distributed steerable phased antenna subarrays for position estimation in the mmWave range. We also propose localization algorithms and a multistage/multiresolution search strategy that resolve the problem of high side lobes, which is inherent in spatially coherent localization. The proposed system is intended

The null space condition for ℓ1 minimization in compressed sensing is a necessary and sufficient condition on the sensing matrices under which a sparse signal can be uniquely recovered from the observation data via ℓ1 minimization. However, verifying the null space condition is known to be computationally challenging. Most of the existing methods can provide only upper and lower bounds on the proportion

This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means (ITKrM) algorithm to learning dictionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low-rank component in the data and provides a strategy for recovering this low-rank component again from incomplete data. Several synthetic experiments

In this paper, we propose a new method for support detection and estimation of sparse and approximately sparse signals from compressed measurements. Using a double Laplace mixture model as the parametric representation of the signal coefficients, the problem is formulated as a weighted ℓ 1 minimization. Then, we introduce a new family of iterative shrinkage-thresholding algorithms based on double Laplace

Periodic nonuniform sampling occurs in many applications, and the Nyquist folding receiver (NYFR) is an efficient, low complexity, and broadband spectrum sensing architecture. In this paper, we first derive that the radio frequency (RF) sample clock function of NYFR is periodic nonuniform. Then, the classical results of periodic nonuniform sampling are applied to NYFR. We extend the spectral reconstruction

In this paper, we investigate the problem of low probability of intercept (LPI)-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems, where the radar system optimizes the transmitted waveform such that the interference caused to the cellular communication systems is strictly controlled. Assuming that the precise knowledge of the target

A priori signal-to-noise ratio (SNR) estimation and noise estimation are important for speech enhancement. In this paper, a novel modified decision-directed (DD) a priori SNR estimation approach based on single-frequency entropy, named DDBSE, is proposed. DDBSE replaces the fixed weighting factor in the DD approach with an adaptive one calculated according to change of single-frequency entropy. Simultaneously

Modern radar and communication systems require the detection and parameter estimation of signal under a broadband radio frequency (RF) environment. The Nyquist folding receiver (NYFR) is an efficient analog-to-information (A2I) architecture. It can use the compressive sensing (CS) techniques to break the limitations of the analog-to-digital converter (ADC). This paper demonstrates the restricted isometry

Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving with a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/BPSK) hybrid modulated signal is a novel kind of low probability interception signal with wide bandwidth. The NYFR is an effective architecture to intercept the LFM/BPSK signal and the LFM/BPSK

In this paper, a new adaptive robustified filter algorithm of recursive weighted least squares with combined scale and variable forgetting factors for time-varying parameters estimation in non-stationary and impulsive noise environments has been proposed. To reduce the effect of impulsive noise, whether this situation is stationary or not, the proposed adaptive robustified approach extends the concept

We present a novel distributed QR factorization algorithm for orthogonalizing a set of vectors in a decentralized wireless sensor network. The algorithm is based on the classical Gram-Schmidt orthogonalization with all projections and inner products reformulated in a recursive manner. In contrast to existing distributed orthogonalization algorithms, all elements of the resulting matrices Q and R are

We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber, an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework, the diffusion ellipsoid had

We present an application of a modified Kalman-Filter (KF) framework for data fusion to the estimation of respiratory rate from multiple physiological sources which is robust to background noise. A novel index of the underlying signal quality of respiratory signals is presented and then used to modify the noise covariance matrix of the KF which discounts the effect of noisy data. The signal quality

Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally