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Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in low image quality. Most of the previous works on low-light imaging focus either only on a single task such as illumination adjustment, color enhancement, or noise removal; or on a joint illumination adjustment and denoising task that heavily relies on short-long exposure image pairs from specific camera models. These approaches are less practical and generalizable in real-world settings where camera-specific joint enhancement and restoration is required. In this paper, we propose a low-light imaging framework that performs joint illumination adjustment, color enhancement, and denoising to tackle this problem. Considering the difficulty in model-specific data collection and the ultra-high definition of the captured images, we design two branches: a coefficient estimation branch and a joint operation branch. The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint operation branch works in a full-resolution space and progressively performs joint enhancement and denoising. In contrast to existing methods, our framework does not need to recollect massive data when adapted to another camera model, which significantly reduces the efforts required to fine-tune our approach for practical usage. Through extensive experiments, we demonstrate its great potential in real-world low-light imaging applications. [ABSTRACT FROM AUTHOR]

Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have instance-dependent noise (IDN) and suffer from high observer variability. In this paper, we systematically discuss the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from biased aggregation of individual annotations. We then propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task. We design a dual-uncertainty estimation approach to measure the disagreement label noise and single-target label noise via improved Direct Uncertainty Prediction and Monte-Carlo-Dropout. A boosting-based curriculum training procedure is later introduced for robust learning. We demonstrate the effectiveness of our method by conducting extensive experiments on three different diseases with synthesized and real-world label noise: skin lesions, prostate cancer, and retinal diseases. We also release a large re-engineered database that consists of annotations from more than ten ophthalmologists with an unbiased golden standard dataset for evaluation and benchmarking. The dataset is available at https://mmai.group/peoples/julie/. [ABSTRACT FROM AUTHOR]

This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the scaling coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Lévy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero. [ABSTRACT FROM AUTHOR]

Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspace. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum a posteriori criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

The problem of sensor fault estimation has been studied for Unmanned Surface Vehicle (USV) systems in networked environment. Assuming that the process noise is related to the measurement noise and packet loss will occur during the measurement transmission. We use an event-trigger mechanism (ETM) to save communication resources. Accordingly, an observer is proposed to estimate the state and sensor fault, and the trigger threshold is associated with the correlation coefficient. By adjusting the threshold, the observer performance and energy consumption can be balanced in an ideal situation. The observer designed in this paper reduces the effects of correlation noises effectively and good handling network packet losses, and it has been proved that the expected covariance of the estimation error is bounded. Finally, the simulation experiment on the identified model of USV in the laboratory is performed to validate the relevant conclusions. [ABSTRACT FROM AUTHOR]

We recently proposed a direction-of-arrival (DOA) estimation method for the coexistence of uncorrelated and coherent signals, which is related to the uniform linear antenna arrays with mutual coupling (MC) in the presence of unknown nonuniform noise. This technique, however, relies on the assumption of mixed signals and is not suitable for scenarios with purely coherent signals. This paper extends the mixed signal scenario to the purely coherent signals one on one hand, and on the other hand, allows us to directly adopt the ESPPRIT algorithm to perform DOA estimation in MC environments. To be specific, with the help of the least squares (LS) criterion, the nonuniform noise is first eliminated, and the traditional middle subarray scheme can then be employed to be immune to the influence of MC. Unlike the former operation, following a different line of attack the latter can make full use of the whole array and provide a larger array antenna aperture. Furthermore, we derive a new strategy to directly determine the DOAs without taking MC into account and performing spectral search. This brings substantial benefits to the DOA estimation accuracy and computational cost. The superiority of the proposed approaches is verified by numerical examples. [ABSTRACT FROM AUTHOR]

Parasitic capacitance has a considerable impact on conductive electromagnetic interference (EMI), especially on the common mode (CM) noise. In vehicular applications, such as more electric aircraft and electrical vehicles, the value of CM stray capacitance can vary during the drive cycle, due to some change in environmental, and operating conditions, resulting in fluctuations in EMI level. Designing EMI filters according to potentially overestimated CM capacitance results in additional weight, volume, and cost which should be avoided. In this paper, for the first time, the parasitic capacitance relevant to CM noise is estimated in real-time using unscented Kalman filter (UKF). The UKF is employed due to its ability in dealing with nonlinearity and stochastic nature of the EMI as well as the measurement noises. The proposed method is tested on a typical dc–dc buck converter in different operating conditions. The effectiveness of the proposed method is tested using some hardware-in-the-loop experiments using dSPACE1104 development platform. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity and noise are two common issues in images but inevitably lead to significant challenges for image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set models yield poor performance when applied to this images. To this end, this paper proposes a novel hybrid level set model, named adaptive variational level set model (AVLSM) by integrating an adaptive scale bias field correction term and a denoising term into one level set framework, which can simultaneously correct the severe inhomogeneous intensity and denoise in segmentation. Specifically, an adaptive scale bias field correction term is first defined to correct the severe inhomogeneous intensity by adaptively adjusting the scale according to the degree of intensity inhomogeneity while segmentation. More importantly, the proposed adaptive scale truncation function in the term is model-agnostic, which can be applied to most off-the-shelf models and improves their performance for image segmentation with severe intensity inhomogeneity. Then, a denoising energy term is constructed based on the variational model, which can remove not only common additive noise but also multiplicative noise often occurred in medical image during segmentation. Finally, by integrating the two proposed energy terms into a variational level set framework, the AVLSM is proposed. The experimental results on synthetic and real images demonstrate the superiority of AVLSM over most state-of-the-art level set models in terms of accuracy, robustness and running time. [ABSTRACT FROM AUTHOR]

This paper investigates the H∞ filtering problem for nonhomogeneous Markovian jump repeated scalar nonlinear systems with multiplicative noises and partially mode-dependent (PM) characterization. A new PM H∞ filter is proposed, which guarantees the stochastic stability of the filtering error systems. The transition probabilities (TPs) of the nonhomogeneous Markovian process are assumed to be polytopic and the probability for successful transmission of mode information is characterized by a Bernoulli distributed sequence. By constructing the Lyapunov functional method, the existence conditions of filter are presented. Finally, the efficiency of the obtained results for PM H∞ filter are demonstrated by an economic system example. [ABSTRACT FROM AUTHOR]

This paper studies the related problems of prediction, covariance estimation, and principal component analysis for the spiked covariance model with heteroscedastic noise. We consider an estimator of the principal components based on whitening the noise, and we derive optimal singular value and eigenvalue shrinkers for use with these estimated principal components. Underlying these methods are new asymptotic results for the high-dimensional spiked model with heteroscedastic noise, and consistent estimators for the relevant population parameters. We extend previous analysis on out-of-sample prediction to the setting of predictors with whitening. We demonstrate certain advantages of noise whitening. Specifically, we show that in a certain asymptotic regime, optimal singular value shrinkage with whitening converges to the best linear predictor, whereas without whitening it converges to a suboptimal linear predictor. We prove that for generic signals, whitening improves estimation of the principal components, and increases a natural signal-to-noise ratio of the observations. We also show that for rank one signals, our estimated principal components achieve the asymptotic minimax rate. [ABSTRACT FROM AUTHOR]

This paper proposes an auto-focusing (AF)-noise suppressor for cell-phone videos based on multiple noise references and spectral gain selection. AF noise generated by a lens actuator, a cam, or a helicoid is suppressed with a spectral gain which is calculated from a combination of multiple noise references prepared in advance. For quickly adjusting the spectral gain to product-dependent noise characteristics, a mixing ratio of noise references is adaptively determined based on an analysis result of the input noisy speech. The spectral gain for AF noise or that for background noise, whichever is smaller, is applied to the input noisy speech for actual suppression so that both AF noise and background noise are suppressed. Simulation results demonstrate that an AF noise recorded with a commercial cell-phone handset is successfully suppressed with little distortion in the target speech. The PESQ score is improved by as much as 2.9 compared to a conventional noise suppressor designed for ordinary background noise. This paper has been employed in commercial products which are shipped worldwide. [ABSTRACT FROM PUBLISHER]

Crowd counting is a challenging problem due to the diverse crowd distribution and background interference. In this paper, we propose a new approach for head size estimation to reduce the impact of different crowd scale and background noise. Different from just using local information of distance between human heads, the global information of the people distribution in the whole image is also under consideration. We obey the order of far- to near-region (small to large) to spread head size, and ensure that the propagation is uninterrupted by inserting dummy head points. The estimated head size is further exploited, such as dividing the crowd into parts of different densities and generating a high-fidelity head mask. On the other hand, we design three different head mask usage mechanisms and the corresponding head masks to analyze where and which mask could lead to better background filtering. Based on the learned masks, two competitive models are proposed which can perform robust crowd estimation against background noise and diverse crowd scale. We evaluate the proposed method on three public crowd counting datasets of ShanghaiTech, UCFQNRF and UCFCC_50. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art crowd counting approaches. [ABSTRACT FROM AUTHOR]

Noise causes unpleasant visual effects in low-light image/video enhancement. In this paper, we aim to make the enhancement model and method aware of noise in the whole process. To deal with heavy noise which is not handled in previous methods, we introduce a robust low-light enhancement approach, aiming at well enhancing low-light images/videos and suppressing intensive noise jointly. Our method is based on the proposed Low-Rank Regularized Retinex Model (LR3M), which is the first to inject low-rank prior into a Retinex decomposition process to suppress noise in the reflectance map. Our method estimates a piece-wise smoothed illumination and a noise-suppressed reflectance sequentially, avoiding remaining noise in the illumination and reflectance maps which are usually presented in alternative decomposition methods. After getting the estimated illumination and reflectance, we adjust the illumination layer and generate our enhancement result. Furthermore, we apply our LR3M to video low-light enhancement. We consider inter-frame coherence of illumination maps and find similar patches through reflectance maps of successive frames to form the low-rank prior to make use of temporal correspondence. Our method performs well for a wide variety of images and videos, and achieves better quality both in enhancing and denoising, compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A novel robust Rauch–Tung–Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters, and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this paper is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers. [ABSTRACT FROM AUTHOR]

In modern microprocessor and SoC designs, supply noise margin has been significantly reduced due to the continuously decreasing supply voltage level. On the other hand, with increasing current density, chips may see larger supply noise variations on various spots and from time to time. As a result, chip robustness and reliability are inevitably deteriorated with more frequent supply noise emergencies. It is therefore crucial to have an efficient supply noise prediction method to enhance design robustness. The state-of-art solutions either try to build a spatial noise estimation framework at the layout-level using the limited distributed physical noise sensors or attempt to develop emergency predictors at the architecture-level thus ignore back-end power delivery details. In this paper, we propose a cross-layer framework for temporal supply noise prediction. Our method not only accounts for the temporal characteristics of workload execution at micro-architecture-level but also incorporates the power delivery model at the circuit-level into such system-level prediction. In order to enable the capability of on-the-fly noise prediction, we first bridge the gap between system-level workload and micro-architectural-level power by employing an ordinary least square-based power estimation model and an adaptive auto-regressive integrated moving average model (ARIMA)-based power prediction model. Then a layout-level supply noise model is developed to explore the correlations between micro-architectural-level power and layout-level supply noise. Compared with existing methods, the proposed ARIMA-based power model improves the prediction performance by up to 37.5%/63.0% in X86/ARM. Moreover, compared with SPICE simulation, our framework is able to estimate present supply noise with an average error of 0.005% and predict future supply noise with an average error of 1.58%/1.17% for X86/ARM architecture. [ABSTRACT FROM AUTHOR]

Quantifying the level of nonlinear distortions and time-varying effects in frequency response function measurements is a first step toward the selection of an appropriate parametric model structure. In this paper, we tackle this problem in the presence of missing data, which is an important issue in large-scale low-cost wireless sensor networks. The proposed method is based on one experiment with a special class of periodic excitation signals. [ABSTRACT FROM AUTHOR]

The acoustic noises caused by the fixed-frequency signal injection scheme limit the applications of saliency tracking based sensorless control methods for interior permanent magnet synchronous machine (IPMSM) drives at low-speed operation. In this paper, a pseudorandom-frequency sinusoidal injection (PRFSI) considering the sample and hold effect in practical applications is proposed to reduce the undesired acoustic noises. Since the power spectral density (PSD) of the excited high-frequency (HF) current could be dispersed and continuously distributed with PRFSI, the additional HF acoustic noises can be reduced. Through the comparative analysis of HF response currents PSD results for PRFSI and fixed-frequency sinusoidal injection (FFSI), the principle of acoustic noises reduction with PRFSI is analyzed theoretically. Furthermore, the optimal selection of the injection signals is derived considering the sample and hold effect. Whereby, a comparative evaluation of the proposed PRFSI versus FFSI is carried out on a 2.2-kW position sensorless IPMSM drive, in terms of position estimation and PSD results, which verifies the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper considers the problem of robust three-dimensional (3-D) angle-of-arrival (AOA) source localization in the presence of impulsive $\alpha$ -stable noise based on the $l_p$ -norm minimization criterion. The iteratively reweighted least-squares algorithm (IRLS) is a well-known technique for solving $l_p$ -norm minimization with the desirable global convergence property. Adopting the IRLS for 3-D AOA localization requires nonlinear-to-pseudolinear transformation of azimuth and elevation angle measurement equations, thus resulting in a new variant of the IRLS, called the iteratively reweighted pseudolinear least-squares estimator (IRPLE). Unfortunately, there exists correlation between the measurement matrix and noise vector in the pseudolinear measurement equations, which consequently makes the IRPLE biased. To counter the bias problem of the IRPLE, a new iteratively reweighted instrumental-variable estimator (IRIVE) is proposed based on the exploitation of instrumental variables. The IRIVE is analytically shown to achieve the theoretical covariance of the general least $l_p$ -norm estimation. Extensive simulation studies are presented to demonstrate the performance advantages of the IRIVE over the IRPLE as well as other existing least-squares and least $l_p$ -norm estimators. The IRIVE is observed to produce nearly unbiased estimates with mean squared error performance very close to the Cramér–Rao lower bound. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits of intentionally adding noise to a Bayesian estimator, which comprises a linear combination of a number of individual Bayesian estimators that are perturbed by mutually independent noise sources and multiplied by a set of adjustable weighting coefficients. We prove that the Bayes risk for the mean square error (MSE) criterion is minimized when the same optimum weighting coefficients are assigned to the identical estimators in the combiner. This property leads to a simplified analysis of the noise benefit to the MSE of the combined Bayesian estimator even when the number of individual estimators tends to infinity. It is shown that, for a sufficiently large number of individual estimators, the MSE of the designed Bayesian estimator approaches a plateau for a wide range of added noise levels. This robust feature facilitates the incorporation of the added noise into the design of Bayesian estimators without tuning the noise level. For an easily implementable Bayesian estimator composed of quantizers, the benefit of the symmetric scale-family noise is demonstrated, and the optimal noise probability density function is approximated by solving a constrained nonlinear optimization problem. We further extend this potential Bayesian estimator to the nonlinear filter design. Finally, examples of the noise benefits in random parameter estimation and nonlinear filtering support the theoretical analyses. [ABSTRACT FROM AUTHOR]

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature (BT) using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis. In the measurements, the negative effect of radio frequency interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on the MUltiple SIgnal Classification (MUSIC) algorithm. Recently, the feasibility of MUSIC direction-of-arrival estimation has been shown for the RFI source detection of the synthetic aperture interferometric radiometer. This paper refines the MUSIC RFI source detection algorithm and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of BT images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution. [ABSTRACT FROM PUBLISHER]

In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature. [ABSTRACT FROM PUBLISHER]

Detection of small changes in natural formations and human-made constructions is a promising application of synthetic aperture radar (SAR). This paper deals with a specific radar, which is a ground-based SAR using noise waveform. Being fully coherent, SAR provides both phase and amplitude information for every pixel in the mapped area. This gives rise to the possibility of comparing phases between the images taken from the same position at different times in order to extract information about the changes that have occurred between subsequent radar measurements. Such a technique, called coherent change detection (CCD), allows changes of a subwavelength size to be detected. The use of a noise waveform for sounding ensures high electromagnetic compatibility and interference immunity. Moreover, it enables long integration time in the detection stage. However, the random nature of probing signals used in noise radars leads to randomness of the output signals and the preservation of residual fluctuation in the images generated. This random residual, along with external noise, influences the overall accuracy of the phase measurement in noise radar. In this paper, the theoretical and experimental investigation of this specific influence of noise on the precision of phase measurements is presented. The theoretical part of the paper is focused on the estimation of the Cramer-Rao lower bound for the phase measurements using both statistical theory and geometrical interpretation of complex signals. In the experimental part, these findings are verified with dedicated measurements of a metallic sphere displacement, carried out with the help of a ground-based noise waveform SAR. [ABSTRACT FROM AUTHOR]

The eigendecompostion of the coherency matrix and the related parameters, i.e., entropy $H$, $\alpha$, and anisotropy $A$, are effective and popular tools for the analysis and quantitative estimation of the physical parameters of polarimetric synthetic aperture radar images. However, the speckle noise constitutes the main obstacle that hinders these goals and should be filtered. In this paper, based on studies obtained from extensive simulated data sets, we tried to determine how this noise is transmitted to sample eigendecomposition parameters. The dependence between means and variances of sample parameters leads to building their speckle models and to the definition of a bias elimination procedure. We found that sample eigenvalues were affected by multiplicative noise, whereas sample entropy and sample anisotropy were affected by a mixture of multiplicative and additive noise sources. In addition to its versatility, independence to knowledge of the equivalent number of looks and high ability to bias compensation, the proposed bias suppression technique reduced the variance of noise. Simulated and real data as well as the existing theories are used for validation in this paper. [ABSTRACT FROM PUBLISHER]

Most cooperative spectrum sensing research activities focus on maximizing the primary user detection and neglect the achieved secondary system performance. This paper proposes a novel capacity-aware cooperative spectrum sensing optimization method, capable of computing the optimal values for the cooperative sensing parameters (e.g., the number of sensed samples, the number of cooperating devices, control channel bandwidth) so that the secondary system capacity is maximized. The proposed method utilizes energy detection with estimated noise power (ENP). We derive and experimentally validate the analytical models for cooperative spectrum sensing based on the ENP method. Additionally, we evaluate the performance of the proposed capacity-aware spectrum sensing described in this paper. We show that the proposed capacity-aware cooperative spectrum sensing based on noise power estimation achieves enhanced secondary system capacity compared to the previous commonly proposed sensing processes that maximize the detection performance. Moreover, the results give insight into the optimal behavior of the proposed capacity-aware cooperative spectrum sensing based on noise power estimation regarding the common cooperative spectrum sensing parameters. [ABSTRACT FROM AUTHOR]

On-line estimation of low-frequency electromechanical oscillations in an interconnected power system has attracted much attention as this provides vital information about the stability condition of the power system. In this paper, a measurement-based method for the estimation of the frequency, magnitude, and damping of an oscillation is proposed. The method can be applied to the phasor measurement data to extract further information about the power system. The proposed method is derived by applying modifications to the existing concept of second-order generalized integrator and adaptive notch filter. This paper details the mathematical derivation and stability properties of the proposed method and presents the simulation results to confirm the analytical derivations and the desirable performance of the proposed method. [ABSTRACT FROM PUBLISHER]

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot’s orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method. [ABSTRACT FROM PUBLISHER]

Accurate voltage and current phasor measurements are required to be provided by phasor measurement units placed at various buses in the system. This paper presents an effective method for estimation of alternating current signal fundamental phasor under dynamic conditions. The model-order estimation of the current signal has been carried out to separate the signal space and the noise space in the signal correlation matrix. The Vandermonde matrix of the total least square estimation of signal parameters via rotational invariance techniques method is extended to second-order Taylor’s series approximation to estimate the dynamic phasor of the current signal. The suggested technique for the phasor estimation has been tested on simultaneous multimode amplitude and phase oscillations with decaying dc offset and noninteger harmonics, in the presence of noise and also for a fault scenario in New England 39-bus system. This paper also provides a few suggestions for a few possible amendments in the IEEE synchrophasor standard. [ABSTRACT FROM PUBLISHER]

Spectrum sensing is a fundamental component of cognitive radio (CR). How to promptly sense the presence of primary users (PUs) is a key issue to a CR network. The time requirement is critical in that violating it will cause harmful interference to the PU, leading to a system-wide failure. The motivation of this paper is to provide an effective spectrum sensing method to detect PUs as soon as possible. In the language of streaming-based real-time data processing, short time means small data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmid's concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV (DTV) signal, show that this proposed method can operate either using smaller data or working under a lower signal-to-noise ratio (SNR) environment. [ABSTRACT FROM PUBLISHER]

This paper studies the zero-delay source-channel coding problem, and specifically the problem of obtaining the vector transformations that optimally map between the $m$ -dimensional source space and $k$ -dimensional channel space, under a given transmission power constraint and for the mean square error distortion. The functional properties of the cost are studied and the necessary conditions for the optimality of the encoder and decoder mappings are derived. An optimization algorithm that imposes these conditions iteratively, in conjunction with the noisy channel relaxation method to mitigate poor local minima, is proposed. The numerical results show strict improvement over prior methods. The numerical approach is extended to the scenario of source-channel coding with decoder side information. The resulting encoding mappings are shown to be continuous relatives of, and in fact subsume as special case, the Wyner–Ziv mappings encountered in digital distributed source coding systems. A well-known result in information theory pertains to the linearity of optimal encoding and decoding mappings in the scalar Gaussian source and channel setting, at all channel signal-to-noise ratios (CSNRs). In this paper, the linearity of optimal coding, beyond the Gaussian source and channel, is considered and the necessary and sufficient condition for linearity of optimal mappings, given a noise (or source) distribution, and a specified a total power constraint are derived. It is shown that the Gaussian source-channel pair is unique in the sense that it is the only source-channel pair for which the optimal mappings are linear at more than one CSNR values. Moreover, the asymptotic linearity of optimal mappings is shown for low CSNR if the channel is Gaussian regardless of the source and, at the other extreme, for high CSNR if the source is Gaussian, regardless of the channel. The extension to the vector settings is also considered where besides the conditions inherited from the scalar case, additional constraints must be satisfied to ensure linearity of the optimal mappings. [ABSTRACT FROM AUTHOR]

The problem of adaptive noisy clustering is investigated. Given a set of noisy observations \(Z_{i}=X_{i}+\epsilon _{i}\) , \(i=1, \ldots , n\) , the goal is to design clusters associated with the law of \(X_{i}\) ’s, with unknown density \(f\) with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach as the popular \(k\) -means is not suitable in this case. In this paper, we propose a noisy \(k\) -means minimization, which is based on the \(k\) -means loss function and a deconvolution estimator of the density \(f\) . In particular, this approach suffers from the dependence on a bandwidth involved in the deconvolution kernel. Fast rates of convergence for the excess risk are proposed for a particular choice of the bandwidth, which depends on the smoothness of the density \(f\) . Then, we turn out into the main issue of this paper: the data-driven choice of the bandwidth. We state an adaptive upper bound using a modified version of Lespki’s method, called Empirical Risk Comparison, where empirical risks associated with different bandwidths are compared. Eventually, we illustrate that the selection rule can be used in many statistical problems of \(M\) -estimation where the empirical risk depends on a nuisance parameter. [ABSTRACT FROM PUBLISHER]

The existing channel estimation methods for millimeter-wave communications, e.g., hierarchical search and compressed sensing, either acquire only one single multipath component (MPC) or require considerably high training overhead. To realize fast yet accurate channel estimation, we propose a multipath decomposition and recovery approach in this paper. The proposed approach has two stages. In the first stage, instead of directly searching the real MPCs, we decompose each real MPC into several virtual MPCs and acquire the virtual MPCs by using the hierarchical search based on a normal-resolution codebook. Then, in the second stage, the real MPCs are recovered from the acquired virtual MPCs in the first stage, which turns out to be a sparse reconstruction problem, where the size of the dictionary matrix is greatly reduced by exploiting the results of the virtual multipath acquisition. Moreover, to make the proposed approach applicable for both analog and hybrid beamforming/combining devices with strict constant-modulus constraint, we particularly design a codebook for the hierarchical search by using an enhanced subarray technique, and the codebook is also applicable in other hierarchical search methods. Performance comparisons show that the proposed approach achieves a superior tradeoff between estimation performance and training penalty over the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other’s original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of $(\epsilon, \delta)$ -data-privacy, defined as the probability of $\epsilon $ -accurate estimate (the difference of an estimation and the original data is within $\epsilon $ ) is no larger than $\delta $ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor’s original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral search approach [25], which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum–difference coarray (SDCA) is derived for larger degrees of freedom (DOFs). Besides, to optimize the selection of regularization parameter, we also construct a new sparse representation algorithm by estimating the errors between the signal and noise parts. Further, an iterative scheme is presented to transform the 2-D grids searching to several times of 1-D searching, where the initial values are obtained by extracting one difference coarray from SDCA. So the proposed method can achieve aperture extension, high estimation performance, and low computational complexity. Besides, the sparse array extension model for multiple-input multiple-output radars is discussed and the Cramér–Rao bound for 2-D DOA estimation with CPPA is also derived in detail. Finally, simulation results demonstrate the effectiveness of proposed method compared to the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Direction of arrival (DOA) estimation in array processing using uniform/sparse linear arrays is concerned in this paper. While sparse methods via approximate parameter discretization have been popular in the past decade, the discretization may cause problems, e.g., modeling error and increased computations due to dense sampling. In this paper, an exact discretization-free method, named as sparse and parametric approach (SPA), is proposed for uniform and sparse linear arrays. SPA carries out parameter estimation in the continuous range based on well-established covariance fitting criteria and convex optimization. It guarantees to produce a sparse parameter estimate without discretization required by existing sparse methods. Theoretical analysis shows that the SPA parameter estimator is a large-snapshot realization of the maximum likelihood estimator and is statistically consistent (in the number of snapshots) under uncorrelated sources. Other merits of SPA include improved resolution, applicability to arbitrary number of snapshots, robustness to correlation of the sources and no requirement of user-parameters. Numerical simulations are carried out to verify our analysis and demonstrate advantages of SPA compared to existing methods. [ABSTRACT FROM PUBLISHER]

In this paper, we provide specific and practical approaches to associate uncertainty with \4 \times \4 transformation matrices, which is a common representation for pose variables in 3-D space. We show constraint-sensitive means of perturbing transformation matrices using their associated exponential-map generators and demonstrate these tools on three simple-yet-important estimation problems: 1) propagating uncertainty through a compound pose change, 2) fusing multiple measurements of a pose (e.g., for use in pose-graph relaxation), and 3) propagating uncertainty on poses (and landmarks) through a nonlinear camera model. The contribution of the paper is the presentation of the theoretical tools, which can be applied in the analysis of many problems involving 3-D pose and point variables. [ABSTRACT FROM PUBLISHER]

In this paper, a phase-based direct average strain estimation method is developed. A mathematical model is presented to calculate axial strain directly from the phase of the zero-lag cross-correlation function between the windowed precompression and stretched post-compression analytic signals. Unlike phase-based conventional strain estimators, for which strain is computed from the displacement field, strain in this paper is computed in one step using the secant algorithm by exploiting the direct phase?strain relationship. To maintain strain continuity, instead of using the instantaneous phase of the interrogative window alone, an average phase function is defined using the phases of the neighboring windows with the assumption that the strain is essentially similar in a close physical proximity to the interrogative window. This method accounts for the effect of lateral shift but without requiring a prior estimate of the applied strain. Moreover, the strain can be computed both in the compression and relaxation phases of the applied pressure. The performance of the proposed strain estimator is analyzed in terms of the quality metrics elastographic signal-to-noise ratio (SNRe), elastographic contrast-tonoise ratio (CNRe), and mean structural similarity (MSSIM), using a finite element modeling simulation phantom. The results reveal that the proposed method performs satisfactorily in terms of all the three indices for up to 2.5% applied strain. Comparative results using simulation and experimental phantom data, and in vivo breast data of benign and malignant masses also demonstrate that the strain image quality of our method is better than the other reported techniques. [ABSTRACT FROM AUTHOR]

Suppose a linear model \bf y=\bf H\bf x+\bf n, where inputs \bf x,n are independent Gaussian mixtures. The problem is to design the transfer matrix \bf H so as to minimize the mean square error (MSE) when estimating \bf x from \bf y. This problem has important applications, but faces at least three hurdles. Firstly, even for a fixed \bf H, the minimum MSE (MMSE) has no analytical form. Secondly, the MMSE is generally not convex in \bf H. Thirdly, derivatives of the MMSE w.r.t. \bf H are hard to obtain. This paper casts the problem as a stochastic program and invokes gradient methods. The study is motivated by two applications in signal processing. One concerns the choice of error-reducing precoders; the other deals with selection of pilot matrices for channel estimation. In either setting, our numerical results indicate improved estimation accuracy—markedly better than those obtained by optimal design based on standard linear estimators. Some implications of the non-convexities of the MMSE are noteworthy, yet, to our knowledge, not well known. For example, there are cases in which more pilot power is detrimental for channel estimation. This paper explains why. [ABSTRACT FROM AUTHOR]

This paper presents a high-accuracy technique for the assessment of harmonics and interharmonics in power systems. In the proposed method, a generalized eigenvalue problem is solved by making use of the matrix pencil method. It is a general method in the sense that it has the ability to predict the frequency, amplitude, and relative phase of frequency components present in a given signal, while the dc-off set and damping factors can be accurately estimated. It also offers a noise-resilient feature which helps to efficiently analyze the noise-contaminated signals. It shows a good performance in dealing with modulated signals as well. Due to its excellent accuracy, the proposed method is foreseen to properly work for the estimation of phasors which is a crucial need in the electrical networks. Different simulation and experimental cases are presented to evaluate the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

In the traditional processing flow of interferometric synthetic aperture radar (SAR) technique, the processing of phase is conducted via two separated and successive steps, i.e., phase denoising and phase unwrapping. That is to say, first, wrapped phases without noise are generated, and then, the true phases without 2\pi-ambiguities are reconstructed (here and in the rest of this paper, true phase refers to the information-induced unwrapped phase without noise). Such separated steps will inevitably bring in extra estimation error because each step has necessary approximations and presumptions which do not always hold. On the contrary, in this paper, we treat phase denoising and unwrapping as a single problem of true phase recovery from observed ones. Following this methodology, an integrated phase denoising and unwrapping algorithm based upon Markov random fields (MRFs) is proposed. Taking a priori knowledge of interferometric phases into account, MRF is used to model the relationship between the elements in the random variable set including both true phases and their observations. After the model is built up, the energy function of this MRF is defined according to the local-independence property inferred from the MRF structure and then minimized to obtain the estimate of the true phase value. In the end of this paper, experiments on simulated and true phase data are conducted, and the comparison with several commonly used unwrapping methods is proposed to verify the efficiency of the proposed MRF algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with fundamental properties of the least-squares-based estimation algorithms of power frequency, such as immunity and insensitivity to harmonics and noise. This paper is motivated by a recent study that dealt with an optimal case of the distance between equidistantly spaced samples. This paper extends and generalizes that approach by decoupling of task of canceling dc component from the least-squares estimation task. Two different algorithms derived as particular cases of this generalized approach are investigated. In addition, besides the basic least-squares (LS) method, the Prony-based method previously formulated by Lobos and Rezmer has also been investigated. The task of estimation frequency from a pure sine signal without dc component presence is also considered. Finally, the third method is derived and its basic properties are shown. [ABSTRACT FROM PUBLISHER]

The fixed-frequency signal is employed in conventional high-frequency (HF) signal injection-based sensorless control schemes for interior permanent magnet synchronous motor (IPMSM) drives, which causes significant electromagnetic and acoustic noises. In order to diminish the additional noises, a novel pseudorandom HF (PRHF) signal injection method is investigated in this paper. Comparative analysis of the power spectra density of the induced HF currents is carried out among three different schemes: fixed-frequency HF, full-period-switch (FPS) PRHF, and half-period-switch (HPS) PRHF square-wave voltage injection. The principles and the spectral characteristics of the three PRHF injection schemes are described. Both theoretical analysis and experimental results are provided, which can agree with each other well. A theoretical basis of frequency determination is presented. Comparison of the spectral performance shows that the HPS–PRHF scheme has continuous spectra and the electromagnetic and acoustic noises can be suppressed significantly. The method is verified via a 2.2-kW IPMSM drive platform. [ABSTRACT FROM AUTHOR]

In this paper, we propose a scheme for the detection of digital amplitude-phase modulated signals in flat fading channels with non-Gaussian noise. The additive noise is modeled by a symmetric alpha-stable distribution, a well-known model of man-made and natural noise that appears in various environments. A major challenge in the design of signal detection schemes is that detectors typically operate without knowledge of the transmitted data, channel state, and noise statistics. The design becomes especially difficult for the considered noise model since conventional signal processing approaches are not applicable. For this reason, we propose a five-stage signal detection scheme that is based on the use of a matched myriad filter. An important contribution of this paper is the development of new algorithms for the estimation of noise distribution parameters in the presence of an unknown signal. Results are presented which show that the proposed detector outperforms a zero-memory non-linearity-based scheme. [ABSTRACT FROM AUTHOR]

In this paper, a novel super resolution (SR) framework is proposed to protect flat regions and edges of the reconstructed high resolution (HR) image simultaneously. In order to remove outliers and constrain the smoothness of the reconstructed HR image, the Lorentzian stochastic estimation is used for measuring the difference between the estimated HR image and each low resolution (LR) image. Moreover, this paper proposes a new regularization item, termed as Lorentzian gradient constraint, which incorporates with bilateral total variation (BTV) to enhance edges and keep flat regions of the reconstructed HR image. The combination of the two regularization items is superior to existing methods only based on BTV because it considers the balance between eliminating outliers and preserving details. Experimental results are presented to show the image quality and practical applicability of the new SR framework, and additionally demonstrate its superiority to existing SR methods. [ABSTRACT FROM AUTHOR]

This paper presents a nonparametric method to measure an improved frequency response function (FRF) of a linear dynamic system excited by a random input. Recently, the local polynomial method (LPM) has been proposed as a technique to reduce the leakage errors on FRF measurements. The noise sensitivity of the LPM was similar to that of the classical windowing methods, while the leakage rejection is improved from an O(N-1) to an O(N-3). This paper shows a methodology, to automatically tune the parameter of the LPM, viz., the local bandwidth that sets how many neighboring frequency lines are combined in a single-point estimate, to get lower noise sensitivity by increasing the smoothening of the original LPM, without any user interaction. The balance between noise reduction and bias error will be automatically retrieved. [ABSTRACT FROM AUTHOR]

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy. [ABSTRACT FROM AUTHOR]