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Registration of diffusion weighted datasets remains a challenging task in the process of quantifying diffusion indexes. Respiratory and cardiac motion, as well as echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for the liver. This work proposes a methodology for the non-rigid registration of multiparametric abdominal diffusion weighted...
It is well recognized that effort estimation is an essential part of successful software management. Among many estimation models, the Case-Base Effort Estimation (CBEE) has been intensively used among researchers and practitioners as a promising model for better and accurate effort prediction. The common challenges with this model are: (1) finding the nearest cases to the new case, (2) selecting...
In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic...
This paper proposes an improved principal component analysis (PCA) algorithm for anomaly detection of hydropower units (HUs). Operation conditions of HUs are identified first. Then PCA model is updated by two adaptive updating methods under different operation conditions. And in steady operation conditions, the proper window size, an important parameter, is obtained by the estimation of model stability...
Advanced robotic technology has become a significant component in many medical specialties, including in rehabilitation tasks such as physiotherapy. Rehabilitation programs are a practical approach created to assist patients, such as stroke victims, in retrieving their missing functional capacity, obtaining new skills, and enhancing their quality of life. Nevertheless, rehabilitation treatments need...
Traditional kernelized correlation filter tracking methods use the target position in the current frame to estimate the moving target initial position in the next frame. For fast moving target, these methods lose the target easily. To cope with this problem, a novel scale-adaptive regression position prediction tracking approach is proposed. This algorithm employs regression prediction method to predict...
The purpose of this paper is to derive new asymptotic properties of the robust adaptive normalized matched filter (ANMF). More precisely, the ANMF built with Tyler estimator (TyE-ANMF) is analyzed under the framework of complex elliptically symmetric (CES) distributions. We show that the distribution of TyE-ANMF can be accurately approximated by the well-known distribution of the ANMF built with the...
Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered...
In this paper, we consider robust direction-of-arrival (DOA) estimation for an array that contains mis-calibrated sensors with unknown gain and phase uncertainties. We develop two robust DOA estimation algorithms based on the maximum correntropy criterion (MCC). In the first algorithm, adaptively optimized weighting factors are obtained and applied to each sensor to effectively mitigate the effect...
Contactless respiration monitoring using Doppler radar is an important technology for healthcare applications. The radar measures small displacements of the body surface. In this study, we propose a new algorithm to separate multiple targets placed closely together at the same range but at different lateral positions using ultra-wideband array radar and the Capon method. The Capon method, which is...
Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal x from noisy linear measurements y = Ax + w. Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds under certain large random matrices and that matches the replica prediction of optimality. But while AMP's SE holds...
In the era of deep learning, although beam-forming multi-channel signal processing is still very helpful, it was reported that single-channel robust front-ends usually cannot benefit deep learning models because the layer-by-layer structure of deep learning models provides a feature extraction strategy that automatically derives powerful noise-resistant features from primitive raw data for senone...
An effective method is proposed to estimate the desired-signal (S) subspace by the intersection between the signal-plus-interference (SI) subspace and a reference space covering the angular region where the desired signal is located. The estimated S subspace is robust to steering vector mismatch and overestimation of the SI subspace, capable of detecting the relative strength of the desired signal...
Acoustic beamforming has played a key role in the robust automatic speech recognition (ASR) applications. Accurate estimates of the speech and noise spatial covariance matrices (SCM) are crucial for successfully applying the minimum variance distortionless response (MVDR) beamforming. Reliable estimation of time-frequency (TF) masks can improve the estimation of the SCMs and significantly improve...
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimensionality reduction by first multiplying each data vector by a random Gaussian matrix, and then computing an element-wise sinusoid. Theoretical analysis shows that collecting a sufficient number of such features can be reliably...
When noise is directional instead of diffuse, the majority of conventional direction of arrival (DOA) estimation techniques suffer from performance degradation because of mismatched noise models. In this paper, a novel robust DOA estimation algorithm is developed as an initial investigation into DOA estimation of speech under directional non-speech interference (DNSI) and non-directional background...
This paper presents an exemplar-based image completion via a new quality measure based on phaseless texture features. The proposed method derives a new quality measure obtained by monitoring errors caused in power spectra, i.e., errors of phaseless texture features, converged through phase retrieval. Even if a target patch includes missing pixels, this measure enables selection of the best matched...
We present an algorithm that finds planar structures in a Manhattan world from two pictures taken from different viewpoints with unknown baseline. The Manhattan world assumption constrains the homographies induced by the visible planes on the image pair, thus enabling robust reconstruction. We extend the T-linkage algorithm for multistructure discovery to account for constrained homographies, and...
The regularization parameter is required in most (if not all) adaptive algorithms, while its role becomes very critical in the presence of additive noise. In this paper, we focus on the regularized recursive least-squares (RLS) algorithm and present a method to find its regularization parameter, which is related to the signal-to-noise ratio (SNR). Also, using a proper estimation of the SNR, we further...
In Direction-of-Arrival (DOA) estimation for multiple sources, removal of noisy data points from a set of local DOA estimates increases the resulting estimation accuracy, especially when there are many sources and they have small angular separation. In this work, we propose a post-processing technique for the enhancement of DOA extraction from a set of local estimates using the consistency of these...
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