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Link of Serum Ferritin along with Heart failure Iron Accumulation along with Heart failure Perform throughout Transfusion Reliant Beta-Thalassemia Major Individuals.
Multi-view registration plays a critical role in 3D model reconstruction. To solve this problem, most previous methods align point sets by either partially exploring available information or blindly utilizing unnecessary information, which may lead to undesired results or extra computation complexity. #link# Accordingly, we propose a novel solution for the multi-view registration under the perspective of Expectation-Maximization (EM). The proposed method assumes that each data point is generated from one unique Gaussian Mixture Model (GMM), where its corresponding points in other point sets are regarded as Gaussian centroids with equal covariance and membership probabilities. As it is difficult to obtain real corresponding points in the registration problem, they are approximated by the nearest neighbor in each other aligned point sets. Based on this assumption, it is reasonable to define the likelihood function including all rigid transformations, which require to be estimated for multi-view registration. Subsequently, the EM algorithm is derived to estimate rigid transformations with one Gaussian covariance by maximizing the likelihood function. Since the GMM component number is automatically determined by the number of point sets, there is no trade-off between registration accuracy and efficiency in the proposed method. Finally, the proposed method is tested on several benchmark data sets and compared with state-of-the-art algorithms. Experimental results demonstrate its superior performance on the accuracy, efficiency, and robustness for multi-view registration.Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. However, this raises privacy concerns, especially when face images collected for biometric recognition purposes are used for attribute analysis without the person's consent. To address this problem, we develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology. The image perturbation is undertaken using a GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet - that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender). Extensive experiments using multiple face matchers, multiple age/gender/race classifiers, and multiple face datasets demonstrate the generalizability of the proposed multi-attribute privacy enhancing method across multiple face and attribute classifiers.The Deep learning of optical flow has been an active area for its empirical success. For the difficulty of obtaining accurate dense correspondence labels, unsupervised learning of optical flow has drawn more and more attention, while the accuracy is still far from satisfaction. By holding the philosophy that better estimation models can be trained with betterapproximated labels, which in turn can be obtained from better estimation models, we propose a self-taught learning framework to continually improve the accuracy using self-generated pseudo labels. link2 The estimated optical flow is first filtered by bidirectional flow consistency validation and occlusion-aware dense labels are then generated by edge-aware interpolation from selected sparse matches. Moreover, by combining reconstruction loss with regression loss on the generated pseudo labels, the performance is further improved. The experimental results demonstrate that our models achieve state-of-the-art results among unsupervised methods on the public KITTI, MPI-Sintel and Flying Chairs datasets.This paper describes the characterization and analysis of the effects an additional polymer layer has on a high overtone bulk acoustic wave resonator based on Ba0.5Sr0.5TiO3 (BSTO) thin film by studying its spectral information. From both simulations (numerical model) and experimental results of the resonator with and without coating, significant difference of both cases is evident in the spacing of the parallel resonance frequencies (SPRF), effective coupling coefficient (k2eff), and Quality factor distribution of the resonator. The acoustic velocity of the coated material (SU-8) was calculated from the new periodicity introduced in the SPRF distribution. The SPRF of the SU-8 coated resonator decreases overall as expected due to the additional layer introduced but increases sharply in regions defined by the thickness and acoustic velocity of the SU-8 layer. The mechanical loss of the added layer has significant effect on the parameters of the resonator. The study reveals that this method of characterization can be used to approximate the mechanical loss of materials such as polymers or polymer composites. Simulation with finite element method agrees with the experimental result.Ultrasonic guided waves (UGW) propagating in long cortical bone can be measured via the axial transmission method. The characterization of long cortical bone using UGW is a multiparameter inverse problem. The optimal solution of the inverse problem often includes a complex solving process. Deep neural networks (DNNs) are essentially powerful multiparameter predictors based on universal approximation theorem, which are suitable for solving parameter predictions in the inverse problem by constructing the mapping relationship between UGW and cortical bone material parameters. In this study, we investigate the feasibility of applying the multichannel crossed convolutional neural network (MCC-CNN) to simultaneously estimate cortical thickness and bulk velocities (longitudinal and transverse). Unlike the multiparameter estimation in most previous studies, the technique mentioned in this work avoids solving a multiparameter optimization problem directly. The finite-difference time-domain method (FDTD) is performed to obtain the simulated UGW array signals for training the MCC-CNN. The network that is exclusively trained on simulated datasets can predict cortical parameters from the experimental UGW data. The proposed method is confirmed by using FDTD simulation signals and experimental data obtained from four bone-mimicking plates and from ten exvivo bovine cortical bones. The estimated root-mean-squared error (RMSE) in the simulated test data for the longitudinal bulk velocity (VL), transverse bulk velocity (VT), and cortical thickness (Th) is 97 m/s, 53 m/s and 0.089 mm, respectively. The predicted RMSE in the bone-mimicking phantom experiments for VL., VT., and Th is 120 m/s, 80 m/s, and 0.14 mm, respectively. The experimental dispersion trajectories are matched with the theoretical dispersion curves calculated by the predicted parameters in ex-vivo bovine cortical bone experiments. Our proposed method demonstrates a feasible approach for the accurate evaluation of long cortical bones based on UGW.Transmission coefficient spectra of two ferroelectret films (showing several thickness resonances) measured with air-coupled ultrasound (0.2-3.5MHz) are presented and an explanation for the observed behavior is provided by proposing a film layered sandwich mesostructure (skin/core/skin) and by solving the inverse problem, using a simulated annealing algorithm. This permits to extract the value of the ultrasonic parameters of the different layers in the film as well as overall film parameters. It is shown that skin layers are thinner, denser and softer than core layers and also present lower acoustic impedance. Similarly, it is also obtained that the denser film also presents lower overall acoustic impedance. Scanning Electron Microscopy was employed to analyze the films cross-section, revealing that both denser films and film layers present more flattened cells and that close to the surface cells tends to be more flattened (supporting the proposed sandwich model). The fact that more flattened cells contributes to a lower elastic modulus and acoustic impedance can be explained, as it has been made previously by several authors, by the fact that the macroscopic film elastic response is furnished by cell micromechanics which is governed, mainly, by cell wall bending. Consistency of extracted parameters with trends shown by a simple model based on a honeycomb microstructure is discussed as well as the possibilities that this sandwich mesostructure and the associated impedance gradient could offer to improve the performance of FE films in ultrasonic transducers.Dynamic temperature sensing and infrared detection/imaging near room temperature are critical in many applications including invasive safety alarming, energy conversion, and public health, in which ferroelectric (FE) materials play an extremely important role due to their pyroelectricity. As a result, over the past few decades many efforts have been made to improve the understanding of pyroelectrics, explore new pyroelectric materials, and promote their practical applications. In this review, we consider the pyroelectric parameters and the two pyroelectric operation modes. Then based on the operation modes, we review recent achievements in the FE ceramic materials for pyroelectric detection applications, including Pb(Zr,Ti)O3-based, (Bi,Na)TiO3-based, (Sr,Ba)NbO3-based, Pb(Sc,Ta)O3-based, (Ba,Sr)TiO3-based, and Pb(Zr,Sn,Ti)O3-based systems. This review will attempt to provide guidance for further improvements of the pyroelectric properties of these materials and consider future exploration of new FE and other material candidates for use in temperature and infrared sensing/detection applications.Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. QNZ price based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. link3 Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.
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