Newtonmckay7205

By introducing these new features, our proposal is expected to become a good candidate for examining basic physics (such as frequency dependence) in the fields of medicine, biology, industry, etc.Adaptive beamforming has been widely studied for ultrasound imaging over the past few decades. The minimum variance (MV) and generalized coherence factor (GCF) approaches have been validated as effective methods. However, the MV method had a limited contribution to contrast improvement, while the GCF method suffered from severe speckle distortion in previous studies. In this paper, a novel ultrasound beamforming approach based on MV and GCF beamformers is proposed to enhance the spatial resolution and contrast in synthetic aperture (SA) ultrasound imaging. First, the MV optimization problem is conceptually redefined by minimizing the total power of the transmitting and receiving outputs. Estimation of the covariance matrices in transmit and receive apertures is carried out and then utilized to determine adaptive weighting vectors. Second, a data-compounding method, viewed as a spatial low-pass filter, is introduced to the GCF method to optimize the spatial spectrum of echo signals and obtain better performance. Robust principal component analysis (RPCA) processing is additionally employed to obtain the final output. Simulation, experimental and in vivo studies are conducted on different datasets. Relative to the traditional delay-and-sum (DAS) beamformer, mean improvements in the full-width-at-halfmaximum and contrast ratio of 89% and 94%, respectively, are achieved. Thus, considerable enhancement of the spatial resolution and contrast is obtained by the proposed method. Moreover, the proposed method performs better in terms of the computational complexity. In summary, the proposed scheme effectively enhances ultrasound imaging quality.Ultrashort echo time (UTE) MRI is capable of detecting signals from protons with very short T2 relaxation times, and thus has potential for skull-selective imaging as a radiation-free alternative to computed tomography. However, relatively long scan times make the technique vulnerable to artifacts from involuntary subject motion. Here, we developed a self-navigated, three-dimensional (3D) UTE pulse sequence, which builds on dual-RF, dual-echo UTE imaging, and a retrospective motion correction scheme for motion-resistant skull MRI. Full echo signals in the second readout serve as a self-navigator that yields a time-course of center of mass, allowing for adaptive determination of motion states. Furthermore, golden-means based k-space trajectory was employed to achieve a quasi-uniform distribution of sampling views on a spherical k-space surface for any subset of the entire data collected, thereby allowing reconstruction of low-resolution images pertaining to each motion state for subsequent estimation of rigid-motion parameters. Finally, the extracted trajectory of the head was used to make the whole k-space datasets motion-consistent, leading to motion-corrected, high-resolution images. Additionally, we posit that hardware-related k-space trajectory errors, if uncorrected, result in obscured bone contrast. Thus, a calibration scan was performed once to measure k-space encoding locations, subsequently used during image reconstruction of actual imaging data. In vivo studies were performed to evaluate the effectiveness of the proposed correction schemes in combination with approaches to accelerated bone-selective imaging. Results illustrating effective removal of motion artifacts and clear depiction of skull bone voxels suggest that the proposed method is robust to intermittent head motions during scanning.Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect strategies that estimate prediction uncertainties and visualize model support in the input image space, the ability to explicitly query a prediction model regarding its image content offers a more direct way to determine the behavior of trained models. To this end, we present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.In the past half of the decade, object detection approaches based on convolutional neural network have been widely studied and successfully applied in many computer vision applications. However, detecting objects in inclement weather conditions remains a major challenge because of poor visibility. CADD522 In this paper, we address the object detection problem in the presence of fog by introducing a novel dual-subnet network (DSNet) that can be trained end-to-end and jointly learn three tasks visibility enhancement, object classification, and object localization. DSNet attains complete performance improvement by including two subnetworks detection subnet and restoration subnet. We employ RetinaNet as a backbone network (also called detection subnet), which is responsible for learning to classify and locate objects. The restoration subnet is designed by sharing feature extraction layers with the detection subnet and adopting a feature recovery (FR) module for visibility enhancement. Experimental results show that our DSNet achieved 50.84% mean average precision (mAP) on a synthetic foggy dataset that we composed and 41.91% mAP on a public natural foggy dataset (Foggy Driving dataset), outperforming many state-of-the-art object detectors and combination models between dehazing and detection methods while maintaining a high speed.In this article, we investigate the problem of the dissipativity-based resilient sliding-mode control design of cyber-physical systems with the occurrence of denial-of-service (DoS) attacks. First, we analyze the physical layer operating without DoS attacks to ensure the input-to-state practical stability (ISpS). The upper bound of the sample-data rate in this situation can be identified synchronously. Next, for systems under DoS attacks, we present the following results 1) combined with reasonable hypotheses of DoS attacks, the ISpS as well as dissipativity of the underlying system can be guaranteed; 2) the upper bound of the sample-data rate in the presence of DoS attacks can be derived; and 3) the sliding-mode controller is synthesized to achieve the desired goals in a finite time. Finally, two examples are given to illustrate the applicability of our theoretical derivation.