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Evaluating the Portable Phone-Delivered Message Prompt Intervention to Reduce Child Vaccination Dropout throughout Arua, Uganda: Method for any Randomized Governed Test.
Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its predecessor standard High Efficiency Video Coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of the coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. selleck kinase inhibitor First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving a desirable trade-off between complexity and RD performance. The experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%~66.88% with a negligible Bjøntegaard delta bit-rate (BD-BR) of 1.322%~3.188%, significantly outperforming other state-of-the-art approaches.In ultrasound Nondestructive Testing (NDT), a widespread approach is to take synthetic aperture measurements from the surface of a specimen to detect and locate defects within it. Based on these measurements, imaging is usually performed using the Synthetic Aperture Focusing Technique (SAFT). However, SAFT is sub-optimal in terms of resolution and requires oversampling in time domain to obtain a fine grid for the Delay-and-Sum (DAS). On the other hand, parametric reconstruction algorithms give better resolution, but their usage for imaging becomes computationally expensive due to the size of the parameter space and the large amount of measurement data in realistic 3-D scenarios when using oversampling. In the literature, the remedies to this are twofold First, the amount of measurement data can be reduced using state of the art sub-Nyquist sampling approaches to measure Fourier coefficients instead of time domain samples. Second, parametric reconstruction algorithms mostly rely on matrix-vector operations that can be implemented efficiently by exploiting the underlying structure of the model. In this paper, we propose and compare different strategies to choose the Fourier coefficients to be measured. Their asymptotic performance is compared by numerically evaluating the Cramér-Rao-Bound (CRB) for the localizability of the defect coordinates. These subsampling strategies are then combined with an ℓ1-minimization scheme to compute 3-D reconstructions from the low-rate measurements. Compared to conventional DAS, this allows us to formulate a fully physically motivated forward model matrix. To enable this, the projection operations of the forward model matrix are implemented matrix-free by exploiting the underlying 2-level Toeplitz structure. Finally, we show that high resolution reconstructions from as low as a single Fourier coefficient per A-scan are possible based on simulated data as well as on measurements from a steel specimen.In many industrial sectors, Structural Health Monitoring (SHM) is considered as an addition to Non-Destructive Testing (NDT) that can reduce maintenance effort during lifetime of a technical facility, structural component or vehicle. A large number of SHM methods is based on ultrasonic waves, whose properties change depending on structural health. However, the wide application of SHM systems is limited due to the lack of suitable methods to assess their reliability. The evaluation of the system performance usually refers to the determination of the Probability of Detection (POD) of a test procedure. Up to now, only few limited methods exist to evaluate the POD of SHM systems, which prevent them from being standardised and widely accepted in industry. The biggest hurdle concerning the POD calculation is the large amount of samples needed. A POD analysis requires data from numerous identical structures with integrated SHM systems. Each structure is then damaged at different locations and with various degrees of severity. All of this is connected to high costs. Therefore, one possible way to tackle this problem is to perform computer-aided investigations. In this work, the POD assessment procedure established in NDT according to the Berens model is adapted to guided wave-based SHM systems. The approach implemented here is based on solely computer-aided investigations. After efficient modelling of wave propagation phenomena across an automotive component made of a carbon fibre-reinforced composite, the POD curves are extracted. Finally, the novel concept of a POD map is introduced to look into the effect of damage position on system reliability.Taking selfies has become one of the major photographic trends of our time. In this study, we focus on the selfie stick, on which a camera is mounted to take selfies. We observe that a camera on a selfie stick typically travels through a particular type of trajectory around a sphere. Based on this finding, we propose a robust, efficient, and optimal estimation method for relative camera pose between two images captured by a camera mounted on a selfie stick. We exploit the special geometric structure of camera motion constrained by a selfie stick and define this motion as spherical joint motion. Utilizing a novel parametrization and calibration scheme, we demonstrate that the pose estimation problem can be reduced to a 3-degrees of freedom (DoF) search problem, instead of a generic 6-DoF problem. This facilitates the derivation of an efficient branch-and-bound optimization method that guarantees a global optimal solution, even in the presence of outliers. Furthermore, as a simplified case of spherical joint motion, we introduce selfie motion, which has a fewer number of DoF than spherical joint motion.
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