Generalized Derivative Operator (GDO) is one of the first-order derivative filters that could control derivative’s direction by modifying value azimuth () and dip parameters. This study aims to examine those GDO parameters on synthetic Bouguer anomaly apply them field data Silver Peak geothermal identify geological structures. We use Python programs conduct other operators such as horizontal gr...

Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on feature maps. The optimal resolution may vary significantly depending dataset. Modern CNNs hard-code their hyper-parameters network architecture which makes tuning such cumbersome. We propose to do away with hard-coded a...

We develop a theory of stratified $LM$-filters which generalizes the theory of stratified $L$-filters. Our stratification condition explicitly depends on a suitable mapping between the lattices $L$ and $M$. If $L$ and $M$ are identical and the mapping is the identity mapping, then we obtain the theory of stratified $L$-filters. Based on the stratified $LM$-filters, a general theory of lattice-v...

background: the aim of this study was to prevent of increasing end tidal co2(et co 2) with changing of vital capacity and respiratory rate, when using of birthing filter in infants. materials and method: in a randomized clinical trial study, ninety four infant' patients were studied in three groups . basic values, such as: peak inspiratory pressure, tidal volume, minute ventilation, respiratory...

The performance characteristics of gradient-based, tensor-based, and phase-based low-level motion estimators are studied analytically. All three techniques yield exact results under ideal conditions (constant motion field and perfect discretization). With appropriate derivative filters, accuracy well below a hundredth pixel/frame can be achieved. All techniques are quite insensitive to inhomoge...

In many applications, it is desirable to design filters that affect the magnitude of the Fourier transform of the input in some way – for example, by attenuating certain frequency components, while affecting the phase as little as possible. Unfortunately, for causal systems, it is not possible to achieve zero phase. Instead, as a design goal, we can try to make the phase as close to linear as p...

Object recognition systems relying on local descriptors are increasingly used because of their perceived robustness with respect to occlusions and to global geometrical deformations. Descriptors of this type -based on a set of oriented Gaussian derivative filters -are used in our recognition system. In this paper, we explore a multiview 3D object recognition system that does not use explicit ge...

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of oriented Gaussian derivative filters-is used in our recognition system. We report here an evaluation of several techniques for orientation estimation to achieve rotation invariance...

State estimation and estimator based predictive control of nonlinear autonomous hybrid systems poses a challenging problem as these systems involve discontinuities that are introduced by switching of the discrete variables. In this paper, we propose a state estimation scheme for an autonomous hybrid system using an ensemble Kalman filter (EnKF), which belongs to the class of particle filters an...

The paper deals with implicit and explicit approaches for fractional nonlinear model order estimation using a benchmark model relating applied angular rate and neuron’s firing intensity within the vestibular system. The implicit approach is based on an interacting multiple models scheme, where several extended Kalman filters with fixed fractional order nonlinear models are running in parallel. ...