There are many applications in which particle filters outperform traditional signal processing algorithms. Some of these applications include tracking, joint detection and estimation in wireless communication, and computer vision. However, particle filters are not used in practice for these applications mainly because they cannot satisfy real-time requirements. This paper presents an efficient ...

The RR interval series is non-stationary and unevenly spaced in time. For estimating its power spectral density (PSD) using traditional techniques like FFT, require resampling at uniform intervals. The researchers have used different interpolation techniques as resampling methods. All these resampling methods introduce the low pass filtering effect in the power spectrum. The lomb transform is a...

Internal validity of a risk model can be studied efficiently with bootstrapping to assess possible optimism in model performance. Assumptions of the regular bootstrap are violated when the development data are clustered. We compared alternative resampling schemes in clustered data for the estimation of optimism in model performance. A simulation study was conducted to compare regular resampling...

Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) problem in traditional LDA. When applied to face recognition under varying lighting conditions and different facial expressions, neither method may work robustly due to limited number of training samples for each class in the train...

Cross-validation and bootstrap, or resampling methods in general, are examined for applying them to optimizing the number of units of competitive associative nets called CAN2. There are a number of resampling methods available, but the performance depends on the neural network to be applied and functions to be learned. So, we apply several resampling methods to the CAN2 which has been shown eff...

This paper develops a family of multi-pass image resampling algorithms that use one-dimensional filtering stages to achieve high-quality results at low computational cost. Our key insight is to perform a frequency-domain analysis to ensure that very little aliasing occurs at each stage in the multi-pass transform and to insert additional stages where necessary to ensure this. Using one-dimensio...

Real-time three-dimensional (RT3D) ultrasound technique based on matrix phased array transducers is likely to become predominant for dynamic screening in cardiac and obstetric practice. With these transducers large data volumes are acquired in spherical coordinates and require resampling to be visualized in Cartesian coordinates. A fast 3D resampling method was implemented and five interpolatio...

The processes of discretization, interpolation, and resampling are frequently used in data analysis. Here the formalism of functional spaces is used as a framework for the description and characterization of both the measurement operation and these subsequent processes. The tools provided by this formalism are applied to the problem of resampling of atmospheric volume mixing ratio vertical prof...

Particle filters are numerical methods for approximating the solution of the filtering problem which use systems of weighted particles that (typically) evolve according to the law of the signal process. These methods involve a corrective/resampling procedure which eliminates the particles that become redundant and multiplies the ones that contribute most to the resulting approximation. The corr...