Skew-pipeline and interleaved pipeline structures for 2-D recursive filtering

2002 ◽  
Author(s):  
T. Lu ◽  
M.R. Azimi-Sadjadi ◽  
A.R. Rostampour
Keyword(s):  
Recursive Filtering ◽  
Pipeline Structures

scholarly journals How to train your differentiable filter

2021 ◽  
Author(s):  
Alina Kloss ◽  
Georg Martius ◽  
Jeannette Bohg
Keyword(s):  
Bayesian Filtering ◽  
Learning Approaches ◽  
Filtering Algorithms ◽  
Recursive Filtering ◽  
Process Dynamics ◽  
Noise Characteristics ◽  
Complex Models ◽  
End To End ◽  
Robotic Applications ◽  
And Training

AbstractIn many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

A revised scheme for real time ECG Signal denoising based on recursive filtering

2016 ◽  
Vol 27 ◽  
pp. 134-144 ◽  
Author(s):  
S. Cuomo ◽  
G. De Pietro ◽  
R. Farina ◽  
A. Galletti ◽  
G. Sannino
Keyword(s):  
Real Time ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Recursive Filtering

An Optimal Consensus Control for Multiple Agents System with Time-Delay and Disturbances

2014 ◽  
Vol 490-491 ◽  
pp. 828-831 ◽  
Author(s):  
Dong Hao Wang ◽  
Jian Yuan ◽  
Juan Xu ◽  
Zhong Hai Zhou
Keyword(s):  
Time Delay ◽  
Transformation Method ◽  
Consensus Control ◽  
Recursive Filtering ◽  
Control Time ◽  
Disturbance Rejection Control ◽  
System States ◽  
Feedback Optimal Control ◽  
Persistent Disturbances ◽  
System With Time Delay

The optimal disturbance rejection control problem is considered for a kind of consensus with control time-delay affected by external persistent disturbances and noise. An transformation method is used to convert the consensus with control time-delay to the consensus system without time-delay. The optimal estimated values of the converted consensus system states are obtained by recursive filtering with Kalman filter. Then the feedforward-feedback optimal control law is deduced by solving the Riccati equations and matrix equations. Lastly, simulations show the result is effectiveness to the consensus system with time-delay with respect to external persistent disturbances and noise.

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

2019 ◽  
Vol 17 (01) ◽  
pp. 1950003
Author(s):  
Apurba Roy ◽  
Santi P. Maity
Keyword(s):  
Image Reconstruction ◽  
Linear Prediction ◽  
Random Noise ◽  
Curvelet Transform ◽  
Conditional Entropy ◽  
Mr Images ◽  
Recursive Filtering ◽  
Entropy Maximization ◽  
Residual Noise ◽  
Incomplete Measurement

In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

scholarly journals ACCELERATING 3D NON-RIGID REGISTRATION USING GRAPHICS HARDWARE

2008 ◽  
Vol 08 (01) ◽  
pp. 81-98 ◽  
Author(s):  
NICOLAS COURTY ◽  
PIERRE HELLIER
Keyword(s):  
Image Analysis ◽  
Classical Method ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Recursive Filtering ◽  
Rigid Registration ◽  
Guided Surgery ◽  
Graphical Processing Unit Implementation ◽  
Demons Algorithm ◽  
Graphical Processing

There is an increasing need for real-time implementation of 3D image analysis processes, especially in the context of image-guided surgery. Among the various image analysis tasks, non-rigid image registration is particularly needed and is also computationally prohibitive. This paper presents a GPU (Graphical Processing Unit) implementation of the popular Demons algorithm using a Gaussian recursive filtering. Acceleration of the classical method is mainly achieved by a new filtering scheme on GPU which could be reused in or extended to other applications and denotes a significant contribution to the GPU-based image processing domain. This implementation was able to perform a non-rigid registration of 3D MR volumes in less than one minute, which corresponds to an acceleration factor of 10 compared to the corresponding CPU implementation. This demonstrated the usefulness of such method in an intra-operative context.

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