Relation between Irregular Sampling and Estimated Covariance for Closed-Loop Tracking Method

2014 ◽  
pp. 836-843
Author(s):  
Bei-bei Miao ◽  
Xue-bo Jin
Keyword(s):  
Closed Loop ◽  
Irregular Sampling ◽  
Tracking Method

scholarly journals Closed-Loop Estimation for Randomly Sampled Measurements in Target Tracking System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jin Xue-bo ◽  
Lian Xiao-feng ◽  
Su Ting-li ◽  
Shi Yan ◽  
Miao Bei-bei
Keyword(s):  
Target Tracking ◽  
Dynamic Model ◽  
Closed Loop ◽  
Tracking System ◽  
Estimation Method ◽  
Dynamic Parameter ◽  
Sampling Interval ◽  
Recursive Estimation ◽  
Irregular Sampling ◽  
Sampled Measurements

Many tracking applications need to deal with the randomly sampled measurements, for which the traditional recursive estimation method may fail. Moreover, getting the accurate dynamic model of the target becomes more difficult. Therefore, it is necessary to update the dynamic model with the real-time information of the tracking system. This paper provides a solution for the target tracking system with randomly sampling measurement. Here, the irregular sampling interval is transformed to a time-varying parameter by calculating the matrix exponential, and the dynamic parameter is estimated by the online estimated state with Yule-Walker method, which is called the closed-loop estimation. The convergence condition of the closed-loop estimation is proved. Simulations and experiments show that the closed-loop estimation method can obtain good estimation performance, even with very high irregular rate of sampling interval, and the developed model has a strong advantage for the long trajectory tracking comparing the other models.

Real-time removal of stimulation artifacts in closed-loop deep brain stimulation

2021 ◽  
Author(s):  
Yingnan Nie ◽  
Xuanjun Guo ◽  
Xiao Li ◽  
Xinyi Geng ◽  
Yan Li ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Real Time ◽  
Brain Stimulation ◽  
Sampling Method ◽  
Closed Loop ◽  
Low Frequency ◽  
Irregular Sampling ◽  
Neural Feedback ◽  
Deep Brain

Abstract Objective. Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters. Approach. We proposed an irregular sampling method for the real-time removal of stimulation artifacts. The artifact peaks were detected by applying a threshold to the raw recordings, and the samples within the contaminated period of the stimulation pulses were excluded and replaced with the interpolation of the samples prior to and after the stimulation artifact duration. This method was evaluated with both simulation signals and in vivo closed-loop DBS applications in Parkinsonian animal models. Main results. The irregular sampling method was able to remove the stimulation artifacts effectively with the simulation signals. The relative errors between the power spectral density of the recovered and true signals within a wide frequency band (2-150 Hz) were 2.14%, 3.93%, 7.22%, 7.97% and 6.25% for stimulation at 20 Hz, 60 Hz, 130 Hz, 180 Hz, and stimulation with variable low and high frequencies, respectively. This stimulation artifact removal method was verified in real-time closed-loop DBS application in vivo, and the artifacts were effectively removed during stimulation with frequency continuously changing from 130 Hz to 1 Hz and stimulation adaptive to beta oscillations. Significance. The proposed method provides an approach for real-time removal in closed-loop DBS applications, which is effective in stimulation with low frequency, high frequency, and variable frequency. This method can facilitate the development of more advanced closed-loop DBS strategies.

Intestinal Bacteriology of Closed Loop, Strangulated Obstruction in Dogs.

1961 ◽  
Vol 41 (3) ◽  
pp. 245-250 ◽  
Author(s):  
George H. Bornside ◽  
Isidore Cohn
Keyword(s):  
Closed Loop

The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück
Keyword(s):  
Empirical Research ◽  
Visual Feedback ◽  
Closed Loop ◽  
Motor Planning ◽  
Visual Input ◽  
Closed Loop Control ◽  
Loop Control ◽  
Feedback Type ◽  
Effective Use ◽  
The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

Generalized full/partial closed loop transmit diversity algorithm for WCDMA

2003 ◽  
Vol 14 (5) ◽  
pp. 471-477
Author(s):  
Dejan M. Novakovic ◽  
Markku J. Juntti ◽  
Miroslav L. Dukic
Keyword(s):  
Closed Loop ◽  
Transmit Diversity

A closed-loop, ACT-R approach to modeling approach and landing with and without synthetic vision system (SVS) technology

2004 ◽  
Author(s):  
Michael D. Byrne ◽  
Alex Kirlik ◽  
Michael D. Fleetwood ◽  
David G. Huss ◽  
Alex Kosorukoff ◽  
...  
Keyword(s):  
Closed Loop ◽  
Vision System ◽  
Modeling Approach ◽  
Synthetic Vision
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