scholarly journals Concurrent multifunction transmit and receive applications with dynamic filtering

2010 ◽  
Author(s):  
T Snow ◽  
E J Naglich ◽  
W J Chappell
Keyword(s):  
Dynamic Filtering

Analytic signal space partitioning and symbolic dynamic filtering for degradation monitoring of electric motors

2009 ◽  
Vol 4 (4) ◽  
pp. 399-403 ◽  
Author(s):  
Subhadeep Chakraborty ◽  
Asok Ray ◽  
Aparna Subbu ◽  
Eric Keller
Keyword(s):  
Analytic Signal ◽  
Symbolic Dynamic ◽  
Electric Motors ◽  
Space Partitioning ◽  
Signal Space ◽  
Degradation Monitoring ◽  
Symbolic Dynamic Filtering ◽  
Dynamic Filtering

Adaptive shaper-based filters for fast dynamic filtering of load cell measurements

2022 ◽  
Vol 167 ◽  
pp. 108541
Author(s):  
Dario Richiedei
Keyword(s):  
Load Cell ◽  
Dynamic Filtering ◽  
Fast Dynamic

scholarly journals Dynamic filtering improves attentional state prediction with fNIRS

2016 ◽  
Vol 7 (3) ◽  
pp. 979 ◽  
Author(s):  
Angela R. Harrivel ◽  
Daniel H. Weissman ◽  
Douglas C. Noll ◽  
Theodore Huppert ◽  
Scott J. Peltier
Keyword(s):  
Attentional State ◽  
State Prediction ◽  
Dynamic Filtering

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

2008 ◽  
Vol 3 (2) ◽  
pp. 101-114 ◽  
Author(s):  
Chinmay Rao ◽  
Asok Ray ◽  
Soumik Sarkar ◽  
Murat Yasar
Keyword(s):  
Comparative Evaluation ◽  
Symbolic Dynamic ◽  
Symbolic Dynamic Filtering ◽  
Dynamic Filtering

A novel improved CKF with adaptive generation of the cubature points and weights for target tracking

2021 ◽  
Author(s):  
Hongpo Fu ◽  
Yongmei Cheng ◽  
Cheng Cheng
Keyword(s):  
Target Tracking ◽  
Superior Performance ◽  
Inner Product ◽  
Estimation Accuracy ◽  
Cubature Rule ◽  
Cubature Kalman Filter ◽  
One Step ◽  
Tracking Model ◽  
Dynamic Filtering ◽  
High Degree

Abstract In the nonlinear state estimation, the generation method of cubature points and weights of the classical cubature Kalman filter (CKF) limits its estimation accuracy. Inspired by that, in this paper, a novel improved CKF with adaptive generation of the cubature points and weights is proposed. Firstly, to improve the accuracy of classical CKF while considering the calculation efficiency, we introduce a new high-degree cubature rule combining third-order spherical rule and sixth-degree radial rule. Next, in the new cubature rule, a novel method that can generate adaptively cubature points and weights based on the distance between the points and center point in the sense of the inner product is designed. We use the cosine similarity to quantify the distance. Then, based on that, a novel high-degree CKF is derived that use much fewer points than other high-degree CKF. In the proposed filter, based on the actual dynamic filtering process, the simultaneously adaptive generation of cubature points and weight can make the filter reasonably distribute the cubature points and allocate the corresponding weights, which can obviously improve the approximate accuracy of one-step state and measurement prediction. Finally, the superior performance of the proposed filter is demonstrated in a benchmark target tracking model.

scholarly journals Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies

Science ◽  
2019 ◽  
Vol 364 (6440) ◽  
pp. 593-597 ◽  
Author(s):  
Caleb J. Bashor ◽  
Nikit Patel ◽  
Sandeep Choubey ◽  
Ali Beyzavi ◽  
Jané Kondev ◽  
...  
Keyword(s):  
Signal Processing ◽  
Self Assembly ◽  
Design Principle ◽  
Complex Signal ◽  
Synthetic Gene Circuits ◽  
Cellular Decision Making ◽  
Eukaryotic Genes ◽  
Transcription Factor Complexes ◽  
Dynamic Filtering ◽  
Cooperative Assembly

Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic circuits.

Damage Detection of Bridge Network With Spatiotemporal Pattern Network

2016 ◽  
Author(s):  
Chao Liu ◽  
Yongqiang Gong ◽  
Simon Laflamme ◽  
Brent Phares ◽  
Soumik Sarkar
Keyword(s):  
Damage Detection ◽  
Streaming Data ◽  
Dynamic Responses ◽  
Data Driven ◽  
Spatiotemporal Pattern ◽  
Simulation Data ◽  
Strain Data ◽  
Symbolic Dynamic Filtering ◽  
Robustness To Noise ◽  
Dynamic Filtering

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. In particular, scalable data-driven approaches is of great interest, because it can deal with large volume of streaming data without requiring models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and inference and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is applied to explore spatiotemporal behaviors in bridge network. Data from strain gauges installed on two bridges are simulated by finite element method, and the causality among strain data in spatial and temporal resolutions is analyzed. Case studies are conducted for truck identification and damage detection from simulation data. Results show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, and (iii) detecting and localizing damage via the comparison of behaviors within the bridge network.

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