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Filters
Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
[article]
2017
arXiv
pre-print
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structure of the signal. ...
The resulting batch and online algorithms automatically learn to process highly nonlinear temporal information extracted from the input signal, which is implicitly encoded in the kernel values. ...
MULTIKERNEL (RMK) STRATEGIES. ...
arXiv:1706.03533v1
fatcat:uklhpkyvrbacta6ca3nptvkn7q
Recursive multikernel filters exploiting nonlinear temporal structure
2017
2017 25th European Signal Processing Conference (EUSIPCO)
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structure of the signal. ...
The resulting batch and online algorithms automatically learn to process highly nonlinear temporal information extracted from the input signal, which is implicitly encoded in the kernel values. ...
MULTIKERNEL (RMK) STRATEGIES. ...
doi:10.23919/eusipco.2017.8081696
dblp:conf/eusipco/VaerenberghSS17
fatcat:ruloo35znjdlbbcbnbxe5f4fzi
Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
2018
Zenodo
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MULTIKERNEL (RMK) STRATEGIES. ...
Note, however, that nothing prevents the use of more advanced multi-kernel or ensemble strategies to further exploit the multi-kernel structure. Let us consider the batch case first. ...
doi:10.5281/zenodo.1159431
fatcat:lpe7nrlubfcitouz6cirnrsai4
TUNING CHAOS SYNCHRONIZATION AND ANTI-SYNCHRONIZATION FOR APPLICATIONS IN TEMPORAL PATTERN RECOGNITION
2005
International Journal of Bifurcation and Chaos in Applied Sciences and Engineering
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To this end, to exploit the qualitative resonance phenomenon in concrete applications, the synchronization feedback loop must be opportunely tuned. ...
Then, by exploiting the selective properties of the recently illustrated phenomenon of Qualitative Resonance [De Feo, 2004a , 2004b , this model can be employed within a feedback-synchronization-based ...
This algorithm has been developed within a larger research framework, whose aim is a temporal pattern recognition system, which exploits chaotic behavior for representing the intrinsic variability of patterns ...
doi:10.1142/s0218127405014386
fatcat:c6hbg36wi5auvb2yngk2rzxifu
2020 Index IEEE Transactions on Circuits and Systems II: Express Briefs Vol. 67
2020
IEEE Transactions on Circuits and Systems - II - Express Briefs
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, G., Ning, X., and Wang, S., Recursive Constrained Maximum Correntropy Criterion Algorithm for Adaptive Filtering; TCSII Oct. 2020 2229-2233 Qian, J., Lu, M., and Huang, N., Radar and Communication ...
., Quantized Fuzzy Finite-Time Control for Nonlinear Semi-Markov Switching Systems; TCSII Nov. 2020 2622-2626 Qi, X., see Liu, W., 1249-1253 Qian, G., see Dong, F., TCSII Dec. 2020 3587-3591 Qian ...
,TCSII Jan. 2020 177-181 Robust Multikernel Maximum Correntropy Filters. ...
doi:10.1109/tcsii.2020.3047305
fatcat:ifjzekeyczfrbp5b7wrzandm7e
A review of heterogeneous data mining for brain disorder identification
2015
Brain Informatics
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For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and nonlinear separability. ...
He et al. study the problem of supervised tensor learning with nonlinear kernels which can preserve the structure of tensor data [13] . ...
In general, a variety of filtering criteria are proposed. ...
doi:10.1007/s40708-015-0021-3
pmid:27747561
pmcid:PMC4883173
fatcat:rhvqh4vmeffnnoxts7esxwxlsq
2020 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 50
2020
IEEE Transactions on Systems, Man & Cybernetics. Systems
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Zhang, Z., +, TSMC Dec. 2020 5106-5118 Recursive filters Event-Triggered Distributed Fusion Estimation of Networked Multisensor Systems With Limited Information. ...
, D., +, TSMC June 2020 2273-2283 Diagnosis of Structural and Temporal Faults for k-Bounded Non-Markovian Stochastic Petri Nets. ...
doi:10.1109/tsmc.2021.3054492
fatcat:zartzom6xvdpbbnkcw7xnsbeqy
2020 Index IEEE Transactions on Instrumentation and Measurement Vol. 69
2020
IEEE Transactions on Instrumentation and Measurement
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., The Prism: Recursive FIR Signal Processing for Instrumentation Applications; TIM April 2020 1519-1529 Heo, S., see Jung, J.H., TIM Oct. 2020 7530-7541 Hernandez, A., see Aparicio-Esteve, E., TIM Aug ...
., +, TIM
Dec. 2020 9586-9598
Filtering theory
A Moreau Envelope-Based Nonlinear Filtering Approach to Denoising
Physiological Signals. ...
Gao, X., +, Robust Estimator-Based Nonlinear Filtering Approach to Piecewise Biosignal Reconstruction. ...
doi:10.1109/tim.2020.3042348
fatcat:a5f4fsqs45fbbetre6zwsg3dly
Joint Functional Brain Network Atlas Estimation and Feature Selection for Neurological Disorder Diagnosis With Application to Autism
2019
Medical Image Analysis
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Finally, band-pass filtering (0.01-0.1 Hz) was performed on the time series of each voxel (Price et al., 2014; Huang et al., 2017) . ...
This might indicate that alternative network fusion methods simply average all networks without capturing their nonlinear relationship. ...
doi:10.1016/j.media.2019.101596
pmid:31739282
fatcat:dlwejthh6rfx5hsy3ilhmuis6q
A Review of Complex Systems Approaches to Cancer Networks
[article]
2020
arXiv
pre-print
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Data then undergoes cell clustering using an unsupervised schema called single-cell interpretation via multikernel learning (SIMLR). ...
Dynamic Bayesian Networks (DBN), an extension of the Bayesian network considers temporal information in the data. ...
arXiv:2009.12693v2
fatcat:kt3e4bqaufgwlbhx2wbgzftnpe
2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70
2021
IEEE Transactions on Instrumentation and Measurement
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., +, TIM 2021 6500509 Exploiting Local Temporal Characteristics via Multinomial Decomposition Algorithm for Real-Time Activity Recognition. ...
Wen, Z., +, TIM 2021 3503117 Multiconstraint Spatial and Temporal Calibration of Rotating Line Structured Light Vision Sensor. ...
doi:10.1109/tim.2022.3156705
fatcat:dmqderzenrcopoyipv3v4vh4ry
Combining complex networks and data mining: Why and how
2016
Physics reports
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2016 pre-print arXiv:1604.08816v1 fatcat:mmozaxqqb5du3ag6abyonwfmd4Other Versions
An HCRF generates sequences of labels for sequences of input samples, and thus allows exploiting the temporal structure present in EEG data. ...
Apply the procedure recursively on the obtained subset. ...
doi:10.1016/j.physrep.2016.04.005
fatcat:dp33n23k7vhdfg7nm6lfo57adu
Combining complex networks and data mining: why and how
[article]
2016
bioRxiv
pre-print
Preserved Fulltext
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An HCRF generates sequences of labels for sequences of input samples, and thus allows exploiting the temporal structure present in EEG data. ...
Apply the procedure recursively on the obtained subset. ...
doi:10.1101/054064
fatcat:ncnw5vdvnfawxiq52vyzqtziuu
Orchestrating Multiple Data-Parallel Kernels on Multiple Devices
2015
2015 International Conference on Parallel Architecture and Compilation (PACT)
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MKMD is a two phased approach that combines coarse grain scheduling of indivisible kernels followed by opportunistic fine-grained workgroup-level partitioning to exploit idle resources. ...
Overall, Algorithm 1 shows a high-level description of multikernel partitioning. ...
First, OpenCL does not allow recursive calls, so expensive inter-procedural analysis can be avoided by inlining function calls. ...
doi:10.1109/pact.2015.14
dblp:conf/IEEEpact/LeeSM15
fatcat:25oo43zbjnayhgy5ovwkan2mny
Learning Capacity in Simulated Virtual Neurological Procedures
2020
Journal of WSCG
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This research proposes the implementation of the multikernel multi-scale steerable filtering task of digital images represented as Cartesian complexes, on graphical processing units (GPUs), using a parallel ...
PARALLELIZATION SCHEMA The possibilities of parallel computation provided by the GPUs were explored in the context of the multikernel multi-scale steerable filtering task proposed in [13] and reformulated ...
Additionally, a heuristic is proposed to determine the algorithm parameters without have to review manually all structures to segmentation. ...
doi:10.24132/csrn.2020.3001.13
fatcat:uytlm7nytrhmnk553ellfhl54a
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