Results 41 to 50 of about 139,689 (265)
Difference Frequency Gridless Sparse Array Processing
IEEE Open Journal of Signal ProcessingThis paper introduces a DOA estimation method for sources beyond the aliasing frequency. The method utilizes multiple frequencies of sources to exploit the frequency difference between them, enabling processing at a frequency below the aliasing frequency.
Yongsung Park, Peter Gerstoft
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SoftwareX, 2018 Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem.
Ervin Sejdić +2 more
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Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
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Frontiers in Neural CircuitsNeurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse connectivity for a network’s function?
Rieke Fruengel +3 more
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Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features
Shock and Vibration, 2016 Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable.
Wei Peng +3 more
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Knot Selection in Sparse Gaussian Processes
CoRR, 2020 Knot-based, sparse Gaussian processes have enjoyed considerable success as scalable approximations to full Gaussian processes. Problems can occur, however, when knot selection is done by optimizing the marginal likelihood. For example, the marginal likelihood surface is highly multimodal, which can cause suboptimal knot placement where some knots serve
Nathaniel Garton +2 more
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Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
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Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint
IEEE Access, 2018 In this paper, an enhanced proportionate normalized least mean square (PNLMS) algorithm with the hybrid l2,0-norm constraint is proposed for block-sparse signal processing.
Yingsong Li +4 more
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Sparse approximations in signal and image processing [PDF]
Signal Processing, 2006 Guest editorial of the special issue on Sparse Approximations in Signal and Image ...
Rémi Gribonval, Morten Nielsen 0002
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