Results 41 to 50 of about 322,181 (285)

Quantization in Compressive Sensing: A Signal Processing Approach

IEEE Access, 2020
The influence of finite-length registers and the corresponding quantization effects on the reconstruction of sparse and approximately sparse signals from a reduced set of measurements is analyzed in this paper.
Isidora Stankovic, Milos Brajovic, Milos Dakovic, Cornel Ioana, Ljubisa Stankovic   +4 more
doaj   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Robust iterative hard thresholding for compressed sensing

, 2014
Compressed sensing (CS) or sparse signal reconstruction (SSR) is a signal processing technique that exploits the fact that acquired data can have a sparse representation in some basis.
Kim, Hyon-Jung, Koivunen, Visa, Ollila, Esa   +2 more
core   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gabor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source

A survey of sparse representation: algorithms and applications

, 2015
Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision and pattern recognition.
Li, Xuelong, Xu, Yong, Yang, Jian, Zhang, David, Zhang, Zheng   +4 more
core   +3 more sources

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Sparse seismic imaging using variable projection

, 2012
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated.
Aravkin, Aleksandr Y., Tu, Ning, van Leeuwen, Tristan   +2 more
core   +1 more source

Natural Products as Geroprotective Modulators in Diabetic Nephropathy: A Mechanistic Framework Integrating Aging Hallmarks and the AMPK–SIRT1–Nrf2 Axis

Aging and Cancer, EarlyView.
Natural products target the aging kidney in diabetic nephropathy by restoring the AMPK–SIRT1–Nrf2 axis, reducing oxidative stress, inflammation, fibrosis, and cellular senescence while enhancing mitochondrial biogenesis and antioxidant defenses.
Sherif Hamidu, Latif Adams, Eunice Ampem‐Danso, Justice Kumi, Abigail Aning, Fatima Hamidu, Mustapha Abdul Mumin Mohammed, Ayuba Nii Darku Ankrah, Seth Kwabena Amponsah   +8 more
wiley   +1 more source

Application Research of the Sparse Representation of Eigenvector on the PD Positioning in the Transformer Oil

International Journal of Antennas and Propagation, 2016
The partial discharge (PD) detection of electrical equipment is important for the safe operation of power system. The ultrasonic signal generated by the PD in the oil is a broadband signal.
Qing Xie, Dan Liu, Ying Zhang, Shuguo Gao, Tong Li, Xinjie Wang, Fangcheng Lü   +6 more
doaj   +1 more source

Sparse Image and Signal Processing

, 2015
This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators.
Starck, Jean-Luc, Murtagh, Fionn, Fadili, Jalal   +2 more
openaire   +2 more sources