Results 21 to 30 of about 122,061 (212)
Compressively Sensed Image Recognition [PDF]
2018 7th European Workshop on Visual Information Processing (EUVIP), 2018 6 pages, submitted/accepted, EUVIP ...
Degerli A. +4 more
openaire +6 more sources
Quasi-linear Compressed Sensing [PDF]
Multiscale Modeling & Simulation, 2014 Inspired by significant real-life applications, in particular, sparse phase retrieval and sparse pulsation frequency detection in Asteroseismology, we investigate a general framework for compressed sensing, where the measurements are quasi-linear.
Martin Ehler +2 more
openaire +2 more sources
Randomness and isometries in echo state networks and compressed sensing
, 2018 Although largely different concepts, echo state networks and compressed sensing models both rely on collections of random weights; as the reservoir dynamics for echo state networks, and the sensing coefficients in compressed sensing.
Prater-Bennette, Ashley
core +1 more source
On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis [PDF]
, 2015 Despite the linearity of its encoding, compressed sensing may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dimensional measurements (ciphertexts).
Cambareri, Valerio +4 more
core +2 more sources
Hierarchical Compressed Sensing
, 2022 Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this
Eisert, Jens +4 more
openaire +2 more sources
, 2013 Traditional compressed sensing considers sampling a 1D signal. For a multidimensional signal, if reshaped into a vector, the required size of the sensing matrix becomes dramatically large, which increases the storage and computational complexity ...
Fang, Hao +3 more
core +1 more source
Compressed Sensing in Hilbert Spaces [PDF]
, 2017 In many linear inverse problems, we want to estimate an unknown vector belonging to a high-dimensional (or infinite-dimensional) space from few linear measurements. To overcome the ill-posed nature of such problems, we use a low-dimension assumption on the unknown vector: it belongs to a low-dimensional model set. The question of whether it is possible
Traonmilin, Yann +3 more
openaire +6 more sources
FEBS Letters, EarlyView.B cells sense external mechanical forces and convert them into biochemical signals through mechanotransduction. Understanding how malignant B cells respond to physical stimuli represents a groundbreaking area of research. This review examines the key mechano‐related molecules and pathways in B lymphocytes, highlights the most relevant techniques to ...
Marta Sampietro +2 more
wiley +1 more source
Molecular Oncology, EarlyView.Large multidimensional digital images of cancer tissue are becoming prolific, but many challenges exist to automatically extract relevant information from them using computational tools. We describe publicly available resources that have been developed jointly by expert and non‐expert computational biologists working together during a virtual hackathon
Sandhya Prabhakaran +16 more
wiley +1 more source
Molecular Oncology, EarlyView.TOMM20 increases cancer aggressiveness by maintaining a reduced state with increased NADH and NADPH levels, oxidative phosphorylation (OXPHOS), and apoptosis resistance while reducing reactive oxygen species (ROS) levels. Conversely, CRISPR‐Cas9 knockdown of TOMM20 alters these cancer‐aggressive traits.
Ranakul Islam +9 more
wiley +1 more source