Results 51 to 60 of about 3,407,717 (355)
Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction [PDF]
IEEE Global Conference on Signal and Information Processing, 2017 Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals.
M. Ménoret +3 more
semanticscholar +1 more source
TERRESTRIAL LASER SCANNER DATA DENOISING BY DICTIONARY LEARNING OF SPARSE CODING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 Point cloud processing is basically a signal processing issue. The huge amount of data which are collected with Terrestrial Laser Scanners or photogrammetry techniques faces the classical questions linked with signal or image processing.
E. Smigiel, E. Alby, P. Grussenmeyer
doaj +1 more source
Near-Optimal Graph Signal Sampling by Pareto Optimization
Sensors, 2021 In this paper, we focus on the bandlimited graph signal sampling problem. To sample graph signals, we need to find small-sized subset of nodes with the minimal optimal reconstruction error.
Dongqi Luo +4 more
doaj +1 more source
Recovery of Graph Signals from Sign Measurements [PDF]
arXiv, 2021 Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most achievements are about continuous signals. While in a lot of signal processing tasks, signals are not fully observed, and only the signs of signals are available, for example
arxiv
Random Sampling of Bandlimited Graph Signals from Local Measurements [PDF]
arXiv, 2023 The random sampling on graph signals is one of the fundamental topics in graph signal processing. In this letter, we consider the random sampling of k-bandlimited signals from the local measurements and show that no more than O(klogk) measurements with replacement are sufficient for the accurate and stable recovery of any k-bandlimited graph signals ...
arxiv
Synchronization of sampling in distributed signal processing systems [PDF]
IEEE International Symposium on Intelligent Signal Processing, 2003, 2004 In distributed signal processing systems, every node samples analog signals by its own AD converter. Sampling is controlled by autonomous clocks, that are generally not synchronizable. In order to ensure synchronized operation among the different nodes of the distributed system, both the drift of these clocks, and the jitter of the sampling is handled.
Gábor Péceli +2 more
openaire +2 more sources
Frontiers in Neurology, 2015 We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion gradient directions for high angular resolution diffusion magnetic resonance imaging.
Farshid eSepehrband +12 more
doaj +1 more source
Wideband Mixed Signal Separation Based on Photonic Signal Processing
Telecom, 2021 The growing needs for high-speed and secure communications create an increasing challenge to the contemporary framework of signal processing. The coexistence of multiple high-speed wireless communication systems generates wideband interference.
Yang Qi, Taichu Shi, Ben Wu
doaj +1 more source
Half-infinite sampling and its FT [PDF]
arXiv, 2021 In the digital world, signals are discrete and finite. The Fourier representation of discrete and finite signals is FT convolution of the finite sampling function and the continuous signal. Conventionally, finite sampling is treated as a segment of infinite sampling.
arxiv
Adaptive Rate Sampling and Filtering Based on Level Crossing Sampling
EURASIP Journal on Advances in Signal Processing, 2009 The recent sophistications in areas of mobile systems and sensor networks demand more and more processing resources. In order to maintain the system autonomy, energy saving is becoming one of the most difficult industrial challenges, in mobile computing.
Saeed Mian Qaisar +2 more
doaj +1 more source