Results 11 to 20 of about 2,461,002 (382)
The choice of signal-process models in Kalman-Bucy filtering
Journal of Mathematical Analysis and Applications, 1971 AbstractKalman and Bucy have shown how to obtain the linear least-squares estimate of a signal, given observations of the signal plus independent white noise, and given a lumped-parameter or state-variable model for the process. The filter producing the signal estimate produces it as a linear functional of an estimate of the state of the model; and ...
Thomas Kailath, Brian D. O. Anderson
openaire +3 more sources
Extending Classical Multirate Signal Processing Theory to Graphs—Part II: M-Channel Filter Banks
IEEE Transactions on Signal Processing, 2017 Oguzhan Teke, P. Vaidyanathan
semanticscholar +2 more sources
Graph Filters for Signal Processing and Machine Learning on Graphs [PDF]
IEEE Transactions on Signal Processing, 2022 Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks ...
Elvin Isufi +3 more
semanticscholar +1 more source
Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid [PDF]
IEEE Transactions on Signal Processing, 2021 The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework.
Raksha Ramakrishna, A. Scaglione
semanticscholar +1 more source
SSVEP-EEG Feature Enhancement Method Using an Image Sharpening Filter
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance.
Wenqiang Yan +3 more
doaj +1 more source
Graphon Signal Processing [PDF]
IEEE Transactions on Signal Processing, 2020 Graphons are infinite-dimensional objects that represent the limit of convergent sequences of graphs as their number of nodes goes to infinity. This paper derives a theory of graphon signal processing centered on the notions of graphon Fourier transform ...
Luana Ruiz +2 more
semanticscholar +1 more source
A User Guide to Low-Pass Graph Signal Processing and Its Applications: Tools and Applications [PDF]
IEEE Signal Processing Magazine, 2020 The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter.
Raksha Ramakrishna +2 more
semanticscholar +1 more source
Signal Processing on Directed Graphs: The Role of Edge Directionality When Processing and Learning From Network Data [PDF]
IEEE Signal Processing Magazine, 2020 This article provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transportation, biological) networks, and it should play an integral role in ...
A. Marques, Santiago Segarra, G. Mateos
semanticscholar +1 more source
Features of the quasi-optimal discrete signal processing [PDF]
Радиофизика и электроника, 2019 Subject and Purpose. The subject of the research is a digital signal processing system of a radar, consisting of an input filter, an analog-to-digital converter and a processor that implements an algorithm for detecting of information signal in ...
O.V. Sytnik
doaj +1 more source
Filter-enhanced MLP is All You Need for Sequential Recommendation [PDF]
The Web Conference, 2022 Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation.
Kun Zhou +3 more
semanticscholar +1 more source