Results 251 to 260 of about 4,252,298 (276)

Series concatenation of 2D convolutional codes

2015 IEEE 9th International Workshop on Multidimensional (nD) Systems (nDS), 2015
In this paper we study two-dimensional (2D) con-volutional codes which are obtained from series concatenation of two 2D convolutional codes. In this preliminary work we confine ourselves to dealing with finite-support 2D convolutional codes and make use of the so-called Fornasini-Marchesini input-state-output (ISO) model representations. In particular,
Joan-Josep Climent, Diego Napp, Raquel Pinto, Rita Simões   +3 more
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Convolutional PCA for Multiple Time Series

IEEE Signal Processing Letters, 2020
We study a fundamental generalization of principal component analysis (PCA) that looks for a small set of common time series, i.e., principal components (PCs), whose filtered versions can explain most of the variances of multiple observed time series. This problem boils down to PCA in the frequency domain, in principle. But, frequency domain processing
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The convolution sums of MacMahon’s q-series

The Ramanujan Journal
In his classical work on partitions and divisor functions, MacMahon introduced the two \(q\)-series \(A_k(q)\) and \(C_k(q)\), which have since been shown to be quasimodular forms and are closely linked to partition functions, modular forms, and infinite product identities.
Xia, Ernest X. W., Fan, Yan, Yao, Olivia X. M.   +2 more
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Weighted and Ergodic Theorems for Series of Differences of Convolutions

Journal of Fourier Analysis and Applications, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lorente, María, de la Torre, Alberto
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CONVOLUTIONS OF HECKE SERIES AND THEIR VALUES AT LATTICE POINTS

Mathematics of the USSR-Sbornik, 1977
An algebraicity theorem is proved for the values of convolutions of Dirichlet series connected with modular forms of various weights at lattice points of the critical strip, -adic estimates of these values are obtained.Bibliography: 24 titles.
Manin, Yu. I., Panchishkin, A. A.
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Mapping Properties for Convolution Involving Hypergeometric Series

Ukrainian Mathematical Journal, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aouf, M. K., Mostafa, A. O., Zayed, H. M.   +2 more
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Convolutional Neural Networks for Time Series Classification

2017
This article concerns identifying objects generating signals from various sensors. Instead of using traditional hand-made time series features we feed the signals as input channels to a convolutional neural network. The network learned low- and high-level features from data.
Mariusz Zebik, Marcin Korytkowski, Rafal A. Angryk, Rafal Scherer   +3 more
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Time series convolution kernel estimation

AIP Conference Proceedings, 2018
This paper focuses on descriptive analysis of time series using artificial neural networks. We used special type of neural networks architecture called convolutional neural networks to for estimation of convolutional kernels. These kernels were estimated from lookbehind window using Adam backpropagation algorithm and as such can be indicative of future
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Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Remote Sensing, 2022
Andrew D B Leakey, Sebastian Varela
exaly  

Starlikeness properties for convolutions involving hypergeometric series

1998
Summary: For ...
Ponnusamy, Saminathan, Rønning, Frode
openaire   +1 more source