Results 41 to 50 of about 1,028,974 (354)
Convolutional neural networks in automatic control systems: The state-of-the-art [PDF]
Tehnika, 2023 Convolutional neural networks are type of deep neural networks used for classification, identification, prediction and object detection. They are sutable for dealing with input data of various dimensions, such as signals, images and videos.
Perišić Natalija B. +1 more
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International Statistical Review / Revue Internationale de Statistique, 1999 SummaryA general convolution theorem within a Bayesian framework is presented. Consider estimation of the Euclidean parameter θ by an estimator T within a parametric model. Let W be a prior distribution for θ and define G as the W‐average of the distribution of T ‐ θ under θ.
van den Heuvel, E.R., Klaassen, C.A.J.
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Convolution algebra for extended Feller convolution
Semigroup Forum, 2020 We apply the recently introduced framework of admissible homomorphisms in the form of a convolution algebra of $$\mathbb{C}^2$$ -valued admissible homomorphisms to handle two-dimensional uni-directional homogeneous stochastic kernels.
Wha-Suck Lee, Christiaan Le Roux
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Remote Sensing, 2012 Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects ...
Roeland de Kok
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Multiservice Loss Models for Cloud Radio Access Networks
IEEE Access, 2021 In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units which form a common pool of computational resource units.
Iskanter-Alexandros Chousainov +3 more
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Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions [PDF]
Computer Vision and Pattern Recognition, 2020 Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e.
Ricard Durall +2 more
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Skew Convolutional Codes [PDF]
Entropy, 2020 A new class of convolutional codes, called skew convolutional codes, that extends the class of classical fixed convolutional codes, is proposed. Skew convolutional codes can be represented as periodic time-varying convolutional codes but have a description as compact as fixed convolutional codes. Designs of generator and parity check matrices, encoders,
Vladimir Sidorenko +3 more
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International Journal of Metrology and Quality Engineering, 2021 The concepts of convolution and deconvolution are well known in the field of physical measurement. In particular, they are of interest in the field of metrology, since they can positively influence the performance of the measurement.
Fanton Jean-Pierre
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Error Function and Certain Subclasses of Analytic Univalent Functions [PDF]
Sahand Communications in Mathematical Analysis, 2023 In the present investigation, our main aim is to introduce a certain subclass of analytic univalent functions related to the Error function. We discuss the implications of our main results, including the coefficient bound, extreme points, weighted mean ...
Seyed Hadi Sayedain Boroujeni +1 more
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