Search results for "Convolution"
showing 10 items of 334 documents
An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling
2017
The goal of modeling sentences is to accurately represent their meaning for different tasks. A variety of deep learning architectures have been proposed to model sentences, however, little is known about their comparative performance on a common ground, across a variety of datasets, and on the same level of optimization. In this paper, we provide such a novel comparison for two popular architectures, Recursive Neural Tensor Networks (RNTNs) and Convolutional Neural Networks (CNNs). Although RNTNs have been shown to work well in many cases, they require intensive manual labeling due to the vanishing gradient problem. To enable an extensive comparison of the two architectures, this paper empl…
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
2020
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…
The range of non-surjective convolution operators on Beurling spaces
1996
AbstractLet μ ≠ 0 be an ultradistribution of Beurling type with compact support in the space . We investigate the range of the convolution operator Tμ on the space of non-quasianalytic functions of Beurling type associated with a weight w, in the case the operator is not surjective. It is proved that the range of TM always contains the space of real-analytic functions, and that it contains a smaller space of Beurling type for a weight σ ≥ ω if and only if the convolution operator is surjective on the smaller class.
A reexamination of voltage distortion for classical carrier-based vs B-Spline modulation of three-phase Voltage Sources Inverters
2015
Voltage waveform improvement has been the object of several investigations for many years and a manifold of different solutions have been proposed to reduce the harmonic content in Voltage Source Inverters (VSI) power application. In many cases this improvements have been obtained by modifying the reference voltage modulating signal. The recent introduction of cardinal B-spline functions, used as carrier signals, has given rise to a new modulation technique whose main characteristic is a lower value of the Total Harmonic Distortion (THD). After the discussion on the B-Spline modulation principle and on its computational effort, a performance comparisons is carried out by means of Total Harm…
Multi-feature Counting of Dense Crowd Image Based on Multi-column Convolutional Neural Network
2020
The crowd counting task is an important research problem. Now more and more people are concerned about safety issues. When the population density reaches a very high peak, the population density counts, the alarm is sent out, and the crowds are diverted. The trampling of the Shanghai New Year’s stampede will not happen again. The final density map is produced by two steps: at first, extract feature maps from multiple layers, and then adjust their output so that they are all the same size, all these resized layers are combined into the final density map. We also used texture features and target edge detection to reduce the loss of density map detail to better integrate with our convolutional…
Spline Algorithms for Deconvolution and Inversion of Heat Equation
2014
In this chapter, we present algorithms based on Tikhonov regularization for solving two related problems: deconvolution and inversion of heat equation. The algorithms, which utilize the SHA technique, provide explicit solutions to the problems in one and two dimensions.
Deconvolution of Multiple Spectral Lines Shapes by Means of Tikhonov’s Regularization Method
2013
We present deconvolution of multiple narrow Zeeman split Hg lines, emitted from Hg/Xe micro-size capillary and measured by the Fourier Transform spectrometer. The ill-posed inverse problem was solved using the Tikhonov& rsquo;s regularization method.
Beurling ultradistributions of Lp-growth
2003
We study the convolutors and the surjective convolution operators acting on spaces of ultradistributions of Lp-growth. In the case p = 2 we obtain complete characterizations. Some results on hypoellipticity are also included. 2003 Elsevier Science (USA). All rights reserved.
CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study
2020
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…
MR3714763 Reviewed Bargetz, C.(A-INSB); Nigsch, E. A.(A-WIEN-WPI); Ortner, N.(A-INSB) Convolvability and regularization of distributions. (English su…
2018
Referring to the theory of vector-valued distributions due to L. Schwartz, the authors, starting from a formulation due to Hirata and Shiraishi, carry out a study about generalizations of the convolvability and regularization of distributions, without test functions but by means of kernels. Further topological features, such as boundedness and relative compactness of subsets of distributions, are exhibited in light of previous results.