3,313 research outputs found
Weighted Integral Means of Mixed Areas and Lengths under Holomorphic Mappings
This note addresses monotonic growths and logarithmic convexities of the
weighted (, , ) integral
means and
of the mixed area and the mixed length
( and ) of and
under a holomorphic map from the unit disk into the finite
complex plane
Composition Operators between Analytic Campanato Spaces
This note characterizes both boundedness and compactness of a composition
operator between any two analytic Campanato spaces on the unit complex disk
Propagation Path Loss Prediction Model of Multi-Sensor Network in Forest
AbstractDuring the process of carrying on the master plan and design of multi-sensor network in forest, We must consider the coverage of the signal, how to find the best position, through predicting it from launching and checking to accepting the loss value of the electromagnetic wave checked, Can carry on planning and design. Based on the radio wave propagation loss model in free space and the characteristics of radio wave propagation in forest, this paper proposes the generalized predicting model of radio wave propagation loss, To validate the model, a radio propagation measurement campaign was carried out, The modeling results by measuring the parameters of some trees are good agreement with that of the literatur
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs),
CNNs are successfully used for image denoising. However, they suffer from the
following drawbacks: (i) deep network architecture is very difficult to train.
(ii) Deeper networks face the challenge of performance saturation. In this
study, the authors propose a novel method called enhanced convolutional neural
denoising network (ECNDNet). Specifically, they use residual learning and batch
normalisation techniques to address the problem of training difficulties and
accelerate the convergence of the network. In addition, dilated convolutions
are used in the proposed network to enlarge the context information and reduce
the computational cost. Extensive experiments demonstrate that the ECNDNet
outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201
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