Weighted Integral Means of Mixed Areas and Lengths under Holomorphic Mappings

This note addresses monotonic growths and logarithmic convexities of the weighted ($(1-t^2)^\alpha dt^2$, $-\infty<\alpha<\infty$, $0<t<1$) integral means $\mathsf{A}_{\alpha,\beta}(f,\cdot)$ and $\mathsf{L}_{\alpha,\beta}(f,\cdot)$ of the mixed area $(\pi r^2)^{-\beta}A(f,r)$ and the mixed length $(2\pi r)^{-\beta}L(f,r)$ ($0\le\beta\le 1$ and $0<r<1$) of $f(r\mathbb D)$ and $\partial f(r\mathbb D)$ under a holomorphic map $f$ from the unit disk $\mathbb D$ into the finite complex plane $\mathbb C$

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