Weather condition is an important factor that considered for various decisions. In the industrial world, weather classification very useful, such as in development of self-driving cars, smart transportation systems, and outdoor vision systems. Manual by humans inconsistent takes a long time. forecast information obtained from internet not real time at specific location. image has unique charact...

Cellular Neural Networks (CNN) is a massive computing paradigm which became very popular in the last decades. A Cellular Neural Network Universal Machine is an extension of the CNN concept. An implementation of CNN-UM on Field Programmable Gate Arrays (FPGA) appears attractive because their full computational power comes to a life only in hardware. Besides FPGA there are many different possibil...

Centrosomin (Cnn) is a required core component in mitotic centrosomes during syncytial development and the presence of Cnn at centrosomes has become synonymous with fully functional centrosomes in Drosophila melanogaster. Previous studies of Cnn have attributed this embryonic function to a single isoform or splice variant. In this study, we present new evidence that significantly increases the ...

BACKGROUND Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). NEW METHOD T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transform...

Cyclic nigerosyl nigerose (CNN) is a cyclic tetrasaccharide that exhibits properties distinct from other conventional cyclodextrins. Herein, we demonstrate that treatment of B16 melanoma with CNN results in a dose-dependent decrease in melanin synthesis, even under conditions that stimulate melanin synthesis, without significant cytotoxity. The effects of CNN were prolonged for more than 27 day...

Convolutional Neural Network (CNN) was firstly introduced in Computer Vision for image recognition by LeCun et al. in 1989. Since then, it has been widely used in image recognition and classification tasks. The recent impressive success of Krizhevsky et al. in ILSVRC 2012 competition demonstrates the significant advance of modern deep CNN on image classification task. Inspired by his work, many...

Early detection and forecast of extreme weather events such as tropical cyclones wildfires are becoming increasingly crucial for mitigating their catastrophic damages. Remotely-sensed satellite data enable us to detect assimilate these into models improve global-scale forecasts. Despite the significant advances in imagery image processing techniques, assimilation methods currently employ conven...

XIE Jiang-jian DING Chang-qing; LI Wen-bin; CAI Cheng-hao (1 School of Technology,Beijing Forestry University, Beijing, 100083, P. R. China. 2 School of Nature Conservation,Beijing Forestry University, Beijing, 100083, P. R. China.) Abstract 1. Deep convolutional neural networks (DCNN) have achieved breakthrough performance on bird species identification tasks based on spectrogram features, but...

Although the current vehicle detection and recognition framework based on deep learning has its own characteristics advantages, it is difficult to effectively combine multi-scale multi category features, there still room for improvement in performance. Based this, an improved fast R-CNN convolutional neural network proposed detect dim targets complex traffic environment. The model of introduced...

This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional neural network (called pixel-level CNN) is constructed by modifying the VGGNet architecture to perform multiscale feature learning, based on which an image-to-image predict...