Clouds play a vital role in climate prediction. Rainfall prediction also majorly depends on the status and types of clouds present sky. Therefore, cloud identification is most exciting topic meteorology attracts researchers from other areas. This paper presents transfer learning technique to predict based ground-based Cloud images responsible for rains. It will estimated by identifying type tak...

This paper presents a comprehensive study of applying the convolutional neural network (CNN) to solving the demosaicing problem. The paper presents two CNN models that learn end-to-end mappings between the mosaic samples and the original image patches with full information. In the case the Bayer color filter array (CFA) is used, an evaluation on popular benchmarks confirms that the data-driven,...

This paper discusses a facial expression recognition model and description generation to build descriptive sentences for images expressions of people in images. Our study shows that YOLOv5 achieves better results than traditional CNN all emotions on the KDEF dataset. In particular, accuracies models emotion are 0.853 0.938, respectively. A generating descriptions based merged architecture is pr...

Abstract Aiming at the egg crack detection task, a 6-position image acquisition method is proposed. EfficientNet was used to classify cracks. Using transfer learning technology, model pre-trained with CIFAR-10 data set obtain initial weights. The efficiency of training improved. Compared current mainstream CNN models Alexnet, VGG16 and ResNet50, in terms recognition, average correct recognition...

Centrosomes are important cell organizers. They consist of a pair of centrioles surrounded by pericentriolar material (PCM) that expands dramatically during mitosis-a process termed centrosome maturation. How centrosomes mature remains mysterious. Here, we identify a domain in Drosophila Cnn that appears to be phosphorylated by Polo/Plk1 specifically at centrosomes during mitosis. The phosphory...

A fast time-domain simulation technique of plane circuits via two-layer Cellular Neural Network (CNN) -based modeling, which is necessary for power/signal integrity evaluation in VLSIs, printed circuit boards, and packages, is presented. Using the new notation expressed by the two-layer CNN, 1,553 times faster simulation is achieved, compared with Berkeley SPICE (ngspice). In CNN community, CNN...

In this paper, the technique of image noise cancellation is presented by employing cellular neural networks (CNN) and linear matrix inequality (LMI). The main objective is to obtain the templates of CNN by using a corrupted image and a corresponding desired image. A criterion for the uniqueness and global asymptotic stability of the equilibrium point of CNN is presented based on the Lyapunov st...

A brain tumor is a disorder caused by the growth of abnormal cells. The survival rate patient affected with difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role determining site; however, manual detection time-consuming challenging procedure that cause some errors res...

This paper presents a configurable convolutional neural network accelerator (CNNA) for system-on-chip (SoC). The goal was to accelerate inference in different deep learning networks on an embedded SoC platform. presented CNNA has scalable architecture that uses high-level synthesis (HLS) and SystemC the hardware . It can any (CNN) exported from Keras Python supports combination of convolutional...

Advanced digital solutions are increasingly introduced into manufacturing systems to make them more intelligent. Intelligent Waste Management Systems in industries allow for data collection and analysis better-informed decisions, monitor manage processes remotely, improve waste management. In many industries, scrap is collected large containers located on the factory floor, usually close its so...