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...

R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification. However, the proposal generation phase in this paradigm is usually time consuming, which would slow down the whole detection time in testing. This paper suggests that the value discrepancies among features in deep convolutional feature maps con...

A prolific nucleator program n studies of Drosophila cells, aurora-A kinase and centrosomin (CNN) are sometimes used as markers for centrosomes, but, other than their localization, relatively little is known about these proteins. On page 757, Terada et al. show that aurora-A and CNN must physically interact to be translocated to the centro-some, and, once there, CNN acts as a powerful microtubu...

A prolific nucleator program n studies of Drosophila cells, aurora-A kinase and centrosomin (CNN) are sometimes used as markers for centrosomes, but, other than their localization, relatively little is known about these proteins. On page 757, Terada et al. show that aurora-A and CNN must physically interact to be translocated to the centro-some, and, once there, CNN acts as a powerful microtubu...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

In this paper, to improve image performance of biomedical data, Markov Random Field (MRF) and Cellular Neural Network (CNN) structures are combined and a new approach, Markov Random Field-Cellular Neural Networks (MRF-CNN) is introduced. MRF-CNN structure can be applied to biomedical data for various image processing problems such as noise filtering, edge detecting, blank filing etc., with nois...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...