A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology. A genetic algorithm based CNN gene design methodology is als...

Deep Convolutional Neural Networks (CNN) have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit. For the image classification task, most of the current deep CNNbased approaches take the whole size-normalized image as input and have achieved quite pro...

This study investigated the combined prognostic value of pretreatment anemia and cervical node necrosis (CNN) in patients with nasopharyngeal carcinoma (NPC). Retrospective review of 1302 patients with newly diagnosed nonmetastatic NPC treated with intensity-modulated radiotherapy (IMRT) ± chemotherapy. Patients were classified into four groups according to anemia and CNN status. Survival was c...

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

This presentation investigates the dynamics of discrete-time cellular neural networks (DT-CNN). In contrast to classical neural networks that are mostly gradient-like systems, DT-CNN possesses both complete stability and chaotic behaviors as different parameters are considered. An energylike function which decreases along orbits of DT-CNN as well as the existence of a globally attracting set ar...

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, as well as robotic and biological visions. The designs for CNN templates are one of the important issues for the practical applications of CNNs. This paper first describes and proves the local rules of the binary Point Extract (PE) CNN introduced by Roska et al., then extends the PE CNN to a gr...

Cellular neural networks (CNN) were introduced by Chua and Yang in 1998 [1]. The idea of the CNN was inspired from the architecture of the cellular automata and the neural networks. Unlike the conventional neural networks, the CNN has local connectivity property. Since the structure of the CNN resembles the structure of animals retina, the CNN can be used for various image processing applicatio...

Cellular Neural Network (CNN) is constructed the grating coupling of analog circuits. The performance of the CNN depends on the parameters called the template. Template can use for various image processing applications. In this study, we apply the CNN with delay output (D-CNN) to image processing. In a general way, the D-CNN is used by motion picture processing. However, we apply the D-CNN to i...

Background: To analyze the prognostic value of cervical node necrosis (CNN) observed on pretreatment magnetic resonance imaging (MRI) in patients with nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT). Patients and Methods: The medical records of 1423 NPC patients with cervical node metastasis who underwent IMRT were retrospectively reviewed. Lymph nodes in the...

The formation of the pericentriolar matrix (PCM) and a fully functional centrosome in syncytial Drosophila melanogaster embryos requires the rapid transport of Cnn during initiation of the centrosome replication cycle. We show a Cnn and Polo kinase interaction is apparently required during embryogenesis and involves the exon 1A-initiating coding exon, suggesting a subset of Cnn splice variants ...