This paper presents an analysis of the stability and convergence properties of the Full Signal Range CNN model. These properties are demonstrated to be similar to those of the Chua-Yang's model, and the I/O mapping of known applications is shown to be unaffected by the modification introduced in this new model. In this modified CNN model, the dynamic range of the cell state-variables equals the dynamic range of the cell output variables, and is invariant with the application. This feature results in simpler circuit implementations, thus allowing higher cell densities and improving the robustness of CNN integrated circuits. In particular the Full Signal Range CNN model is specially well-suited for programmable CNN integrated circuits wi...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The paper investigates the potential for a packet switching network for real-time image processing b...
Cellular Neural Networks (CNN's) represent a remarkable improvement in hardware implementation of Ar...
Stability and convergency results are reported for a modified continuous-time CNN model. The signal ...
A new Cellular Neural Network model is proposed which allows simpler and faster VLSI implementation ...
The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary ...
The work carried out is oriented to extend classical topologies of Cellular Neural Networks (CNN) to...
Describes an analogue hardware implementation of a programmable full-range CNN. The used technology ...
This paper compares the dynamical behaviour of the standard (S) cellular neural networks (CNNs) and ...
The paper considers a class of CNNs, named dynamic-memristor (DM) CNNs, where each cell has an ideal...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
This paper describes the design of a programmable Cellular Neural Network (CNN) chip, with additiona...
The paper considers a class of Full-Range (FR) cellular neural networks (CNNs) characterized by a fi...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The paper investigates the potential for a packet switching network for real-time image processing b...
Cellular Neural Networks (CNN's) represent a remarkable improvement in hardware implementation of Ar...
Stability and convergency results are reported for a modified continuous-time CNN model. The signal ...
A new Cellular Neural Network model is proposed which allows simpler and faster VLSI implementation ...
The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary ...
The work carried out is oriented to extend classical topologies of Cellular Neural Networks (CNN) to...
Describes an analogue hardware implementation of a programmable full-range CNN. The used technology ...
This paper compares the dynamical behaviour of the standard (S) cellular neural networks (CNNs) and ...
The paper considers a class of CNNs, named dynamic-memristor (DM) CNNs, where each cell has an ideal...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
This paper describes the design of a programmable Cellular Neural Network (CNN) chip, with additiona...
The paper considers a class of Full-Range (FR) cellular neural networks (CNNs) characterized by a fi...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The paper investigates the potential for a packet switching network for real-time image processing b...
Cellular Neural Networks (CNN's) represent a remarkable improvement in hardware implementation of Ar...