Image Processing Using SETMOS-based CNN

There are many cases where image processing is performed using the neural network. The content of this Chapter is implementation of CNN (Cellular Neural Network) on a chip in a new original digital approach. First, testing of some features of the proposed cell of the neural network is evaluated. Then the design of the basic circuit containing the cell for the CNN will be introduced. The idea is to use it for a more complex chip with image processing application. CNN have been extensively used in various image processing applications. One of the most important problems in signal processing is noise removal. The input signals mostly arrive from the real world and therefore they contain relatively high amounts of noise. The CNN circuits are capable of removing this noise partly. The possibility of noise removing depends on the template (weight) coefficients between the cells. CNN networks are based on relatively simple principles, very similar to biological neurons. The input signals are multiplied by appropriate weights, the weight matrix being given intuitively, as it is typical for CNN networks. Then they are added and multiplied by the transfer function. We describe also the settings of weights coefficients. CNN have been extensively used in various image processing applications ( Matsumoto & Yokohama, 1990 ) or ( Sziranyi & Csicsvari, 1993 ). The main problem of CNN implementations on a chip is the chip area consumption. The most area is reserved for the multiplexer, so we looked for alternative multiplications. We describe the achieved results with the designed chip. During the creation of the chip architecture we proposed and introduced special original coding for the weight coefficients. After simulations we recognized that the results were better with rounding than without it, but we found that the rounding during multiplication was not as important as we previously expected. Therefore we decided – instead of a hardware multiplexer – to use multiplication realized by simple gate timing of the special signal. The circuit was designed as a digital synchronized circuit. For the design and simulation we used the Xilinx tool. The cell is based on sub-circuits as it will be shown in detail. The Chapter describes the designed circuit. We introduce also our novel simulator for the CNN using the program tool Visual Basic for Application, and its algorithm which was based on the same principle as the planned designed circuit. The network can process the patterns with 400 points of recognition. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

A practical system approach for time-multiplexing cellular neural network (CNN) implementations suitable for processing large and complex images using small CNN arrays is presented. For real size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware cells and all the pixels in the image involved. This paper presents a practical solution by processing the input image, block by block, with the number of pixels in a block being the same as the number of CNN cells in the array. Furthermore, unlike other implementations in which the output is observed at the hard-limiting block, the very large scale integrated (VLSI) architecture hereby described monitors the outputs from the state node. While previous implementations are mostly suitable for black and white applications because of the thresholded outputs, our approach is especially suitable for applications in color (gray) image processing due to the analog nature of the state node. Experimental complementary metal-oxide-semiconductor (CMOS) chip results in good agreement with theoretical results are presented.

CNNEDGEPOT: CNN based edge detection of 2D near surface potential field data

Cellular neural networks (CNNs) are a powerful analog architecture that can outperform traditional von Neumann architecture for spatio-temporal information processing applications, e.g., image processing and speech recognition. Much existing work reports energy dissipation for CNNs at the chip level, which includes dissipation of sensors, actuators, and other components. As such, the impacts of various system variables, e.g., application templates, characteristics of the resistive element, etc., on the energy profile of a CNN cannot be easily determined. In this work, we propose analytical models to estimate CNN power and performance (measured by settling time). Power dissipations, and settling times obtained via the models for different linear, and non-linear characteristics are verified through circuit simulation. Simulation results show that the proposed models predict power dissipation and settling time with less than 1% and 3% errors, respectively. By using these models, we have also performed case studies for a tactile sensing problem, and a pattern recognition problem to compare power and performance between tunneling field effect transistor (TFET) based non-linear CNN and conventional linear resistor based CNN.

A Cellular Neural Network (CNN) is a highly-parallel, analog processor that can significantly outperform von Neumann architectures for certain classes of problems. Here, we show how emerging, beyond-CMOS devices could help to further enhance the capabilities of CNNs, particularly for solving problems with non-binary outputs. We show how CNNs based on devices such as graphene transistors -- with multiple steep current growth regions separated by negative differential resistance (NDR) in their I-V characteristics -- could be used to recognize multiple patterns simultaneously. (This would require multiple steps given a conventional, binary CNN.) Also, we demonstrate how tunneling field effect transistors (TFETs) can be used to form circuits capable of performing similar tasks. With this approach, more exotic device I-V characteristics are not required -- which should be an asset when considering issues such as cell-to-cell mismatch, etc. As a case study, we present a CNN-cell design that employs TFET-based circuitry to realize ternary outputs. We then illustrate how this hardware could be employed to efficiently solve a tactile sensing problem. The total number of computation steps as well as the required hardware could be reduced significantly when compared to an approach based on a conventional CNN.

A new methodology for real-time processing of DNA chip images is proposed. The idea developed here is to use the cellular neural network (CNN) array to analyze the DNA microarray. A CNN is an analog dynamic processor array that reflects this property: the processing elements interact directly within a finite local neighborhood. Due to its architecture, a two-dimensional CNN array is widely used to solve image processing and pattern recognition problems; moreover, the parallelism characteristic of this structure allows one to perform the most computationally expensive image analysis tasks three orders of magnitude faster than a classical CPU-based computer. This approach, thanks to the supercomputing capabilities of the CNN architecture, makes the whole DNA chip methodology fully parallel and also makes the processing phase, until now very time consuming, a real-time step. We discuss the results of testing an algorithm based on the CNN universal machine (CNN-UM) that has been designed to classify the image data. The algorithm is implemented in an analogic (analog and logic) microprocessor.

A new one-chip texture-classifier system is demonstrated with an execution time of about a few /spl mu/secs. Cellular neural networks (CNN) provide a new fast parallel computational method for VLSI image processing. CNN contain one or more layers of 2D cell-arrays with local cell-interconnections and in-cell dynamics. In this paper it is demonstrated that many of the feature mapping early vision effects can be simultaneously executed as convolution/deconvolution, cross-correlation, pattern-shifting, halftoning etc. The parameters of the CNN (a template) are trained through a genetic-like learning algorithm. Several types of Brodatz textures have been examined to test our method for classification and segmentation. Choosing 4 Brodatz textures which are close to each other in their main characteristics, they can be discriminated at a classification error of about 1-5% and segmentation error of about 5-10 pixels by using only one CNN template, even in the case of noisy or nonuniform CNN VLSI parameters. Using a 44*44 CNN array and one template, 16 Brodatz textures can be successfully discriminated.

The cellular neural network (CNN) method is applied to solve the Pennes bioheat transfer equation, and its feasibility is demonstrated. Numerical solutions were obtained for a cellular neural network for a two-dimensional steady-state temperature field obtained from focused and unfocused ultrasound heat sources. Transient-state temperature fields were also studied and compared with experimental results obtained elsewhere. The cellular neural networks' key features of asynchronous parallel processing, continuous-time dynamics and local interaction enable real-time temperature field estimation for clinical hyperthermia.

Cellular neural networks (CNNs) have been successfully applied in many areas such as classification of patterns, image processing, associative memories, etc. Since they are inherently local in nature, they can be easily implemented in very large scale integration. In the processing of static images, CNNs without delay are often applied whereas in the processing of moving images, CNNs with delay have been found more suitable. This paper proposes a more general model of CNNs with unbounded delay, which may have potential applications in processing such motion related phenomena as moving images, and studies global convergence properties of this model. The dynamic behaviors of CNNs, especially their convergence properties, play important roles in applications. This paper: (1) introduces a class of CNNs with unbounded delay; (2) gives some interesting properties of a network's output function; (3) establishes relationships between a network's state stability and its output stability; and (4) obtains simple and easily checkable conditions for global convergence by functional differential equation methods.

In this paper, a fault tolerant CNN (cellular neural network) using a small world network connection for image processing in embedded systems is described. In embedded systems, there are problems such as compactness, power consumption, reliability and speed, so new processor architecture is required. In this paper, we apply a small world network connection to cellular neural networks. The small world network features small links to the destination from the place of origin using local connection information of a random nature. The concept is derived from sociology. We then provide neural image processing algorithms suitable for CNN with the small world network model. Special attention has been given to typical neural algorithms for image processing, such as noise reduction and edge extraction. Further, we are proposing a fault tolerant architecture for the CNN, using majority voting.

This paper presents a novel class of cellular neural networks (CNNs), where output of a cell in the CNN is given by the piecewise-linear (PWL) function having multiple constant regions or a quantization function. CNNs with one of these output functions allow us to extend CNNs to image processing with multiple gray levels. Since each cell of the original CNN has the PWL output function with two saturation regions, the image-processing tasks are mainly developed for black and white output images. Hence, the proposed architecture will extend the promising nature of the CNN further. Moreover, the hysteresis characteristics are introduced for these functions, which make tolerance to a noise robust. It is demonstrated mathematically that under a mild assumption, the stability of the CNN, which has an output function with hysteresis characteristics, is guaranteed, and the impressive simulation results are also presented.

The researches and applications of image processing based on the cellular neural network (CNN) have made great progress. The fingerprint feature extraction CNN are two kinds of CNN, which are able to extract the endings and bifurcations, two important features in a fingerprint image. This paper makes a further contribution to this topic. We first present the global tasks and the local rules of these two kinds of CNN. Then, we proof the correctness of those local rules mathematically.

Shock filter is an important type of the filters in image processing. In recent years, research on shock filters has been an interested topic in signal and image processing. In this paper we propose a method to realize the shock filter by CNN (Cellular Neural Network). The method offers fast processing time in comparison with that on the serial digital computers and can be used in many real-time image processing systems. We focus on development of the shock filter algorithm realizing edge enhancement and de-noising on the Bi-I CNN visual computer. Besides presenting the algorithm, many experiments on Bi-I are given for demonstration.

Because of the locally connected lattice structure and high-speed parallel processing, cellular neural network (CNN) have been widely used in image processing.Traditional processing methods typically employ fixed templates, which impose significant limitations on practical complex image processing.However, the hardware implementation of large-scale CNNs becomes impossible due to the bottleneck of traditional CMOS technology.In this paper, a new threshold-adaptive algorithm is proposed by considering pixel space distributions based on human visual perception, which can overcome the aforementioned limitation.Then, the memristor, a twoterminal nonlinear device with unique high-speed switching, nonvolatility, and nanometer scale is used to solve the circuit realization problem.Specifically, we design a spintronic memristor-based CNN (SMCNN) to facilitate the proposed threshold-adaptive algorithm.Finally, by using the example of color image processing, the effectiveness of the proposed SMCNN is demonstrated by means of numerical simulations and comparative analysis.

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 i ssues for the practical applications of CNNs. This paper combines two CNN to implement the D ilation CNNs and Erosion CNN for gray scale images and proposes two theorems of robustness designs. The parameters of the templates can range between a hyper plane and a hyper surface in the first quartile. The simulations have been given. The results show the effectiveness of the theoretical results to be implemented in computer simulations. Keywords: Cellular neural network, Robust Design, Dilation, Erosion. 1. INTRODUCTION The CNN was firstly introduced by Chua & Yang [1,2] in 1988. Its original intention is to find out a structure of neural network easier to implement than the Hopfield neural networks[3], which requires fully-connected and grows exponentially with the size of the array. Now CNN has played important roles in many fields such as image and video signal processing, robotic and biological visions, and data prediction[3-8]. In an analog cellular neural network, the parameter levers usually have 5% ý 10% of perturbation [9]. So the robustness designs for CNN template parameters are important for the practical applications of CNN. Chua and Dogaru [3,5] have studied the robust designs of a large kind of CNN-uncoupled Boolean CNNs, which provides optimal design schemes for CNNs with prescribed tasks. Since then, some robust designs for uncoupled and coupled CNNs have been studied (for example see [10-20]), which have been used in image processing and pattern recognitions. Dilation CNN and Erosion CNN for binary image is proposed in Chua and Roska’s book[6]. Then Shu Jian[21] et al. designed the robust of them and proposed two theorems to guarantee the parameters of the templates can perform prescribed dilation or erosion CNN. The pa rameters of templates in [21] will range according the image. In this paper, we separate the original local rules of dilation or erosion CNN into two local rules, then we set up two parameters inequalities of templates to perform each local rules. The parameters of our templates need not range according the image, and parameters values of each template range between one hyper plane and hyper surface. The rest of this paper has organized as follows. Section 2.1 introduces the robust designs of the CNN. Section 2.2 describes and proves two local rules of the Combination C NN which implements the dilati on CNN for gray scale image. Section 2.3 describes and proves two local rules of the Combination CNN which implements erosion CNN for gray scale image. Section 3 gives the examples of the combination CNN. Conclusions have been addressed in Section 4.