Memory Access Patterns for Cellular Automata Using GPGPUs

Today\u27s graphical processing units have hundreds of individual processing cores that can be used for general purpose computation of mathematical and scientific problems. Due to their hardware architecture, these devices are especially effective when solving problems that exhibit a high degree of spatial locality. Cellular automata use small, local neighborhoods to determine successive states of individual elements and therefore, provide an excellent opportunity for the application of general purpose GPU computing. However, the GPU presents a challenging environment because it lacks many of the features of traditional CPUs, such as automatic, on-chip caching of data. To fully realize the potential of a GPU, specialized memory techniques and patterns must be employed to account for their unique architecture. Several techniques are presented which not only dramatically improve performance, but, in many cases, also simplify implementation. Many of the approaches discussed relate to the organization of data in memory or patterns for accessing that data, while others detail methods of increasing the computation to memory access ratio. The ideas presented are generic, and applicable to cellular automata models as a whole. Example implementations are given for several problems, including the Game of Life and Gaussian blurring, while performance characteristics, such as instruction and memory accesses counts, are analyzed and compared. A case study is detailed, showing the effectiveness of the various techniques when applied to a larger, real-world problem. Lastly, the reasoning behind each of the improvements is explained, providing general guidelines for determining when a given technique will be most and least effective

Utilizing an enhanced cellular automata model for data mining

Data mining deals with clustering and classifying large amounts of data, in order to discover new knowledge from the existent data by identifying correlations and relationships between various data-sets. Cellular automata have been used before for classification purposes. This paper presents a cellular automata enhanced classification algorithm for data mining. Experimental results show that the proposed enhancement gives better performance in terms of accuracy and execution time than previous work using cellular automata

A Survey on Continuous Time Computations

We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing results, and point to relevant references in the literature

Copyright Protection for Surveillance System Multimedia Stream with Cellular Automata Watermarking

Intelligent Surveillance Systems are attracting extraordinary attention from research and industry. Security and privacy protection are critical issues for public acceptance of security camera networks. Existing approaches, however, only address isolated aspects without considering the integration with established security technologies and the underlying platform. Easy availability of internet, together with relatively inexpensive digital recording and storage peripherals has created an era where duplication, unauthorized use and misdistribution of digital content has become easier. The ease of availability made digital video popular over analog media like film or tape. At the same time it demands a sharp attention regarding the ownership issue. The ownership and integrity can easily be violated using different audio and video editing softwares. To prevent unauthorized use, misappropriation, misrepresentation; authentication of multimedia contents achieved a broad attention in recent days and to achieve secure copyright protection we embedded some information in audio and videos and that audio or video is called copyright protected. Digital watermarking is a technology to embed additional information into the host signal to ensure security and protection of multimedia data. The embedded information can’t be detected by human but some attacks and operations can tamper that information to breach protection. So in order to find a secure technique of copyright protection, we have analyzed different techniques. After having a good understanding of these techniques we have proposed a novel algorithm that generates results with high effectiveness, additionally we can use self-extracted watermark technique to increase the security and automate the process of watermarking. Forensic digital watermarking is a promising tool in the fight against piracy of copyrighted motion imagery content, but to be effective it must be (1) imperceptibly embedded in high-definition motion picture source, (2) reliably retrieved, even from degraded copies as might result from camcorder capture and subsequent very-low-bitrate compression and distribution on the Internet, and (3) secure against unauthorized removal. Audio and video watermarking enables the copyright protection with owner or customer authentication and the detection of media manipulations. The available watermarking technology concentrates on single media like audio or video. But the typical multimedia stream consists of both video and audio data. Our goal is to provide a solution with robust and fragile aspects to guarantee authentication and integrity by using watermarks in combination with content information. We show two solutions for the protection of audio and video data with a combined robust and fragile watermarking approach. The first solution is to insert a time code into the data: We embed a signal as a watermark to detect gaps or changes in the flow of time. The second solution is more complex: We use watermarks to embed information in each media about the content of the other media. In our paper we present the problem of copyright protection and integrity checks for combined video and audio data. Both the solutions depend upon cellular automata, cellular automata are a powerful computation model that provides a simple way to simulate and solve many difficult problems in different fields. The most widely known example of Cellular Automata is the Game-of-Life. Cellular automaton growth is controlled by predefined rule or programs .The rule describes how the cell will interact with its neighborhood. Once the automaton is started it will work on its own according to the rule specified.

Cellular Automata and Randomization: A Structural Overview

The chapter overviews the methods, algorithms, and architectures for random number generators based on cellular automata, as presented in the scientific literature. The variations in linear and two-dimensional cellular automata model and their features are discussed in relation to their applications as randomizers. Additional memory layers, functional nonuniformity in space or time, and global feedback are examples of such variations. Successful applications of cellular automata random number/signal generators (both software and hardware) reported in the scientific literature are also reviewed. The chapter includes an introductory presentation of the mathematical (ideal) model of cellular automata and its implementation as a computing model, emphasizing some important theoretical debates regarding the complexity and universality of cellular automata