Browsing by Subject "Cellular automata"
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Item A multiple-row-based leaf cell generator(Texas Tech University, 1990-08) Rao, Satish KrishnaNot availableItem Adder and multiplier design and analysis in quantum-dot cellular automata(2006) Cho, Heumpil; Swartzlander, Earl E., Jr.Quantum-dot cellular automata (QCA) is an emerging nanotechnology for electronic circuits. Its advantages such as faster speed, smaller size, and lower power consumption are very attractive. The fundamental device, a quantum-dot cell, can be used to make gates, wires, and memories. As such it is the basic building block of nanotechnology circuits. While the physical nature of the nanoscale materials is complicated, the circuit designer can concentrate on the logical and structural design, so the design effort is reduced. Because of its novelty, the current literature shows only simple circuit structures. This research broadens the QCA circuit designs with larger circuits, explores the characteristics of QCA circuit designs, and shows analysis based on those design results. This dissertation proposes three kinds of adder designs in QCA from the conventional adder design approaches. Ripple carry adders, carry lookahead adders, and conditional sum adders are designed for optimization with QCA technology and simulated with several different operand sizes. Using the newly discovered knowledge of the QCA circuit characteristics, new designs for serial adders and multipliers are presented, which are the carry flow adder and the serial multiplier. The carry flow adder design is compared with the previous three adder designs. From the filter design methodology, the carry shift multiplication and the carry delay multiplication algorithms are proposed. The serial multipliers are implemented with both algorithms. The final designs are compared according to the complexity, area, and delay.Item Mapping textures on 3d terrains: a hybrid cellular automata approach(Texas A&M University, 2007-04-25) Sinvhal, SwapnilIt is a time consuming task to generate textures for large 3D terrain surfaces in computer games, flight simulations and computer animations. This work explores the use of cellular automata in the automatic generation of textures for large surfaces. I propose a method for generating textures for 3D terrains using various approaches - in particular, a hybrid approach that integrates the concepts of cellular automata, probabilistic distribution according to height and Wang tiles. I also look at other hybrid combinations using cellular automata to generate textures for 3D terrains. Work for this thesis includes development of a tool called "Texullar" that allows users to generate textures for 3D terrain surfaces by configuring various input parameters and choosing cellular automata rules. I evaluate the effectiveness of the approach by conducting a user survey to compare the results obtained by using different inputs and analyzing the results. The findings show that incorporating concepts of cellular automata in texture generation for terrains can lead to better results than random generation of textures. The analysis also reveals that incorporating height information along with cellular automata yields better results than using cellular automata alone. Results from the user survey indicate that a hybrid approach incorporating height information along with cellular automata and Wang tiles is better than incorporating height information along with cellular automata in the context of texture generation for 3D meshes. The survey did not yield enough evidence to suggest whether the use of Wang tiles in combination with cellular automata and probabilistic distribution according to height results in a higher mean score than the use of only cellular automata and probabilistic distribution. However, this outcome could have been influenced by the fact that the survey respondents did not have information about the parameters used to generate the final image - such as probabilistic distributions, the population configurations and rules of the cellular automata.Item Self-repair and adaptation in collective and parallel computational networks: a statistical approximation(Texas Tech University, 1990-08) Phoha, Vir ViranderHogg and Huberman have defined the global dynamics of a system made up of elementary computational cells which can be used to model processes such as speech and image recognition. In training a neural net model for adaptive behavior, such that a given set of inputs will result in specific outputs, Hogg and Huberman reported the so-called frustration effect, whereby outputs never converged to the desired class. Stability under parameter changes and general behavior of this model are open research issues. At a more fundamental level Hogg and Huberman hoped for the development of a theory of recognition of fuzzy inputs in such a way that the neural net parameters could be trained to produce specific responses to a desired set of training inputs. Towards such a theory, this work formulates an analytical model for approximating the outputs of the Hogg and Huberman model after k iterations through the M*N neural network. The analytical model is a best fit to the dynamic process in the sense of mean square error. Under regularity conditions such that the analytical model is a good fit, the well estabUshed theory of multivariate statistics can be used to understand the stability properties of the neural net.