Browsing by Subject "Electronic noise"
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Item Item Electronic noise in nanostructures: limitations and sensing applications(Texas A&M University, 2007-04-25) Kim, Jong UnNanostructures are nanometer scale structures (characteristic length less than 100 nm) such as nanowires, ultra-small junctions, etc. Since nanostructures are less stable, their characteristic volume is much smaller compared to defect sizes and their characteristic length is close to acoustical phonon wavelength. Moreover, because nanostructures include significantly fewer charge carriers than microscale structures, electronic noise in nanostructures is enhanced compared to microscale structures. Additionally, in microprocessors, due to the small gate capacitance and reduced noise margin (due to reduced supply voltage to keep the electrical field at a reasonable level), the electronic noise results in bit errors. On the other hand, the enhanced noise is useful for advanced sensing applications which are called fluctuation-enhanced sensing. In this dissertation, we first survey our earlier results about the limitation of noise posed on specific nano processors. Here, single electron logic is considered for voltage controlled logic with thermal excitations and generic shot noise is considered for current-controlled logic. Secondly, we discuss our recent results on the electronic noise in nanoscale sensors for SEnsing of Phage-Triggered Ion Cascade (SEPTIC, for instant bacterial detection) and for silicon nanowires for viral sensing. In the sensing of the phage-triggered ion cascade sensor, bacteriophage-infected bacteria release potassium ions and move randomly at the same time; therefore, electronic noise (i.e., stochastic signals) are generated. As an advanced model, the electrophoretic effect in the SEPTIC sensor is discussed. In the viral sensor, since the combination of the analyte and a specific receptor located at the surface of the silicon nanowire occurs randomly in space and time, a stochastic signal is obtained. A mathematical model for a pH silicon nanowire nanosensor is developed and the size quantization effect in the nanosensor is also discussed. The calculation results are in excellent agreement with the experimental results in the literature.Item Film grain noise limitations in interferometric measurements(Texas Tech University, 1973-05) De Journett, Stewart LanceNot Available.Item Filtering and estimating methodology in wildlife telemetry(Texas Tech University, 2003-12) Randeniya, Duminda I BEstimating the location of a free-ranging animal accurately from noisy directional data is treated here in this study by using two widely used filtering techniques namely Extended Kalman filter and Sampling Importance Resample Particle filter. The mathematical model used in this study consists a discrete time linear difference equation. Due to the fact that the measurements are taken every second, time step is taken discrete. Both the process noise and the measurement noise are taken to be Gaussian white noise. Simulations are carried out to compare the performance of both the Extended Kalman filter and the SIR Particle filter for two different sets of data.Item Noise effects and fault tolerance in Hopfield-type neural networks(Texas Tech University, 1990-05) Jong, Tai-LangResearch interest in neural networks has grown rapidly in recent years. Studies covering many aspects of neural networks, from new models, simulations and theoretical analyses, to implementations and applications, have been reported. Little research, however, has been performed on noise effects and fault tolerance in neural networks. In this dissertation, the focus is on the investigation of Hopfield-type neural networks (HNNs) by both numerical simulations and theoretical analyses. Computer simulations and a linear combination concept are employed to study HNNs from a quantitative point of view. A statistical method and models are then proposed for various situations in analyzing different aspects of HNNs. Contributions of this dissertation include the following: First, a complementary Hopfield model (CHNN) is presented for improving the performance of the original Hopfield model. A generalized three-layered model capable of systematically describing HNNs and their extensions, e.g., higher-order, exponential order, and winner-take-all nets is then proposed. A rigorous analysis of HNNs, using a statistical technique, clearly displays their characteristics. Analyses and comparisons of first-order modifications, higher-order nets, and exponential order nets are also performed. The differences between even- and odd-order nets, as well as auto- and hetero-associative memories are pointed out and discussed. Various models for implementation error and/or noise sources, including detector/thresholding device noise, 2-D matrix mask noise, and gain variation noise, are then proposed and an "excess" noise concept is developed, which then leads to new results on the analysis of implementation noise effects in HNNs. This technique is then extended and successfully applied to the analysis of fault tolerance problems, including synaptic interconnect faults and neuron stuck-at faults, in HNNs.Item Space domain suppression of film-grain noise(Texas Tech University, 1975-05) Abbott, Barton LewisNot availableItem Statistical modeling of film grain noise(Texas Tech University, 1976-05) Whited, John LeonardNot available