Existing opportunities in advanced interceptor, satellite guidance and aircraft navigation technologies, requiring higher signal processing speeds and lower noise environments, are demanding Ring Laser Gyro (RLG) based Inertial Systems to reduce initialization and operational data latency as well as correlated noise magnitudes. Existing signal processing algorithms are often less than optimal when considering these requirements. Advancements in micro-electronic processes have made Application Specific Integrated Circuits (ASIC) a fundamental building block for system implementation when considering higher-level signal processing algorithms. Research of real time adaptive signal processing algorithms embedded in ASICs for use in RLG based inertial systems will help to understand the trade-off in finite register length effects to correlated noise magnitude, organizational complexity, computational efficiency, rate of convergence, and numerical stability. Adaptive filter structures selected will directly affect meeting inertial system performance requirements for data latency, residual noise budgets and real time processing throughput. Research in this area will help to target specific adaptive noise cancellation algorithms for RLG based inertial systems in a variety of military and commercial space applications. Of particular significance is an attempt to identify an algorithm embedded in an ASIC that will reduce the correlated noise components to the theoretical limit of the RLG sensor itself. This would support a variety of applications for the low noise space environments that the RLG based inertial systems are beginning to find promise for such as advanced military interceptor technology and commercial space satellite navigation, guidance and control systems.

This study is concerned in reducing high dimensionality problem of auxiliary variables in the calibration estimation with the presence of nonresponse. The calibration estimation is a weighting method assists to compensate for the nonresponse in the survey analysis. Calibration estimation using principal components (PCs) is new idea in the literatures. Principal component analysis (PCA) is used in reduction dimension of the auxiliary variables. PCA in calibration estimation is presented as an alternative method for choosing the auxiliary variables. In this study, simulation on the real data is used and nonresponse mechanism is applied on the sampled data. The calibration estimator is compared using different criteria such as varying the nonresponse rate and increasing the sample size. From the results, although the calibration estimation based on the principal components have reasonable outputs to use instead of the whole auxiliary variables for the means, the variance is very large compared with based on original auxiliary variables. Finally, we identified the principal component analysis is not efficient in the reduction of high dimensionality problem of auxiliary variables in the calibration estimation for large sample sizes.

This paper designs and implements a DSP based intelligent battery charger and residual capacity estimator for electric vehicle. This system uses the proposed new electric circuit structure and the intelligent fuzzy charge algorithm to charge batteries, and the improved coulometric measurement with accurate residual capacity estimation to estimate the residual capacity of batteries. From the experimental results, the charger can achieve the purpose of fast and uniform charge with charging time six (6) to eight (8) hours, and will not cause the damage of battery because of using the intelligent fuzzy charge algorithm can give different charging current depend on the difference of voltage, capacity and temperature of battery; the residual capacity estimator can accurate estimate the residual capacity of batteries due to calculating the increment current and considering the aging factor.

This paper Management and Diagnosis a DSP based intelligent battery charger and residual capacity estimator for electric vehicle. This system uses the proposed new electric circuit structure and the intelligent fuzzy charge algorithm to charge batteries, and the improved coulometric measurement with accurate residual capacity estimation to estimate the residual capacity of batteries. From the experimental results, the charger can achieve the purpose of fast and uniform charge with charging time six (6) to eight (8) hours, and will not cause the damage of battery because of using the intelligent fuzzy charge algorithm can give different charging current depend on the difference of voltage, capacity and temperature of battery; the residual capacity estimator can accurate estimate the residual capacity of batteries due to calculating the increment current and considering the aging factor.

This paper designs and implements a DSP based intelligent battery charger and residual capacity estimator. This system uses the proposed structure of the series circuit and battery equalizer with the intelligent fuzzy charge algorithm to charge batteries, and the improved coulometric measurement with accurate residual capacity estimation to estimate the residual capacity of batteries. Because of using the intelligent fuzzy charge algorithm can give different charging current depend on the difference of voltage, capacity and temperature of battery; And because of using the battery equalizer can adjust the voltage of battery. The charger can charge the battery safely without causing any damage. From the experimental results, the charger can achieve the purpose of fast and uniform charge with charging time six (6) to eight (8) hours, the residual capacity estimator can accurate estimate the residual capacity of batteries due to calculating the increment current and considering the aging factor.

This thesis is to design an intelligent battery charger and residual capacity estimator with DSP. This system uses the proposed structure of the series circuit and battery equalizer with the intelligent fuzzy charge algorithm to charge battery, The internal resistance measurement can accurately estimate the residual capacity of battery. Because of using the intelligent fuzzy charge algorithm, it can give different charging current depends on voltage, capacity and temperature of battery. Because of using battery equalizer, it can adjust the voltage of battery to guarantee the battery be charged safely. According to experimental results, the charger can achieve the goal of fast and uniform charge within 6 to 8 hours. On the residual capacity estimator, We measure internal resistance to accurately estimate residual capacity of battery, because internal resistance is affected by environmental temperature, battery corrosion, aging factor and output current .

The causal system is more practical then the noncausal system in the world. Causality implies only the past input can effect the future output. As a consequence, noncausal system is seldom investigation. The purpose of this thesis is to study the signal recury for a noncausal system. The principle of signal estimation is based upon the Wiener-Hopf equation. Therefore, the correlation computation is very important. By transforming the noncausal dynamic equations to a causal equation, we achieve a partial recursive computation structure for correlation computation. However the current input is not independent of the past signal in the noncausal system. Hence, the Mason Rule is applied to solved this problem to make the above recursive structure complete. Furthermore, a recursive computation of Mason Rule for stage propagation is developed in this thesis to accelerating the processing speed. Our algorithm is applied to image restoration. We first segment the image to find the required generating input ponen for each correlated region. Secondly, we extend our 1-D algorithms to 2-D algorithm to restore the image. Our method is compared with the method developed base upon the Gaussian Markov model. The experiments results demonstrate the advantage of method in both visual quailty and numerical results.

The paper presents a posteriori error estimators for the stationary Stokes problem. We consider anisotropic finite element discretizations (i.e. elements with very large aspect ratio) where conventional, isotropic error estimators fail. Our analysis covers two- and three-dimensional domains, conforming and nonconforming discretizations as well as different elements. This large variety of settings requires different approaches and results in different estimators. Furthermore many examples of finite element pairs that are covered by the analysis are presented. Lower and upper error bounds form the main result with minimal assumptions on the elements. The lower error bound is uniform with respect to the mesh anisotropy with the exception of nonconforming 3D discretizations made of pentahedra or hexahedra. The upper error bound depends on a proper alignment of the anisotropy of the mesh which is a common feature of anisotropic error estimation. In the special case of isotropic meshes, the results simplify, and upper and lower error bounds hold unconditionally. Some of the corresponding results seem to be novel (in particular for 3D domains), and cover element pairs of practical importance. The numerical experiments confirm the theoretical predictions and show the usefulness of the anisotropic error estimators.

Rotor Position information is essential in the operation of the Switched Reluctance Motor (SRM) for properly controlling its phase currents. This thesis uses Field Programmable Gate Array (FPGA) technology to implement a method to estimate the SRMs rotor position using the inverse inductance value of the SRMs phases. The estimated rotor position is given as input to the Commutator circuit, also implemented in the FPGA, to determine when torque-producing currents should be input in the SRM phase windings. The Estimator and Commutator design is coded using Verilog HDL and is simulated using Xilinx tools. This circuit is implemented on a Xilinx Virtex XCV800 FPGA system. The experimentally generated output is validated by comparing it with simulation results from a Simulink model of the Estimator. The performance of the FPGA based SRM rotor position estimator in terms of calculation time is compared to a digital signal processor (DSP) implementation of the same position estimator algorithm. It is found that the FPGA rotor position Estimator with a 5MHz clock can update its rotor position estimate every 7s compared to an update time of 50s for a TMS320C6701-150 DSP implementation using a commercial DSP board. This is a greater than 7 to one reduction in the update time.

Underwater communication is necessary for a variety of applications, including transmission of diver speech, communication between manned and/or unmanned underwater vehicles, and data harvesting for environmental monitoring, to name a few. Examples of communication between underwater vehicles include unmanned or autonomous underwater vehicles (UUV or AUV) for deep water construction, military UUVs such as submarine drones, repair vehicles for deep water oil wells, scientific or resource exploration, etc. Examples of underwater communication between fixed submerged devices are sensor networks deployed on the ocean floor for seismic monitoring and tsunami prediction, pollution monitoring, tactical surveillance, analysis of resource deposits, oceanographic studies, etc. The underwater communication environment is a challenging one. Radio signals experience drastic attenuation, while optical signals suffer from dispersion. Because of these issues, acoustic (sound) signals are usually used for underwater communication. Unfortunately, acoustics has its own problems, including limited bandwidth, slow propagation, and signal distortion. Some of these limitations can be overcome with advanced modulation and coding, but to do so requires better understanding of the underwater acoustic propagation environment, which is significantly different than air- or space-based radio propagation. The underwater environment must be studied and characterized to exploit these advanced modulation and coding techniques. This thesis addresses some of these concerns by proposing a derivation of the envelope level crossing rate of the underwater channel, as well as a simulation model for the channel, both of which agree well with the measured results. A velocity estimator is also proposed, but suffers from a high degree of root mean square error