A model-based estimation process is implemented in simulation of a water transmission line for the purpose of leak detection. The objective of this thesis is aimed at determining, through simulation results, the effectiveness of the Extended Kalman Filter for leak detection. Water distribution systems often contain large amounts of unknown losses. The range in magnitude of losses varies from 10 to over 50 percent of the total volume of water pumped. The result is a loss of product, including water and the chemicals used to treat it, environmental damage, demand shortfalls, increased energy usage and unneeded pump capacity expansions. It is clear that more control efforts need to be implemented on these systems to reduce losses and increase energy efficiencies. The problems of demand shortfalls, resulting from lost product, are worsened by the limited availability of water resources and a growing population and economy. The repair of leakage zones as they occur is not a simple problem since the vast majority of leaks, not considered to be major faults, go undetected. The leak detection process described in the work of this thesis is model based. A transient model of a transmission line is developed using the Method of Characteristics. This method provides the most accurate results of all finite-difference solutions to the two partial differential equations of continuity and momentum that describe pipe flow. Simulations are run with leakage within the system and small transients are added as random perturbations in the upstream reservoir head. The head measurements at the two pipe extremes are used as inputs into the filter estimation process. The Extended Kalman Filter is used for state estimation of leakage within the transmission line. The filter model places two artificial leakage states within the system. The estimates of these fictitious leakage states are then used to locate the actual position and magnitude of leakage within the transmission line. This method is capable of locating one leak within the line. The results of the Extended Kalman Filter (EKF) process show that it is capable of locating the position and magnitude of small leaks within the line. It was concluded that the EKF could be used for leak detection, but field tests need to be done to better quantify the ability of these methods. It is recommended that a multiple filtering method be implemented that may be able to locate the occurrence of multiple leakage.

Many problems in tracking control have been identified over the years, such as the availability of systems states, the presence of noise and system uncertainties, and speed of response, just to name a few. This thesis is concerned with developing novel integrated control and estimation algorithms to overcome some of these problems in order to achieve an efficient tracking performance. Since there are some significant advantages associated with Sliding Mode Control (SMC) or Variable Structure Control (VSC), (fast regulation rate and robustness to uncertainties), this research reviews and extends new filtering concepts for state estimation, referred to as the Variable Structure Filter (VSF)and Smooth Variable Structure Filter (SVSF). These are based on the philosophy of Sliding Mode Control.<p>The VSF filter is designed to estimate some of the states of a plant when noise and uncertainties are presented. This is accomplished by refining an estimate of the states in an iterative fashion using two filter gains, one based on a noiseless system with no uncertainties and the second gain which reflects these uncertainties. The VSF is combined seamlessly with the Sliding Mode Controller to produce an integrated controller called a Sliding Mode Controller and Filter (SMCF). This new controller is shown to be a robust and effective integrated control strategy for linear systems. For nonlinear systems, a novel integrated control strategy called the Smooth Sliding Mode Controller and Filter (SSMCF), fuses the SMC and SVSF in a particular form to address nonlinearities. The gain term in the SVSF is redefined to form a new algorithm called the SVSF with revised gain in order to obtain a better estimation performance. Its performance is compared to that of the Extended Kalman Filter (EKF) when applied to a particular nonlinear plant.<p>The SMCF and SSMCF are applied to the experimental prototype of a precision positioning hydraulic system called an ElectroHydraulic Actuator (EHA) system. The EHA system is known to display nonlinear characteristics but can approximate linear behavior under certain operating conditions, making it ideal to test the robustness of the proposed controllers.<p>The main conclusion drawn in this research was that the SMCF and SSMCF as developed and implemented, do exhibit robust and high performance state estimation and trajectory tracking control given modeling uncertainties and noise. The controllers were applied to a prototype EHA which demonstrated the use of the controllers in a real world application. It was also concluded that the application of the concepts of VSC for the controller can alleviate a challenging mechanical problem caused by a slip-stick characteristic in friction. Another conclusion is that the revised form of the SVSF could obtain robust and fast state estimation for nonlinear systems.<p>The original contributions of the research include: i) proposing the SMCF and SSMCF, ii) applying the Sliding Mode Controller to suppress cross-over oscillations caused by the slip-stick characteristics in friction which often occur in mechanical systems, iii) the first application of the SVSF for state estimation and iv) a comparative study of the SVSF and Extended Kalman Filter (EKF) to the EHA demonstrating the superiority of the SVSF for state estimation performance under both steady-state and transient conditions for the application considered.<p>The dissertation is written in a paper format unlike the traditional Ph.D thesis manuscript. The content of the thesis discourse is based on five manuscripts which are appended at the end of the thesis. Fundamental principles and concepts associated with SMC, VSF, SVSF and the fused controllers are introduced. For each paper, the objectives, approaches, typical results, conclusions and major contributions are presented. Major conclusions are summarized and original contributions reiterated.

A model based algorithm of pipeline flow is developed and tested to determine if the model is capable of detecting a leak in a pipeline. The overall objective of this research is to determine the feasibility of applying the Extended Kalman Filter and a new technique defined as the Extended Boundary Approach to the detection of leakages in a physical water distribution system. <p>The demands on the water supply system increase as the human population grows and expands throughout the world. Water conservation is required to ensure an adequate supply of water remains for future generations. One way to conserve this water is by reducing the leakages in underground water distribution systems. Currently between 10 to 50 percent of the pumped water is lost due to unrecognized leakages. This results in a huge revenue loss of water, chemicals and energy that is required for transporting the water. The detection of underground leakages is a very complex problem because many leakages are small and go unnoticed by todays leak detection technology. <p>A model based leak detection technique is developed and tested in this thesis. The Method of Characteristics is used to develop a model of a single pipeline. This method is extensively used and provides the most accurate results of the two partial differential equations of continuity and momentum that describe pipe flow. The Extended Kalman Filter is used to estimate two fictitious leakages at known locations along the pipeline. In order to ensure the model is observable four pressure measurements are needed at equally spaced nodes along the pipeline. With the development of the Extended Boundary Approach only the upstream and downstream pressure measurements are required, however; the upstream and downstream flow measurements are also required. Using the information from the two fictitious leaks the actual leak location and magnitude can be determined. This method is only capable of detecting one leak in a single pipeline. <p>The results of the developed model show that the approach is capable of theoretically determining the leak location and magnitude in a pipeline. However, at this time, the feasibility of implementing the proposed leak detection method is limited by the required level of accuracy of the sensors which is beyond that found in todays technology. It was also found that the EKF used primarily steady state information to predict the leakage. It is recommended that further research explore alternate models which might better enhance the EKF approach using transient information from the pipeline. This may allow implementation on a real pipeline.

TThe objective of the current study was to investigate the backwashing behavior of granular media filters used in water treatment under realistic conditions and to develop better models of the backwash process based on both fundamental and practical considerations. The focus of this study was on water only backwash but the applicability of the results to auxiliary backwash systems is discussed. The effects of filter backwash rate, coagulant used, degree of filter clogging and age of filter deposits on backwash behavior and efficiency were investigated in a pilot scale in-line filtration plant treating low turbidity raw water from a large dam. The results of these experiments and their implications both for modeling and managing filter backwash are discussed. The initial stages of backwashing are shown to be dominated by mixing and flow localization effects not accounted for in existing models of backwash. These effects appear to be dependent on both the equipment and the experimental conditions making the development of an accurate model of transient backwash behavior extremely difficult. However, it is shown that the overall efficiency of backwash can be predicted based on data about the filter and backwash design and operation that should be available at any treatment plant. This is an important first step in the development of modeling tool for the design and optimization of the complete filter cycle. A significant finding of this study was that the average age of filter deposits is one of the most important factors determining the ease with which they are detached during backwashing. Deposits become more difficult to remove the longer they remain in the filter. This has important implications for the robust design and operation of filters in applications where optimal backwash cannot be guaranteed. The rate of accumulation of mud in a filter over multiple filter cycles was determined experimentally for one set of backwash conditions and a procedure for estimating the useful life of a filter bed with sub-optimal backwash is proposed.

Two kinds of companding filters are presented in this dissertation. The first one is a square-root domain filter based on operational transconductance amplifier (OTA). This one is compact and simple. The total are of the circuit excluding pads is 0.013 mm2. The supply voltage is 1.5V and the cutoff frequency can be tuned from 1.1 kHz to 35.2 kHz when the external capacitance C is 1nF. The total harmonic distortions is 0.93% and the power consumption is 152.29 £gW for a 10£gA DC input current. The other one is a tunable log-domain filter. The log domain filters uses parasitic vertical bipolar junction transistor (VBJT) in standard CMOS process for high frequency. The cut-off frequency is from 8.6 MHz to 25.8 MHz and the power dissipation is 585 £gW. All experimental results in a TSMC 0.35 £gm 2P4M CMOS process confirm the feasibility of the methodology.

The purpose of Morphology is to capture features and attributes of image, such as boundaries and contours. It has been widely applied to computer vision, the analysis and processing of image and even industry examinations and medical image processing. The reason why the Morphology is widely applied is that we can use its simple structure elements to process images, and get up to our requirements. Therefore, it¡¦s become our primary study to find out those suitable structure elements. In this paper, we used the laws of judging the multiple mask relationship to find out filters which came up to the condition that we set, and also came up with an efficient way to find out filters that we want to get.

The major problems encountered in wireless location are the effects caused by non-line of sight (NLOS) propagation and multipath interference. In the thesis, we propose an approach to mitigate NLOS error. First of all, we use improved biased Kalman filter (IBKF) based on time of arrival (TOA) measurement to identify and mitigate NLOS error. Applying the statistic characteristic that the standard deviation of the NLOS propagation errors is generally much larger than that of measurement noises in the LOS condition, we combine hypothesis test and sliding window to identify NLOS error. According to the feedback identification and the calculated standard deviation, IBKF switches biased or unbiased to process TOA measurement. Nevertheless, the performance of IBKF-TOA is still affected slightly by NLOS error. Since extended Kalman filter (EKF) based on received signal strength (RSS) measurement is designed for prespecified environments, the effect of NLOS mitigation is more obvious. Moreover, EKF-RSS not only exists higher error probability in NLOS identification, but also needs longer time to converge in the cases that start with NLOS. Comparing IBKF-TOA with EKF-RSS, we adopt interacting multiple model (IMM) in the proposed data fusion structure for processing TOA and RSS measurements. In the proposed scheme, we reserve the basic IMM structure and add the step of NLOS identification into basic IMM structure. By accurate NLOS identification results and soft decision of IMM, the proposed scheme will switch to adequate filter mode and obtain better estimation. With simulation in UWB channel, the analysis and performance evaluation show advantages and disadvantages of using IBKF-TOA, EKF-RSS, and proposed scheme. Simulation results reveal that NLOS error can be mitigated effectively by data fusion of TOA and RSS measurements and can achieve high accuracy in positioning and tracking.

This thesis focuses on the design and analysis of an optimal filter that is capable of making one-step-ahead prediction of a bandlimited signal while attenuating unwanted noise. First, the filter optimization based on the least mean-square-error criterion is presented. Then, an exact expression for the achievable minimum mean square error (MMSE) is derived with the aid of the Toeplitz form method and Szego theory. Based on this MMSE expression, the formulae for estimating the optimal filter¡¦s in-band prediction error and out-of-band noise attenuation are derived. Finally, the optimal filter is applied to sigma-delta modulation. It shows that the modulation performance and stability are intimately related to the filter performance and can be accurately estimated by the derived formulae.

Traditional transfer function of integrators have warping effect in high frequency, this isn¡¦t good for make filter circuit. In reference[3] they mention a new transfer function to improve this error, but we found that the design of the previous circuit doesn¡¦t conform to the new transfer function. In this thesis, a different structure of integrator is presented, it use the method of double sampling to realize the modified bilinear transfer function, in addition, we also add a grounded-gate amplifier to decrease the input impedance and dummy switch technique what can reduce the charge injection error, then we use the central circuit to make the second order bandpass filter. The proposed circuit employ Hspice to simulate and design the form of the circuit layout, then use TSMC 0.35£gm CMOS process to make chip. The sampling frequency is 10MHz, the central frequency is 1MHz, and the power consumption is 1.78mW.

We consider the topic of target tracking in this thesis. Target tracking is one of the applications in wireless sensor networks (WSNs). Clustering approach prolongs sensor¡¦s lifetime and provides better data aggregation for WSNs. Most previous researches assumed that cluster regions are disjointed, while others assigned overlapping cluster regions, and utilized them in some applications, including inter-cluster routing and time synchronization. However, in overlapping clustering, processing of redundant sensing data may impair system performance. We present a regular distributed overlapping WSN in this thesis. The network is based on two kinds of sensors: (1) high-capability sensors, which are assigned as cluster heads (CHs), responsible for data processing and inter-cluster communication, (2) normal sensors, which are in a larger number when comparing with the high-capability sensors, the function of normal sensors are to provide data to the CHs. We define several operating modes of CHs and sensors. WSN works more efficient under the settings. Since a target may be located in the overlapping region, redundant data processing problem exists. To solve the problem, we utilize Cholesky decomposition to decorrelate the measurement noise covariance matrices. The correlation will be eliminated during the process. In addition, we modify extended information filter (EIF) and adapt to the decorrelated data. The CHs track the target, fuse the information from other CHs, and implement distributed positioning. The simulations are based on ultra-wideband (UWB) environment, we have verified that the proposed scheme works more efficient under the setting of different modes. The performance with decorrelated measurement is better than that with correlated ones.