Hydrocarbon use has been increasing and will continue to increase for the foreseeable future in even the most pessimistic energy scenarios. Over the past few decades, natural gas has become the major player and revenue source for many countries and multinationals. Its presence and power share will continue to grow in the world energy mix. Much of the current gas reserves are found in gas condensate reservoirs. When these reservoirs are allowed to deplete, the pressure drops below the dew point pressure and a liquid condensate will begin to form in the wellbore or near wellbore formation, possibly affecting production. A field optimization includes determining the number of wells, type (vertical, horizontal, multilateral, etc.), trajectory and location of wells. Optimum well placement has been studied extensively for oil reservoirs. However, well placement in gas condensate reservoirs has received little attention when compared to oil. In most cases involving a homogeneous gas reservoir, the optimum well location could be determined as the center of the reservoir, but when considering the complexity of a heterogeneous reservoir with initial compositional variation, the well placement dilemma does not produce such a simple result. In this research, a horizontal well placement problem is optimized by using a modified Genetic Algorithm. The algorithm presented has been modified specifically for gas condensate reservoirs. Unlike oil reservoirs, the cumulative production in gas reservoirs does not vary significantly (although the variation is not economically negligible) and there are possibly more local optimums. Therefore the possibility of finding better production scenarios in subsequent optimization steps is not much higher than the worse case scenarios, which delays finding the best production plan. The second modification is developed in order to find optimum well location in a reservoir with geological uncertainties. In this modification, for the first time, the probability of success of optimum production is defined by the user. These modifications magnify the small variations and produce a faster convergence while also giving the user the option to input the probability of success when compared to a Standard Genetic Algorithm.

Various civil and military applications of UAVs, or ground robots, require a set of vehicles to monitor a group of targets. Routing problems naturally arise in this setting where the operators of the vehicles have to plan the paths suitably in order to optimize the use of resources available such as sensors, fuel etc. These vehicles may differ either in their structural (design and dynamics) or functional (sensing) capabilities. This thesis addresses an important routing problem involving two heterogeneous vehicles. As the addressed routing problem is NP-Hard, we develop an approximation algorithm and heuristics to solve the problem. Our approach involves dividing the routing problem into two sub-problems: Partitioning and Sequencing. Partitioning the targets involves finding two distinct sets of targets, each corresponding to one of the vehicles. We then find a sequence in which these targets need to be visited in order to optimize the use of resources to the maximum possible extent. The sequencing problem can be solved either by Christofides algorithm or the Lin-Kernighan Heuristic (LKH). The problem of partitioning is tackled by solving a Linear Program (LP) obtained by relaxing some of the constraints of an Integer Programming (IP) model for the problem. We observe the performance of two LP models for the partitioning. The first LP model is obtained by relaxing only the integrality constraints whereas in the second model relaxes both integrality and degree constraints. The algorithms were implemented in a C++ environment with the help of Concert Technology for CPLEX, and Boost Graph Libraries. The performance of these algorithms was studied for 50 random instances of varying problem sizes. It was found that on an average, the algorithms based on the first LP model provided better (closer to the optimum) solutions as compared to those based on the second LP model. We also observed that for both the LP models, the average quality of solutions given by the heuristics were found to be better ( within 5% of the optimum) than the average quality of solutions obtained from the approximation algorithm (between 30 - 60% of the optimum depending on the problem size).

The development of telerobotic systems has evolved from Single Operator Single Robot (SOSR) systems to Multiple Operator Multiple Robot (MOMR) systems. The relationship between human operators and robots follows the master-slave control architecture and the requests for controlling robot actuation are completely generated by human operators. Recently, the fast evolving advances in network and computer technologies and decreasing size and cost of sensors and robots enable us to further extend the MOMR system architecture to incorporate heterogeneous components such as humans, robots, sensors, and automated agents. The requests for controlling robot actuation are generated by all the participants. We term it as the MOMR++ system. However, to reach the best potential and performance of the system, there are many technical challenges needing to be addressed. In this dissertation, we address two major challenges in the MOMR++ system development. We first address the robot coordination and planning issue in the application of an autonomous crowd surveillance system. The system consists of multiple robotic pan-tilt-zoom (PTZ) cameras assisted with a fixed wide-angle camera. The wide-angle camera provides an overview of the scene and detects moving objects, which are required for close-up views using the PTZ cameras. When applied to the pedestrian surveillance application and compared to a previous work, the system achieves increasing number of observed objects by over 210% in heavy traffic scenarios. The key issue here is given the limited number (e.g., p (p > 0)) of PTZ cameras and many more (e.g., n (n >> p)) observation requests, how to coordinate the cameras to best satisfy all the requests. We formulate this problem as a new camera resource allocation problem. Given p cameras, n observation requests, and [epsilon] being approximation bound, we develop an approximation algorithm running in O(n/[epsilon]³ + p²/[epsilon]⁶) time, and an exact algorithm, when p = 2, running in O(n³) time. We then address the automatic object content analysis and recognition issue in the application of an autonomous rare bird species detection system. We set up the system in the forest near Brinkley, Arkansas. The camera monitors the sky, detects motions, and preserves video data for only those targeted bird species. During the one-year search, the system reduces the raw video data of 29.41TB to only 146.7MB (reduction rate 99.9995%). The key issue here is to automatically recognize the flying bird species. We verify the bird body axis dynamic information by an extended Kalman filter (EKF) and compare the bird dynamic state with the prior knowledge of the targeted bird species. We quantify the uncertainty in recognition due to the measurement uncertainty and develop a novel Probable Observation Data Set (PODS)-based EKF method. In experiments with real video data, the algorithm achieves 95% area under the receiver operating characteristic (ROC) curve. Through the exploration of the two MOMR++ systems, we conclude that the new MOMR++ system architecture enables much wider range of participants, enhances the collaboration and interaction between participants so that information can be exchanged in between, suppresses the chance of any individual bias or mistakes in the observation process, and further frees humans from the control/observation process by providing automatic control/observation. The new MOMR++ system architecture is a promising direction for future telerobtics advances.

Parts orienting is an important part of automated manufacturing. Sensorless manipulation has proven to be a useful paradigm in addressing parts orienting, and the manipulation of deformable objects is a growing area of interest. Until now, these areas have remained separate because existing orienting approaches utilize forces that if applied to deformable parts violate the assumptions used by existing algorithms, and could potentially break the part. We introduce a new algorithm and manipulator actions that, when provided with the geometric description and a deformation model of choice for the part, exploits the deformation and generates a Plan that consists of the shortest sequence of manipulator actions guaranteed to orient the part up to symmetry from any unknown initial orientation and pose. Additionally, the algorithm estimates whether a given manipulator is sufficiently precise to perform the actions which guarantee the final orientation. This is dictated by the particular part geometry, deformation model, and the manipulator action path planner which contains simple end-effector constraints and any standard motion planner. We illustrate the success of the algorithm with multiple parts through 192 trials of experiments that were performed with low-precision robot manipulators and six parts made of four types of materials. The experimental trials resulted in 154 successes, which show the feasibility of deformable parts orienting. The analysis of the failures showed that for success the assumptions of zero friction are essential for this work, increased manipulator precision would be beneficial but not necessary, and a simple deformation model can be sufficient. Finally, we note that the algorithm has applications to truly sensorless manipulation of non-deformable parts.

Mobile robots are used on many areas and its demand on extreme terrain, hazardous area, or life-threatening place is increasing to reduce the loss of life. A good decision making capability is essential for successful navigation of autonomous robot and it affect finding the shortest or optimal path within given condition. The wavefront algorithm is simple to apply, yet yield an optimal path for a robot to follow in many different configurations. Although the path created using wavefront algorithm is an optimal in the sense that every node has the same cost, the result is not the best result in global perspective because of the algorithm is inconsiderate on the surrounding condition. To solve this issue and create the best result on global perspective, risk factor analysis method was implemented on the wavefront algorithm to improve the performance. In this work, the relationship between the wavefront algorithm and dynamic programming will be explained to show that the wavefront algorithm obeys the principle of optimality. The simulation result displays better performance on safety, while keeping the travelling distance minimum, if the risk factor is used on the wavefront algorithm and the robot on actual test behave accordingly. This work will contribute on decision making of mobile robot using risk factor method to create a most desirable and safe path. In addition to that, it will demonstrate how the risk factor method can be applied to the mobile robot navigation.

The objective of this thesis is to enhance the efficiency of Mass Rapid Transit (MRT) system and improve the power quality by reducing harmonic distortion. The energy consumption of an MRT system by considering the annual ridership and the stochastic operation characteristics of train sets are used to find the optimal placement of traction substations to enhance the operation efficiency of MRT systems. To mitigate the harmonic distortion, the installation location and capacity of harmonic filters are designed and verified by computer simulation. The software programs for AC/DC load flow study and harmonic distortion analysis have been developed and integrated to perform power system simulation of MRT operation. The mathematical model of 12-pulse uncontrolled rectifiers without interphase transformers is derived and implemented in the programs to obtain more accurate simulation results. The optimal inverter substation planning is solved by minimizing the overall cost of power consumption and inverter investment for mass rapid transit power systems with immune algorithm. The objective function and constraints are expressed as antigen, and all feasible solutions are expressed as antibody. The diversity of antibody is then enhanced by proximity of antigen so that the global optimization during the solution process can be obtained. It is found that the energy regeneration can be restored effectively with the optimal planning of inverters by the proposed immune algorithm. Based on the computer simulation of Taipei MRT system, the voltage harmonic distortion is varied dramatically with the dynamic load behavior of train sets. The stochastic harmonic load flow analysis is performed to investigate the power quality problem for an electrified rapid transit system. Different strategies of harmonic distortion mitigation have been proposed by minimizing the objective function to solve the optimal sizes and locations of harmonic filters so that the harmonic distortion can be reduced and reactive power compensation can be obtained at the same time. By performing the immune algorithm, the harmonic filters with proper capacity and the corresponding switching time for filter commitment are determined. It is found that the harmonic distortion can be effectively reduced for the MRT system by the proper design of harmonic filters.

In this thesis, the multi-carrier (MC) code division multiple access (CDMA) system in Rayleigh fading channel is considered. The system performance will be degraded due to multiple access interference (MAI) or background noise. It is know that linearly constrained inverse QR-decomposition (LC-IQRD) recursive least-square algorithm can overcome the problems. The main concern of this thesis is to implement the circuit of LC-IQRD algorithm. FPGA components and sets up a high efficient programmable hardware module. In this thesis, we implemented the circuit of LC-IQRD algorithm via a chip of Field Programmable Gate Array (FPGA) with Verilog HDL. The conventional IQRD circuit design employs systolic array architecture. The advantages of systolic array architecture include modularity and hardware simplicity. These properties are extremely desirable for VLSI implementation. In fact, we expect to reduce the execution time of the conventional IQRD algorithm circuit design. Therefore, in this thesis a modified IQRD circuit design is proposed to improve the effect of circuit implementation. It also has advantage of modularity and reduces the execution time. In order to degrade complexity of LC-IQRD algorithm circuit design, the area and speed of circuit are the consideration in this thesis. The data source is produced by Matlab software. We verify the performance of the system in terms of BER (bit error rate) and SINR (signal to interference and noise ratio).Finally, LC-IQRD algorithm circuit is realized in the Altera EP20k1500EFC-33 chip and on the Quartus II of Altera. The algorithm circuit uses 51536 logic elements (LE) for 30 bits fixed point design.

The Ultra-Wideband (UWB) communication technology has been extensively attended in recent years. In this thesis, we propose the improved selective-Rake receiver and analyze the performance on UWB channels. The UWB transmission channels are modeled with statistical methods and its fading characteristics are discussed. Different impulse radio properties for the UWB communication system are analyzed. The system performance and design complexity issues of selective-Rake receiver (SRake) are studied. Rake receiver has difficulties achieving desired system performance in the dense multipath environment. The main ideas of SRake receiver are to obtain the SNR level on known multipath channel and to determine the desired number of Rake fingers. Matched filters and maximum likelihood detectors are utilized in the implementation of the SRake to estimate the signal time delay. The CLEAN algorithm is then used in selecting the paths with relatively high energy. Furthermore, we also propose a noise cancellation scheme for performance improvement in the SRake receiver. In the noise cancellation scheme, the multiresolution property of wavelet transform is used for filtering the noise interference caused by the rapid fluctuation factor. In addition, a two-stage search is combined with the original CLEAN algorithm to increase the accuracy of path selection. From our simulation results on the UWB channels, the improved SRake receiver, with noise cancellation and two-stage search, indeed has high SRake output SNR and better path accuracy than the original SRake receiver.

It is well known that the biological function of a protein depends on its 3D structure. Therefore, solving the problem of protein structures is one of the most important works for studying proteins. However, protein structure prediction is a very challenging task because there is still no clear feature about how a protein folds to its 3D structure yet. In this thesis, we propose a genetic algorithm (GA) based on the lattice model to predict the 3D structure of an unknown protein, target protein, whose primary sequence and secondary structure elements (SSEs) are assumed known. Hydrophobic-hydrophilic model (HP model) is one of the most simplified and popular protein folding models. These models consider the hydrophobic-hydrophobic interactions of protein structures, but the results of prediction are still not encouraged enough. Therefore, we suggest that some other features should be considered, such as SSEs, charges, and disulfide bonds. That is, the fitness function of GA in our method considers not only how many hydrophobic-hydrophobic pairs there are, but also what kind of SSEs these amino acids belong to. The lattice model is in fact used to help us get a rough folding of the target protein, since we have no idea how they fold at the very beginning. We show that these additional features do improve the prediction accuracy by comparing our prediction results with their real structures with RMSD.

Energy saving, despite recent advances in extending battery life, is still an important issue in wireless ad hoc networks. An often adopted method is power management, which can help in reducing the transmission power consumption and thus can prolong the battery life of mobile nodes. In this paper, we present a new approach of power management for the wireless ad-hoc networks. Firstly, we propose a clustering algorithm. The clustering algorithm is incooperated with power adjustment and energy-efficient routing procedure to achieve the goal of reducing the transmission power. We use clusterheads to monitor a mobile node's transmission power and to conduct the routing path between any source-destination pair. Not only the lifetime of network is increased but also the interference in communication channel is reduced. As a result, the transmission quality is improved and the network throughput is enhanced. By simulation, we showed that our algorithm outperforms the traditional clustering algorithm both in power saving and in throughput.