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Articles 571 - 600 of 709

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Biology-Inspired Approach For Communal Behavior In Massively Deployed Sensor Networks, Kennie H. Jones Jul 2008

Biology-Inspired Approach For Communal Behavior In Massively Deployed Sensor Networks, Kennie H. Jones

Computer Science Theses & Dissertations

Research in wireless sensor networks has accelerated rapidly in recent years. The promise of ubiquitous control of the physical environment opens the way for new applications that will redefine the way we live and work. Due to the small size and low cost of sensor devices, visionaries promise smart systems enabled by deployment of massive numbers of sensors working in concert. To date, most of the research effort has concentrated on forming ad hoc networks under centralized control, which is not scalable to massive deployments. This thesis proposes an alternative approach based on models inspired by biological systems and reports …

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Improving The Availability Of Manufacturability Information Through Decentralization Of Process Planning, Eliab Opiyo Jun 2008

Improving The Availability Of Manufacturability Information Through Decentralization Of Process Planning, Eliab Opiyo

Tanzania Journal of Engineering and Technology (TJET)

Process planning is part of the general product development and production process that usually follows design and precedes manufacturing. Manufacturability and process planning information in general play central role in many product development and production activities, including paradoxically, conceptual and detail design - the activities that take place before process planning. The need of conducting some of the process planning activities formally before or during design is thus rather obvious. One of the main research issues is therefore the identification of the process planning activities that can be performed before the traditional process planning phase and handling of the process …

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Modeling Of A Spray Assisted Natural Draft Cooling Tower, Kaunde K Jun 2008

Modeling Of A Spray Assisted Natural Draft Cooling Tower, Kaunde K

Tanzania Journal of Engineering and Technology (TJET)

Cooling towers are one of the largest heat and mass transfer devices that are in common use. A novel type of cooling tower has been proposed in which air flow rate into the tower is drawn by ejector action of sprays instead of fans as is done in conventional mechanical forced or induced draft cooling towers. This novel design offers the potential of saving the energy cost for driving the fan. The paper presents mathematical models for momentum transfer which is the driving force causing the entrainment of air. Also the heat transfer model for co-current flow of liquid and …

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Missouri S&T Mote-Based Demonstration Of Energy Monitoring Solution For Network Enabled Manufacturing Using Wireless Sensor Networks (Wsn), James W. Fonda, Maciej Jan Zawodniok, Al Salour, Jagannathan Sarangapani, Donald Miller Apr 2008

Missouri S&T Mote-Based Demonstration Of Energy Monitoring Solution For Network Enabled Manufacturing Using Wireless Sensor Networks (Wsn), James W. Fonda, Maciej Jan Zawodniok, Al Salour, Jagannathan Sarangapani, Donald Miller

Electrical and Computer Engineering Faculty Research & Creative Works

In this work, an inexpensive electric utilities monitoring solution using wireless sensor networks is demonstrated that can easily be installed, deployed, maintained and eliminate unnecessary energy costs and effort. The monitoring solution is designed to support network enabled manufacturing (NEM) program using Missouri University of Science and Technology (MST), formerly the University of Missouri-Rolla (UMR), motes.

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Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Mar 2008

Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under …

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A Suite Of Robust Controllers For The Manipulation Of Microscale Objects, Qinmin Yang, Jagannathan Sarangapani Feb 2008

A Suite Of Robust Controllers For The Manipulation Of Microscale Objects, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A suite of novel robust controllers is introduced for the pickup operation of microscale objects in a microelectromechanical system (MEMS). In MEMS, adhesive, surface tension, friction, and van der Waals forces are dominant. Moreover, these forces are typically unknown. The proposed robust controller overcomes the unknown contact dynamics and ensures its performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive critic-based neural network (NN) controller, the unknown dynamic forces are estimated online. It consists of an action NN for compensating the unknown system …

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Guidelines For Deploying Wireless Sensor Network In Manufacturing: Star Topology Study Using The Rssi To Test The Network Performance, A. Hameed Madwar Jan 2008

Guidelines For Deploying Wireless Sensor Network In Manufacturing: Star Topology Study Using The Rssi To Test The Network Performance, A. Hameed Madwar

Dissertations and Theses @ UNI

Wireless Sensor Networks (WSNs) are varying in kinds and can be used for many purposes. It can be extensively used in monitoring, reporting, and decision routines. Therefore, WSNs are very important to integrate with manufacturing since they can help not only in monitoring and decision making but also they can increase productivity and decrease the wiring costs. However, WSNs are facing some challenges in manufacturing as they need to resist the interferences that could be generated by the electrical and mechanical fields created during the manufacturing processes. Some of these interferences could be due to the high temperatures, blocks of …

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Generalized Hamilton-Jacobi-Bellman Formulation-Based Neural Network Control Of Affine Nonlinear Discrete-Time Systems, Zheng Chen, Jagannathan Sarangapani Jan 2008

Generalized Hamilton-Jacobi-Bellman Formulation-Based Neural Network Control Of Affine Nonlinear Discrete-Time Systems, Zheng Chen, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time (DT) systems. The method is based on least squares successive approximation solution of the generalized Hamilton-Jacobi-Bellman (GHJB) equation which appears in optimization problems. Successive approximation using the GHJB has not been applied for nonlinear DT systems. The proposed recursive method solves the GHJB equation in DT on a well-defined region of attraction. The definition of GHJB, pre-Hamiltonian function, HJB equation, and method of updating the control function for the affine nonlinear DT systems under small perturbation assumption are proposed. …

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Implementing An Architectural Framework To Define And Deliver Net-Centric Capability To Legacy Military Air Assets Operating Within A System Of Systems, Mark S. Anderson, S. M. Martin, Cihan H. Dagli, Ann K. Miller Jan 2008

Implementing An Architectural Framework To Define And Deliver Net-Centric Capability To Legacy Military Air Assets Operating Within A System Of Systems, Mark S. Anderson, S. M. Martin, Cihan H. Dagli, Ann K. Miller

Engineering Management and Systems Engineering Faculty Research & Creative Works

The United States Air Force (USAF) is implementing an integrated net-centric system of systems for airborne operations in support of the global war on terror (GWOT). The GWOT demands that a successful architecture framework transforms and delivers net-centric assets to the war-fighter in a timely manner. A critical component of this implementation is the transformation of legacy strategic air platforms into net-centric air power assets operating within a system of systems. The System Architectural (SA) framework, and the Department of Defense Architectural Framework (DoDAF) are ways of managing complexity and organizing information within a system of systems network. This paper …

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Effects Of Electromagnetic Interference On Control Area Network Performance, Fei Ren, Y. Rosa Zheng, Maciej Jan Zawodniok, Jagannathan Sarangapani Nov 2007

Effects Of Electromagnetic Interference On Control Area Network Performance, Fei Ren, Y. Rosa Zheng, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the effects of electromagnetic interference (EMI) on control area network (CAN) communications are investigated by hardware experiments. Distinct CAN bit rates, communication cables, and networks are used to test effects of EMI on CAN bus. Waveforms of CAN data frames in EMI environment are observed and analyzed for figuring out details of effects. Experiments show that the EMI pulses frequently encountered in automobile and off-road machinery can cause the reduction of bit rates and errors in high-speed CAN communications. Replacing traditional unshielded parallel communication cables with shielded communication cables is proved to be an effective method of …

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Predictive Congestion Control Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani Nov 2007

Predictive Congestion Control Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks, result in a large number of packet drops, unfair scenarios and low throughputs with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, this paper presents a novel, decentralized, predictive congestion control (DPCC) for wireless sensor networks (WSN). The DPCC consists of an adaptive flow and adaptive back-off interval selection schemes that work in concert with energy efficient, distributed power control (DPC). The DPCC detects the onset of congestion using queue utilization and the embedded channel …

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Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes Oct 2007

Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a suite of neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.

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Energy-Efficient Hybrid Key Management Protocol For Wireless Sensor Networks, Timothy J. Landstra, Maciej Jan Zawodniok, Jagannathan Sarangapani Oct 2007

Energy-Efficient Hybrid Key Management Protocol For Wireless Sensor Networks, Timothy J. Landstra, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we propose a subnetwork key management strategy in which the heterogeneous security requirements of a wireless sensor network are considered to provide differing levels of security with minimum communication overhead. Additionally, it allows the dynamic creation of high security subnetworks within the wireless sensor network and provides subnetworks with a mechanism for dynamically creating a secure key using a novel and dynamic group key management protocol. The proposed energy-efficient protocol utilizes a combination of pre-deployed group keys and initial trustworthiness of nodes to create a level of trust between neighbors in the network. This trust is later …

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Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes Oct 2007

Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes

Electrical and Computer Engineering Faculty Research & Creative Works

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the …

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Workshop - Building Reflective Team Skills With A T-Group, Ray Luechtefeld, Steve Eugene Watkins Oct 2007

Workshop - Building Reflective Team Skills With A T-Group, Ray Luechtefeld, Steve Eugene Watkins

Engineering Management and Systems Engineering Faculty Research & Creative Works

ABET criteria require that engineering graduates have the ability to "function on multidisciplinary teams" and "communicate effectively". An important component of these skills is the ability to reflect on one's personal actions and the dynamics occurring within the group. This workshop is intended to provide participants with a practical exercise that can help students become more self-reflective and aware of group dynamics, while demonstrating the use of the "virtual facilitator" system to improve group dialogue. The workshop will engage the participants in a self- directed learning exercise modeled after T-Groups. This exercise will help participants: 1) Become aware of their …

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Expert System For Team Facilitation Using Observational Learning, Ray Luechtefeld, R. K. Singh, Steve Eugene Watkins Oct 2007

Expert System For Team Facilitation Using Observational Learning, Ray Luechtefeld, R. K. Singh, Steve Eugene Watkins

Engineering Management and Systems Engineering Faculty Research & Creative Works

While ABET criteria require that engineering graduates be able to "function on multidisciplinary teams" and "communicate effectively", the need for effective team skills goes far deeper. One solution is the use of a computationally intelligent "virtual facilitator" that contains a subset of the expert knowledge of a skilled facilitator. The "virtual facilitator" models behaviors of an expert facilitator to engineering student teams as they are working together. Albert Bandura's theory of observational learning suggests that skills can be developed through observation of expert "others" engaged in practice. Preliminary research indicates that students can increase beneficial team behaviors (such as inquiry) …

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Distributed Energy Resources: Issues And Challenges, Badrul H. Chowdhury, Chung-Li Tseng Aug 2007

Distributed Energy Resources: Issues And Challenges, Badrul H. Chowdhury, Chung-Li Tseng

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

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Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani Jul 2007

Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for general multi-input and multi- output affine unknown nonlinear discrete-time systems in the presence of bounded disturbances. Adaptive critic designs consist of two entities, an action network that produces optimal solution and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function and the action network is adapted simultaneously based on the information from the critic. In our online learning method, one NN is designated as the …

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Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jul 2007

Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …

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Selecting Electricity Generation Sources In Remote Locations, Kelly E. Kwan Mar 2007

Selecting Electricity Generation Sources In Remote Locations, Kelly E. Kwan

Theses and Dissertations

The purpose of this research was to investigate the impact of using a decision analysis technique for the selection of an electrical generation system for remote locations. Specifically, this thesis sought to answer five research questions addressing the types of energy sources used in remote locations, the decision-making processes used to identify these sources, the types of constraints incorporated in such a process, other valued factors, and their level of importance in relation to each other. The research questions were answered through a comprehensive literature review and the 10-Step Value-Focused Thinking Process on a specific case study in the National …

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Automatic Target Recognition User Interface Tool, David A. Kerns Mar 2007

Automatic Target Recognition User Interface Tool, David A. Kerns

Theses and Dissertations

A computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and presents results in a meaningful way. It is written for Microsoft Excel. An ATR algorithm assigns a class label to a recognized target. General designations can include "Friend" and "Foe." The error of designating "Friend" as "Foe" as well as "Foe" as "Friend" comes with a high cost. Studying each algorithm's error can minimize this cost. Receiver Operating Characteristic (ROC) curves provide only information on the probabilities given …

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Route Aware Predictive Congestion Control Protocol For Wireless Sensor Networks, Carl Larsen, Maciej Jan Zawodniok, Jagannathan Sarangapani Jan 2007

Route Aware Predictive Congestion Control Protocol For Wireless Sensor Networks, Carl Larsen, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Congestion in wireless sensor networks (WSN) may lead to packet losses or delayed delivery of important information rendering the WSN-based monitoring or control system useless. In this paper a routing-aware predictive congestion control (RPCC) yet decentralized scheme for WSN is presented that uses a combination of a hop by hop congestion control mechanism to maintain desired level of buffer occupancy, and a dynamic routing scheme that works in concert with the congestion control mechanism to forward the packets through less congested nodes. The proposed adaptive approach restricts the incoming traffic thus preventing buffer overflow while maintaining the rate through an …

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Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jan 2007

Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate …

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Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jan 2007

Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …

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Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani Jan 2007

Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel neural network (NN) based online reinforcement learning controller is designed for nonaffine nonlinear discrete-time systems with bounded disturbances. The nonaffine systems are represented by nonlinear auto regressive moving average with exogenous input (NARMAX) model with unknown nonlinear functions. An equivalent affine-like representation for the tracking error dynamics is developed first from the original nonaffine system. Subsequently, a reinforcement learning-based neural network (NN) controller is proposed for the affine-like nonlinear error dynamic system. The control scheme consists of two NNs. One NN is designated as the critic, which approximates a predefined long-term cost function, whereas an …

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Spatial Diversity In Signal Strength Based Wlan Location Determination Systems, Anil Ramachandran, Jagannathan Sarangapani Jan 2007

Spatial Diversity In Signal Strength Based Wlan Location Determination Systems, Anil Ramachandran, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Literature indicates that spatial diversity can be utilized to compensate channel uncertainties such as multipath fading. Therefore, in this paper, spatial diversity is exploited for locating stationary and mobile objects in the indoor environment. First, space diversity technique is introduced for small scale motion and temporal variation compensation of received signal strength and it is demonstrated analytically that it enhances location accuracy. Small scale motion refers to movements of the transmitter and/or the receiver of the order of sub-wavelengths while temporal effects refer to environmental variations with time. A novel metric is introduced for selection combining in order to improve …

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An Online Approximator-Based Fault Detection Framework For Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani Jan 2007

An Online Approximator-Based Fault Detection Framework For Nonlinear Discrete-Time Systems, Balaje T. Thumati, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a fault detection scheme is developed for nonlinear discrete time systems. The changes in the system dynamics due to incipient failures are modeled as a nonlinear function of state and input variables while the time profile of the failures is assumed to be exponentially developing. The fault is detected by monitoring the system and is approximated by using online approximators. A stable adaptation law in discrete-time is developed in order to characterize the faults. The robustness of the diagnosis scheme is shown by extensive mathematical analysis and simulation results.

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Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke Jan 2007

Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke

Electrical and Computer Engineering Faculty Research & Creative Works

The development and deployment of radio frequency identification (RFID) systems render a novel distributed sensor network which enhances visibility into manufacturing processes. In RFID systems, the detection range and read rates will suffer from interference among high-power reading devices. This problem grows severely and degrades system performance in dense RFID networks. Consequently, medium access protocols (MAC) protocols are needed for such networks to assess and provide access to the channel so that tags can be read accurately. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a …

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Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani Jan 2007

Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for affine nonlinear discrete-time systems with applications to nanomanipulation. In the online NN reinforcement learning method, one NN is designated as the critic NN, which approximates the long-term cost function by assuming that the states of the nonlinear systems is available for measurement. An action NN is employed to derive an optimal control signal to track a desired system trajectory while minimizing the cost function. Online updating weight tuning schemes for these two NNs are also derived. By using the Lyapunov approach, …

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Multi-Phase Flow Analysis In Oil And Gas Engineering Systems And Its Modelling, Orlando F. Ayala, Luis F. Ayala, Orlando M. Ayala Jan 2007

Multi-Phase Flow Analysis In Oil And Gas Engineering Systems And Its Modelling, Orlando F. Ayala, Luis F. Ayala, Orlando M. Ayala

Engineering Technology Faculty Publications

[Introduction] Two-phase flow is very common in industrial processes and its applications were already in use in ages as remote as the era of Archimedes. At the present time, many industrial processes rely on multi-phase phenomena for the transport of energy and mass or for material processing. During the last century, the nuclear, chemical and petroleum industries propelled intense research activity in the area. Their efforts have been aimed at the demystification of the mechanisms taking place during this complex flow situation.

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