Characterization and modeling of multi-conductor transmission line using Finite-difference Time-Domain method

A two-dimensional, 2D, finite-difference time-domain (FDTD) method is used to analyze two different models of multi-conductor transmission lines (MTL). The first model is a two-conductor MTL and the second is a threeconductor MTL. Apart from the MTL's, a three-dimensional, 3D, FDTD method is used to analyze a three-patch microstrip parasitic array. While the MTL analysis is entirely in time-domain, the microstrip parasitic array is a study of scattering parameter Sn in the frequency-domain. The results clearly indicate that FDTD is an efficient and accurate tool to model and analyze multiconductor transmission line as well as microstrip antennas and arrays.

Customization of the design of antenna using genetic algorithm and finite-difference time-domain technique

Antenna design is an iterative process in which structures are analyzed and changed to comply with certain performance parameters required. The classic approach starts with analyzing a "known" structure, obtaining the value of its performance parameter and changing this structure until the "target" value is achieved. This process relies on having an initial structure, which follows some known or "intuitive" patterns already familiar to the designer. The purpose of this research was to develop a method of designing UWB antennas. What is new in this proposal is that the design process is reversed: the designer will start with the target performance parameter and obtain a structure as the result of the design process. This method provided a new way to replicate and optimize existing performance parameters. The base of the method was the use of a Genetic Algorithm (GA) adapted to the format of the chromosome that will be evaluated by the Electromagnetic (EM) solver. For the electromagnetic study we used XFDTD™ program, based in the Finite-Difference Time-Domain technique. The programming portion of the method was created under the MatLab environment, which serves as the interface for converting chromosomes, file formats and transferring of data between the XFDTD™ and GA. A high level of customization had to be written into the code to work with the specific files generated by the XFDTD™ program. Two types of cost functions were evaluated; the first one seeking broadband performance within the UWB band, and the second one searching for curve replication of a reference geometry. The performance of the method was evaluated considering the speed provided by the computer resources used. Balance between accuracy, data file size and speed of execution was achieved by defining parameters in the GA code as well as changing the internal parameters of the XFDTD™ projects. The results showed that the GA produced geometries that were analyzed by the XFDTD™ program and changed following the search criteria until reaching the target value of the cost function. Results also showed how the parameters can change the search criteria and influence the running of the code to provide a variety of geometries.

Characterization of ultra wideband antennas for personal communication devices using finite difference time domain technique

The current mobile networks don't offer sufficient data rates to support multimedia intensive applications in development for multifunctional mobile devices. Ultra wideband (UWB) wireless technology is being considered as the solution to overcome data rate bottlenecks in the current mobile networks. UWB is able to achieve such high data transmission rates because it transmits data over a very large chunk of the frequency spectrum. As currently approved by the U.S. Federal Communication Commission it utilizes 7.5 GHz of spectrum between 3.1 GHz and 10.6 GHz. ^ Successful transmission and reception of information data using UWB wireless technology in mobile devices, requires an antenna that has linear phase, low dispersion and a voltage standing wave ratio (VSWR) ≤ 2 throughout the entire frequency band. Compatibility with an integrated circuit requires an unobtrusive and electrically small design. The previous techniques that have been used to optimize the performance of UWB wireless systems, involve proper design of source pulses for optimal UWB performance. The goal of this work is directed towards the designing of antennas for personal communication devices, with optimal UWB bandwidth performance. Several techniques are proposed for optimal UWB bandwidth performance of the UWB antenna designs in this Ph.D. dissertation. ^ This Ph.D. dissertation presents novel UWB antenna designs for personal communication devices that have been characterized and optimized using the finite difference time domain (FDTD) technique. The antenna designs reported in this research are physically compact, planar for low profile use, with sufficient impedance bandwidth (>20%), antenna input impedance of 50-Ω, and an omni-directional (±1.5 dB) radiation pattern in the operating bandwidth. ^

Numerical calculation of diffraction coefficients from building edges using FDTD method

Finite Difference Time Domain (FDTD) Method and software are applied to obtain diffraction waves from modulated Gaussian plane wave illumination for right angle wedges and Fast Fourier Transform (FFT) is used to get diffraction coefficients in a wideband in the illuminated lit region. Theta and Phi polarization in 3-dimensional, TM and TE polarization in 2-dimensional cases are considered respectively for soft and hard diffraction coefficients. Results using FDTD method of perfect electric conductor (PEC) wedge are compared with asymptotic expressions from Uniform Theory of Diffraction (UTD). Extend the PEC wedges to some homogenous conducting and dielectric building materials for diffraction coefficients that are not available analytically in practical conditions. ^

A high efficiency method for exploiting an ultra fast opto-electric signal generator

Simulations suggest that photomixing in resonant laser-assisted field emission could be used to generate and detect signals from DC to 100 THz. It is the objective of this research to develop a system to efficiently couple the microwave signals generated on an emitting tip by optical mixing. Four different methods for coupling are studied. Tapered Goubau line is found to be the most suitable. Goubau line theory is reviewed, and programs are written to determine loss on the line. From this, Goubau tapers are designed that have a 1:100 bandwidth. These tapers are finally simulated using finite difference time domain, to find the optimum design parameters. Tapered Goubau line is an effective method for coupling power from the field emitting tip. It has large bandwidth, and acceptable loss. Another important consideration is that it is the easiest to manufacture of the four possibilities studied, an important quality for any prototype.

Determining polarization dependent diffraction coefficients from building edges using FDTD method

Electromagnetic waves in suburban environment encounter multiple obstructions that shadow the signal. These waves are scattered and random in polarization. They take multiple paths that add as vectors at the portable device. Buildings have vertical and horizontal edges. Diffraction from edges has polarization dependent characteristics. In practical case, a signal transmitted from a vertically polarized high antenna will result in a significant fraction of total power in the horizontal polarization at the street level. Signal reception can be improved whenever there is a probability of receiving the signal in at least two independent ways or branches. The Finite-Difference Time-Domain (FDTD) method was applied to obtain the two and three-dimensional dyadic diffraction coefficients (soft and hard) of right-angle perfect electric conductor (PEC) wedges illuminated by a plane wave. The FDTD results were in good agreement with the asymptotic solutions obtained using Uniform Theory of Diffraction (UTD). Further, a material wedge replaced the PEC wedge and the dyadic diffraction coefficient for the same was obtained.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

A micro-opto-electro-mechanical system (MOEMS) for microstructure manipulation

Microstructure manipulation is a fundamental process to the study of biology and medicine, as well as to advance micro- and nano-system applications. Manipulation of microstructures has been achieved through various microgripper devices developed recently, which lead to advances in micromachine assembly, and single cell manipulation, among others. Only two kinds of integrated feedback have been demonstrated so far, force sensing and optical binary feedback. As a result, the physical, mechanical, optical, and chemical information about the microstructure under study must be extracted from macroscopic instrumentation, such as confocal fluorescence microscopy and Raman spectroscopy. In this research work, novel Micro-Opto-Electro-Mechanical-System (MOEMS) microgrippers are presented. These devices utilize flexible optical waveguides as gripping arms, which provide the physical means for grasping a microobject, while simultaneously enabling light to be delivered and collected. This unique capability allows extensive optical characterization of the structure being held such as transmission, reflection, or fluorescence. The microgrippers require external actuation which was accomplished by two methods: initially with a micrometer screw, and later with a piezoelectric actuator. Thanks to a novel actuation mechanism, the "fishbone", the gripping facets remain parallel within 1 degree. The design, simulation, fabrication, and characterization are systematically presented. The devices mechanical operation was verified by means of 3D finite element analysis simulations. Also, the optical performance and losses were simulated by the 3D-to-2D effective index (finite difference time domain FDTD) method as well as 3D Beam Propagation Method (3D-BPM). The microgrippers were designed to manipulate structures from submicron dimensions up to approximately 100 μm. The devices were implemented in SU-8 due to its suitable optical and mechanical properties. This work demonstrates two practical applications: the manipulation of single SKOV-3 human ovarian carcinoma cells, and the detection and identification of microparts tagged with a fluorescent "barcode" implemented with quantum dots. The novel devices presented open up new possibilities in the field of micromanipulation at the microscale, scalable to the nano-domain.

Improving Analytical Travel Time Estimation for Transportation Planning Models

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

A model for continuous measurement of drilled shaft diameter during construction

Non-Destructive Testing (NDT) of deep foundations has become an integral part of the industry's standard manufacturing processes. It is not unusual for the evaluation of the integrity of the concrete to include the measurement of ultrasonic wave speeds. Numerous methods have been proposed that use the propagation speed of ultrasonic waves to check the integrity of concrete for drilled shaft foundations. All such methods evaluate the integrity of the concrete inside the cage and between the access tubes. The integrity of the concrete outside the cage remains to be considered to determine the location of the border between the concrete and the soil in order to obtain the diameter of the drilled shaft. It is also economic to devise a methodology to obtain the diameter of the drilled shaft using the Cross-Hole Sonic Logging system (CSL). Performing such a methodology using the CSL and following the CSL tests is performed and used to check the integrity of the inside concrete, thus allowing the determination of the drilled shaft diameter without having to set up another NDT device.^ This proposed new method is based on the installation of galvanized tubes outside the shaft across from each inside tube, and performing the CSL test between the inside and outside tubes. From the performed experimental work a model is developed to evaluate the relationship between the thickness of concrete and the ultrasonic wave properties using signal processing. The experimental results show that there is a direct correlation between concrete thicknesses outside the cage and maximum amplitude of the received signal obtained from frequency domain data. This study demonstrates how this new method to measuring the diameter of drilled shafts during construction using a NDT method overcomes the limitations of currently-used methods. ^ In the other part of study, a new method is proposed to visualize and quantify the extent and location of the defects. It is based on a color change in the frequency amplitude of the signal recorded by the receiver probe in the location of defects and it is called Frequency Tomography Analysis (FTA). Time-domain data is transferred to frequency-domain data of the signals propagated between tubes using Fast Fourier Transform (FFT). Then, distribution of the FTA will be evaluated. This method is employed after CSL has determined the high probability of an anomaly in a given area and is applied to improve location accuracy and to further characterize the feature. The technique has a very good resolution and clarifies the exact depth location of any void or defect through the length of the drilled shaft for the voids inside the cage. ^ The last part of study also evaluates the effect of voids inside and outside the reinforcement cage and corrosion in the longitudinal bars on the strength and axial load capacity of drilled shafts. The objective is to quantify the extent of loss in axial strength and stiffness of drilled shafts due to presence of different types of symmetric voids and corrosion throughout their lengths.^

Digital signal processing implementation for near real-time wavelet transformation system for binary images

Communication has become an essential function in our civilization. With the increasing demand for communication channels, it is now necessary to find ways to optimize the use of their bandwidth. One way to achieve this is by transforming the information before it is transmitted. This transformation can be performed by several techniques. One of the newest of these techniques is the use of wavelets. Wavelet transformation refers to the act of breaking down a signal into components called details and trends by using small waveforms that have a zero average in the time domain. After this transformation the data can be compressed by discarding the details, transmitting the trends. In the receiving end, the trends are used to reconstruct the image. In this work, the wavelet used for the transformation of an image will be selected from a library of available bases. The accuracy of the reconstruction, after the details are discarded, is dependent on the wavelets chosen from the wavelet basis library. The system developed in this thesis takes a 2-D image and decomposes it using a wavelet bank. A digital signal processor is used to achieve near real-time performance in this transformation task. A contribution of this thesis project is the development of DSP-based test bed for the future development of new real-time wavelet transformation algorithms.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

A Micro-Opto-Electro-Mechanical System (MOEMS) for Microstructure Manipulation

Microstructure manipulation is a fundamental process to the study of biology and medicine, as well as to advance micro- and nano-system applications. Manipulation of microstructures has been achieved through various microgripper devices developed recently, which lead to advances in micromachine assembly, and single cell manipulation, among others. Only two kinds of integrated feedback have been demonstrated so far, force sensing and optical binary feedback. As a result, the physical, mechanical, optical, and chemical information about the microstructure under study must be extracted from macroscopic instrumentation, such as confocal fluorescence microscopy and Raman spectroscopy. In this research work, novel Micro-Opto-Electro-Mechanical-System (MOEMS) microgrippers are presented. These devices utilize flexible optical waveguides as gripping arms, which provide the physical means for grasping a microobject, while simultaneously enabling light to be delivered and collected. This unique capability allows extensive optical characterization of the structure being held such as transmission, reflection, or fluorescence. The microgrippers require external actuation which was accomplished by two methods: initially with a micrometer screw, and later with a piezoelectric actuator. Thanks to a novel actuation mechanism, the “fishbone”, the gripping facets remain parallel within 1 degree. The design, simulation, fabrication, and characterization are systematically presented. The devices mechanical operation was verified by means of 3D finite element analysis simulations. Also, the optical performance and losses were simulated by the 3D-to-2D effective index (finite difference time domain FDTD) method as well as 3D Beam Propagation Method (3D-BPM). The microgrippers were designed to manipulate structures from submicron dimensions up to approximately 100 µm. The devices were implemented in SU-8 due to its suitable optical and mechanical properties. This work demonstrates two practical applications: the manipulation of single SKOV-3 human ovarian carcinoma cells, and the detection and identification of microparts tagged with a fluorescent “barcode” implemented with quantum dots. The novel devices presented open up new possibilities in the field of micromanipulation at the microscale, scalable to the nano-domain.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.