With the rapid growth in modern cities and congestion on major freeways and local streets, public transit services have become more and more important for urban transportation. As an important component of Intelligent Transportation Systems (ITS), Transit Signal Priority (TSP) systems have been extensively studied and widely implemented to improve the quality of transit service by reducing transit delay. The focus of this research is on the development of a platform with the physical queue representation that can be employed to evaluate and/or improve TSP strategies with the consideration of the interaction between transit vehicles and queues at the intersection.This dissertation starts with deterministic analyses of TSP systems based on a physical queue model. A request oriented TSP decision process is then developed which incorporates a set of TSP decision regions defined on a time-space diagram with the physical queue representation. These regions help identify the optimal detector location, select the appropriate priority control strategy, and handle the situations with multiple priority requests. In order to handle uncertainties in TSP systems arising in bus travel time and dwell time estimation, a type-2 fuzzy logic forecasting system is presented and tested with field data. Type-2 fuzzy logic is very powerful in dealing with uncertainty. The use of Type-2 fuzzy logic helps improve the performance of TSP systems. The last component of the dissertation is the development of a Colored Petri Net (CPN) model for TSP systems. With CPN tools, computer simulation can be performed to evaluate various TSP control strategies and the decision process. Examples for demonstrating the process of implementing the green extension strategy and the proposed TSP decision process are presented in the dissertation. The CPN model can also serve as an interface between the platform developed in this dissertation and the implementation of the control strategies at the controller level.

ABSTRACT Detection of and discrimination between different nanoparticles and biomolecules are vital steps in analytical, biochemical, and diagnostic biomedical procedures used in life sciences. Synthetic micro/nanopores in solid-state membranes form an emerging class of single-molecule detectors capable of detecting and probing the properties of particles and biomolecules with high throughput and resolution: The particles or biomolecules to be analyzed are added to an electrolyte solution in one of the two reservoirs of the detector system separated by a thin membrane containing a single micro/nanopore. An outer electric field induces an open-pore ionic current (Iopen) through the pore, dragging the particles with itself. Transient changes occur when a particle slightly smaller than the pore translocates through the pore. This electrical signal can be analyzed to derive information regarding to the particle or biomolecule size and even its morphology, concentration in the solution, and the affinity for the pore. Many detectors are based on self-assembled, naturally occurring protein pores in lipid bilayer membranes. Most solid-state pore-based detectors reported in literature use artificial pores in silicon nitride or silicon oxide membranes. Applying polymers as a membrane potentially offers advantages over the aforementioned types, including good electrical insulation, improved wettability thanks to higher hydrophilicity, and long-term stable yet low-cost and disposable devices. The present study aims at exploiting such advantages by developing the proof-of-concept for a single-material, all-polymer, nanopore detector allowing the continuous variation of target pore size in the range from micrometers to a few nanometers for best pore size adaption to the biomolecules to be investigated. The research comprises materials selection, system design, development of a fabrication and assembly sequence, device fabrication, and functional device testing. Poly (methyl methacrylate) (PMMA) was selected as it combines advantageous microfluidic properties know from competing materials, such as polyimide, polystyrene, polycarbonate, or polyethylene terephthalate, with outstanding micropatterning capabilities. The membrane thickness is set to be 1 µm, based on a compromise between robustness during fabrication and operation on one side, and electrochemical performance on the other. After spincoating the membrane onto a sacrificial wafer, pores with diameters of typically several hundred nanometers are patterned by electron beam lithography. In combination with thermal post processing leading to polymer reflow, diameters one order of magnitude smaller can be achieved. The present study focuses on 450 nm and 22 nm pores, respectively. Besides these pores fabricated in a top-down approach, self-assembled -hemolysin protein pores of 1.5 nm diameter are integrated in a combined top-down and bottom-up approach so that single digit, double digit, and triple digit nanometer pores are available. Systems integration is achieved by capillary-forced based release from the sacrificial substrate and the application of UV-initiated glue. Test sequences proved and qualified the device functionality: Electrical characterization was performed in aqueous KCl electrolyte solution. The devices exhibit a stable, time-independent ionic current. The current-voltage curves are linear and scale with the electrolyte concentration. System verification was performed using silica nanospheres of 100 nm and 150 nm diameter as known test particles. Translocation through a 450 nm pore induced current blockades for about 1 ms with an amplitude of 30 pA to 55 pA for 100 nm particles and in excess of 70 pA for 150 nm particles. This is in close agreement with results obtained by a mathematical model used in this study. Biomolecules relevant to many life science applications, double-stranded DNA (dsDNA) and bovine serum albumin (BSA) were subsequently analyzed to prove the device concept. Post-processed pores of 22 nm diameter were used at 600 mV driving voltage and 0.1 molar electrolyte in a slightly acidic regime of pH = 6. Typical current blockade amplitudes for complete translocations of dsDNA are Iblock = 22 pA for a translocation time of tD = 0.2 ms, and an almost threefold current blockade (Iblock = 60 pA) for the larger BSA molecules, respectively. The results demonstrate that the PMMA-based nanopores are sensitive enough to not only detect translocating biomolecules, but to also sense them by distinguishing between different biomolecules. The molecule-specific and distinct translocation signals through the pores using both, standardized silica nanoparticles and biomolecules of different dimensions, prove the concept of an all-PMMA electrophoretic flow detector with adjustable pore diameters. Devices with pore diameters covering three orders of magnitude in the nanometer range were successfully built, tested, and characterized. The results suggest such detectors are promising candidates for biomolecule detecting applications.

The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.

Thesis (PhD)--University of South Australia, 2001

Bibliography: p. 235-243. / xxvi, 243 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Concerned with the mathematical characterisation and adaptive processing of narrowband high frequency signals received by a very wide aperture antenna array after reflection from the ionosphere. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 2000

This thesis presents methods for improving the detection processing of active sonar systems. Measures to compensate for or even exploit particular characteristics of the detection problem for these systems are considered. Reverberation is the result of scattering of the transmitted signal from non-target features. Multipath and variability are particularly pronounced for underwater sound signals because propagation is very sensitive to spatial and temporal temperature variations. Another problem is the low pulse repetition rate due to the relatively low speed of sound. This low data rate reduces tracking and detection performance. / Reverberation often arises as the sum of many small contributions so that received data has a multivariate Gaussian distribution. Estimating the large numbers of parameters in the distribution requires a lot of data. This data is not available because of the low data rate. Representing the scattering as an autoregressive process reduced the data requirement but at some cost to modelling accuracy. A coupled estimator algorithm is developed to estimate the parameters. Detection performance is compared to other models and estimators that assume Gaussian statistics. / To counter multipath distortion the delays and strength of the paths are estimated using a version of the expectation maximisation (EM) algorithm. The magnitude of path amplitudes is then used to decide if a target is present. The EM algorithm is also suggested as a way to find the likely amplitude of reverberation from a few large scatterers that that form non-Gaussian reverberation. / Non-parametric methods are considered for detection of short duration incoherent signals in a duct. These detectors compare the ranks of the data in a region being tested for target present to another region assumed to have no target. Simulations are used to explore performance and what happens when the independent samples assumption is violated by the presence of reverberation. / More data can improve detection. Exploiting data from multiple transmissions is difficult because the slow speed of sound allows targets to move out of detection cells between transmissions. Tracking the movements of potential targets can counter this problem. The usefulness of Integrated Probabalistic Data Association (IPDA), which calculates a probability of true track as well as track properties, is considered as a detection algorithm. Improvements when multiple receivers are used as well as limitations when sensor positions are uncertain are investigated.

Thesis (Eng.D.) - University of Glasgow, 2007. / Eng.D. thesis submitted to the Faculty of Engineering, Department of Civil Engineering, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Jacob A. Abraham. Vita. Includes bibliographical references. Also available from UMI.

Zugl.: Frankfurt (Main), Univ., Diss., 2008

Techn. Universiẗat, Diss., 2006--München.