Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot

Velocity tracking is one of the important objectives of vehicle, machines and mobile robots. A two wheeled inverted pendulum (TWIP) is a class of mobile robot that is open loop unstable with high nonlinearities which makes it difficult to control its velocity because of its nature of pitch falling if left unattended. In this work, three soft computing techniques were proposed to track a desired velocity of the TWIP. Fuzzy Logic Control (FLC), Neural Network Inverse Model control (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were designed and simulated on the TWIP model. All the three controllers have shown practically good performance in tracking the desired speed and keeping the robot in upright position and ANFIS has shown slightly better performance than FLC, while NN consumes more energy

An Acoustic Charge Transport Imager for High Definition Television Applications: Reliability Modeling and Parametric Yield Prediction of GaAs Multiple Quantum Well Avalanche Photodiodes

Reliability modeling and parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs), which are of interest as an ultra-low noise image capture mechanism for high definition systems, have been investigated. First, the effect of various doping methods on the reliability of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiode (APD) structures fabricated by molecular beam epitaxy is investigated. Reliability is examined by accelerated life tests by monitoring dark current and breakdown voltage. Median device lifetime and the activation energy of the degradation mechanism are computed for undoped, doped-barrier, and doped-well APD structures. Lifetimes for each device structure are examined via a statistically designed experiment. Analysis of variance shows that dark-current is affected primarily by device diameter, temperature and stressing time, and breakdown voltage depends on the diameter, stressing time and APD type. It is concluded that the undoped APD has the highest reliability, followed by the doped well and doped barrier devices, respectively. To determine the source of the degradation mechanism for each device structure, failure analysis using the electron-beam induced current method is performed. This analysis reveals some degree of device degradation caused by ionic impurities in the passivation layer, and energy-dispersive spectrometry subsequently verified the presence of ionic sodium as the primary contaminant. However, since all device structures are similarly passivated, sodium contamination alone does not account for the observed variation between the differently doped APDs. This effect is explained by the dopant migration during stressing, which is verified by free carrier concentration measurements using the capacitance-voltage technique

A gradient descent algorithm built on approximate discrete gradients

We propose an optimization method obtained by the approximation of a novel discretization approach for gradient dynamics recently proposed by the authors. It is shown that the proposed algorithm ensures convergence for all amplitudes of the step size, contrarily to classical implementations

Gaussian process regression for virtual metrology of microchip quality and the resulting strategic sampling scheme

Manufacturing of integrated circuits involves many sequential processes, often ex- ecuted to nanoscale tolerances, and the yield depends on the often unmeasured quality of intermediate steps. In the high-throughput industry of fabricating microelectronics on semi-conducting wafers, scheduling measurements of product quality before the electrical test of the complete IC can be expensive. We therefore seek to predict metrics of product quality based on sensor readings describing the environment within the relevant tool during the processing of each wafer, or to apply the concept of virtual metrology (VM) to monitor these intermediate steps. We model the data using Gaussian process regression (GPR), adapted to simultaneously learn the nonlinear dynamics that govern the quality characteristic, as well as their operating space, expressed by a linear embedding of the sensor traces’ features. Such Bayesian models predict a distribution for the target metric, such as a critical dimension, so one may assess the model’s credibility through its predictive uncertainty. Assuming measurements of the quality characteristic of interest are budgeted, we seek to hasten convergence of the GPR model to a credible form through an active sampling scheme, whereby the predictive uncertainty informs which wafer’s quality to measure next. We evaluate this convergence when predicting and updating online, as if in a factory, using a large dataset for plasma-enhanced chemical vapor deposition (PECVD), with measured thicknesses for ~32,000 wafers. By approximately optimizing the information extracted from this seemingly repetitive data describing a tightly controlled process, GPR achieves ~10% greater accuracy on average than a baseline linear model based on partial least squares (PLS). In a derivative study, we seek to discern the degree of drift in the process over the several months the data spans. We express this drift by how unusual the relevant features, as embedded by the GPR model, appear as the in- puts compensate for degrading conditions. This method detects the onset of consistently unusual behavior that extends to a bimodal thickness fault, anticipating its flagging by as much as two days.Mechanical Engineerin

Towards Next Generation of Optoelectronics: from Quantum Plasmonics and 2D Materials to Advanced Optimization Techniques of Nanophotonic Devices

In this thesis, we explore different novel concepts and materials for the next-generation of nanophotonic and optoelectronic devices that could be used both in classical and quantum settings. First, we study quantum coherence properties of surface plasmon polaritons (SPPs) in the regime of extreme dispersion. Most experiments to date, that tested quantum coherence properties of SPPs, used essentially weakly-confined plasmons, which experience limited light-matter hybridization, thus restricting the potential for decoherence. Our setup is based on a hole-array chip supporting SPPs near the surface plasma frequency, where plasmonic dispersion and confinement is much stronger than in previous experiments, making the plasmons much more susceptible for decoherence processes. We generated polarization-entangled pairs of photons and transmitted one of the photons through this plasmonic hole array. Our results show that the quality of photon entanglement after the highly-dispersive plasmonic channel is unperturbed. Our findings provide a lower bound of 100 femtoseconds for the pure dephasing time of dispersive plasmons in our materials, and show that even in a highly dispersive regime, surface plasmons preserve quantum mechanical correlations, making possible harnessing the power of extreme light confinement for integrated quantum photonics. Second, we systematically study different passivation schemes of sulfur vacancies in 2D molybdenum disulfide using first-principles calculations based on density functional theory. We aim at building a microscopic understanding of passivation mechanisms of treatment with TFSI superacid - a popular approach of to improve optical properties. Since superacids have a strong ability to donate protons, we consider hydrogenation and protonation of sulfur vacancies as a possible passivation scheme. Our calculations show that effects of protonation and hydrogenation on properties of 2D molybdenum disulfide are very similar. Moreover, we find that four hydrogen atoms can fully "heal" sulfur vacancies in this material. Our results are an important step towards controllable defects design in 2D transition metal dichalcogenides. And third, we study applications of advanced methods of optimization and machine learning to the design of different nanophotonic devices. We explore feasibility of using novel multi-fidelity Gaussian processes optimization technique to optimize plasmonic mirror filters for hyperspectral imaging. We compare our results with other common optimization approaches. Then we apply deep-learning inspired techniques to optimize control voltages of individual pixels of active metasurfaces to achieve dynamic beamsteering. We obtain interesting results that pave the way for future experiments both in nanophotonics and machine learning fields.</p

Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods

Although electrochemical noise (EN) has been studied for decades, the optimal approach for the analysis of EN data remains uncertain. This research innovatively combined the use of recurrence quantification analysis of electrochemical noise data and machine learning methods to develop models for corrosion monitoring and corrosion type identification. Case studies demonstrate that the proposed methodologies are potentially feasible for the development of online corrosion monitoring programs

Smart polymeric temperature sensors – for biological systems

The damaged brain is vulnerable to increase in brain temperature after a severe head injury. Continuous monitoring of intracranial temperature depicts functionality essential to the treatment of brain injury Many innovations have been made in the biomedical industry relying on electronic implants in treating condition such as traumatic brain injury (TBI) or other cerebral diseases. Hence, a methodical and reliable way to measure the temperature is crucial to assess the patient’s situation. In this investigation, an analysis of various approaches to detect the change in the temperature due to resistance, current-voltage characteristics with respect to time has been evaluated. Also, studies describing various materials used in sensors, their working principles and the results anticipated in these discrete procedures are presented. These smart temperature sensors have provided the accuracy and the stability compared to earlier methods used to detect the change in brain temperature since temperature is one of the most important variables in brain monitoring

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

Chemical Bionics - a novel design approach using ion sensitive field effect transistors

In the late 1980s Carver Mead introduced Neuromorphic engineering in which various aspects of the neural systems of the body were modelled using VLSI1 circuits. As a result most bio-inspired systems to date concentrate on modelling the electrical behaviour of neural systems such as the eyes, ears and brain. The reality is however that biological systems rely on chemical as well as electrical principles in order to function. This thesis introduces chemical bionics in which the chemically-dependent physiology of specific cells in the body is implemented for the development of novel bio-inspired therapeutic devices. The glucose dependent pancreatic beta cell is shown to be one such cell, that is designed and fabricated to form the first silicon metabolic cell. By replicating the bursting behaviour of biological beta cells, which respond to changes in blood glucose, a bio-inspired prosthetic for glucose homeostasis of Type I diabetes is demonstrated. To compliment this, research to further develop the Ion Sensitive Field Effect Transistor (ISFET) on unmodified CMOS is also presented for use as a monolithic sensor for chemical bionic systems. Problems arising by using the native passivation of CMOS as a sensing surface are described and methods of compensation are presented. A model for the operation of the device in weak inversion is also proposed for exploitation of its physical primitives to make novel monolithic solutions. Functional implementations in various technologies is also detailed to allow future implementations chemical bionic circuits. Finally the ISFET integrate and fire neuron, which is the first of its kind, is presented to be used as a chemical based building block for many existing neuromorphic circuits. As an example of this a chemical imager is described for spatio-temporal monitoring of chemical species and an acid base discriminator for monitoring changes in concentration around a fixed threshold is also proposed