DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

Design and implementation of a 3D computer game controller using inertial MEMS sensors

Though 3D computer graphics has seen tremendous advancement in the past two decades, most available mechanisms for computer interaction in 3D are high cost and targeted for industry and virtual reality applications. Recent advances in Micro-Electro-Mechanical-System (MEMS) devices have brought forth a variety of new low-cost, low-power, miniature sensors with high accuracy, which are well suited for hand-held devices. In this work a novel design for a 3D computer game controller using inertial sensors is proposed, and a prototype device based on this design is implemented. The design incorporates MEMS accelerometers and gyroscopes from Analog Devices to measure the three components of the acceleration and angular velocity. From these sensor readings, the position and orientation of the hand-held compartment can be calculated using numerical methods. The implemented prototype is utilizes a USB 2.0 compliant interface for power and communication with the host system. A Microchip dsPIC microcontroller is used in the design. This microcontroller integrates the analog to digital converters, the program memory flash, as well as the core processor, on a single integrated circuit. A PC running Microsoft Windows operating system is used as the host machine. Prototype firmware for the microcontroller is developed and tested to establish the communication between the design and the host, and perform the data acquisition and initial filtering of the sensor data. A PC front-end application with a graphical interface is developed to communicate with the device, and allow real-time visualization of the acquired data.

Inductive-Capacitive Resonant Circuit Sensors for Structural Health and Environmental Monitoring

Inductive-capacitive (LC) resonant circuit sensors are low-cost, wireless, durable, simple to fabricate and battery-less. Consequently, they are well suited to sensing applications in harsh environments or in situations where large numbers of sensors are needed. They are also advantageous in applications where access to the sensor is limited or impossible or when sensors are needed on a disposable basis. Due to their many advantages, LC sensors have been used for sensing a variety of parameters including humidity, temperature, chemical concentrations, pH, stress/pressure, strain, food quality and even biological growth. However, current versions of the LC sensor technology are limited to sensing only one parameter. The purpose of this work is to develop new types of LC sensor systems that are simpler to fabricate (hence lower cost) or capable of monitoring multiple parameters simultaneously. One design presented in this work, referred to as the multi-element LC sensor, is able to measure multiple parameters simultaneously using a second capacitive element. Compared to conventional LC sensors, this design can sense multiple parameters with a higher detection range than two independent sensors while maintaining the same overall sensor footprint. In addition, the two-element sensor does not suffer from interference issues normally encountered while implementing two LC sensors in close proximity. Another design, the single-spiral inductive-capacitive sensor, utilizes the parasitic capacitance of a coil or spring structure to form a single layer LC resonant circuit. Unlike conventional LC sensors, this design is truly planar, thus simplifying its fabrication process and reducing sensor cost. Due to the simplicity of this sensor layout it will be easier and more cost-effective for embedding in common building or packaging materials during manufacturing processes, thereby adding functionality to current products (such as drywall sheets) while having a minor impact on overall unit cost. These modifications to the LC sensor design significantly improve the functionality and commercial feasibility of this technology, especially for applications where a large array of sensors or multiple sensing parameters are required.

Development and characterization of fluorescent pH sensors based on porous silica and hydrogel support matrices

The hydrogen ion activity (pH) is a very important parameter in environment monitoring, biomedical research and other applications. Optical pH sensors have several advantages over traditional potentiometric pH measurement, such as high sensitivity, no need of constant calibration, easy for miniaturization and possibility for remote sensing. Several pH indicators has been successfully immobilized in three different solid porous materials to use as pH sensing probes. The fluorescent pH indicator fluorescein-5-isothiocyanate (FITC) was covalently bound onto the internal surface of porous silica (pore size ~10 nm) and retained its pH sensitivity. The excited state pK* a of FITC in porous silica (5.58) was slightly smaller than in solution (5.68) due to the free silanol groups (Si-OH) on the silica surface. The pH sensitive range for this probe is pH 4.5 - 7.0 with an error less than 0.1 pH units. The probe response was reproducible and stable for at least four month, stored in DI water, but exhibit a long equilibrium of up to 100 minutes. Sol-gel based pH sensors were developed with immobilization of two fluorescent pH indicators fluorescein-5-(and-6)-sulfonic acid, trisodium salt (FS) and 8-hydroxypyrene- 1,3,6-trisulfonic acid (HPTS) through physical entrapment. Prior to immobilization, the indicators were ion-paired with a common surfactant hexadecyltrimethylammonium bromide (CTAB) in order to prevent leaching. The sol-gel films were synthesized through the hydrolysis of two different precursors, ethyltriethoxysilane (ETEOS) and 3- glycidoxypropyltrimethoxysilane (GPTMS) and deposited on a quartz slide through spin coating. The pK a of the indicators immobilized in sol-gel films was much smaller than in solutions due to silanol groups on the inner surface of the sol-gel films and ammonium groups from the surrounding surfactants. Unlike in solution, the apparent pK a of the indicators in sol-gel films increased with increasing ionic strength. The equilibrium time for these sensors was within 5 minutes (with film thickness of ~470 nm). Polyethylene glycol (PEG) hydrogel was of interest for optical pH sensor development because it is highly proton permeable, transparent and easy to synthesize. pH indicators can be immobilized in hydrogel through physical entrapment and copolymerization. FS and HPTS ion-pairs were physically entrapped in hydrogel matrix synthesized via free radical initiation. For covalent immobilization, three indicators, 6,8-dihydroxypyrene-1,3- disulfonic acid (DHPDS), 2,7-dihydroxynaphthalene-3,6-disulfonic acid (DHNDS) and cresol red were first reacted with methacrylic anhydride (MA) to form methacryloylanalogs for copolymerization. These hydrogels were synthesized in aqueous solution with a redox initiation system. The thickness of the hydrogel film is controlled as ~ 0.5 cm and the porosity can be adjusted with the percentage of polyethylene glycol in the precursor solutions. The pK a of the indicators immobilized in the hydrogel both physically and covalently were higher than in solution due to the medium effect. The sensors are stable and reproducible with a short equilibrium time (less than 4 minutes). In addition, the color change of cresol red immobilized hydrogel is vivid from yellow (acidic condition) to purple (basic condition). Due to covalently binding, cresol red was not leaching out from the hydrogel, making it a good candidate of reusable "pH paper".

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

DESIGN AND APPLICATION OF WIRELESS PASSIVE MAGNETOELASTIC RESONANCE AND MAGNETOHARMONIC FORCE SENSORS

The objective of the work described in this dissertation is the development of new wireless passive force monitoring platforms for applications in the medical field, specifically monitoring lower limb prosthetics. The developed sensors consist of stress sensitive, magnetically soft amorphous metallic glass materials. The first technology is based on magnetoelastic resonance. Specifically, when exposed to an AC excitation field along with a constant DC bias field, the magnetoelastic material mechanically vibrates, and may reaches resonance if the field frequency matches the mechanical resonant frequency of the material. The presented work illustrates that an applied loading pins portions of the strip, effectively decreasing the strip length, which results in an increase in the frequency of the resonance. The developed technology is deployed in a prototype lower limb prosthetic sleeve for monitoring forces experienced by the distal end of the residuum. This work also reports on the development of a magnetoharmonic force sensor comprised of the same material. According to the Villari effect, an applied loading to the material results in a change in the permeability of the magnetic sensor which is visualized as an increase in the higher-order harmonic fields of the material. Specifically, by applying a constant low frequency AC field and sweeping the applied DC biasing field, the higher-order harmonic components of the magnetic response can be visualized. This sensor technology was also instrumented onto a lower limb prosthetic for proof of deployment; however, the magnetoharmonic sensor illustrated complications with sensor positioning and a necessity to tailor the interface mechanics between the sensing material and the surface being monitored. The novelty of these two technologies is in their wireless passive nature which allows for long term monitoring over the life time of a given device. Additionally, the developed technologies are low cost. Recommendations for future works include improving the system for real-time monitoring, useful for data collection outside of a clinical setting.