Browsing by Subject "Sensor"

The treasure hunt problem to determine how a computational agent can maximize its ability to detect and/or classify multiple targets located in a region of interest (ROI) populated with multiple obstacles. One particular instance of this problem involves optimizing the performance of a sensor mounted on an unmanned aerial vehicle (UAV) flying over a littoral region in order to detect mines buried underground.

Buried objects (including non-metallic ones) have an effect on the thermal conductivity and heat retention of the soil in which they reside. Because of this, objects that are not very deep below the surface often create measurable thermal anomalies on the surface soil. Because of this, infrared (IR) sensors have the potential to find mines and minelike objects (referred to in this thesis as clutters).

As the sensor flies over the ROI, sensor data is obtained. The sensor receives the data as pixellated infrared light signatures. Using this, ground temperature measurements are recorded and used to generate a two-dimensional thermal profile of the field of view (FOV) and map that profile onto the geography of the ROI.

The input stream of thermal data is then passed to an image processor that estimates the size and shape of the detected target. Then a Bayesian Network (BN) trained from a database of known mines and clutters is used to provide the posterior probability that the evidence obtained by the IR sensor for each detected target was the result of a mine or a clutter. The output is a confidence level (CL), and each target is classified as a mine or a clutter according to the most likely explanation (MLE) for the sensor evidence. Though the sensor may produce incomplete, noisy data, inferences from the BN attenuate the problem.

Since sensor performance depends on altitude and environmental conditions, the value of the IR information can be further improved by choosing the flight path intelligently. This thesis assumes that the UAV is flying through an environmentally homogeneous ROI and addresses the question of how the optimal altitude can be determined for any given multi-dimensional environmental state.

In general, high altitudes result in poor resolution, whereas low altitudes result in very limited FOVs. The problem of weighing these tradeoffs can be addressed by creating a scoring function that is directly dependent on a comparison between sensor outputs and ground truth. The scoring function provides a flexible framework through which multiple mission objectives can be addressed by assigning different weights to correct detections, correct non-detections, false detections, and false non-detections.

The scoring function provides a metric of sensor performance that can be used as feedback to optimize the sensor altitude as a function of the environmental conditions. In turn, the scoring function can be empirically evaluated over a number of different altitudes and then converted to empirical Q scores that also weigh future rewards against immediate ones. These values can be used to train a neural network (NN). The NN filters the data and interpolates between discrete Q-values to provide information about the optimal sensor altitude.

The research described in this thesis can be used to determine the optimal control policy for an aircraft in two different situations. The global maximum of the Q-function can be used to determine the altitude at which a UAV should cruise over an ROI for which the environmental conditions are known a priori. Alternatively, the local maxima of the Q-function can be used to determine the altitude to which a UAV should move if the environmental variables change during flight.

This thesis includes the results of computer simulations of a sensor flying over an ROI. The ROI is populated with targets whose characteristics are based on actual mines and minelike objects. The IR sensor itself is modeled by using a BN to create a stochastic simulation of the sensor performance. The results demonstrate how Q-learning can be applied to signals from a UAV-mounted IR sensor whose data stream is preprocessed by a BN classifier in order to determine an optimal flight policy for a given set of environmental conditions.

As society becomes increasingly data-driven, the appetite of individuals, corporations, and algorithms for data sources swells, strengthening the demand for sensors. Chemical sensors are of particular interest as they provide highly human-relevant information, such as DNA sequences, cancer biomarker concentrations, blood glucose levels, antibody detection, and viral testing, to name a few. Among the most promising transduction elements for chemical sensors are nanomaterial field-effect transistors (FETs). The nanoscale size of these devices allows them to operate using very small sample sizes (an extremely small volume of patient blood, for instance), be strongly influenced by low concentrations of the target chemical, and be produced at low-cost, potentially using the same methods developed for consumer electronics (which have achieved a cost of less than 0.000001 cents per device). Nanomaterial FET-based chemical sensors also have the advantage of directly transducing a chemical presence or change to an electrical output signal. This avoids components such as lasers, optics, fluorophores, and more, that are frequently used as a part of the transduction chain in other types of chemical sensors, adding size, complexity, and cost. Much work has focused on demonstrating one-off nanomaterial FET-based sensors, but less work has been done to determine the underlying mechanisms that lead to sensitivity by mapping sensitivity against other variables in experimental devices. With challenges of consistency and reproducible operation stifling progress in this field, there is a significant need to improve understanding of nanomaterial-based FET sensitivity and operation mechanisms.

The work contained in this dissertation maps the sensitivity of nanomaterial FETs across a range of parameters, including space, time, device operating point, and analyte charge. This mapping is performed in an effort to yield insight into the underlying mechanisms that govern the sensitivity of these devices to nearby charges. In order to both draw comparisons between different device types and to make the results of this work broadly applicable to the field as a whole, four types of devices were studied that span a broad range of characteristics. The device types spanned from channels of one-dimensional nanotubes to three-dimensional nanostructures, and from partially printed fabrication to cleanroom-based nanofabrication. Specifically, the devices explored herein are carbon nanotube (CNT) FETs, molybdenum disulfide (MoS2) FETs, silicon nanowire FETs, and carbon nanotube thin-film transistors (CNT-TFTs). Fabrication processes were developed to build devices of each of these types that are capable of undergoing long-term electronic testing with reliable contact strategies. Passivation schemes were also developed for each device type to enable testing in solution and formation of solution-based sensors so that results could be extended to the case of biosensors. An automated experimentation platform was developed to enable tight synchronization between characterization instruments so that each variable impacting device sensitivity could be controlled and measured in tandem, in some cases for months on end.

Many of the obtained results showed similar trends in sensitivity between device types, while some findings were unique to a given channel material. All tested devices showed stability after a period of drain current settling caused by the occupation equilibration of charge trap states – an effect that was found to severely reduce sensitivity and dynamic range. For CNTs specifically, two new decay modes were discovered (intermediate between device stability and breakdown) along with respective onset voltages that can be used to avoid them. For CNT-TFTs, it was found that the relationship between signal-to-noise ratio (SNR) and device operating point remained consistent between ambient air and solution environments, indicating that this relationship is governed primarily by properties of the device. A simple chemical sensor made from the same devices showed a clear peak in the SNR near the device threshold voltage – a result that became increasingly meaningful when combined with similar observations in other device types obtained via separate experimental methods.

For both silicon nanowire and MoS2 FETs, sensitivity was mapped in space with sub-nanometer precise control over analyte position. Both device types manifested distinct sensitivity hotspots spread across the geometry of the channel. These hotspots were found to be stable in time, but their prominence depended heavily on the device operating point. When SNR was mapped across a range of operating points for these devices, a clear peak was discovered, with the hotspot intensity culminating at the peak. Ideal operating points were identified to be near the threshold voltage for both device types, with findings (and a developed numerical model) in MoS2 indicating that the operating point where SNR is maximized may depend upon the extent of the channel that is influenced by the analyte. Observations from multiple devices and approaches revealed that SNR peaks below the point of maximal transconductance, offering increased resolution to a matter that has previously been of some debate in the literature. In MoS2 FETs, a significant asymmetry was discovered in the response of devices to analytes of opposing polarity, with analytes that modulate devices toward their off-state eliciting a much larger response (and, correspondingly, SNR). This asymmetry was confirmed by a numerical model that suggested it to be a general result applicable to all FET-based charge detection sensors, leading to the recommendation that sensor designers select FETs that will be turned off by the target analyte.

Each finding contributed by this dissertation provides insight into future sensor designs and increases clarity of the underlying mechanisms leading to sensitivity in nanomaterial FET-based sensors. The discovery of decay modes, hotspots, ideal operating points, asymmetries, and other trends comprise substantial scientific advancements and propel the field closer to the goal of providing ubiquitous access to critical information, diagnoses, and measurements that promptly and correctly inform decisions.

Integrated sensing systems are designed to address a variety of problems, including clinical diagnosis, water quality testing, and air quality testing. The growing prevalence of tropical diseases in the developing world, such as malaria, trypanosomiasis (sleeping sickness), and tuberculosis, provides a clear and present impetus for portable, low cost diagnostics both to improve treatment outcomes and to prevent the development of drug resistance in a population. The increasing scarcity of available clean, fresh water, especially noticeable in the developing world, also presents a motivation for low-cost water quality diagnostic tools to prevent exposure of people to contaminated water supplies and to monitor those water supplies to effectively mitigate their contamination. In the developed world, the impact of second-hand cigarette smoke is receiving increased attention, and measuring its effects on public health have become a priority. The `point-of-need' technologies required to address these sensing problems cannot achieve a widespread and effective level of use unless low-cost, small form-factor, portable sensing devices can be realized. Optical sensors based on low cost polymer materials have the potential to address the aforementioned `point-of-need' sensing problems by leveraging low-cost materials and fabrication processes. For portable clinical diagnostics and water quality testing in particular, on-chip sample preparation is a necessity. Electrowetting-on-dielectric (EWD) technology is an enabling technology for chip-scale sample preparation, due to its very low power consumption compared to other microfluidics technologies and the ability to move fluids without bulky external pumps. Potentially, these technologies could be combined into a cell phone sized portable sensing device.

Towards the goal of developing a portable diagnostic device using EWD microfluidics with an embedded polymer microresonator sensor, this thesis describes a viable fabrication process for the system and explores the design trade-offs of such a system. The main design challenges for this system are optimization of the sensor's limit-of-detection, minimization of the insertion loss of the optical system, and maintaining the ability to actuate droplets onto and off of the sensor embedded in the microfluidic system. The polymer microresonator sensor was designed to optimize the limit-of-detection (LOD) using SU-8 polymer as the bus waveguide and microresonator material and SiO2 as the substrate cladding material. The fabrication process and methodology were explored with test devices using a tunable laser system working around a wavelength of 1550 nm using glucose solutions as a refractive index standard. This sensor design was then utilized to embed the sensor and bus waveguides into an EWD top plate in order to minimize the impact of the sensor integration on microfluidic operations. Finally, the performance of the embedded sensor embedded was evaluated in the same manner and compared to the performance of the sensor without the microfluidic system.

The primary result of this research was the successful demonstration of a high performance polymer microresonator sensor embedded in the top plate of an electrowetting microfluidic device. The embedded sensor had the highest reported figure-of-merit for any microresonator integrated with electrowetting microfluidics. The embedded microresonator sensor was also the first fully-embedded microresonator in an EWD system. Because the sensor was embedded in the top plate, full functionality of the EWD system was maintained, including the ability to move droplets onto and off of the sensor and to address the sensor with single droplets. Furthermore, the highest figure-of-merit for an SU-8 microresonator sensor yet reported at a probe wavelength of 1550 nm was measured on a test device fabricated with the embedded sensor structure described herein. Optimization of the sensor sensitivity utilized recently developed waveguide sensor design theory, which accurately predicted the measured sensitivity of the sensors. Altogether, the results show that embedding of a microresonator sensor in an EWD microfluidics system is a viable approach to develop a portable diagnostic system with the high efficiency sample preparation capability provided by EWD microfluidics and the versatile sensing capability of the microresonator sensor.

The spatial spectrum, also known as a field directionality map, is a description of the spatial distribution of energy in a wavefield. By sampling the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered.

First, the problem of underwater a hydrophone array towed behind a maneuvering platform is considered. The array consists of many hydrophones mounted to a flexible cable that is pulled behind a ship. The towed cable will bend or distort as the ship performs maneuvers. The motion of the cable through the turn can be used to resolve ambiguities that are inherent to nominally linear arrays. The first significant contribution is a method to estimate the spatial spectrum using a time-varying array shape in a dynamic field and broadband temporal data. Knowledge of the temporal spectral shape is shown to enhance detection performance. The field is approximated as a sum of uncorrelated planewaves located at uniform locations in angle, forming a gridded map on which a maximum likelihood estimate for broadband source power is derived. Uniform linear arrays also suffer from spatial aliasing when the inter-element spacing exceeds a half-wavelength. Broadband temporal knowledge is shown to significantly reduce aliasing and thus, in simulation, enhance target detection in interference dominated environments.

As an extension, the problem of towed array shape estimation is considered when the number and location of sources are unknown. A maximum likelihood estimate of the array shape using the field directionality map is derived. An acoustic-based array shape estimate that exploits the full 360$^\circ$ field via field directionality mapping is the second significant contribution. Towed hydrophone arrays have heading sensors in order to estimate array shape, but these sensors can malfunction during sharp turns. An array shape model is described that allows the heading sensor data to be statistically fused with heading sensor. The third significant contribution is method to exploit dynamical motion models for sharp turns for a robust array shape estimate that combines acoustic and heading data. The proposed array shape model works well for both acoustic and heading data and is valid for arbitrary continuous array shapes.

Finally, the problem of array manifold ambiguities for static under-sampled linear arrays is considered. Under-sampled arrays are non-uniformly sampled with average spacing greater than a half-wavelength. While spatial aliasing only occurs in uniformly sampled arrays with spacing greater than a half-wavelength, under-sampled arrays have increased spatial resolution at the cost of high sidelobes compared to half-wavelength sampled arrays with the same number of sensors. Additionally, non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same length can be reconstructed in wide sense stationary fields at the cost of increased variance. The forth significant contribution is a reduced rank processing method for fully augmentable arrays to reduce the variance from augmentation with limited snapshots. Array gain for reduced rank adaptive processing with diagonal loading for snapshot deficient scenarios is analytically derived using asymptotic results from random matrix theory for a set ratio of sensors to snapshots. Additionally, the problem of near-field sources is considered and a method to reduce the variance from augmentation is proposed. In simulation, these methods result in significant average and median array gains with limited snapshots.