Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in the image. This hardware has not been studied in the context of autonomy as the sensors are large, expensive, and have non-trivial image capture times. This work presents three novel contributions: a Labeled Hyperspectral Image Dataset (LHID) of ground-level, outdoor objects based on typical scenes that a vehicle or pedestrian may encounter, an open-source hyperspectral interface software package (HSImage), and a feature selection based sensor design algorithm for object detection sensors (DLSD). These three contributions are novel and useful in the fields of hyperspectral data analysis, visual sensor design, and hyperspectral machine learning. The hyperspectral dataset and hyperspectral interface software were used in the design and testing of the sensor design algorithm. The LHID is shown to be useful for machine learning tasks through experimentation and provides a unique data source for hyperspectral machine learning. HSImage is shown to be useful for manipulating, labeling and interacting with hyperspectral data, and allows wavelength and classification based data retrieval, storage of labeling information and ambient light data. DLSD is shown to be useful for creating wavelength bands for a sensor design that increase the accuracy of classifiers trained on data from the LHID. DLSD shows accuracy near that of the full spectrum hyperspectral data, with a reduction in features on the order of 100 times. It compared favorably to other state-of-the-art wavelength feature selection techniques and exceeded the accuracy of an RGB sensor by 10%. / Ph. D. / To allow for better performance of autonomous vehicles in the complex road environment, identifying different objects in the roadway or near it is very important. Typically, cameras are used to identify objects and there has been much research into this task. However, the type of camera used is an RGB camera, the same used in consumer electronics, and it has a limited ability to identify colors. Instead, it only detects red, green, and blue and combines the results of these three measurements to simulate color. Hyperspectral cameras are specialized hardware that can detect individual colors, without having to simulate them. This study details an algorithm that will design a sensor for autonomous vehicle object identification that leverages the higher amount of information in a hyperspectral camera, but keep the simpler hardware of the RGB camera. This study presents three separate novel contributions: A database of hyperspectral images useful for tasks related to autonomous vehicles, a software tool that allows scientific study of hyperspectral images, and an algorithm that provides a sensor design that is useful for object identification. Experiments using the database show that it is useful for research tasks related to autonomous vehicles. The software tool is shown to be useful to interfacing between image files, algorithms and external software, and the sensor design algorithm is shown to be comparable to other such algorithms in accuracy, but outperforms the other algorithms in the size of the data required to complete the goal.

This thesis details several modifications to the mechanical design of the Orthotic Lower Limb Exoskeleton (OLL-E) that improve upon the functionality and manufacturability of parts and their assemblies. The changes made to these parts maintain or improve the factor of safety against yield and fatigue failure as compared to the original designs. Design changes are verified by FEA simulations and hand calculations. The changes included in this thesis also allowed parts that were previously difficult or impossible to manufacture using traditional methods to be made in house or outsourced to another machine shop. In addition to the mechanical design changes, this thesis also details the design and implementation of a six axis force-torque sensor built into the foot of OLL-E. The purpose of this sensor is to provide feedback to the central control system and allow OLL-E to be self-balancing. This foot sensor design is calibrated and initial results are discussed and shown to be favorable. / Master of Science / Recent developments in the fields of robotics and exoskeleton design have increased their feasibility for use in medical rehabilitation and mobility enhancement for persons with limited mobility. The Orthotic Lower Limb Exoskeleton (OLL-E) is an exoskeleton specifically designed for enhancing mobility by allowing users with lower limb disabilities such as spinal cord injuries or paraplegia to walk. The research detailed in this thesis explains the design and manufacturing processes used to make OLL-E as well as providing design details for a force sensor built into the exoskeleton foot. Before manufacturing could take place some parts needed to be redesigned and this thesis provides insight into the reasons for these changes. After the manufacturing and design process was completed the OLL-E was assembled and the project can now move forward with physical testing.

A compliant force/torque sensor for robot force control has been developed. This thesis presents methods of designing, testing, and implementing the sensor on a robotic system. The sensor uses an orthoplanar spring equipped with Hall-effect sensors to measure one component of force and two moment components. Its unique design allows for simple and cost effective manufacturing, high reliability, and compactness. The device may be used in applications where a robot must control contact forces with its environment, such as in surface cleaning tasks, manipulating doors, and removing threaded fasteners. The compliant design of the sensor improves force control performance and reduces impact forces. Sensor design considerations are discussed, followed by a discussion of the proposed design concept. Theoretical compliance and stress analysis of the orthoplanar spring is presented that allows for rapid design calculations; these calculations are validated via finite element analysis. A mechanical design method is given which uses the results of the compliance and stress analysis. Transducer design is then addressed by developing a model of the sensor. The design methods are used to design a prototype sensor which is tested to determine its instrument uncertainty. Finally, the sensor is implemented on a robotic platform to test its performance in force control.

The acoustic emission technique is an effective way to acquire crack information from material bodies at the microscopic level. Monitoring of the acoustic emission events provides a deeper understanding regarding the structural health status of critical constructions such as bridges, railways, pipelines, pressure vessels, etc. Thanks to the acoustic emission monitoring systems, it is possible to avoid catastrophic events and save lives, time, and money. For this reason, efforts to develop new acoustic emission sensor technologies, as well as the use of current acoustic emission sensors in new research fields, will contribute to the limited literature sources. Optical sensing systems provide good alternatives to the existing sensing technologies because of their wide range of detection bandwidths, adaptation to harsh environments, and low sensitivity to electromagnetic interference. For this reason, the first part of this thesis demonstrates an optoacoustic sensing methodology that enables the detection of acoustic emissions by optics. This sensing system consists of thin-film optical filters (TFOF) and an elastic microcavity layer. The sensing mechanism is similar to the Fabry Perot structures and it relies on resonance shifts of the cavity when there is a change in the cavity thickness similar to the Fabry Perot structures. Thus, the design, fabrication, and demonstration steps of a Fabry Perot elastic microcavity have been presented. Throughout the fabrication efforts, a new deposition protocol was developed. This deposition technique has enabled the deposition of TFOF on flexible substrates via the RF-sputtering technique. Thus, a new sensing configuration has been developed using flexible optical components. In the second chapter, an optical sensing methodology based on tunable spectral filters and flexible optical components is introduced. The design, fabrication, realization, and characterization of a proof-of-concept optomechanical sensor have been presented. The design step includes optical, mechanical, and optoacoustic correlation simulations using the Transfer Matrix Method, finite element analysis, and analytical models. Moreover, the fabrication part includes multilayer deposition on silica and flexible substrates using the RF-Sputtering technique and integration of these optical components into a 3D-printed housing together with electronic components. Eventually, the performance evaluation of the optomechanical sensor has been carried out and the experimental results showed that the sensor resonance frequency is around 515 Hz and the sensor is capable of detecting static loadings from 50 Pa to 235 Pa values. In the fourth chapter, seismic vulnerability analysis of a coupled Tank-Piping System has been performed using traditional acoustic emission sensors. Real-time performance evaluation of the pipeline as well as the structural health status of the critical parts were monitored. As a result, deformation levels of each critical part were investigated, and the processing of acoustic emission signals provided a more in-depth view of damage level of the analyzed components. Throughout the thesis, TFOFs are an integral part of this thesis. Therefore, both the design and simulation of TFOFs play a crucial role throughout this research work. The Transfer Matrix Method is used to simulate the optical performance of TFOFs. Moreover, in the final chapter, an automated design framework is presented for the design of TFOFs using a nature-inspired machine learning approach called Genetic algorithm. This design approach enables the design of sophisticated geometric configurations with unique optical capabilities. Therefore, not only the improvement of sensor response but also the new ways in the development of novel optical systems are demonstrated in this final chapter.

In order to aid federal agencies and private companies in the ever-growing problem of invasive species target detection, an investigation has been done on classification accuracy data cubes for use in the determination of spectral, spatial, and temporal sensor resolution requirements. The data cube is the result of a developed automated target recognition system that begins with ?ideal? hyperspectral data, and then reduces and combines spectral and spatial resolutions. The reduced data is subjected to testing methods using the Best Spectral Bands (BSB) and the All Spectral Bands (ASB) approaches and classification methods using nearest mean (NM), nearest neighbor (NN), and maximum likelihood (ML) classifiers. The effectiveness of the system is tested via two target-nontarget case studies, namely, terrestrial Cogongrass (Imperata cylindrica)-Johnsongrass (Sorghum halepense), and aquatic Water Hyacinth (Eichhornia crassipes)-American Lotus (Nelumbo lutea). Results reveal the effects, or trade-offs, of spectral-spatial-temporal resolution combinations on the ability of an ATR system to accurately detect the target invasive species. For example, in the aquatic vegetation case study, overall classification accuracies of around 90% or higher can be obtained during the month of August for spectral resolutions of 80 ? 1000nm FWHM for target abundances of 70 ? 100% per pixel. Furthermore, the ATR system demonstrates the use of resolution cubes that can be readily used to design or select cost-effective sensors for use in invasive species target detection, since lower resolution combinations may be acceptable in order to gain satisfactory classification accuracy results.

The development of Fully Depleted Monolithic Active Pixel Sensors (FD-MAPS) represents nowadays a hot-topic in the radiation detector community. The advantages in terms of production costs and easiness of manufacturing in comparison to the state-of-the-art hybrid detectors boost the research effort in the direction of developing new CMOS compatible detector technologies. To this end, the INFN ARCADIA project targeted the design of a sensor platform for the production of FD-MAPS to be employed in different scientific, medical and space applications. The sensor technology has been developed in collaboration with LFoundry on the basis of a standard 110nm CMOS production process with some modifications needed to meet the project requirements. High resistivity n-type silicon substrates have been chosen for the sensor active volume and a n-type epitaxial layer has been included at the sensor frontside to delay the onset of the punch-through current flowing between the frontside and backside p-type implants. The sensor n-type collection electrodes are surrounded by pwells, which can host the embedded analog and digital frontend electronics, and deep pwells have been included below the pwells to shield them from the sensor substrate. Three engineering runs have been submitted and the produced wafers have been delivered in 2021, 2022 and 2023, respectively. An additional p-type implant has been added in the third production run to create an embedded gain layer below the n-type collection electrodes, to enhance the signal through avalanche multiplication. A selection of the main results obtained from the TCAD simulations and of the most relevant measurements performed on the designed MAPS passive test structures will be presented and discussed in chapter 4. In an analogous way, the experimental results obtained from the characterization of an active sensor designed for brachytherapy, called COBRA, are reported in chapter 5. The calibration of the capacitance included in the internal charge injection circuit of two TJ-Monopix2 MAPS having different substrate types is reported in chapter 6. These sensors represent examples of fully functional and full scale monolithic prototypes realized in a 180nm Tower-Jazz CMOS process, that have been characterized using X-rays fluorescence techniques at the SiLab of the University of Bonn. Finally, in the Conclusions section the main results of the research activity are summarized and the possible future spin-offs of the project are presented.

Prior research has shown that indoor air quality (IAQ) impacts cognitive performance. At Cal Poly, many older buildings are unable to maintain appropriate IAQ because of their outdated ventilation systems and the increasing number of students in the rooms. This work analyzes the IAQ of different buildings at Cal Poly, with a focus on Building 20. Carbon dioxide, temperature, and relative humidity inside classrooms are collected using an integrated circuit sensor and a microcontroller. A total of 38 hours of data was collected, with 22 of those hours in Building 20 specifically. We find that unlike temperature and relative humidity, CO2 levels routinely exceed 1,000 ppm—a concentration that hinders cognitive function. A questionnaire distributed to Cal Poly students suggests that while students can recognize poor IAQ in classrooms, they erroneously attribute these poor conditions to temperature and humidity instead of CO2. This data is then used to propose a system which can collect long-term data based on optimal placement, storage, and power requirements.

The temperature sensor design discussed in this thesis, is meant mainly to monitor temperature at power outlets. Current variations in power cords have a direct impact on the surrounding temperature. Sensing these variations ,enables us to take necessary measures to prevent any hazards due to temperature rise. Thus, for this application we require a sensor with a moderate temperature error (_10C) over a sensing range of -200C to 1500C. Low power consumption and simple digitizing scheme alleviate measurement errors due to self heating effects of the sensor. A current starved inverter based ring oscillator was chosen for the sensor design in 130nm technology. The inverter delay variation with temperature is used for sensing. Linearity and process invariancy of these characteristics are fundamental to the sensor design. We observed through simulations, and confirmed by mathematical analysis, that the sensing characteristics are governed by bias current dependence on temperature. Control voltage for the bias circuitry of the oscillator determines current through the inverter stages. Hence, for linear sensing characteristics, a control voltage(Vc) just above the maximum threshold voltage of bias transistor is used. This enables generation of PTAT saturation current for current starved inverters, due to dominance of threshold voltage decrease with temperature over mobility decrease. I.Another limitation, process dependency of the sensing characteristics, was overcome through the proposed calibration based compensation technique. A changing Vc proportional to threshold voltage variation with process, process independent bias current and current temperature characteristics were obtained. This compensated for the process variation effects on frequency. Thus, a variable Vc was generated using a reference with low temperature sensitivity of 17.6_V=0C, and resistive divider combinations for various processes. Incorporating this compensation technique we achieved good linearity in sensor characteristics and a maximum temperature error of± 1.60C over the sensing range. The sensor consumes a low power of 0.29mW and also occupies minimal area.

<p>This dissertation describes three computational sensors. The first sensor is a scanning multi-spectral aperture-coded microscope containing a coded aperture spectrometer that is vertically scanned through a microscope intermediate image plane. The spectrometer aperture-code spatially encodes the object spectral data and nonnegative</p> <p>least squares inversion combined with a series of reconfigured two-dimensional (2D spatial-spectral) scanned measurements enables three-dimensional (3D) (x, y, &#955) object estimation. The second sensor is a coded aperture snapshot spectral imager that employs a compressive optical architecture to record a spectrally filtered projection</p> <p>of a 3D object data cube onto a 2D detector array. Two nonlinear and adapted TV-minimization schemes are presented for 3D (x,y,&#955) object estimation from a 2D compressed snapshot. Both sensors are interfaced to laboratory-grade microscopes and</p> <p>applied to fluorescence microscopy. The third sensor is a millimeter-wave holographic imaging system that is used to study the impact of 2D compressive measurement on 3D (x,y,z) data estimation. Holography is a natural compressive encoder since a 3D</p> <p>parabolic slice of the object band volume is recorded onto a 2D planar surface. An adapted nonlinear TV-minimization algorithm is used for 3D tomographic estimation from a 2D and a sparse 2D hologram composite. This strategy aims to reduce scan time costs associated with millimeter-wave image acquisition using a single pixel receiver.</p> / Dissertation

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