This thesis reports on two graphene-based structures that have been proposed and fabricated as possible prototypes for high-spatial-resolution Hall sensors with potential application in research on high-density magnetic recording technology such as bit patterned magnetic recording (BPMR) and other areas where the measurement of highly inhomogeneous fields is required. There is a direct graphene-metal contact in the first structure, which is named as TYPE I in this thesis, so that the anomalous Hall effect (AHE) in the ferromagnetic islands deposited on the graphene could be detected. Meanwhile, the graphene and the metal are isolated by an h-BN layer in the second structure which is named as TYPE II, so that only the stray field from the islands can be detected using the ordinary Hall effect (OHE).The transport measurements performed on TYPE I devices revealed there is no AHE or stray field signal detectable, and their Hall resistance relations are non-linear and do not pass through the origin point. A finite element simulation comparing the resistance of the empty graphene cross and the island-occupied cross indicates that the current in the graphene may not redistribute through the metallic islands due to interface current blocking, resulting in the non-appearance of the expected AHE signal. Moreover, an analysis on the data of the longitudinal magnetoresistance (MR) reveals that a two-fluid model and effective medium theory (EMT) model might be the major graphene MR mechanisms in the regime away from and near to the charge neutrality point (CNP) respectively. As a combined result of the above findings, a joint MR-Hall effect model under the condition of the presence of a pre-existing transverse offset current, is proposed to explain the unusual behaviour of the Hall measurement data of the TYPE I devices. The model gives qualitatively correct fitting for all longitudinal and transverse transport data of TYPE I devices. In addition, the nature of the graphene/metal contact is considered as the reason responsible for the non-appearance of the expected AHE and stray field signal, although further experimental work is needed, and suggested in the thesis, to clarify this issue. On the other hand, the TYPE II devices have shown their potential to be developed as a Hall sensor being able to detect a sub-micron magnetic island in the future, but there is still a large space for the performance of the devices to be improved. At the end of the thesis, future experimental work, which could lead to the eventual development of a high-sensitivity high-spatial-resolution Hall sensor on the basis of TYPE I and TYPE II structures, are suggested and described.

Wireless Sensor Networks (WSNs) have been applied to a wide range of application areas, including battle fields, transportation systems, and hospitals. The security issues in WSNs are still hot research topics. The constrained capabilities of sensors and the environments in which sensors are deployed, such as hostile and non-reachable areas, make the security more complicated. This dissertation describes the development and testing of a novel two-phase security mechanism for hierarchical WSNs that is capable of defending both outside and inside attacks. For the outside attacks, the attackers are usually malicious intruders that entered the network. The computation and communication capabilities of the sensors restrict them from directly defending the harmful intruders by performing traditionally encryption, authentication, or other cryptographic operations. However, the sensors can assist the more powerful nodes in a hierarchical structured WSN to track down these intruders and thereby prevent further damage. To fundamentally improve the security of a WSN, a multi-target tracking algorithm is developed to track the intruders. For the inside attacks, the attackers are compromised insiders. The intruders manipulate these insiders to indirectly attack other sensors. Therefore, detecting these malicious insiders in a timely manner is important to improve the security of a network. In this dissertation, we mainly focus on detecting the malicious insiders that try to break the normal communication among sensors, which creates holes in the WSN. As the malicious insiders attempt to break the communication by actively using HELLO flooding attack, we apply an immune-inspired algorithm called Dendritic Cell Algorithm (DCA) to detect this type of attack. If the malicious insiders adopt a subtle way to break the communication by dropping received packets, we implement another proposed technique, a short-and-safe routing (SSR) protocol to prevent this type of attack. The designed security mechanism can be applied to different sizes of both static and dynamic WSNs. We adopt a popular simulation tool, ns-2, and a numerical computing environment, MATLAB, to analyze and compare the computational complexities of the proposed security mechanism. Simulation results demonstrate effective performance of the developed corrective and preventive security mechanisms on detecting malicious nodes and tracking the intruders.

Steel is widely used as building material for large-scale structures, such as oil and gas pipelines, due to its high strength-to-weight ratio. However, corrosion attack has been long recognized as one of the major reasons of steel pipeline degradation and brings great threat to safety in normal operation of structure. To mitigate the corrosion attacks, coatings are generally applied to protect steel pipelines against corrosion and improve durability of the associated structures for longer service life. Although have higher corrosion resistance, coated pipelines will still get corroded in a long run, as coatings may subject to damages such as cracks. Cracks on coatings could lower the effectiveness of protection for associated structures. Timely updates of up-to-date corrosion rate, corrosion location, and coating conditions to the pipeline risk management model and prompt repairs on these damaged coatings would significantly improve the reliability of protected structures against deterioration and failure. In this study, a corrosion risk analysis system is developed to detect and locate the corrosion induced coating cracks on coated steel using embedded fiber Bragg grating (FBG) sensors. The coatings investigated include high velocity oxygen fuel (HVOF) thermal sprayed Al-Bronze coating, wire arc sprayed Al-Zn coating, and soft coating. Theoretical models of corrosion risk assessment system were carried out followed by systematic laboratory experiments, which shows that the developed system can quantitatively detect corrosion rate, corrosion propagations, and accurately locate the cracks initialized in the coating in real time. This real-time corrosion information can be integrated into pipeline risk management model to optimize the corrosion related risk analysis for resource allocation. To place the sensing units of the system in the most needed locations along the huge pipeline systems for an effective corrosion risk assessment, an example case study is conducted in this study to show how to locate the most critical sensor placement locations along the pipeline using worst case oil and gas discharge analysis. Further applications of the developed system can be integrated with pipeline management system for better maintenance resource allocations. / USDOT-PHMSA

This thesis describes the design and implementation of mobile robot IEEE Micromouse category. The aim is to build a functional design of robot usable to testing methods of mapping and localization. The thesis also deals with the design of electronics for motion control. Electronic design of optical sensors operating on the principle of reflection of infrared light and the signal processing.

Orientador: Marcelo Augusto Assunção Sanches / Resumo: Recentes estudos têm abordado o aprimoramento de sensores de pressão com a finalidade de reproduzir a sensibilidade da pele humana para ser utilizada em robôs. Dentre diversos materiais disponíveis na literatura, destaca-se o material piezoresistivo à base do elastômero Polidimetilsiloxano e Cobre Dendritico (Cu-PDMS), devido à tecnologia empregada na produção destes sensores. Este trabalho trata a síntese e a caracterizações de compósitos piezoresistivo Cu-PDMS para confecção de sensores de pressão, na forma matricial, para aplicações biomédicas, como palmilhas instrumentadas, sensor on/off, dentre outros. Com finalidade de análise do material atuando como sensor de pressão, foram fabricadas e testadas amostras com diferentes composições. Para o estudo das propriedades de cada amostra, foram realizadas caracterizações elétricas (resistência elétrica com pressão variável, condutividade ao longo do tempo e espectroscopia de impedância), mecânicas (caracterização mecânica do material, ensaio de tração e ensaio termogravimétrico) e Microscopia Eletrônica de Varredura (MEV). Os resultados obtidos mostram as faixas possíveis para utilização do material como sensor de pressão, e os fatores que podem influenciar o seu emprego. / Abstract: Recent studies have addressed the improvement of pressure sensors in order to reproduce the sensitivity of human skin to be used in robots. Among the various materials available in the literature, the piezoresistive material based on the polydimethylsiloxane and Dendritic Copper (Cu-PDMS) elastomer stands out, due to the technology used in the production of these sensors. This work deals with the synthesis and characterization of Cu-PDMS piezoresistive composites for making pressure sensors, in matrix form, for biomedical applications such as instrumented insoles, on / off sensor, among others. In order to analyze the material acting as a pressure sensor, samples with different compositions were manufactured and tested. For the study of the properties of each sample, electrical characterizations (electrical resistance with variable pressure, conductivity over time and impedance spectroscopy), mechanical characterizations (mechanical characterization of the material, tensile test and thermogravimetric test) and Scanning Electron Microscopy were performed (ME V). The results obtained show the possible ranges for using the material as a pressure sensor, and the factors that can influence its use. / Mestre

The ability to detect crop rows and release sensors in large areas to ensure homogeneous coverage is crucial to monitor and increase the yield of crop rows. Aerial robotics in the agriculture field helps to reduce soil compaction. We report a release mechanics system based on image processing for crop row detection, which is essential for field navigation-based machine vision since most plants grow in a row. The release mechanics system is fully automated using embedded hardware and operated from a UAV. Once the crop row is detected, the release mechanics system releases lightweight, flexible multi-sensory devices on top of each plant to monitor the humidity and temperature conditions. The capability to monitor the local environmental conditions of plants can have a high impact on enhancing the plant’s health and in creasing the output of agriculture. The proposed algorithm steps: image acquisition, image processing, and line detection. First, we select the Region of Interest (ROI) from the frame, transform it to grayscale, remove noise, and then skeletonize and remove the background. Next, apply a Hough transform to detect crop rows and filter the lines. Finally, we use the Kalman filter to predict the crop row line in the next frame to improve the performance. This work’s main contribution is the release mechanism integrated with embedded hardware with a high-performance crop row detection algorithm for field navigation. The experimental results show the algorithm’s performance achieved a high accuracy of 90% of images with resolutions of (900x470) the speed reached 2 Frames Per Second (FPS).

Indiana University-Purdue University Indianapolis (IUPUI) / Indoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems.

Indiana University-Purdue University Indianapolis (IUPUI) / Self driving cars becoming more popular nowadays, which transport with it's own intelligence and take appropriate actions at adequate time. Safety is the key factor in driving environment. A simple fail of action can cause many fatalities. Computer Vision has major part in achieving this, it help the autonomous vehicle to perceive the surroundings. Detection is a very popular technique in helping to capture the surrounding for an autonomous car. At the same time tracking also has important role in this by providing dynamic of detected objects. Autonomous cars combine a variety of sensors such as RADAR, LiDAR, sonar, GPS, odometry and inertial measurement units to perceive their surroundings. Driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collision Mitigation by Breaking (CMbB) ensure safety while driving. Perceiving the information from environment include setting up sensors on the car. These sensors will collect the data it sees and this will be further processed for taking actions. The sensor system can be a single sensor or multiple sensor. Different sensors have different strengths and weaknesses which makes the combination of them important for technologies like Autonomous Driving. Each sensor will have a limit of accuracy on it's readings, so multi sensor system can help to overcome this defects. This thesis is an attempt to develop a multi sensor multi object tracking method to perceive the surrounding of the ego vehicle. When the Object detection gives information about the presence of objects in a frame, Object Tracking goes beyond simple observation to more useful action of monitoring objects. The experimental results conducted on KITTI dataset indicate that our proposed state estimation system for Multi Object Tracking works well in various challenging environments.

Solar Textiles came out of the idea that everyone has windows in their homes which need to be shaded. The question was simple, why are we not utilizing the sun’s rays which are hitting the shades throughout the day. The project explored the idea of creating solar curtains which would collect the sun’s energy and put it back into the curtain itself. Solar power, solar sensing, fabrics, shapes and movement is what this thesis is intending to explore. How to bring all of these aspects into a simple curtain that could be put into any household;making the world a more beautiful and ecologically friendly place.

Nowadays, water pollution significant jeopardizes the continuous clean drinking water supply that results in the damage of human health, and economy development. Adequate sensors are not only able to greatly benefit the treatment process but also can continuous monitoring of the watershed for contaminates which help effectively control pollution and manage the water resources. However, the commercial available sensors are expensive and required frequently maintenance. These limitations make these sensors not sufficient in continuous water monitoring application. In this thesis, sensors for some of the essential sensing targets including dissolved oxygen, phosphate and chlorine are developed. These sensors are low cost, easy operation and minimum maintenance required. These advantages make the sensors suitable to be applied in the continuously water quality monitoring system in multiple water systems such as drinking water, surface water and wastewater. Furthermore, an all solid state rechargeable palladium nanostructure based reference electrode and a universal dopamine/PEG/albumin antifouling coating technique are also studied in order to further extend the lifetime of these sensor thus reduces the need of maintenance. / Thesis / Doctor of Philosophy (PhD)