A differential steered, 13.6 kg robot was developed as an intelligent tire testing system and was used to investigate the potential of using piezoelectric film sensors in small tube-type pneumatic tires to characterize tire-ground interaction.<br />One focus of recent research in the tire industry has been on instrumenting tires with sensors to monitor the tire, vehicle, or external environment. On small robots, tire sensors that measure the forces and deflections in the contact patch could be used to improve energy efficiency and/or mobility during a mission.<br />The robot was assembled from a SuperDroid Robots kit and instrumented with low-cost piezoelectric film sensors from Measurement Specialties between the inner tube and the tire.  An unlaminated and a laminated sensor were placed circumferentially along the tread and an unlaminated sensor was placed along the sidewall.  A slip ring transferred the signals from the tire to the robot. There, the signal conditioning circuit extended the time constant of the sensors and filtered electromagnetic interference.  The robot was tested with a controlled power sequence carried out on polished cement, ice, and sand at three power levels, two payload levels, and with two tire sizes.<br />The results suggest that the sensors were capable of detecting normal pressure, deflection, and/or longitudinal strain.  Added payload increased the amplitude of the signals for all sensors.  On the smaller tires, sensors generally recorded a smaller, wider signal on sand compared to cement, indicating the potential to detect contact patch pressure and length.  The signals recorded by the unlaminated sensor along the tread of the smaller tire were lower on ice compared to cement, indicating possible sensitivity to tractive force.  Results were less consistent for the larger tires, possibly due to the large tread pattern. / Master of Science

Today's technology is becoming more interactive with voice assistants like Siri. However, interactive systems such as Siri make mistakes. The purpose of this thesis is to explore using affect as an implicit feedback channel so that such mistakes would be easily corrected in real time. The CAfFEINE Framework, which was created by Dr. Saha, is a context-aware affective feedback loop in an intelligent environment. For the research described in this thesis, the focus will be on analyzing a user's physiological response to the service provided by an intelligent environment. To test this feedback loop, an experiment was constructed using an on-screen, step-by-step assembly guide for a Tangram puzzle. To categorize the user's response to the experiment, baseline readings were gathered for a user's stressed and non-stressed state. The Paced Stroop Test and two other baseline tests were conducted to gather these two states. The data gathered in the baseline tests was then used to train a support vector machine to predict the user's response to the Tangram experiment. During the data analysis phase of the research, the results for the predictions on the Tangram experiment were not as expected. Multiple trials of training data for the support vector machine were explored, but the data gathered throughout this research was not enough to draw proper conclusions. More focus was then given to analyzing the pre-processed data of the baseline tests in an attempt to find a factor or group of factors to determine if the user's physiological responses would be useful to train the Support Vector Machine. There were trends found when comparing the area under the curves of the Paced Stroop Test phasic driver plots. It was found that these comparison factors might be a useful approach for differentiating users based upon their physiological responses during the Paced Stroop Test. / Master of Science / The purpose of this thesis was to use the CAfFEINE Framework, proposed by Dr. Saha, in a real-world environment. Dr. Saha's Framework utilizes a user's physical responses, i.e. heart rate, in a smart environment to give information to the smart devices. For example, if Siri were to give a user directions to someone's home and told that user to turn right when the user knew they needed to turn left. That user would have a physical reaction as in their heart rate would increase. If the user were wearing a smart watch, Siri would be able to see the heart rate increase and realize, from past experiences with that user, that the information she gave to the user was incorrect. Then she would be able to correct herself. My research focused on measuring user reaction to a smart service provided in a real-world situation using a Tangram puzzle as a mock version of an industrial assembly situation. The users were asked to follow on-screen instructions to assemble the Tangram puzzle. Their reactions were recorded through a smart watch and analyzed post-experiment. Based on the results of a Paced Stroop Test they took before the experiment, a computer algorithm would predict their stress levels for each service provided by the step-by-step instruction guide. However, the results did not turn out as expected. Therefore, the rest of the research focused more on why the results did not support Dr. Saha's previous Framework results.

Recent industry trends toward more complex and interconnected systems have increased the demand for more reliable pressure sensors. One of the best methods to ensure reliability is by regularly calibrating the sensor, checking its functionality and accuracy. By integrating a micro-actuator with a pressure sensor, the sensor can self-calibrate, eliminating the complexities and costs associated with traditional sensor calibration methods. The present work is focused on furthering understanding and improving the thermal performance of a thermopneumatic actuated self-calibrating pressure sensor. A transient numerical model was developed in ANSYS and was calibrated using experimental testing data. The model provided insights into the sensor's performance not previously observed in experimental testing, such as the temperature gradient within the sensor and its implications. Furthermore, the model was utilized for two design studies. First, the sensor's inefficiencies were studied, and it was found that a substrate with low thermal conductivity and high thermal diffusivity is ideal for both the sensor's efficiency and a faster transient response time. The second design study showed that decreasing the size of the sealed reference cavity, decreases power consumption and transient response time. The study also showed that decreasing the cavity base dimension has a larger effect on decreasing power consumption and response time. Overall, the present work increases understanding of the self-calibrating pressure sensor and provides insight into potential design improvements, moving closer to true self-calibrating pressure sensors. / Master of Science / Pressure sensors are used in most engineering applications, and the demand is ever increasing due to emerging fields such as the Internet of things (IOT), automations, and autonomy. One drawback of current pressures sensor technology is their need to be calibrated, ensuring accuracy and function. Sensor calibration requires equipment, trained personnel, and must be done regularly, resulting in significant costs. Borrowing technology, methods, and materials from the integrated circuit industry, the costs of sensor calibration can be addressed by the development of an intelligent MEMS (micro-electromechanical system) pressure sensor with self-calibration capabilities. The self-calibrating capability is achieved by combining a micro-actuator and a micro- pressures sensor into one system. This work focuses on complementing previously obtained experimental testing data with a thermal finite element model to provide a deeper understanding and insight. The model is implemented in the commercial software ANSYS and model uncertainties were addressed via model calibration. The model revealed a temperature gradient within the sensor, and insight into its potential effects. The model is also used as a design tool to reduce energy inefficiencies, decrease the time it takes the sensor to respond, and to study the effects of reducing the sensor size. The studies showed that the power consumption can potentially be decreased up to 92% and the response time can be decreased up to 99% by changing the sensor's substrate material. Furthermore, by halving the sensor reference cavity size, the cavity temperature can be increased by 45% and the time for the sensor to respond can be decrease by 59%.

Intelligent cruise control (ICC) has the potential to impact both roadway throughput and safety by assisting drivers in maintaining safe headways. This thesis explores this potential through comparisons of ICC to conventional cruise control (CCC) and manual driving. Accordingly, descriptions are given of both CCC and ICC systems. Furthermore, descriptions of ICC evaluation studies and car-following models are presented. The evaluation of ICC is conducted using data collected as part of the Field Operational Test (FOT) performed in Ann Arbor, Michigan. Two levels of analysis are presented in this thesis. The first level of analysis compares the usage of ICC to CCC from a macro level. This study demonstrated that ICC was used more along similar trips. In addition, it was shown that there was no difference in usage of the ON, SET, CANCEL and RESUME buttons. ICC resulted in a higher usage of the ACCEL button and a lower usage of the COAST button compared to CCC. Furthermore, the number of brake interventions while ICC was engaged was higher than CCC. Lastly, the macro-level analysis indicated that there was no difference in the number of near encounters for ICC and CCC. The second analysis makes comparisons at a micro level. The most probable speed, acceleration and headway for each driving mode as well as the probability of using cruise control (based on speed) were determined. The probability of ICC use exceeded CCC use for every freeway speed bin and all but two high-speed arterial speed bins. Finally, a car-following behavior comparison was performed. Manual driving resulted in larger headway values for speeds less than 80 km/h. The ICC speed-headway curve was similar to the CCC speed-headway curve created from high-speed arterial data. The mean headway-speed charts, however, indicated that ICC was more similar to manual driving. Exploration into the specific differences is needed in order to determine the impact of ICC on system safety. / Master of Science

Next-generation wireless technologies are being actively researched to meet the growing demands for higher data rates, massive connectivity, enhanced reliability, and extended coverage. Recently, reconfigurable intelligent surfaces (RISs) and extremely large antenna arrays (ELAAs) have garnered significant attention as new physical-layer transmission technologies capable of achieving unprecedented spectral and energy efficiency gains. Consequently, RIS and ELAA are considered as promising key enabling technologies for the sixth-generation (6G) and future wireless standards.This dissertation investigates RIS and simultaneously transmitting and reflecting (STAR)-RIS assisted wireless communications, emphasizing design, optimization, and analysis across various practical settings. It presents wireless channel modelling techniques, system design aspects, fundamental performance limits/metrics, including outage probability, average achievable rate, average symbol error rate (SER), diversity order, computational complexity, and algorithmic foundations. This doctoral research also develops algorithms for optimizing RIS/STAR-RIS phase shifts and transmit power allocation in multi-user massive multiple-input multiple-output (MIMO) systems. Moreover, this dissertation characterizes unique propagation characteristics of ELAAs, and thereby impacts of visibility regions (VRs) and spatial non-stationarity in extra-large (XL) RIS communication set-ups with XL-massive MIMO base stations (BS) are analyzed.The dissertation begins with a fundamental performance analysis of RIS-assisted systems operating over Nakagami-m fading channels. It quantifies optimal phase-shifts to maximize received signal-to-noise ration (SNR) and derives the probability distribution of the SNR. The findings include closed-form expressions for outage probability, average SER, and achievable rate, demonstrating that these metrics improve as the number of RIS reflective elements increases. The study also reveals that the achievable diversity order scales linearly with the number of passive RIS elements, resulting in significant diversity gains without additional radio-frequency (RF) chains. Further investigation into STAR-RIS systems with discrete phase-shifts highlights the performance under different protocols, such as energy splitting (ES), mode switching (MS), and time splitting (TS), considering both unicast and multicast transmissions. The analysis demonstrates that employing four-bit phase-shift quantization significantly narrows the performance gap between discrete and continuous phase-shifts. Additionally, it is found that the average achievable rate and SER reach saturation levels at high transmit SNRs, influenced by power allocation coefficients at the transmitter. The dissertation also presents an achievable rate analysis and RIS phase-shift optimization for multi-cell RIS-aided massive MIMO, with for imperfect channel state information (CSI), co-channel interference, and spatially correlated fading. A statistical CSI-based transmit power allocation algorithm is proposed, reducing channel estimation overhead and ensuring user fairness. In exploring STAR-RIS aided multi-user massive MIMO systems, statistical CSI-based STAR-RIS phase-shift and transmit power optimization techniques are used to maximize composite channel gains and ensure fair user rates. The study quantifies the impacts of CSI imperfections, residual interference, and spatially correlated fading. Lastly, the effects of visibility regions in XL RIS setups are examined, deriving achievable user rates and employing phase-shift optimization to maximize user channel covariance. A max-min power allocation algorithm is utilized to address near-far user effects, ensuring system-wide user fairness. Overall, this dissertation provides comprehensive insights and advanced optimization techniques for enhancing RIS and STAR-RIS technologies in wireless communication systems.

Adaptive tutorial dialogues have been successfully employed by ITSs to facilitate deep learning of conceptual domain knowledge. But none of the approaches used for generating dialogues have been used across instructional domains and tasks. The objective of this project was twofold: (i) to propose a general model that provides adaptive dialogue support in both well- and ill-defined instructional tasks (ii) to explore whether adaptive tutorial dialogues are better than non-adaptive dialogues in acquiring domain knowledge. Our model provides adaptive dialogue support by identifying the concepts that the student has most difficulty with, and then selecting the tutorial dialogues corresponding to those concepts. The dialogues are customised based on the student’s knowledge and explanation skills, in terms of the length and the exact content of the dialogue. The model consists of three parts: an error hierarchy, tutorial dialogues and rules for adapting them. We incorporated our model into EER-Tutor, a constraint-based tutor that teaches database design. The effectiveness of adaptive dialogues compared to non-adaptive dialogues in learning this ill-defined task was evaluated in an authentic classroom environment. The results revealed that the acquisition of the domain knowledge (represented as constraints) of the experimental group who received adaptive dialogues was significantly higher than their peers in the control group with non-adaptive dialogues. We also incorporated our model into NORMIT, a constraint-based tutor that teaches data normalization. We repeated the experiment using NORMIT in a real-world class room environment with a much smaller group of students (18 in NORMIT study vs 65 in EER-Tutor study) but did not find significant differences. We also investigated whether our model could support dialogues in logical database design and fraction addition using paper-based methods. Our evaluation studies and investigations on paper indicated that our model can provide adaptive support for both ill-and well-defined tasks associated with a well-defined domain theory. The results also indicated that adaptive dialogues are more effective than non-adaptive dialogues in teaching the ill-defined task of database design.

The purpose of this dissertation is to examine the impact of domain and methodological tutorials on the attitude and performance of end-users who are neither well-versed in the domain area nor well-versed with an expert system which is designed to assist them in solving software selection tasks. With respect to these tasks and the mechanism for accomplishing them, the end-users can be categorized as "non-technical users." The design of this experiment was a 2 x 2 full factorial laboratory experiment employing eighty novice users as subjects. Each of the experimental subjects was randomly assigned to one of the four treatment groups corresponding to receipt or lack of receipt of tutorials concerning the problem domain and methodology employed by an expert system. The results of this research indicate that there is a significant interaction between receiving the application and expert system tutorial videos; better performance in terms of correct categorization of problems was observed in subjects who saw either both or neither video whereas worse performance was observed in subjects who saw only one video. In general, the video treatments were unrelated to a variety of attitude measures applied to the subjects. However, it was found that prior attitudes towards the use of computers were significantly related to the majority of the (posttest) attitude measures. Further, the general pattern was for attitudes towards computers to improve as a result of undergoing the experimental process with the viewing of the expert system video to be significant in the level of improvement.

D.Phil. / A modern day N-P complete problem is the assigning of frequencies to transmitters in a cellular network in such a manner that, ideally, no two transmitters in the same cell or neighbouring cells use the same frequency. Considering that an average cellular network provider has over 29 000 transmitters and only 55 frequencies, choosing these frequencies in an optimal way is a very difficult computational problem. Swarm intelligence allows the acceptable minimization and optimization of the frequency assignment problem (FAP). Swarm intelligence is a concept modelling the processes in natural systems such as ant colonies, beehives, human immune systems and the human brain. These systems are selforganizational and display high efficiency in the execution of their tasks. A number of simple automated agents interacting with each other and the environment form a collective. Specifically, there is no "central agent" directing the others. A collective can display surprising intelligence which emerges out of the interaction of the individual agents. This collective intelligence, referred to as swarm intelligence, is displayed in ant colonies when ants build elaborate nests, regulate nest temperature and efficiently search for food in very complex environments. In this thesis a proposal is made to utilize swarm intelligence to build a swarm automatic frequency planner (swarm AFP). The swarm AFP produces frequency plans that are better, or on par with existing frequency planning tools, and in a fraction of the time. A swarm AFP is presented through an in-depth investigation into complex adaptive systems, agent architectures and emergence. Based on an understanding of these concepts, a swarm intelligence model called ACEUS is constructed. ACEUS forms the platform of the swarm AFP. It is a contribution to multi-agent technology as it is a new multi-agent framework that exhibits swarm intelligence and complex distributed computation. What differentiates ACEUS from other multi-agent technologies is that ACEUS works on the basis that the tasks or constructions that have been created by the agents actually guide the agents in their endeavours. There is no centralised agent controlling or guiding the process. The agents in ACEUS receive information and stimulation from their tasks or constructions in the environment. As these constructions or tasks alter the environment, the agents receive stimulus from the changing environment and then react to the changing environment. The changing environment acts as an emergent guiding force to the agents. This is the important contribution that stigmergy contributes to ACEUS. Utilizing this concept, ACEUS is used to create a swarm AFP. The swarm AFP is benchmarked against the COST 259 Siemens benchmarks. In all the COST 259 Siemens scenarios the swarm AFP produced the best results in the shortest time. The swarm AFP was also tested in a real cellular network and the resulting statistics before and after the swarm AFP implementation are presented.

The design and validation of a low-level backstepping controller for speed and heading that is adaptive in speed for a twin-hulled underactuated unmanned surface vessel is presented. Consideration is given to the autonomous launch and recovery of an underwater vehicle in the decision to pursue an adaptive control approach. Basic system identification is conducted and numerical simulation of the vessel is developed and validated. A speed and heading controller derived using the backstepping method and a model reference adaptive controller are developed and ultimately compared through experimental testing against a previously developed control law. Experimental tests show that the adaptive speed control law outperforms the non-adaptive alternatives by as much as 98% in some cases; however heading control is slightly sacrificed when using the adaptive speed approach. It is found that the adaptive control law is the best alternative when drag and mass properties of the vessel are time-varying and uncertain. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

In this research, a wind feedforward (FF) controller has been developed to augment closed loop feedback controllers for the position and heading station keeping control of Unmanned Surface Vehicles (USVs). The performance of the controllers was experimentally tested using a 16 foot USV in an outdoor marine environment. The FF controller was combined with three nonlinear feedback controllers, a Proportional–Derivative (PD) controller, a Backstepping (BS) controller, and a Sliding mode (SM) controller, to improve the station-keeping performance of the USV. To address the problem of wind model uncertainties, adaptive wind feedforward (AFF) control schemes are also applied to the FF controller, and implemented together with the BS and SM feedback controllers. The adaptive law is derived using Lyapunov Theory to ensure stability. On-water station keeping tests of each combination of FF and feedback controllers were conducted in the U.S. Intracoastal Waterway in Dania Beach, FL USA. Five runs of each test condition were performed; each run lasted at least 10 minutes. The experiments were conducted in Sea State 1 with an average wind speed of between 1 to 4 meters per second and significant wave heights of less than 0.2 meters. When the performance of the controllers is compared using the Integral of the Absolute Error (IAE) of position criterion, the experimental results indicate that the BS and SM feedback controllers significantly outperform the PD feedback controller (e.g. a 33% and a 44% decreases in the IAE, respectively). It is also found that FF is beneficial for all three feedback controllers and that AFF can further improve the station keeping performance. For example, a BS feedback control combined with AFF control reduces the IAE by 25% when compared with a BS feedback controller combined with a non-adaptive FF controller. Among the eight combinations of controllers tested, SM feedback control combined with AFF control gives the best station keeping performance with an average position and heading error of 0.32 meters and 4.76 degrees, respectively. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection