Given the proliferation of new-generation internet capable devices in our society, they are now commonly used for a variety of purposes and by a variety of ages, including young children. The vast amount of new media content, available through these devices, cause parents to worry about what their children have access to. In this thesis we investigated how parents and children can work together towards the goal of content control and filtering. One problem to the current content control filtering tools and approaches is that they do not involve children in the filtering process, thus missing an opportunity of educating children about content appropriateness. Therefore, we propose a kid-in-the-loop approach to content control and filtering where parents and children collaboratively configure restrictions and filters, an approach that focuses on education rather than simple rule setting. We conducted an exploratory qualitative study with results highlighting the importance that parents place on avoiding inappropriate content. Building on these findings, we designed an initial kid-in-the-loop prototype which allows parents to work with their children to select appropriate applications, providing parents with the opportunity to educate their children on what they consider to be appropriate or inappropriate. We further validate our proposed approach by conducting a qualitative study with sets of parents and children in the six to eight year-old age group, which revealed an overwhelmingly favorable response to this approach. We conclude this thesis with a comprehensive analysis of our approach, which can be leveraged in designing content control systems targeting both parents and children.

The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From this, the performance of online parameter identification can be tested with respect to different noise levels. This research is based on simulation work showcasing how effective the Kalman filtering techniques are in dealing with analytical uncertainties of data. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

A smart manufacturing network connects machines via sensing, communication, and actuation networks. The data generated from the networks are used in data-driven modeling and decision-making to improve quality, productivity, and flexibility while reducing the cost. This dissertation focuses on improving the data-driven modeling of the quality-process relationship in smart manufacturing networks. The quality-process variable relationships are important to understand for guiding the quality improvement by optimizing the process variables. However, several challenges emerge. First, the big data sets generated from the manufacturing network may be information-poor for modeling, which may lead to high data transmission and computational loads and redundant data storage. Second, the data generated from connected machines often contain inexplicit similarities due to similar product designs and manufacturing processes. Modeling such inexplicit similarities remains challenging. Third, it is unclear how to select representative data sets for modeling in a manufacturing network setting, considering inexplicit similarities. In this dissertation, a data filtering method is proposed to select a relatively small and informative data subset. Multi-task learning is combined with latent variable decomposition to model multiple connected manufacturing processes that are similar-but-non-identical. A data filtering and modeling framework is also proposed to filter the manufacturing data for manufacturing network modeling adaptively. The proposed methodologies have been validated through simulation and the applications to real manufacturing case studies. / Doctor of Philosophy / The advancement of the Internet-of-Things (IoT) integrates manufacturing processes and equipment into a network. Practitioners analyze and apply the data generated from the network to model the manufacturing network to improve product quality. The data quality directly affects the modeling performance and decision effectiveness. However, the data quality is not well controlled in a manufacturing network setting. In this dissertation, we propose a data quality assurance method, referred to as data filtering. The proposed method selects a data subset from raw data collected from the manufacturing network. The proposed method reduces the complexity in modeling while supporting decision effectiveness. To model the data from multiple similar-but-non-identical manufacturing processes, we propose a latent variable decomposition-based multi-task learning model to study the relationships between the process variables and product quality variable. Lastly, to adaptively determine the appropriate data subset for modeling each process in the manufacturing network, we further proposed an integrated data filtering and modeling framework. The proposed integrated framework improved the modeling performance of data generated by babycare manufacturing and semiconductor manufacturing.

The usage of Unmanned Aerial Systems (UASs), such as quadcopters and hexacopters, has steadily increased over the past few years in both recreational and commercial use. This increased availability to purchase such systems has also given rise to many safety and security concerns. A common concern is that the misuse of a UAS can cause damage to airplanes and helicopters in and around airports. Another growing concern is the use of UASs for terrorist intentions such as using the UAS as a remote controlled bomb. There is clearly a need to be able to detect the presence of unwanted UASs in restricted areas. This thesis work presents the beginning work towards a method to detect the presence of these UASs using the blade pass frequency (BPF) of the motors and rotors of a home made quadcopter. A low cost uniform linear microphone array is first used to perform a simple delay-and-sum beamformer to spatially filter out noise sources. The beamformer output is then divided into sub-bands using three bandpass filters centered on the expected location of the fundamental BPF and its 2nd and 3rd harmonics. For each sub-band, an adaptive filter called an adaptive line enhancer is used to extract and enhance the narrowband signals. The response of the adaptive filters are then used to detect the quadcopter by looking for the presence of the 2nd and 3rd harmonics of the fundamental BPF. Static tests of the quadcopter out in a field showed promising results for this method with the ability to detect up to the 3rd harmonic 90ft away and the 2nd harmonic 130 ft away. / Master of Science / The usage of Unmanned Aerial Systems (UASs), such as quadcopters and hexacopters, has steadily increased over the past few years in both recreational and commercial use. This increased availability to purchase such systems has also given rise to many safety and security concerns. A common concern is that the misuse of a UAS can cause damage to airplanes and helicopters in and around airports. Another growing concern is the use of UASs for terrorist intentions such as using the UAS as a remote controlled bomb. There is clearly a need to be able to detect the presence of unwanted UASs in restricted areas. This thesis work presents the beginning work towards a method to detect the presence of a home made quadcopter based on the sound it produces. A series of microphone are first used to remove surrounding sounds that could interfere with the quadcopter’s sound. The output of this processes is then divided into smaller sections using three filters centered on the expected location of the most important and information rich parts of the quadcopter’s sound. For each section, a final filter is used to extract and enhance the signals of interest produced by the quadcopter. The response of these filters are then used to detect whether the quadcopter is present or not. Static tests of the quadcopter out in a field showed promising results for this method with the ability to detect the quadcopter 90 to 130 ft away.

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A synthetic aperture microwave near-field system is used to image biophysical objects in order to investigate the nature of radar-target interaction. Two different imaging algorithms for focusing data collected over a two-dimensional planar aperture are investigated. The first of these is the single frequency backward propagation technique which is mathematically simple to implement and provides a high degree of resolution. Secondly, a multifrequency development of the backward propagation algorithm is presented and derived from two separate perspectives. This latter algorithm, known as the auto-focusing algorithm, requires no information about the range of the target from the aperture. Full characterisation by simulation of both algorithms is carried out and different filtering techniques are investigated. The backward propagation algorithm is applied to the polarimetric imaging of three different leafless trees and a sugar beet plant at the X-band frequency of 10GHz. The images so produced demonstrate that the backscattered signal is dependent on the orientation of individual tree elements with respect to the polarisation. Furthermore, multiple scattering terms can be identified within the structure of the tree. The auto-focusing algorithm is applied to the polarimetric imaging of two trees at 10GHz and repeat measurements are made over several months. As with the single frequency measurements, the backscattered signal is dependent on the orientation of individual tree elements relative to the polarisation. The relative contributions from the leaves and branches of the trees to the backscattered signal are assessed and found to be seasonally dependent. Measurements are also carried out to investigate the variation of backscatter from a beech tree with varying incidence angle. It is demonstrated that at small angles of incidence, the leaves are the dominant source of backscatter but at large incidence angles, the branches and trunk of the tree have the greatest contrbution.

Recent years have seen ambitious robotic exploration missions to other planets and a renewed interest in sending humans beyond low Earth orbit. These activities give rise to a need for autonomous spacecraft operation. Of particular interest here is the ability of a spacecraft to navigate independent of contact with Earth-based resources. Optical navigation techniques are proposed as a solution to the problem of navigating in a planetary system without requiring navigation information from Earth. A detailed discussion of optical sensor hardware and error sources leads to new high fidelity math models for optical sensor performance that may be used in navigation simulations. Algorithms are developed that allow optical data to be used for the estimation of spacecraft position, velocity, and attitude. Sequential measurements are processed using traditional filtering techniques. Additionally, for the case of attitude estimation, a new attitude filter called Sequential Optimal Attitude Routine (SOAR) is presented. The models and techniques developed in this dissertation are demonstrated in two case studies: (1) navigation of a spacecraft performing a planetary fly-by using real images from the June 2007 MESSENGER fly-by of Venus and (2) navigation of a spacecraft in cislunar space on a return trajectory from the Moon. / text

Voice over IP (VoIP) is the group of techniques for delivering voice communications over Internet Protocol (IP) networks. It has mainly served as the possible substitution for regular PSTN over the last decades, but has recently gained an increased interest in various areas such as alarm applications and customer service. Acoustic echo is the situation were a distorted version of the sent signal is transmitted back to the sender, due to acoustic feedback between loudspeaker and microphone. There already exists several algorithms to solve this problem, and this thesis provides a study of the performance in relation to the computational complexity of the algorithms. This is in order to indicate which approaches are better suited for implementation in an embedded system, where resources are limited. During the thesis a number of algorithms were tested, including variations of the LMS algorithm, some other approaches utilizing the correlation between echo and signal, and the RLS algorithm. They were first tested in MATLAB, on speech signals recorded at Syntronic and distorted by adding echo, then tested by implementation in C, and run on speech signals recorded in a simulated VoIP system at Syntronic. The results were then evaluated in terms of efficiency and computational complexity.

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