This dissertation aims to develop a formal foundation to design an adaptive output feedback predictor for a class of unknown systems where parameters and order are unknown or high-dimensional. We present a reduced-order adaptive output-predictor scheme based on modal reduction and Lyapunov's method. Moreover, the credibility of the proposed reduced-order adaptive output-predictor scheme is validated by mathematical proof, and numerical and experimental studies, such as single pendulum, double pendulum, six-link pendulum, rope as a high-dimensional rope, and EEG data. Then the dissertation goal is to experimentally validate the proposed reduced-order model parameterization technique for tracking uncertain linear time-invariant (LTI) single-input, single-output (SISO) systems. The proposed theory focuses on parameterizing a high-dimensional, uncertain model and introduces a reduced-order adaptive output predictor capable of forecasting the system's output. This predictor utilizes auto-regressive filtered vectors, incorporating the input and output history. The adaptive output predictor is a simplified and known model, making it suitable for controlling high-dimensional, uncertain SISO systems without access to full-state measurements. Specifically, this work establishes the foundation for parameterizing uncertain models, creating a virtual structure that emulates the actual system, and offering a more manageable model for control when the objective is solely to regulate the system's output. The primary focus of this research is to assess the effectiveness and output-tracking capabilities of the proposed approach. These capabilities are extensively examined across diverse platforms and hardware configurations, relying solely on input and output data from the models without incorporating any additional information on the system dynamics. In the first experiment, the predictor's ability to track the angle of a single pendulum, including additional dynamics, is evaluated using only input-output data. The second experiment targets tracking the endpoint of a rope connected to a single pendulum, where the rope emulates a high-dimensional model. A vision system is designed and employed to acquire the rope endpoint position data. Before the rope experiment, a set of experiments is conducted on single pendulum hardware to ensure the accuracy of the vision system's data collection. Comparative analysis between data from object tracking via vision and data acquired through an encoder demonstrates negligible error. Finally, the input and the endpoint output data from the rope experiment are fed into the predictor to assess its capability to track the rope endpoint position without utilizing specific knowledge of the experimental hardware. Achieving negligible error in tracking implies that the predictor provides a simple and accurate representation of the rope dynamics. Consequently, designing a controller for this known model is equivalent to designing a controller for the actual rope system dynamics. The predictor, by closely emulating the behavior of the rope, becomes a reliable surrogate model for control design, simplifying the task of controller design for the complex and uncertain high-dimensional system. Finally, this study introduces a novel approach to enhance controller design for complex brain dynamics by employing a reduced-order adaptive output predictor proposed in [1], fine-tuned with chirp binaural beats. The proposed technique is promising for developing closed-loop controllers in non-invasive brain stimulation therapies, such as binaural beats stimulation, to improve working memory. The study focuses on parameterizing uncertain models and creates a predictor that utilizes auto-regressive filtered vectors to forecast mean phase lock values generated by binaural beats stimulation. The simplified and known model of the predictor proves effective in tracking brain responses, as demonstrated in experiments evaluating its ability to track mean phase locking values. The results indicate negligible tracking error, suggesting the predictor's reliability in representing brain dynamics and simplifying the task of controller design for the complex and uncertain high-dimensional system. / Doctor of Philosophy / This dissertation explores the development of a reduced-order adaptive output predictor for unknown systems with unknown or high-dimensional parameters and order. A reduced-order adaptive output predictor scheme is introduced, validated through mathematical proof, and tested in diverse scenarios, including pendulum systems and EEG data. The focus is on parameterizing uncertain models and creating a simplified adaptive output predictor capable of forecasting system output, specifically for SISO systems. Experimental validation involves tracking the angle of a single pendulum and the endpoint of a high-dimensional rope, demonstrating the predictor's accuracy without detailed knowledge of system dynamics. The study extends its application to complex brain dynamics, using the predictor fine-tuned with chirp binaural beats. Results show promise for developing closed-loop controllers in non-invasive brain stimulation therapies, offering a novel approach to improve working memory via helping to design closed-loop controllers.

The default mode network (DMN) is active in the absence of task demands and during self-referential thought. Considerable evidence suggests that the DMN is involved in normative aspects of social cognition, and as such, disruptions in the function of DMN would be expected in disorders characterized by alterations in social function. Consistent with this notion, work in autism spectrum disorder (ASD) and social anxiety disorder (SAD) has demonstrated altered activation of several core regions of the DMN relative to neurotypical controls. Despite emergent evidence for alterations within the same brain systems in SAD and ASD, as well as a behavioral continuum of social impairments, no study to date has examined what is unique and what is common to the brain systems within these disorders. Therefore, the primary aim of the current study is to precisely characterize the topology of neural connectivity within the DMN in SAD and ASD and neurotypical controls in order to test the following hypotheses through functional and structural connectivity analyses of the DMN. Our analyses demonstrate increased coavtivation of the dorsomedial prefrontal cortex in ASD and SAD compared to controls, as well as over and under connectivity in structural brain connectivity in ASD. These results may reflect general deficits in social function at rest, and disorder specific alterations in structural connectivity in ASD. / Master of Science

The thesis analyzes Baltimore City's food network, and seeks a site which has the potential for several factors: site accessibility, renewable resources, solar exposure, and connecting the community. These factors serve as the basis in which to build a hybrid prototype that is able to expose people to the process of food production through a combination of traditional outdoor farming methods and indoor hydroponics in the form of a vertical farm. / Master of Architecture

LTE heterogeneous wireless networks promise significant increase in data rates and improved coverage through (i) the deployment of relays and cell densification, (ii) carrier aggregation to enhance bandwidth usage and (iii) by enabling nodes to have dual connectivity. These emerging cellular networks are complex and large systems which are difficult to optimize with centralized control and where mobiles need to balance spectral efficiency, power consumption and fairness constraints. In this dissertation we focus on how decentralized and autonomous mobiles in multihop cellular systems can optimize their own local objectives by taking into account end-to-end or network-wide conditions. We propose several link-adaptive schemes where nodes can adjust their transmit power, aggregate carriers and select points of access to the network (relays and/or macrocell base stations) autonomously, based on both local and global conditions. Under our approach, this is achieved by disseminating the dynamic congestion level in the backhaul links of the points of access. As nodes adapt locally, the congestion levels in the backhaul links can change, which can in turn induce them to also change their adaptation objectives. We show that under our schemes, even with this dynamic congestion feedback, nodes can distributedly converge to a stable selection of transmit power levels and points of access. We also analytically derive the transmit power levels at the equilibrium points for certain cases. Moreover, through numerical results we show that the corresponding system throughput is significantly higher than when nodes adapt greedily following traditional link layer optimization objectives. Given the growing data rate demand, increasing system complexity and the difficulty of implementing centralized cross-layer optimization frameworks, our work simplifies resource allocation in heterogeneous cellular systems. Our work can be extended to any multihop wireless system where the backhaul link capacity is limited and feedback on the dynamic congestion levels at the access points is available. / Ph. D.

In the early 20th century, downtown areas were the primary centers of commerce, industry, and cultural activity in many American cities. However, with the rise of suburbanization, many of these downtown areas began to experience economic decline and population loss. Factors that contributed to this decline included the decentralization of jobs and economic activity to suburban areas, as well as the increasing availability of affordable automobiles that allowed people to commute longer distances. As more people moved to the suburbs, downtown areas became associated with problems such as crime, poverty, and decay. This led to a further decline in urban areas as businesses and residents left for the suburbs. This is how the edge cities started to rise. Transit development is a significant impact on the shift of population to edge cities. The term "edge cities"was coined by Joel Garreau in his 1991 book "Edge Cities: Life on the New Frontier". which are suburban areas that have become significant employment centers and have developed downtown-like characteristics. These urban areas are driven by factors such as the desire for walkable neighborhoods, access to cultural amenities, and job opportunities that are like urban downtowns. The Thesis proposal explores combining both suburban and urban lifestyles resulting in a hybrid environment that offers the best of both worlds. It will provide the sense of community and neighborhood that is often found in suburban areas, along with the convenience and accessibility to urban amenities and services. The concept will be addressing how horizontal living (Suburban) and how vertical living (Downtown) would address a different lifestyle that will give access to a range of shops, restaurants, and entertainment options within a short walk or bike from home, as well as having park spaces and playgrounds connecting between the buildings will help to thrive for better and healthy communities. This project will also offer the benefits of urban living, such as the opportunity to work in a dynamic and diverse environment, access to cultural events and activities, and the convenience of public transportation. This idea of convergence is focusing on the newly proposed purple line transit corridor and Adelphi-West Metro Station, Maryland. It envisions a unique urban fabric that will set into action to reduce reliance on cars by promoting more sustainable way of life. / Master of Science / Rapid urbanization has impacted the natural landscape in the United States. The shift of population to suburbs in the United States was primarily driven by a combination of factors that arose in the mid-20th century, including the growth of automobile use, the development of the interstate highway system, and the expansion of affordable single-family homes in suburban areas. One of the main reasons people are moving to these areas is the job market, quality of life, and affordability. In recent years, there has been a growing trend of people moving from Washington, DC to the nearby communities of Bethesda and Silver Spring. Both places are easily accessible from downtown by means of transportation. These places are known for their vibrant downtown areas with a range of shops, restaurants, and cultural attractions. They also offer a few outdoor amenities, including park spaces, trails, and other recreational opportunities. This shift in population has been observed in many other areas on the Purple Line corridor. In these one of the major potential places would be Adelphi-West. This thesis is going to propose an adaptive master plan by critiquing the existing master plan for the Adelphi-West Metro Station in three strategic ways. Ecology Connectivity Efficient uses (spaces) Secondly, it is creating an urban fabric by providing a suburban and a downtown lifestyle that addresses achieving vibrant and diverse communities which offer a range of amenities, including shopping centers, restaurants, and entertainment venues, making them attractive destinations for both workers and residents.

Two main theories of neural atypicality have been postulated in autism. One theory proposes that autism can be explained as the result of atypical patterns of hypo and hyper functional connectivity (FC) within and between brain areas. A complementary theory suggests that atypical functional communication in autism could result from an altered ratio between excitatory and inhibitory input (E:I imbalance). These theories have been previously explored as they might apply to all individuals with a behavioral diagnosis. However, given the multiscale heterogeneity characterizing autism, different subsets of individuals with autism may display different patterns of functional connectivity atypicalities and E:I imbalance. This thesis sets out to explore how neural atypicalities in connectivity and E:I imbalance might be differentially expressed in subsets of the autistic population. To this end, two empirical investigations were conducted. First, the connectivity hypothesis was explored by investigating whether behaviorally-defined subtypes were associated with different patterns of FC atypicalities. Behaviorally-defined subtypes were obtained by stratifying autistic individuals based on their relative balance between social communication (SC) and restricted and repetitive behaviors (RRBs) core symptom domains. This approach yielded three behaviorally-based subtypes: SC>RRB, SCRRB displayed hypoconnectivity between somatomotor and perisylvian circuitry, while subtypes SC=RRB showed hypoconnectivity between somatomotor and visual association areas and hyperconnectivity between medial motor and anterior salience networks. Finally, these subtype-specific FC alterations were shown to be enriched for partially distinct genetic mechanisms, some of which related to excitatory-inhibitory neurons and astrocytes. In a second study, the EI imbalance hypothesis was explored by investigating whether autism subtypes could be identified based on an E:I-sensitive metric computed from electroencephalographic (EEG) data. Specifically, the Hurst exponent (H) – a metric that has been shown to be affected by changes in excitatory input – was computed on EEG time-series data, obtained in two resting state conditions of eyes open and closed. H-based clustering revealed two E:I-based neurosubtypes across conditions with opposing patterns of E:I imbalance compared to neurotypical controls. Autism neurosubtype 1 showed on-average higher H values, while neurosubtype 2 displayed on-average lower H. These opposing E:I balance patterns were present globally across the brain, with the limited exception of an orthogonal larger decrease in H in non-frontal electrodes in neurosubtype 2. Finally, investigation at the behavioral level identified distinct multivariate brain-behavior relationships between age, intelligence, autistic traits and H. Taken together, these empirical findings demonstrate that the two major theories of neural atypicality in autism – FC alteration and E:I imbalance – do not apply equally to all individuals with a behavioral diagnosis. Rather, different subtypes of autism exist that display contrasting patterns of neural atypicality compared to typically-developing individuals. These contrasting patterns might be driven by differentially altered primary or compensatory E:I mechanisms shaping distinct atypical cortical organizations within the subtypes. Interestingly, the relationship between specific neural atypicalities and variability at the behavioral and genetic level is, however, subtle across the subtypes. This limited multiscale association could suggest that heterogeneity in autism might be due to the presence, within the larger population, of subtype-specific mosaic-like patterns of atypicalities at the behavioral and biological level. Further research is required to thoroughly characterize how these levels map onto one another within the subtypes and determine the pathophysiological mechanisms driving their development.

Floodplain inundation often provides water quality benefits by trapping sediment and biogeochemically transforming other pollutants. Hydrologic floodplain connectivity is a measure of water exchanges and interactions between the main channel and the floodplain via surface (inundation) and subsurface (groundwater) connections. Using an automated model combining GIS and numerical analysis software, this study examined floodplain inundation patterns and measured floodplain connectivity for the Mahantango Creek watershed (Pennsylvania, USA). Connectivity was quantified by developing a metric that included inundation area and duration. Long-term hydrographs at each reach in the watershed were developed via QPPQ (Flow-Percentile-Percentile-Flow) methodology using regional regression analysis to calculate the ungauged flow duration curves (FDC). Inundation area (normalized to stream length) was found to increase with drainage area, suggesting larger streams have more area available for biogeochemical activity. Annual connectivity increased with drainage area, suggesting larger streams, having higher connectivity, should be the focus of individual reach restoration projects due to higher potential for water quality benefits. Across the watershed as a whole, however, the total annual connectivity across first order streams was greater than higher order streams, suggesting the collection of small streams in a watershed may have a stronger effect on outlet water quality. Connectivity was consistently higher during the non-growing season, which was attributed to higher flows. Despite higher connectivity during the non-growing season, increased floodplain biological activity may be negated by low temperatures, reducing microbial activity. Correlations between land use and connectivity were also found, emphasizing dynamics between flow, channel morphology, and floodplain inundation. / Master of Science

Restoring hydrologic connectivity between the channel and floodplain is a common practice in stream and river restoration. Floodplain hydrology and hydrogeology impact biogeochemical processing and potential nutrient removal, yet rigorous field evaluations of surface and groundwater flows during overbank floods are rare. We conducted five sets of experimental floods to mimic floodplain reconnection. Experimental floods entailed pumping stream water onto an existing floodplain swale, and were conducted throughout the year to capture seasonal variation. Each set of experimental floods entailed two replicate floods occurring on successive days to test the effect of varying antecedent moisture. Water levels and specific conductivity were measured in surface water, shallow soils, and deep soils, along with surface flow into and out of the floodplain. Total flood water storage increased as vegetation density increased and or antecedent moisture decreased. Hydrologic flow mechanisms were spatially and temporally heterogeneous in surface water, in groundwater, as well as in exchange between the two and appeared to coexist in small areas. Immediate propagation of hydrostatic pressure into deep soils was suggested at some locations. Preferential groundwater flow was suggested in locations where the pressure and electrical conductivity signals propagated too fast for bulk Darcy flow through porous media. Preferential flow was particularly obvious where the pressure signal bypassed an intermediate depth but was observed at a deeper depth. Bulk Darcy flow in combination with preferential flow was suggested at locations where the flood pressure and electrical conductivity signal propagated more slowly yet arrived too quickly to be described using Darcy's Law. Finally, other areas exhibited no transmission of pressure or conductivity signals, indicating a complete lack of groundwater flow. Antecedent moisture affected the flood pulse arrival time and in some cases vertical connectivity with deeper sediments while vegetation density altered surface water storage volume. Understanding the variety of exchange mechanisms and their spatial variability will help understand the observed variability of floodplain impacts on water quality, and ultimately improve the effectiveness of floodplain restoration in reducing excess nutrient in river basins. / Master of Science

Purpose: In a previous neuroimaging study, patients with taste loss showed stronger activations in gustatory cortices compared to people with normal taste function during taste stimulations. The aim of the current study was to examine whether there are changes in central-nervous functional connectivity in patients with taste loss. Methods: We selected 26 pairs of brain regions related to taste processing as our regions of interests (ROIs). Functional magnetic resonance imaging (fMRI) was used to measure brain responses in seven patients with taste loss and 12 healthy controls as they received taste stimulations (taste condition) and water (water condition). The data were analysed using ROI-to-ROI functional connectivity analysis (FCA). Results: We observed weaker functional connectivity in the patient group between the left and right orbitofrontal cortex in the taste condition and between the left frontal pole and the left superior frontal gyrus in the water condition. Conclusion: These results suggested that patients with taste loss experience changes of functional connectivity between brain regions not only relevant to taste processing but also to cognitive functions. While further studies are needed, fMRI might be helpful in diagnosing taste loss as an additional tool in exceptional cases.

Yes / Sensing coverage and network connectivity are two of the most fundamental issues to ensure that there are effective environmental sensing and robust data communication in a WSN application. Random positioning of nodes in a WSN may result in random connectivity, which can cause a large variety of key parameters within the WSN. For example, data latency and battery lifetime can lead to the isolation of nodes, which causes a disconnection between nodes within the network. These problems can be avoided by using mobile data sinks, which travel between nodes that have connection problems. This research aims to design, test and optimise a data collection system that addresses the isolated node problem, as well as to improve the connectivity between sensor nodes and base station, and to reduce the energy consumption simultaneously. In addition, this system will help to solve several problems such as the imbalance of delay and hotspot problems. The effort in this paper is focussed on the feasibility of using the proposed methodology in different applications. More ongoing experimental work will aim to provide a detailed study for advanced applications e.g. transport systems for civil purposes. / European Union’s Horizon 2020 research and innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.