Ubiquitous computing

A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV.

Classification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we carry out sensitive study of classification algorithms by using feature deletion and addition. Three types of classifiers: (1) weak classifiers; (2) generic and strong classifiers; and (3) ensemble classifiers are validated on three types of data (1) feature dimension data, (2) gene expression data and (3) biomedical document data. In the experiments, we continuously add redundant features to the training and test set in order to observe the classification algorithm performance, and also continuously remove features to find the performance of the underlying
classifiers. Our studies draw a number of important findings, which will help data mining and machine learning community under the genuine performance of common classification algorithms on real-world data.

The objective of the research is to develop various green-house gas (GHG) mitigations scenarios in the energy demand and supply sectors for state of Florida through energy and environment modeling tool called LEAP (Long Range Energy Alternative Planning System Model) for 2010-2050. The GHG mitigation scenarios consist of various demand and supply side scenarios. One of the GHG mitigation scenarios is crafted by taking into account the available renewable resources potential for power generation in the state of Florida and then the comparison has been made for transformation sector and corresponding GHG emissions through this newly developed mitigation scenario versus Business As Usual and Florida State Policy scenario. Moreover two master mitigation scenarios (Electrification and Efficiency and Lifestyle) were crafted through combination of certain GHG mitigation scenarios. The energy demand and GHG emissions assessment is performed for both master mitigation scenarios versus business As Usual scenario for 2010 – 2050.

The coastal (terrestrial) and benthic environments along the southeast Florida
continental shelf show a unique biophysical succession of marine features from a highly
urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a
protective marine sanctuary in the southeast (i.e. Florida Keys National Marine
Sanctuary). However, the establishment of a standard bio-geomorphological
classification scheme for this area of coastal and benthic environments is lacking. The
purpose of this study was to test the hypothesis and answer the research question of
whether new parameters of integrating geomorphological components with dominant
biological covers could be developed and applied across multiple remote sensing
platforms for an innovative way to identify, interpret, and classify diverse coastal and
benthic environments along the southeast Florida continental shelf. An ordered, manageable hierarchical classification scheme was developed to incorporate the categories of Physiographic Realm, Morphodynamic Zone, Geoform, Landform, Dominant Surface Sediment, and Dominant Biological Cover. Six different remote sensing platforms (i.e. five multi-spectral satellite image sensors and one high-resolution aerial orthoimagery) were acquired, delineated according to the new classification scheme, and compared to determine optimal formats for classifying the study area. Cognitive digital classification at a nominal scale of 1:6000 proved to be more accurate than autoclassification programs and therefore used to differentiate coastal marine environments based on spectral reflectance characteristics, such as color, tone, saturation, pattern, and texture of the seafloor topology. In addition, attribute tables were created in conjugation with interpretations to quantify and compare the spatial relationships between classificatory units. IKONOS-2 satellite imagery was determined to be the optimal platform for applying the hierarchical classification scheme. However, each remote sensing platform had beneficial properties depending on research goals, logistical restrictions, and financial support. This study concluded that a new hierarchical comprehensive classification scheme for identifying coastal marine environments along the southeast Florida continental shelf could be achieved by integrating geomorphological features with biological coverages. This newly developed scheme, which can be applied across multiple remote sensing platforms with GIS software, establishes an innovative classification protocol to be used in future research studies.

The Smart Campus project envisions a university campus where technology assists
faculty, staff, students and visitors to improve and more efficiently accomplish their daily
activities. The objective of this project is to develop a smart phone application that assists
users in finding a certain location on campus, locating their friends and professors,
interacting with any student or professors of the campus, get the count of users at certain
locations and remain updated about all the events and campus news. Through this project,
an idea of ‘Futuristic Social Network’ in a Campus is modeled and developed on Android
platform.