Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 409-426). Also available in print.

Thesis (M.H.A.)--University of Michigan, 1969.

Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1992. / Vita. Abstract. Bibliographical references included. Also available via the Internet.

Thesis (B. Arch.)--Roger Williams University, 2009. / Title from title page screen (viewed on June 21, 2010) Includes bibliographical references. Also available in print.

Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This thesis investigates the use of stereo vision sensors for dense autonomous mapping. It characterises and analyses the errors made during the stereo matching process so measurements can be correctly integrated into a 3D grid-based map. Maps are required for navigation and obstacle avoidance on autonomous vehicles in complex, unknown environments. The safety of the vehicle as well as the public depends on an accurate mapping of the environment of the vehicle, which can be problematic when inaccurate sensors such as stereo vision are used. Stereo vision sensors are relatively cheap and convenient, however, and a system that can create reliable maps using them would be beneficial. A literature review suggests that occupancy grid mapping poses an appropriate solution, offering dense maps that can be extended with additional measurements incrementally. It forms a grid representation of the environment by dividing it into cells, and assigns a probability to each cell of being occupied. These probabilities are updated with measurements using a sensor model that relates measurements to occupancy probabilities. Numerous forms of these sensor models exist, but none of them appear to be based on meaningful assumptions and sound statistical principles. Furthermore, they all seem to be limited by an assumption of unimodal, zero-mean Gaussian measurement noise. Therefore, we derive a principled inverse sensor model (PRISM) based on physically meaningful assumptions. This model is capable of approximating any realistic measurement error distribution using a Gaussian mixture model (GMM). Training a GMM requires a characterisation of the measurement errors, which are related to the environment as well as which stereo matching technique is used. Therefore, a method for fitting a GMM to the error distribution of a sensor using measurements and ground truth is presented. Since we may consider the derived principled inverse sensor model to be theoretically correct under its assumptions, we use it to evaluate the approximations made by other models from the literature that are designed for execution speed. We show that at close range these models generally offer good approximations that worsen with an increase in measurement distance. We test our model by creating maps using synthetic and real world data. Comparing its results to those of sensor models from the literature suggests that our model calculates occupancy probabilities reliably. Since our model captures the limited measurement range of stereo vision, we conclude that more accurate sensors are required for mapping at greater distances. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die gebruik van stereovisie sensors vir digte outonome kartering. Dit karakteriseer en ontleed die foute wat gemaak word tydens die stereopassingsproses sodat metings korrek geïntegreer kan word in 'n 3D rooster-gebaseerde kaart. Sulke kaarte is nodig vir die navigasie en hindernisvermyding van outonome voertuie in komplekse en onbekende omgewings. Die veiligheid van die voertuig sowel as die publiek hang af van 'n akkurate kartering van die voertuig se omgewing, wat problematies kan wees wanneer onakkurate sensors soos stereovisie gebruik word. Hierdie sensors is egter relatief goedkoop en gerieflik, en daarom behoort 'n stelsel wat hulle dit gebruik om op 'n betroubare manier kaarte te skep baie voordelig te wees. 'n Literatuuroorsig dui daarop dat die besettingsroosteralgoritme 'n geskikte oplossing bied, aangesien dit digte kaarte skep wat met bykomende metings uitgebrei kan word. Hierdie algoritme skep 'n roostervoorstelling van die omgewing en ken 'n waarskynlikheid dat dit beset is aan elke sel in die voorstelling toe. Hierdie waarskynlikhede word deur nuwe metings opgedateer deur gebruik te maak van 'n sensormodel wat beskryf hoe metings verband hou met besettingswaarskynlikhede. Menigde a eidings bestaan vir hierdie sensormodelle, maar dit blyk dat geen van die modelle gebaseer is op betekenisvolle aannames en statistiese beginsels nie. Verder lyk dit asof elkeen beperk word deur 'n aanname van enkelmodale, nul-gemiddelde Gaussiese metingsgeraas. Ons lei 'n beginselfundeerde omgekeerde sensormodel af wat gebaseer is op fisies betekenisvolle aannames. Hierdie model is in staat om enige realistiese foutverspreiding te weerspieël deur die gebruik van 'n Gaussiese mengselmodel (GMM). Dit vereis 'n karakterisering van 'n stereovisie sensor se metingsfoute, wat afhang van die omgewing sowel as watter stereopassingstegniek gebruik is. Daarom stel ons 'n metode voor wat die foutverspreiding van die sensor met behulp van 'n GMM modelleer deur gebruik te maak van metings en absolute verwysings. Die afgeleide ge inverteerde sensormodel is teoreties korrek en kan gevolglik gebruik word om modelle uit die literatuur wat vir uitvoerspoed ontwerp is te evalueer. Ons wys dat op kort afstande die modelle oor die algemeen goeie benaderings bied wat versleg soos die metingsafstand toeneem. Ons toets ons nuwe model deur kaarte te skep met gesimuleerde data, sintetiese data, en werklike data. Vergelykings tussen hierdie resultate en dié van sensormodelle uit die literatuur dui daarop dat ons model besettingswaarskynlikhede betroubaar bereken. Aangesien ons model die beperkte metingsafstand van stereovisie vasvang, lei ons af dat meer akkurate sensors benodig word vir kartering oor groter afstande.

In the greater Central Hardwood Region, advance regeneration of oak (Quercus spp.) and hickory (Carya spp.) has been in decline for several decades. Facilitated in part by an abrupt change in disturbance regime, coupled with an increase in herbivore density, the existing mid-successional, mast-producing species are being outcompeted by late-successional, mesophytic species. Oak-hickory forests provide keystone resources for a diverse forest wildlife community, and a decline in its dominance will likely impact habitat use and occupancy patterns in the mammal community, but to what extent is unclear. During May-August 2015-2016, I deployed 150 remotely-triggered camera traps in Trail of Tears State Forest (TTSF), Union County, Illinois to investigate single-season, site occupancy patterns and detection probabilities as a function of forest composition and structure for 3 mammals (eastern gray squirrel [Sciurus carolinensis], raccoon [Procyon lotor], and white-tailed deer [Odocoileus virginianus]). I collected microhabitat data at each camera-site and utilized a GIS application to estimate spatial relationships among anthropogenic features and camera-sites. I recorded 404 photographs of 11 endothermic species during 3927 days of survey effort, with white-tailed deer, raccoons, and eastern gray squirrels as the most detected species, respectively. Detection probability of eastern gray squirrels was best explained by the global detection model, indicating no covariate measured explained the variation in detection rates. Raccoon detection probability was best described by a negative relationship with the average temperature recorded during survey period. The best-fitting detection model for white-tailed deer indicated detection probabilities declined throughout the sampling period and across seasons. Eastern gray squirrel site occupancy models received little support, however, ecological land type phase was the most supported model. The best fitting habitat model described a negative relationship between eastern gray squirrel site occupancy probability and coarse woody debris volume. For raccoons, no model with habitat covariates was better fitting than the null model. Raccoon occupancy probability increased with maximum DBH at a site, ground cover, and beech-maple importance values, but decreased with oak-hickory importance values. White-tailed deer occupancy was most positively influenced by ground cover and oak-hickory importance values, but decreased with distance to forest edge, number of understory stems, and beech-maple importance values. My research provides empirical evidence to predictions made regarding the impact of a decline in oak dominance across the Central Hardwood region on a portion of the region’s mammal community. Shifts to late-successional conditions in the Central Hardwood region will likely continue and magnify if forest management approaches continue to minimize the frequency and occurrence of large, anthropogenic disturbances to the forest overstory. A mosaic of forest conditions will be needed to best support a diverse and complete mammal community across the region.

An integrated heating, ventilation and air-conditioning (HVAC) system is one of the most important components to determining the energy consumption of the entire building. For commercial buildings, particularly office buildings and schools, the heating and cooling loads are largely dependent on the occupant behavioral patterns such as occupancy rates and their activities. Therefore, if HVAC systems can respond to dynamic occupancy profiles, there is a large potential to reduce energy consumption. However, currently, most of existing HVAC systems operate without the ability to adjust supply air rate accordingly in response to the dynamic profiles of occupants. Due to this inefficiency, much of the HVAC energy use is wasted, particularly when the conditioned spaces are unoccupied or under-occupied (less occupants than the intended design). The solution to this inefficiency is to control HVAC system based on dynamic occupant profiles. Motivated by this, the research provides a real-time vision-based occupant pattern recognition system for occupancy counting as well as activity level classification. The proposed vision-based system is integrated into the existing HVAC simulation model of a U.S. office building to investigate the level of energy savings as well as thermal comfort improvement compared to traditional existing HVAC control system. The research is divided into two parts. The first part is to use an open source library based on neural network for real-time occupant counting and background subtraction method for activity level classification with a common static RGB camera. The second part utilizes a DOE reference office building model with customized dynamic occupancy schedule, including the number of occupant schedule, activity schedule and clothing insulation schedule to identify the potential energy savings compared with conventional HVAC control system. The research results revealed that vision-based systems can detect occupants and classify activity level in real time with accuracy around 90% when there are not many occlusions. Additionally, the dynamic occupant schedules indeed can bring about energy savings. Details of vision-based system, methodology, simulation configurations and results will be presented in the paper as well as potential opportunities for use throughout multiple types of commercial buildings, specifically focused on office and educational institutes.

Species distributions reflect the suite of resources and range of environmental conditions required by a species. Distributions of many species change seasonally, however, in response to changes in resource availability and environmental conditions, many of which are projected to shift in response to climate change. We sought to identify environmental and anthropogenic factors associated with seasonal changes in the distribution of vertebrates that inhabit the Madrean Archipelago in southern Arizona, which is important for identifying mechanisms through which climate change may affect these species. From July 2012 to February 2015 (966 days), we used remote cameras to survey 200 sites across 16 mountain ranges for a total of 69,434 trap days and used dynamic occupancy models to determine how landscape features, vegetation composition, and anthropogenic factors influenced the distributions and seasonal rates of local colonization and extinction of 13 vertebrates. For these 13 species, we recorded 37,888 detections, with gray foxes, skunks, and squirrels detected most frequently. Bobcats, cottontails, gray foxes, skunks, squirrels, javelina, puma, and coatimundis were all detected across the entire range of elevations surveyed, from 935 to 2395 m. Black bears and wild turkeys were never detected below 1270 m, Sonoran opossums never detected above 1980 m, and coyotes and ringtails never detected above 2020 m. Composition and structure of vegetation in the understory, midstory, and overstory influenced initial occupancy of several species, including skunks, coatimundis, Sonoran opossums, and cottontails. Season, either directly or as an interaction with the estimated amount of solar radiation reaching a site, influenced local extinction rates of all 13 target species and local colonization rates of 9 of 13 species. Elevation influenced local colonization rates of black bears and coatimundis positively, and coyotes, Sonoran opossums, and cottontails negatively, and local extinction rates of skunks negatively and coyotes and black bears positively. These patterns indicate that the distributions of many species in this region change seasonally, likely as a mechanism to meet dietary, behavioral, or physiological needs in response to shifts in environmental conditions and resource availability. Therefore, species that depend on seasonal resources may be at higher risk of distributional shifts or range contractions if the distribution and phenology of these resources change in response to changes in climate.

With the growth of networked smart devices in indoor environments, human information acquisition becomes essential for these devices to make the environment smart and people’s lives more convenient. These networked systems, which are often referred to as Cyber-Physical Systems (CPS), learn and make decisions collaboratively based on data input. The data could come from sensors that perceive various signals in the physical world, human input, etc. In this thesis, I will focus on information acquisition based on data from sensing the physical world. The major challenges to accurately interpreting the information these systems perceive result from the complexity of the physical world. An extreme solution to this problem is to have a large number of sensors or sensing configurations that collect a large amount of data. Ideally, we could then have labeled data for each sensing condition and possible scenario in order to accurately model the world. However, in the real world, such solutions could be difficult if not impossible to achieve due to constraints on the hardware, computational power, and (labeled) dataset. This thesis targets this problem and sets the goal of obtaining accurate indoor human information through limited system configurations and limited labeled data. A new concept of utilizing structures as sensors is presented as the foundation of the system. The intuition is that people induce ambient structures to vibrate all the time, and their activities and information can be inferred from this vibration. To achieve that with the aforementioned constraints, an understanding of the physical world (that has been studied for centuries in multiple disciplines) is used to assist the sensing and learning process for more accurate information acquisition from sensor data.

The purpose of this thesis is to devise a system based on RFID (Radio Frequency IDentification) that can be used for smart healthcare applications. Location estimation, eloping prevention and occupancy detection are monitoring applications of smart healthcare which can provide very useful information for the nursing and administration staff of the nursing-home/hospital. The introduction of ubiquitous networking along with the concepts such as Internet of Things (IoT) can certainly help achieve the goals of smart healthcare. RFID technology has features, such as low power and small size, which makes this technology suitable for researching solutions for smart healthcare. Today several nursing-home/hospital monitoring solutions exist in the market and academia alike. The solutions marketed commercially are very expensive whereas the solutions from academia provides solutions to isolated problems but a comprehensive all in one solution that can meet the need of smart healthcare monitoring applications is missing. In this thesis we present a system that is low cost and suitable for accommodating a number of the smart healthcare applications including occupancy detection, location estimation, eloping prevention and access control. The solution is implemented on a customized Openbeacon Active RFID System (OARS). Active RFID based proximity detection is the core of our system. Practical experiments based on novel Proximity Detection based Weighted Centroid Localization (PD-WCL) method were done to analyze the performance of the system with different applications to highlight the applicability of the system.