Eigen-Adaptation and Distributed Representation of 2-D Phase, Energy, Scale and Orientation in Spatial Vision

Distributed representations (DR) of cortical channels are pervasive in models of spatio-temporal vision. A central idea that underpins current innovations of DR stems from the extension of 1-D phase into 2-D images. Neurophysiological evidence, however, provides tenuous support for a quadrature representation in the visual cortex, since even phase visual units are associated with broader orientation tuning than odd phase visual units (J.Neurophys.,88,455–463, 2002). We demonstrate that the application of the steering theorems to a 2-D definition of phase afforded by the Riesz Transform (IEEE Trans. Sig. Proc., 49, 3136–3144), to include a Scale Transform, allows one to smoothly interpolate across 2-D phase and pass from circularly symmetric to orientation tuned visual units, and from more narrowly tuned odd symmetric units to even ones. Steering across 2-D phase and scale can be orthogonalized via a linearizing transformation. Using the tiltafter effect as an example, we argue that effects of visual adaptation can be better explained by via an orthogonal rather than channel specific representation of visual units. This is because of the ability to explicitly account for isotropic and cross-orientation adaptation effect from the orthogonal representation from which both direct and indirect tilt after-effects can be explained.

An indexing structure and application model for vehicles moving on road networks

Moving objects database systems are the most challenging sub-category among Spatio-Temporal database systems. A database system that updates in real-time the location information of GPS-equipped moving vehicles has to meet even stricter requirements. Currently existing data storage models and indexing mechanisms work well only when the number of moving objects in the system is relatively small. This dissertation research aimed at the real-time tracking and history retrieval of massive numbers of vehicles moving on road networks. A total solution has been provided for the real-time update of the vehicles' location and motion information, range queries on current and history data, and prediction of vehicles' movement in the near future. ^ To achieve these goals, a new approach called Segmented Time Associated to Partitioned Space (STAPS) was first proposed in this dissertation for building and manipulating the indexing structures for moving objects databases. ^ Applying the STAPS approach, an indexing structure of associating a time interval tree to each road segment was developed for real-time database systems of vehicles moving on road networks. The indexing structure uses affordable storage to support real-time data updates and efficient query processing. The data update and query processing performance it provides is consistent without restrictions such as a time window or assuming linear moving trajectories. ^ An application system design based on distributed system architecture with centralized organization was developed to maximally support the proposed data and indexing structures. The suggested system architecture is highly scalable and flexible. Finally, based on a real-world application model of vehicles moving in region-wide, main issues on the implementation of such a system were addressed. ^

Quantitative diatom-based reconstruction of paleoenvironmental conditions in Florida Bay and Biscayne Bay, U.S.A.

The spatial and temporal distribution of modern diatom assemblages in surface sediments, on the most dominant macrophytes, and in the water column at 96 locations in Florida Bay, Biscayne Bay and adjacent regions were examined in order to develop paleoenvironmental prediction models for this region. Analyses of these distributions revealed distinct temporal and spatial differences in assemblages among the locations. The differences among diatom assemblages living on subaquatic vegetation and sediments, and in the water column were significant. Because concentrations of salts, total phosphorus (WTP), total nitrogen (WTN) and total organic carbon (WTOC) are partly controlled by water management in this region, diatom-based models were produced to assess these variables. Discriminant function analyses showed that diatoms can also be successfully used to reconstruct changes in the abundance of diatom assemblages typical for different habitats and life habits. ^ To interpret paleoenvironmental changes, changes in salinity, WTN, WTP and WTOC were inferred from diatoms preserved in sediment cores collected along environmental gradients in Florida Bay (4 cores) and from nearshore and offshore locations in Biscayne Bay (3 cores). The reconstructions showed that water quality conditions in these estuaries have been fluctuating for thousands of years due to natural processes and sea-level changes, but almost synchronized shifts in diatom assemblages occurred in the mid-1960’s at all coring locations (except Ninemile Bank and Bob Allen Bank in Florida Bay). These alterations correspond to the major construction of numerous water management structures on the mainland. Additionally, all the coring sites (except Card Sound Bank, Biscayne Bay and Trout Cove, Florida Bay) showed decreasing salinity and fluctuations in nutrient levels in the last two decades that correspond to increased rainfall in the 1990’s and increased freshwater discharge to the bays, a result of increased freshwater deliveries to the Everglades by South Florida Water Management District in the 1980’s and 1990’s. Reconstructions of the abundance of diatom assemblages typical for different habitats and life habits revealed multiple sources of diatoms to the coring locations and that epiphytic assemblages in both bays increased in abundance since the early 1990’s. ^

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

A conceptual model of ecological interactions in the mangrove estuaries of the Florida Everglades

A brackish water ecotone of coastal bays and lakes, mangrove forests, salt marshes, tidal creeks, and upland hammocks separates Florida Bay, Biscayne Bay, and the Gulf of Mexico from the freshwater Everglades. The Everglades mangrove estuaries are characterized by salinity gradients that vary spatially with topography and vary seasonally and inter-annually with rainfall, tide, and freshwater flow from the Everglades. Because of their location at the lower end of the Everglades drainage basin, Everglades mangrove estuaries have been affected by upstream water management practices that have altered the freshwater heads and flows and that affect salinity gradients. Additionally, interannual variation in precipitation patterns, particularly those caused to El Nin˜o events, control freshwater inputs and salinity dynamics in these estuaries. Two major external drivers on this system are water management activities and global climate change. These drivers lead to two major ecosystem stressors: reduced freshwater flow volume and duration, and sea-level rise. Major ecological attributes include mangrove forest production, soil accretion, and resilience; coastal lake submerged aquatic vegetation; resident mangrove fish populations; wood stork (Mycteria americana) and roseate spoonbill (Platelea ajaja) nesting colonies; and estuarine crocodilian populations. Causal linkages between stressors and attributes include coastal transgression, hydroperiods, salinity gradients, and the ‘‘white zone’’ freshwater/estuarine interface. The functional estuary and its ecological attributes, as influenced by sea level and freshwater flow, must be viewed as spatially dynamic, with a possible near-term balancing of transgression but ultimately a long-term continuation of inland movement. Regardless of the spatio-temporal timing of this transgression, a salinity gradient supportive of ecologically functional Everglades mangrove estuaries will be required to maintain the integrity of the South Florida ecosystem.

Salinity and Chlorophyll a as Performance Measures to Rehabilitate a Mangrove-Dominated Deltaic Coastal Region: the Ciénaga Grande de Santa Marta–Pajarales Lagoon Complex, Colombia

Salinity, water temperature, and chlorophyll a (chl-a) biomass were used as performance measures in the period 1999–2001 to evaluate the effect of a hydrological rehabilitation project in the Ciénaga Grande de Santa Marta (CGSM)–Pajarales lagoon complex, Colombia where freshwater diversions were initiated in 1995 and completed in 1998. The objective of this study was to evaluate how diversions of freshwater into previously hypersaline (>80) environments changed the spatial and temporal distribution of environmental characteristics. Following the diversion, 19 surveys and transects using a flow-through system were surveyed in the CGSM–Pajarales complex to continuously measure selected water quality parameters. Geostatistical analysis indicates that hydrology and salinity regimes and water circulation patterns in the CGSM lagoon are largely controlled by freshwater discharge from the Fundacion, Aracataca, and Sevilla Rivers. Residence times in the CGSM lagoon were similar before (15.5 ± 3.8 days) and after (14.2 ± 2.0 days) the rehabilitation project and indicated that the system is flushed regularly. In contrast, chl-a biomass was highly variable in the CGSM–Pajarales lagoon complex and not related to discharge patterns. Mean annual chl-a biomass (44–250 μg L−1) following the diversion project was similar to values recorded since the 1980s and still remains among the highest reported in coastal systems around the world owing to its unique hydrology regulated by the Magdalena River and Sierra Nevada de Santa Marta watersheds and the high teleconnection to the El Niño Southern Oscillation (ENSO). Our results confirm that the reduction in salinity in the CGSM lagoon and Pajarales complex during 1999–2000 was largely driven by high precipitation (2500 mm) induced by the ENSO–La Niña rather than by the freshwater diversions.

Modeling seasonal dynamics of small fish cohorts in fluctuating freshwater marsh landscapes

Small-bodied fishes constitute an important assemblage in many wetlands. In wetlands that dry periodically except for small permanent waterbodies, these fishes are quick to respond to change and can undergo large fluctuations in numbers and biomasses. An important aspect of landscapes that are mixtures of marsh and permanent waterbodies is that high rates of biomass production occur in the marshes during flooding phases, while the permanent waterbodies serve as refuges for many biotic components during the dry phases. The temporal and spatial dynamics of the small fishes are ecologically important, as these fishes provide a crucial food base for higher trophic levels, such as wading birds. We develop a simple model that is analytically tractable, describing the main processes of the spatio-temporal dynamics of a population of small-bodied fish in a seasonal wetland environment, consisting of marsh and permanent waterbodies. The population expands into newly flooded areas during the wet season and contracts during declining water levels in the dry season. If the marsh dries completely during these times (a drydown), the fish need refuge in permanent waterbodies. At least three new and general conclusions arise from the model: (1) there is an optimal rate at which fish should expand into a newly flooding area to maximize population production; (2) there is also a fluctuation amplitude of water level that maximizes fish production, and (3) there is an upper limit on the number of fish that can reach a permanent waterbody during a drydown, no matter how large the marsh surface area is that drains into the waterbody. Because water levels can be manipulated in many wetlands, it is useful to have an understanding of the role of these fluctuations.

Recent and Historic Drivers of Landscape Change in the Everglades Ridge, Slough, and Tree Island Mosaic

More than half of the original Everglades extent formed a patterned peat mosaic of elevated ridges, lower and more open sloughs, and tree islands aligned parallel to the dominant flow direction. This ecologically important landscape structure remained in a dynamic equilibrium for millennia prior to rapid degradation over the past century in response to human manipulation of the hydrologic system. Restoration of the patterned landscape structure is one of the primary objectives of the Everglades restoration effort. Recent research has revealed that three main drivers regulated feedbacks that initiated and maintained landscape structure: the spatial and temporal distribution of surface water depths, surface and subsurface flow, and phosphorus supply. Causes of recent degradation include but are not limited to perturbations to these historically important controls; shifts in mineral and sulfate supply may have also contributed to degradation. Restoring predrainage hydrologic conditions will likely preserve remaining landscape pattern structure, provided a sufficient supply of surface water with low nutrient and low total dissolved solids content exists to maintain a rainfall-driven water chemistry. However, because of hysteresis in landscape evolution trajectories, restoration of areas with a fully degraded landscape could require additional human intervention.

Novel online data cleaning protocols for data streams in trajectory, Wireless Sensor Networks

The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.

Background traffic modeling for large-scale network simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Rainfall variability in the upper Napo River Basin, Ecuadorian Amazon

The purpose of this research was to investigate the influence of elevation and other terrain characteristics over the spatial and temporal distribution of rainfall. A comparative analysis was conducted between several methods of spatial interpolations using mean monthly precipitation values in order to select the best. Following those previous results it was possible to fit an Artificial Neural Network model for interpolation of monthly precipitation values for a period of 20 years, with input values such as longitude, latitude, elevation, four geomorphologic characteristics and anchored by seven weather stations, it reached a high correlation coefficient (r=0.85). This research demonstrated a strong influence of elevation and other geomorphologic variables over the spatial distribution of precipitation and the agreement that there are nonlinear relationships. This model will be used to fill gaps in time-series of monthly precipitation, and to generate maps of spatial distribution of monthly precipitation at a resolution of 1km2.

An Indexing Structure and Application Model for Vehicles Moving on Road Networks

Moving objects database systems are the most challenging sub-category among Spatio-Temporal database systems. A database system that updates in real-time the location information of GPS-equipped moving vehicles has to meet even stricter requirements. Currently existing data storage models and indexing mechanisms work well only when the number of moving objects in the system is relatively small. This dissertation research aimed at the real-time tracking and history retrieval of massive numbers of vehicles moving on road networks. A total solution has been provided for the real-time update of the vehicles’ location and motion information, range queries on current and history data, and prediction of vehicles’ movement in the near future. To achieve these goals, a new approach called Segmented Time Associated to Partitioned Space (STAPS) was first proposed in this dissertation for building and manipulating the indexing structures for moving objects databases. Applying the STAPS approach, an indexing structure of associating a time interval tree to each road segment was developed for real-time database systems of vehicles moving on road networks. The indexing structure uses affordable storage to support real-time data updates and efficient query processing. The data update and query processing performance it provides is consistent without restrictions such as a time window or assuming linear moving trajectories. An application system design based on distributed system architecture with centralized organization was developed to maximally support the proposed data and indexing structures. The suggested system architecture is highly scalable and flexible. Finally, based on a real-world application model of vehicles moving in region-wide, main issues on the implementation of such a system were addressed.

Resources use and conservation attitudes of local people in the Western Terai landscape, Nepal

Two protected areas: Royal Bardia National Park (RBNP) and Royal Suklaphanta Wildlife Reserve (RSWR) in the Western Terai, Nepal, are under threats due to present political turmoil, uncontrolled immigration, inefficient land reform policies and unsustainable resource use. I did a stratified random questionnaire survey of 234 households to determine how resource use patterns and problems influence conservation attitudes. Chi-square, Student's t, Mann-Whitney and Kruskal-Wallis tests, and multiple regression were used. There was spatio-temporal variability in resource use patterns and dependency. People were collecting eight and seven types of resources in RBNP and RSWR, respectively. However, people in RBNP were more dependent on resources than RSWR. In both areas, the problem of firewood is serious. The mean attitude score of RBNP (8.4 ± 1.44) was significantly higher than the score of RSWR (7.7 ± 1.66; t = 3.24, p = 0.0007). Conservation attitude was determined by variables such as participation in trainings, wildlife damage, and satisfaction towards user groups.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Last Interglacial synthesis of high-latitude temperature: temperature anomalies and associated errors for 4 time slices

The Last Interglacial (LIG, 129-116 thousand of years BP, ka) represents a test bed for climate model feedbacks in warmer-than-present high latitude regions. However, mainly because aligning different palaeoclimatic archives and from different parts of the world is not trivial, a spatio-temporal picture of LIG temperature changes is difficult to obtain. Here, we have selected 47 polar ice core and sub-polar marine sediment records and developed a strategy to align them onto the recent AICC2012 ice core chronology. We provide the first compilation of high-latitude temperature changes across the LIG associated with a coherent temporal framework built between ice core and marine sediment records. Our new data synthesis highlights non-synchronous maximum temperature changes between the two hemispheres with the Southern Ocean and Antarctica records showing an early warming compared to North Atlantic records. We also observe warmer than present-day conditions that occur for a longer time period in southern high latitudes than in northern high latitudes. Finally, the amplitude of temperature changes at high northern latitudes is larger compared to high southern latitude temperature changes recorded at the onset and the demise of the LIG. We have also compiled four data-based time slices with temperature anomalies (compared to present-day conditions) at 115 ka, 120 ka, 125 ka and 130 ka and quantitatively estimated temperature uncertainties that include relative dating errors. This provides an improved benchmark for performing more robust model-data comparison. The surface temperature simulated by two General Circulation Models (CCSM3 and HadCM3) for 130 ka and 125 ka is compared to the corresponding time slice data synthesis. This comparison shows that the models predict warmer than present conditions earlier than documented in the North Atlantic, while neither model is able to produce the reconstructed early Southern Ocean and Antarctic warming. Our results highlight the importance of producing a sequence of time slices rather than one single time slice averaging the LIG climate conditions.