Visualizing the Simulation of 3-D Underwater Sensor Networks

The majority of sensor network research deals with land-based networks, which are essentially two-dimensional, and thus the majority of simulation and animation tools also only handle such networks. Underwater sensor networks on the other hand, are essentially 3D networks because the depth at which a sensor node is located needs to be considered as well. Due to that additional dimension, specialized tools need to be used when conducting simulations for experimentation. The School of Engineering’s Underwater Sensor Network (UWSN) lab is conducting research on underwater sensor networks and requires simulation tools for 3D networks. The lab has extended NS-2, a widely used network simulator, so that it can simulate three-dimensional networks. However, NAM, a widely used network animator, currently only supports two-dimensional networks and no extensions have been implemented to give it three-dimensional capabilities. In this project, we develop a network visualization tool that functions similarly to NAM but is able to render network environments in full 3-D. It is able to take as input a NS-2 trace file (the same file taken as input by NAM), create the environment, position the sensor nodes, and animate the events of the simulation. Further, the visualization tool is easy to use, especially friendly to NAM users, as it is designed to follow the interfaces and functions similar to NAM. So far, the development has fulfilled the basic functionality. Future work includes fully functional capabilities for visualization and much improved user interfaces.

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Organizational perspectives on the management and evaluation of large -scale multicenter controlled clinical trials: An exploratory study of participation in the beta -blocker heart attack trial

A review of literature related to appointment-keeping served as the basis for the development of an organizational paradigm for the study of appointment-keeping in the Beta-blocker Heart Attack Trial (BHAT). Features of the organizational environment, demographic characteristics of BHAT enrollees, organizational structure and processes and previous organizational performance variables were measured so as to provide exploratory information relating to the appointment-keeping behavior of 3,837 participants enrolled at thirty-two Clinical Centers. Results suggest that the social context of individual behavior is an important consideration for the understanding of patient compliance. In particular, the degree to which previous organizational performance--as measured by obtaining recruitment goals--and the ability to utilize resources had particularly strong bivariate associations with appointment-keeping. Implications for future theory development, research and practical implications were provided as was a suggestion for the development of multidisciplinary research efforts conducted within the context of Centers for the study and application of adherence behaviors. ^

Dissolved organic matter molecular composition and concentrations from a large scale mesocosm study KOSMOS 2013 Kristineberg) on ocean acidification

Marine dissolved organic matter (DOM) represents one of the largest active carbon reservoirs on Earth. Changes in pool size or composition could have major impacts on the global carbon cycle. Ocean acidification is a potential driver for these changes because it influences marine primary production and heterotrophic respiration. Here we show that ocean acidification as expected for a 'business-as-usual' emission scenario in the year 2100 (900 µatm) does not affect the DOM pool with respect to its size and molecular composition. We applied ultrahigh-resolution mass spectrometry to monitor the production and turnover of 7,360 distinct molecular DOM features in an unprecedented long-term mesocosm study in a Swedish Fjord, covering a full cycle of marine production. DOM concentration and molecular composition did not differ significantly between present-day and year 2100 CO2 levels. Our findings are likely applicable to other coastal and productive marine ecosystems in general.

Wave height (regular and irregular waves) reduction in 2m water depth over a 40-m-long salt marsh test section in a large scale laboratory flume

Coastal communities around the world face increasing risk from flooding as a result of rising sea level, increasing storminess, and land subsidence. Salt marshes can act as natural buffer zones, providing protection from waves during storms. However, the effectiveness of marshes in protecting the coastline during extreme events when water levels and waves are highest is poorly understood. Here, we experimentally assess wave dissipation under storm surge conditions in a 300-m-long wave flume that contains a transplanted section of natural salt marsh. We find that the presence of marsh vegetation causes considerable wave attenuation, even when water levels and waves are high. From a comparison with experiments without vegetation, we estimate that up to 60% of observed wave reduction is attributed to vegetation. We also find that although waves progressively flatten and break vegetation stems and thereby reduce dissipation, the marsh substrate remained remarkably stable and resistant to surface erosion under all conditions.The effectiveness of storm wave dissipation and the resilience of tidal marshes even at extreme conditions suggest that salt marsh ecosystems can be a valuable component of coastal protection schemes.

Generating Scale-free Networks with Adjustable Clustering Coefficient Via Random Walks

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validity of the scheme, characterizing its tuning capabilities.

Power output fluctuations in large scale PV plants: one year observations with one second resolution and a derived analytic model

The variable nature of the irradiance can produce significant fluctuations in the power generated by large grid-connected photovoltaic (PV) plants. Experimental 1 s data were collected throughout a year from six PV plants, 18 MWp in total. Then, the dependence of short (below 10 min) power fluctuation on PV plant size has been investigated. The analysis focuses on the study of fluctuation frequency as well as the maximum fluctuation value registered. An analytic model able to describe the frequency of a given fluctuation for a certain day is proposed

Ultra Low Power FPGA-Based Architecture for Wake-up Radio in Wireless Sensor Networks

In this paper the capabilities of ultra low power FPGAs to implement Wake-up Radios (WuR) for ultra low energy Wireless Sensor Networks (WSNs) are analyzed. The main goal is to evaluate the utilization of very low power configurable devices to take advantage of their speed, flexibility and low power consumption instead of the more common approaches based on ASICs or microcontrollers. In this context, energy efficiency is a key aspect, considering that usually the instant power consumption is considered a figure of merit, more than the total energy consumed by the application.

Wake up Radio Architecture for Wireless Sensor Networks Using an Ultra Low Power FPGA

In this paper an implementation of a Wake up Radio(WuR) with addressing capabilities based on an ultra low power FPGA for ultra low energy Wireless Sensor Networks (WSNs) is proposed. The main goal is to evaluate the utilization of very low power configurable devices to take advantage of their speed, flexibility and low power consumption instead of the traditional approaches based on ASICs or microcontrollers, for communication frame decoding and communication data control.

Improving target localization accuracy of wireless visual sensor networks

This paper discusses the target localization problem of wireless visual sensor networks. Specifically, each node with a low-resolution camera extracts multiple feature points to represent the target at the sensor node level. A statistical method of merging the position information of different sensor nodes to select the most correlated feature point pair at the base station is presented. This method releases the influence of the accuracy of target extraction on the accuracy of target localization in universal coordinate system. Simulations show that, compared with other relative approach, our proposed method can generate more desirable target localization's accuracy, and it has a better trade-off between camera node usage and localization accuracy.