UAVNet: A Mobile Wireless Mesh Network Using Unmanned Aerial Vehicles

We developed UAVNet, a framework for the autonomous deployment of a flying Wireless Mesh Network using small quadrocopter-based Unmanned Aerial Vehicles (UAVs). The flying wireless mesh nodes are automatically interconnected to each other and building an IEEE 802.11s wireless mesh network. The implemented UAVNet prototype is able to autonomously interconnect two end systems by setting up an airborne relay, consisting of one or several flying wireless mesh nodes. The developed software includes basic functionality to control the UAVs and to setup, deploy, manage, and monitor a wireless mesh network. Our evaluations have shown that UAVNet can significantly improve network performance.

Evaluating the Trade-off Between Energy Efficiency and QoE in Wireless Mesh Networks

The increasing usage of wireless networks creates new challenges for wireless access providers. On the one hand, providers want to satisfy the user demands but on the other hand, they try to reduce the operational costs by decreasing the energy consumption. In this paper, we evaluate the trade-off between energy efficiency and quality of experience for a wireless mesh testbed. The results show that by intelligent service control, resources can be better utilized and energy can be saved by reducing the number of active network components. However, care has to be taken because the channel bandwidth varies in wireless networks. In the second part of the paper, we analyze the trade-off between energy efficiency and quality of experience at the end user. The results reveal that a provider's service control measures do not only reduce the operational costs of the network but also bring a second benefit: they help maximize the battery lifetime of the end-user device.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Early assessment of brain maturation by MR imaging segmentation in neonates and premature infants

PURPOSE: We evaluated the impact of premature extrauterine life on brain maturation. PATIENTS AND METHODS: Twelve neonates underwent MR imaging at 40 (39.64 +/- 0.98) weeks (full term). Fifteen premature infants underwent 2 MR imaging examinations, after birth (preterm at birth) and at 40 weeks (41.03 +/- 1.33) (preterm at term). A 3D MR imaging technique was used to measure brain volumes compared with intracranial volume: total brain volume, cortical gray matter, myelinated white matter, unmyelinated white matter, basal ganglia (BG), and CSF. RESULTS: The average absolute volume of intracranial volume (269.8 mL +/- 36.5), total brain volume (246.5 +/- 32.3), cortical gray matter (85.53 mL +/- 22.23), unmyelinated white matter (142.4 mL +/-14.98), and myelinated white matter (6.099 mL +/-1.82) for preterm at birth was significantly lower compared with that for the preterm at term: the average global volume of intracranial volume (431.7 +/- 69.98), total brain volume (391 +/- 66,1), cortical gray matter (179 mL +/- 41.54), unmyelinated white matter (185.3 mL +/- 30.8), and myelinated white matter (10.66 mL +/- 3.05). It was also lower compared with that of full-term infants: intracranial volume (427.4 mL +/- 53.84), total brain volume (394 +/- 49.22), cortical gray matter (181.4 +/- 29.27), unmyelinated white matter (183.4 +/- 27.37), and myelinated white matter (10.72 +/- 4.63). The relative volume of cortical gray matter (30.62 +/- 5.13) and of unmyelinated white matter (53.15 +/- 4.8) for preterm at birth was significantly different compared with the relative volume of cortical gray matter (41.05 +/- 5.44) and of unmyelinated white matter (43.22 +/- 5.11) for the preterm at term. Premature infants had similar brain tissue volumes at 40 weeks to full-term infants. CONCLUSION: MR segmentation techniques demonstrate that cortical neonatal maturation in moderately premature infants at term and term-born infants was similar.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.

Clinical validation of an automated vessel-segmentation software of the extracranial-carotid arteries based on 3D-MRA: A prospective study

OBJECTIVES: To determine the accuracy of automated vessel-segmentation software for vessel-diameter measurements based on three-dimensional contrast-enhanced magnetic resonance angiography (3D-MRA). METHOD: In 10 patients with high-grade carotid stenosis, automated measurements of both carotid arteries were obtained with 3D-MRA by two independent investigators and compared with manual measurements obtained by digital subtraction angiography (DSA) and 2D maximum-intensity projection (2D-MIP) based on MRA and duplex ultrasonography (US). In 42 patients undergoing carotid endarterectomy (CEA), intraoperative measurements (IOP) were compared with postoperative 3D-MRA and US. RESULTS: Mean interoperator variability was 8% for measurements by DSA and 11% by 2D-MIP, but there was no interoperator variability with the automated 3D-MRA analysis. Good correlations were found between DSA (standard of reference), manual 2D-MIP (rP=0.6) and automated 3D-MRA (rP=0.8). Excellent correlations were found between IOP, 3D-MRA (rP=0.93) and US (rP=0.83). CONCLUSION: Automated 3D-MRA-based vessel segmentation and quantification result in accurate measurements of extracerebral-vessel dimensions.

Effect of mesh distortion on the accuracy of high order vorticity-velocity CFD approaches

The numerical solution of the incompressible Navier-Stokes equations offers an alternative to experimental analysis of fluid-structure interaction (FSI). We would save a lot of time and effort and help cut back on costs, if we are able to accurately model systems by these numerical solutions. These advantages are even more obvious when considering huge structures like bridges, high rise buildings or even wind turbine blades with diameters as large as 200 meters. The modeling of such processes, however, involves complex multiphysics problems along with complex geometries. This thesis focuses on a novel vorticity-velocity formulation called the Kinematic Laplacian Equation (KLE) to solve the incompressible Navier-stokes equations for such FSI problems. This scheme allows for the implementation of robust adaptive ordinary differential equations (ODE) time integration schemes, allowing us to tackle each problem as a separate module. The current algortihm for the KLE uses an unstructured quadrilateral mesh, formed by dividing each triangle of an unstructured triangular mesh into three quadrilaterals for spatial discretization. This research deals with determining a suitable measure of mesh quality based on the physics of the problems being tackled. This is followed by exploring methods to improve the quality of quadrilateral elements obtained from the triangles and thereby improving the overall mesh quality. A series of numerical experiments were designed and conducted for this purpose and the results obtained were tested on different geometries with varying degrees of mesh density.