Robotic swarm control from spatio-temporal specifications

In this paper, we study the problem of controlling a two-dimensional robotic swarm with the purpose of achieving high level and complex spatio-temporal patterns. We use a rich spatio-temporal logic that is capable of describing a wide range of time varying and complex spatial configurations, and develop a method to encode such formal specifications as a set of mixed integer linear constraints, which are incorporated into a mixed integer linear programming problem. We plan trajectories for each individual robot such that the whole swarm satisfies the spatio-temporal requirements, while optimizing total robot movement and/or a metric that shows how strongly the swarm trajectory resembles given spatio-temporal behaviors. An illustrative case study is included.This work was partially supported by the National Science Foundation under grants NRI-1426907 and CMMI-1400167. (NRI-1426907 - National Science Foundation; CMMI-1400167 - National Science Foundation

Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively

Economics of controlling a spreading environmental weed

Weeds can cause significant problems to natural ecosystems. Although there have been numerous studies on the economics of weed control, relatively few of these studies have focused on natural ecosystems. This paper addresses this gap in the literature by assessing the cost-effectiveness of a comprehensive range of control strategies for blackberry (Rubus anglocandicans) in natural environments in Australia. We developed a stochastic dynamic simulation model and a deterministic dynamic optimisation model. The stochastic model calculates the expected net present value (NPV) of a range of control strategies, including any combination of treatment options. The optimisation model identifies the treatment combination that maximises NPV. Both models represent the costs and efficacies of control options over 25 years. The results indicate that using rust (Phragmidium violaceum) as a biological control agent only marginally increases NPV and excluding rust does not affect the optimal choice of other control options. The results also show for a wide range of parameter values that a strategy which combines the herbicide grazon (Triclopyre and picloram) and mowing is optimal. If chemical efficacy decreases by 20 percent it becomes optimal to include grazing blackberry by goats in the control strategy.Environment, Economics, Weed, Stochastic, Optimisation, Management, Environmental Economics and Policy,