Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Benefits of high-speed broadband for Australian households

This report from Deloitte Access Economics examines the benefits to households of high speed broadband in 2020 when the use of digital tools will be widespread across the economy. Deloitte assessed potential household benefits in the areas of communications; e-commerce; e-health; online education; e-government services; savings from an increase in telework; and the flow-on benefits to households from business productivity.Executive summaryHigh-speed broadband is transforming our economy and society, with major implications for households, business, governments and the environment.The report looks over the horizon to 2020 when Australia’s economy will be a fully digital economy, powered by the National Broadband Network (NBN). Recent developments like smartphones, apps and social media will be more deeply embedded, while video content, the cloud and machine-to-machine technologies will be widespread.Households will benefit from improved communications, greater choice and competition from e-commerce, more online services, greater employment opportunities, including through telework, and savings in time and money from reduced travel. They will also experience improvements in goods and services quality and/or lower prices as businesses take up new productivity-boosting applications of the digital economy. There will also be environmental benefits from reduced travel and other applications.Our estimate is average annual household benefits will be worth around $3,800 in 2020, in current dollars. Around two-thirds of these benefits ($2,400) are financial benefits, the rest are the equivalent monetary value of consumer benefits such as travel time savings and convenience of e-commerce. The research reported in this publication was commissioned by the Australian Government Department of Broadband, Communications and the Digital Economy. The information and opinions contained in it do not necessarily reflect the views or policy of the Department of Broadband, Communications and the Digital Economy

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach