Development of a fusion adaptive algorithm for marine debris detection within the post-Sandy restoration framework

Recognition of marine debris represent a difficult task due to the extreme variability of the marine environment, the possible targets, and the variable skill levels of human operators. The range of potential targets is much wider than similar fields of research such as mine hunting, localization of unexploded ordnance or pipeline detection. In order to address this additional complexity, an adaptive algorithm is being developing that appropriately responds to changes in the environment, and context. The preliminary step is to properly geometrically and radiometrically correct the collected data. Then, the core engine manages the fusion of a set of statistically- and physically-based algorithms, working at different levels (swath, beam, snippet, and pixel) and using both predictive modeling (that is, a high-frequency acoustic backscatter model) and phenomenological (e.g., digital image processing techniques) approaches. The expected outcome is the reduction of inter-algorithmic cross-correlation and, thus, the probability of false alarm. At this early stage, we provide a proof of concept showing outcomes from algorithms that dynamically adapt themselves to the depth and average backscatter level met in the surveyed environment, targeting marine debris (modeled as objects of about 1-m size). The project relies on a modular software library, called Matador (Marine Target Detection and Object Recognition)

Reliable and timely event notification for publish/subscribe services over the internet

The publish/subscribe paradigm is gaining attention for the development of several applications in wide area networks (WANs) due to its intrinsic time, space, and synchronization decoupling properties that meet the scalability and asynchrony requirements of those applications. However, while the communication in a WAN may be affected by the unpredictable behavior of the network, with messages that can be dropped or delayed, existing publish/subscribe solutions pay just a little attention to addressing these issues. On the contrary, applications such as business intelligence, critical infrastructures, and financial services require delivery guarantees with strict temporal deadlines. In this paper, we propose a framework that enforces both reliability and timeliness for publish/subscribe services over WAN. Specifically, we combine two different approaches: gossiping, to retrieve missing packets in case of incomplete information, and network coding, to reduce the number of retransmissions and, consequently, the latency. We provide an analytical model that describes the information recovery capabilities of our algorithm and a simulation-based study, taking into account a real workload from the Air Traffic Control domain, which evidences how the proposed solution is able to ensure reliable event notification over a WAN within a reasonable bounded time window. © 2013 IEEE

Girt by sea: understanding Australia’s maritime domains in a networked world

This study aims to provide the background, language and context necessary for an informed understanding of the challenges and dilemmas faced by those responsible for the efficacy of Australia’s maritime domain awareness system. Abstract Against a rapidly changing region dominated by the rise of China, India and, closer to home, Indonesia, Australia’s approaches to understanding its maritime domains will be influenced by strategic factors and diplomatic judgements as well as operational imperatives.  Australia’s alliance relationship with the United States and its relationships with regional neighbours may be expected to have a profound impact on the strength of the information sharing and interoperability regimes on which so much of Australia’s maritime domain awareness depends. The purpose of this paper is twofold.  First, it seeks to explain in plain English some of the principles, concepts and terms that maritime domain awareness practitioners grapple with on a daily basis.  Second, it points to a series of challenges that governments face in deciding how to spend scarce tax dollars to deliver a maritime domain awareness system that is necessary and sufficient for the protection and promotion of Australia’s national interests

Distribution of Accuracy of TRMM Daily Rainfall in Makassar Strait

This research aims to evaluate rainfall estimates of satellite products in regions that have high variations of rainfall pattern. The surrounding area of Makassar Strait have chosen because of its distinctive rainfall pattern between the eastern and western parts of the Makassar Strait. For this purpose, spatial distribution of Pearson’s coefficient correlation and Root Mean Square Error (RMSE) is used to evaluate accuracy of rainfall in the eastern part of Kalimantan Island and the western part of Sulawesi Island. Moreover, we also used the contingency table to complete the parameter accuracy of the TRMM rainfall estimates. The results show that the performance of TRMM rainfall estimates varies depending on space and time. Overall, the coefficient correlation between TRMM and rain observed from no correlation was -0.06 and 0.78 from strong correlation. The best correlation is on the eastern coast of South West Sulawesi located in line with the Java Sea. While, no variation in the correlation was related to flatland such as Kalimantan Island. On the other hand, in the mountain region, the correlation of TRMM rainfall estimates and observed rainfall tend to decrease. The RMSE distribution in this region depends on the accumulation of daily rainfall. RMSE tends to be high where there are higher fluctuations of fluctuating rainfall in a location. From contingency indicators, we found that the TRMM rainfall estimates were overestimate. Generally, the absence of rainfall during the dry season contributes to improving TRMM rainfall estimates by raising accuracy (ACC) in the contingency table

ECONOMICS OF DETECTION AND CONTROL OF INVASIVE SPECIES: WORKSHOP HIGHLIGHTS

Invasive species are species that are not native to an ecosystem, and when introduced into the new ecosystem, they cause economic or environmental damage. Trade is one way in which these species are introduced into new regions, and as trade increases, the introduction of invasive species also rises. The Center for Agricultural Policy and Trade Studies, North Dakota State University, held a workshop on April 30, 2004 in Fargo, ND, titled ?Economics of Detection and Control of Invasive Species? to address these issues. The purpose of this workshop was to present current findings on the subject of invasive species in agricultural trade and to structure the model for an in-depth research project examining this issue. Speakers included experts from the Animal Plant Health Inspection Service and the Economic Research Service of the U.S. Department of Agriculture and from U.S. Customs and Border Patrol, as well as professors of economics from North Dakota State University and other academic institutions. Discussion included the impact of invasive species on agricultural production and trade, the tools used by the U.S. Department of Agriculture and U.S. Customs and Border patrol to detect and control incoming species, and the creation of econometric models to capture and explain these processes and to analyze policy issues. This report contains abstracts from the presentations given at the workshop.Resource /Energy Economics and Policy,

A Bayesian network to manage risks of maritime piracy against offshore oil fields

International audienceIn recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures