A noise robust automatic radiolocation animal tracking system

Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model (ResUnet-1d) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m

Modified Simpson O(n3) algorithm for the full sibship reconstruction problem

Motivation: The problem of reconstructing full sibling groups from DNA marker data remains a significant challenge for computational biology. A recently published heuristic algorithm based on Mendelian exclusion rules and the Simpson index was successfully applied to the full sibship reconstruction (FSR) problem. However, the so-called SIMPSON algorithm has an unknown complexity measure, questioning its applicability range.\ud \ud Results: We present a modified version of the SIMPSON (MS) algorithm that behaves as O(n3) and achieves the same or better accuracy when compared with the original algorithm. Performance of the MS algorithm was tested on a variety of simulated diploid population samples to verify its complexity measure and the significant improvement in efficiency (e.g. 100 times faster than SIMPSON in some cases). It has been shown that, in theory, the SIMPSON algorithm runs in non-polynomial time, significantly limiting its usefulness. It has been also verified via simulation experiments that SIMPSON could run in O(na), where a > 3.\ud \ud Availability: Computer code written in Java is available upon request from the first author

BIOINFORMATICS ORIGINAL PAPER Genetics and population analysis Partition-distance via the assignment problem

Motivation: Accuracy testing of various pedigree reconstruction methods requires an efficient algorithm for the calculation of distance between a known partition and its reconstruction. The currently used algorithm of Almudevar and Field takes a prohibitively long time for certain partitions and population sizes. Results: We present an algorithm that very efficiently reduces the partition-distance calculation to the classic assignment problem of weighted bipartite graphs that has known polynomial-time solutions. The performance of the algorithm is tested against the Almudevar and Field partition-distance algorithm to verify the significant improvement in speed. Availability: Computer code written in java is available upon request from the first author. Contact

Establishing low cost aquatic monitoring networks for developing countries

Effective monitoring of natural resources in developing countries is vital to ensuring the sustainability of the environment and major industries such as aquaculture. Sensor networks are a promising technology that can be employed to remotely gather real-time data on important environmental parameters. This data can be exploited by operators/policy makers to better manage environments and maximize the production from aquaculture ventures. However, developing countries face numerous problems in deploying and using sensor network technology. The main issues include cost, limited technological infrastructure, and inexperience with collecting, storing and analyzing data. This paper examines these issues and defines the level of quality that would be sufficient for providing developing countries with usable environmental data. We describe how a country’s existing infrastructure can be combined with scalable middleware (SAL) to integrate disparate technologies. We also present results from a test sensor network featuring heterogeneous technologies that is being used for environmental monitoring

Biodegradable optically transparent terpinen-4-ol thin films for marine antifouling applications

Sustainable marine antifouling strategies aim to minimize long term environmental impacts while effectively preventing surface colonization. In this study, we report upon a biodegradable antifouling coating for marine applications. In two stages, thin bilayers were produced using plasma-enhanced chemical vapor deposition of terpinen-4-ol, at applied powers of 100 W initially and then 10 or 25 W. The resulting coatings were characterized for solubility, surface energy, surface roughness and optical transmission. Both coatings exhibited similar solubility over the initial 14 days of observation, though structures deposited at 25 W were significantly more stable after 14 days. Coatings were smoother than the control surface upon which they were deposited and had higher hydrophobicity with transmission efficiencies > 90% (400-1000 run). Field assessments of the samples were carried out in Curralea Lake (Townsville, Australia) to assess their real world performance. Results indicate that the environmentally friendly coatings, terpinen-4-ol plasma polymer influenced antifouling. The proposed mechanism for this effect is the dissolution of the coating coupled with possible antimicrobial properties of the terpinen-4-ol. These results point to the potential usefulness of developing multilayer coatings for extended deployments

Biodegradable optically transparent terpinen-4-ol thin films for marine antifouling applications

Sustainable marine antifouling strategies aim to minimize long term environmental impacts while effectively preventing surface colonization. In this study, we report upon a biodegradable antifouling coating for marine applications. In two stages, thin bilayers were produced using plasma-enhanced chemical vapor deposition of terpinen-4-ol, at applied powers of 100 W initially and then 10 or 25 W. The resulting coatings were characterized for solubility, surface energy, surface roughness and optical transmission. Both coatings exhibited similar solubility over the initial 14 days of observation, though structures deposited at 25 W were significantly more stable after 14 days. Coatings were smoother than the control surface upon which they were deposited and had higher hydrophobicity with transmission efficiencies > 90% (400-1000 run). Field assessments of the samples were carried out in Curralea Lake (Townsville, Australia) to assess their real world performance. Results indicate that the environmentally friendly coatings, terpinen-4-ol plasma polymer influenced antifouling. The proposed mechanism for this effect is the dissolution of the coating coupled with possible antimicrobial properties of the terpinen-4-ol. These results point to the potential usefulness of developing multilayer coatings for extended deployments

Developing low-cost intelligent wireless sensor networks for aquatic environments

Aquatic environments are extremely difficult for developing, deploying, and maintaining wireless sensor networks. Networks deployed in aquatic settings face multiple challenges, such as marine fowling of equipment, limited power supply, communications difficulties, and \ud restricted accessibility for maintaining and updating sensor nodes. The SEMAT project is an initiative to create "smart", low-cost, heterogeneous wireless sensor networks, tailored to alleviating the aforementioned constraints. Networks can be instantly deployable with minimal setup overheads and can utilise equipment from multiple vendors. This paper presents our experiences with developing the initial technologies to establish SEMAT for field tests. We present the design methodology and challenges faced for creating a marine-based heterogeneous wireless sensor network platform. The result is a low cost solution, with sufficient accuracy for undertaking a study into the factors contributing to Lyngbya algae blooms in Deception Bay, Queensland. The platform builds a case for the merits of the final SEMAT system, as ultimately many of the software and basic hardware challenges for future aquatic deployments have been overcome. This is significant as it allows researchers to focus on the area under study, rather than the specifics of setting up and managing the network

Overcoming the technical problems associated with effective coastal monitoring systems

Coastal and aquatic ecosystems contain a wealth of information that is largely untapped. Understanding the complex dynamics of marine environments will enable scientists to propose sound methods for how to better manage these valuable resources in a sustainable manner. Emerging wireless sensor network technologies are now providing marine scientists with tools to gather data on key ecological factors in ways never previously thought possible. However, various technical, commercial, and logistical factors make it difficult to effectively develop and utilize coastal monitoring technology. This paper examines the main issues associated with wireless sensor network technologies for use in coastal monitoring applications. It describes the challenges faced by the developers, the conflicting push and pull influences by vendors, and the logistical/operational issues for deploying sensor networks in harsh marine environments. We describe the lesions learnt from several real-world sensor network systems currently in use on the Great Barrier Reef, Deception Bay and Heron Island. The experience gained from these deployments can be used as a blue print for future coastal monitoring applications so that cheaper, more cost effective, and user-friendly systems will result. This will enable end users to better understand the sensitive ecological factors that effect these environments, without being burdened by the underlying technical detail

Benefits of building wireless sensor networks on commodity hardware and software stacks

The majority of wireless sensor networks are built on bespoke platforms, that is, custom designed and built hardware with a light weight software stack. There are a number of advantages to this approach. First, the ability to closely match and minimise the resource requirements (e.g., power consumption and communications protocols) to those that are suitable for the intended deployment. Second, as an entire hardware and software stack is often designed or at least optimised for each deployment, the latest advances can be quickly incorporated. However, this model generally requires the expertise of hardware and software engineers to design and build the system. In turn, this increases the cost and tends to shift the focus away from the initial science towards the development of the wireless sensor networks. This paper explores the utility and practicality of building wireless sensor networks based on commercially available embedded single board computing platforms using standard consumer operating systems. Our test bed was built using Gumstix computing platform, running a Linux Operating System (OS) with a java-based middleware coupled to low-cost scientific grade sensors. Test deployments have found this to be a highly versatile solution, able to leverage the flexibility of commodity hardware and software while maintaining reasonable utility