The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Y2K Interruption: Can the Doomsday Scenario Be Averted?

The management philosophy until recent years has been to replace the workers with computers, which are available 24 hours a day, need no benefits, no insurance and never complain. But as the year 2000 approached, along with it came the fear of the millennium bug, generally known as Y2K, and the computers threatened to strike!!!! Y2K, though an abbreviation of year 2000, generally refers to the computer glitches which are associated with the year 2000. Computer companies, in order to save memory and money, adopted a voluntary standard in the beginning of the computer era that all computers automatically convert any year designated by two numbers such as 99 into 1999 by adding the digits 19. This saved enormous amount of memory, and thus money, because large databases containing birth dates or other dates only needed to contain the last two digits such as 65 or 86. But it also created a built in flaw that could make the computers inoperable from January 2000. The problem is that most of these old computers are programmed to convert 00 (for the year 2000) into 1900 and not 2000. The trouble could therefore, arise when the systems had to deal with dates outside the 1900s. In 2000, for example a programme that calculates the age of a person born in 1965 will subtract 65 from 00 and get -65. The problem is most acute in mainframe systems, but that does not mean PCs, UNIX and other computing environments are trouble free. Any computer system that relies on date calculations must be tested because the Y2K or the millennium bug arises because of a potential for “date discontinuity” which occurs when the time expressed by a system, or any of its components, does not move in consonance with real time. Though attention has been focused on the potential problems linked with change from 1999 to 2000, date discontinuity may occur at other times in and around this period.

Beamforming analysis using Random Forest classifier

Abstract. Wireless communication has a long history that has changed shape throughout the centuries, from smoke signals to electromagnetic radiation. Data transmission evolution has made worldwide communication possible and has contributed to globalisation. Today, information can be shared in real-time—for example, to the other side of the world. Wireless communication has evolved to the point that real-time plays a vital role, and data loss should not occur. For efficient wireless data transmission, a beamforming technique has been developed. This is a signal processing technique used in antennas for directional signal transmission or reception. Beamforming includes numerous variations, making the analysis of beamforming challenging. Due to its complex nature, beamforming is attempted to be understood more simply at a higher level, and for that reason, elements are listed that enable the analysis to check whether beamforming succeeded on the radio. Machine learning is a new trend in different aspects of technology. Problems are aimed to be solved and predicted more efficiently by using suitable machine learning methods. Machine learning enables more precise analysis and error tracking, which are utilised in combination to minimise errors. Furthermore, machine learning has been integrated into various automation systems. This thesis concentrates on analysing the success of beamforming at a high level and aims to automate testing and provide feedback to radio architects who utilise beamforming. For a high-level analysis, a few criteria define the success of beamforming on the radio. In this thesis, a machine learning pipeline is presented from prepossessing to the final model, and we demonstrate the promising results we have been able to achieve using the random forest classifier. Such promising results make it possible to continue with the beamforming classification and serve as motivation to improve and gather detailed feedback for the end-user

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results