The results show that accurate ecological maps can be made using data from only a small portion of the area to be mapped, and that at moderate levels noise or misclassifications in the data have a negligible effect. As such, this experiment aims to develop a framework to create ecological maps from citizen scientist collected video, by leveraging the power of deep learning neural networks in the analysis of ecological image data. Citizen scientists, those who willingly offer their time to aid in the scientific process, are a still massively underutilised resource. In a world of increasing ecological instability due to threats posed by anthropogenic factors such as climate change and habitat loss, it is vital that the worlds' ecologists are equipped with the most recent and reliable data to make decisions regarding conservation and sustainability. Topic: Ecological mapping using citizen science and machine learning My project involved researching existing solutions, performing a component selection then programming a microcontroller to develop a physical prototype. An expected outcome was also to develop a cost effective solution. The aim of my project was to develop an electronic visual aid with a focus on object avoidance and ergonomics suited towards daily use for visually impaired people. We then evaluate the performance of our proposed approach on real flight data, where we are able to successfully detect aircraft at ranges approaching those of human pilots. We train our network on synthetic image sequences of aircraft on collision course encounters. In this paper, we present a new approach for vision-based aircraft detection which uses deep learning for both the spatial and temporal behaviour of the potential collision threat. One of the key barriers with advancement in detecting aircraft is the lack of available data due to the challenging task of acquiring real data. The increasing demand for unmanned aerial vehicles has spurred efforts to safely integrate the routine operation of UAVs and the need to detect potential mid-air collision threats. Topic: Vision Based Aircraft Detection using Deep Learning with Synthetic Data
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Please ensure you register your attendance for catering purposes.Ĭome along to support the future engineers of the profession!ĥ.30 pm Arrival / Check-in and networking Guests of the event are welcome to attend from 5.30 pm and will enjoy some light refreshments. The judging is split by 25% technical content and 75% presentation skills, enabling individuals at varying levels of their studies or career to participate.
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The candidates will give a fifteen-minute presentation on the engineering or technology subject of their choice, followed by a five minute Q&A session.
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Young engineers in the industry and engineering students from the six Queensland universities will be tested for their presentation skills in relation to engineering projects and ideas. Kuwait Qatar Saudi Arabia UAE Bahrain Egypt Oman Turkey Singapore Australia Malaysia Russia Brazil South Africa Morocco Lebanon jordan New Zealand India Chile USA Argentina Austria Italy Belgium Philippines Germany Japan Mexico Netherlands Colombia Indonesia Spain Switzerland Czech Republic Denmark Norway Poland Portugal France Ireland Canada Uk Taiwan Vietnam South Korea Sweden Thailand Ukraine Hong Kong China Finland Iceland Maldives Peru Hungary Iran Angola Bangladesh Belarus Cameroon Costa Rica Dominican Republic Ecuador El Salvador Ethiopia Georgia Ghana Greece Guatemala Kazakhstan Kenya Madagascar Mauritius Mozambique Nicaragua Panama Paraguay Puerto Rico Romania Senegal Slovakia Sri Lanka Tanzania Tunisia Uganda Uzbekistan Venezuela Zambia Zimbabwe Estonia Brunie Anguilla Uruguay Palastine Antigua and Barbuda Saint Vincent Jersey Belize Burundi Greenland Guyana Honduras Isle of Man Liechtenstein North Macedonia Seychelles Slovenia Haiti Lithuania The Bahamas Solomon Islands French Guiana Kyrgyzstan Suriname Albania American Samoa Benin ubuy.The IET QLD / EA Student Presentation competition is an annual IET technical competition.