Augmented reality and data visualization (in aviation)

Present-day technology is so powerful that the perception of reality can be easily and realistically modified with IT tools, providing users withan experience beyond “simple” reality. This is achievable by mixing real-world environment elements supplemented and/or augmented by computer-generated inputs. The current post unpacks this topic, focusing specifically on the data visualization aspects. In brief, augmented reality can take two approaches:

  • First, inventing totally new scenarios, in which the user becomes part of a “parallel universe”. Supplementing the real-world environment with an unreal one; either a virtual place (video game) or a different location (i.e. another real location). This is the case of futuristic 90’s and early 2000’s alike head-mounted displays with users’ eyes looking at full screens recreating other places. The ergonomics aspects are usually modest for most of the applications due to the head-mounted displays weight and size.

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  • The second, and closer to “data visualization” area is the so called “mediated reality”. The real-world environment enhanced by virtual elements displayed in glasses, windscreens etc. In them, additional information/data is provided. The real challenges is to decide what, how and when to display the information, without requiring users to look away from their usual viewpoints, while providing extra value. The integration and user experience is much more natural and enjoyable than the fully immersive systems.
Research project Augmented Reality - contact-analogue Head-Up Display (10/2011)

Research project Augmented Reality – contact-analogue Head-Up Display (10/2011)

In this context, one of the very early examples of head-up displays can be found precisely in aviation, almost 80 years ago. In 1937, the German ReviC12/A fighter aircraft included a basic reflector sight indicating some basic aircraft magnitudes such as speed and turn rate, to reduce the (visual) workload of pilots in case of extreme maneuvering
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Nowadays virtually all modern fighters (F18, F16, Eurofighter) use head-up displays. The most modern versions (F35) do not have head-up displays, and instead include helmet mounted displays, ensuring the proper orientation of the user’s head, for all circumstances.
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One of the trending topics of augmented reality within aviation is its usage in air traffic control (ATC), particularly in Tower environments. Below are two common approaches:

  • Visual information is enhanced to ease identification and tracking of aircraft. This includes tools similar to head-up displays and/or helmets-displays that enhance the information (providing for instance, aircraft ID, scheduled times, etc). This approach could be extremely useful in low visibility conditions by facilitating the tower ATCOs tasks. It also avoids dividing attention between the primary visual field (the window) and the auxiliary tools (surface radar, strips etc).

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  • The extreme version is a complete virtual control tower, the so called “remote tower”. ATC would have remote control rooms with video-sensors on-site, including augmented reality enhancements. The synthetic augmentation of vision increases the situational awareness at the airport, especially during poor visibility conditions, or blocked line-of-sight areas due to airport geometry. It additionally provides benefits in terms of cost saving (no need to build and maintain control tower facilities) and a more efficient use of human resources (potentially serving multiple airports with low traffic events from a centralised location). Research in this field started in FP6 project “ART” and is now being progressed by SESAR WP6. In fact, Örnsköldsvik/Gideå airport is the first on the world deployment of remote tower, in late 2015, by the Swedish LFV. In US, Fort Collins-Loveland Municipal Airport was the first approved and tested airport with a remote tower in 2016.

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For the air passenger and mobility context, augmented reality and the wide range of solutions providing additional real-time information to passengers is taking off as well. (No pun intended.)
These technological innovations include indoor location tracking, real-time information on boarding gates, real-time updates on flight delays, and information on airport facilities and shops. This is also being expanded to knowing the number and location of available parking spaces to facilitate the passenger experience in the (sometimes not so easy) airport processes. For example, Copenhagen airport, in collaboration with SITA, created the very first augmented reality indoor app in 2012. Now there is an endless list of both airlines and airports with similar apps.
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Do you think augmented reality together with innovative data visualization can have a significant impact in future aviation?
What are its challenges and potential benefits?
We’re interested in hearing your thoughts and ideas.

Innaxis at EASA-OPTICS conference. Cologne 12-14 April

Developing the future of a safe and growing aviation business, whilst also reassuring the travelling public that it is safe to fly, is a major vision for both EU and national aviation policies, however:

What role do policy makers play?

What are the recent, implemented safety measures?

Who is guiding the safety topics within aviation research?

EASA, the European Commission, the Advisory Council of Aviation Research & Innovation in Europe (ACARE), and the EU’s OPTICS Project organised a three day event in Cologne (12-14 April) in order to provide answers to these types of imperative questions, and furthermore define the way forward to ensure continued aviation safety in Europe. The event had a number of presentations and workshops within several aviation safety areas.

Two Innaxis’ team members David Perez (dp@innaxis.org) and Hector Ureta (hu@innaxis.org) attended the interesting event and took part in several of the workshops, explaining how can Data Science and BIG data can boost aviation safety. Hector  also presented some of the latest data science techniques and tools in safety research, based on SESAR-COMPASS project, during the third day of the event.

 

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Hector Ureta (Innaxis) presenting the Data Science research done in COMPASS (Cologne 14 April 2016)

 

The presentation, “Data science and data mining techniques to improve aviation safety: features, patterns and precursors”, is available online in this link.

If you’d like further information about data science in aviation, big data or aviation safety research completed by Innaxis, please feel free to contact Innaxis team (innovation@innaxis.org).

 

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More details of the event available in EASA and OPTICS websites:

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Dr. Matthias Ruete and Future Transport

Dr Matthias Ruete, Director general DG “Mobility and Transport” of the European Commission, visited Spain last Wednesday 22 January invited by Fundacion Euroamerica, which organized a lunch sponsored by Innaxis and attended by the Spanish Secretary of State for Infrastructures, Transport and Housing, Mr Rafael Catalá, and other Spanish and foreign officials, as well as by high representatives of the Spanish and European industry and members of the academia. They all had the opportunity to listen Dr Ruete highlighting the future of transport in Europe and the different challenges that the region is facing to remain competitive.

The European Commission adopted the FlightPath2050 plan to target the ambitious goal of having 90% of air travellers in Europe completing their journey within 4 hours door-to-door. Horizon 2020 research initiatives will focus on new paradigms for reducing the impact of disturbances, understanding passenger metrics and customer profiles, modelling airports and airspace and also promoting multimodal (train-aircraft) projects, among others. Reducing the percentage of income that European households spend on transport, currently around 15%, is also a challenge Europe should face in the following decades. While the fragmentation of transport services, through public and private means, is pervasive, new service and business paradigms suggest that service contracts covering a wide variety of transport means would increase the efficiency and sustainability of the provision of transport services.

Becoming environmentally friendly is also an important challenge for Europe in the next decades. From its very early stages most transport means have been powered by fossil fuels. The reason for this is the ease of use and that those fuels represent a highly concentrated, low weighted, relatively compact source of energy. The drawbacks of such a choice are that they are heavily polluting, and rely on limited energy sources. Different renewable forms of energy are being investigated and, combined with more efficient usage of fossil fuels, they will be the basis of breakthroughs on environmental impact, driven again by an ambitious European goal of between 60%-80% reduction of CO2 emissions by 2050. Of course, the Innaxis Foundation is and will be very active promoting interdisciplinary research in these fields on the coming years.

Wh- Questions about Data Science

The five Wh’s of Data Science – What, Why, When, Who and Which.

While preparing the upcoming October workshop in Data Science, Innaxis has gathered wh- questions and simple answers about the “new reality” of data science. We also provide links to pages where more information about these important questions have been provided.

What?

The basic answer to what is Data Science could be “a set of fundamental principles that support and guide the principled extraction of information and knowledge from data”. Definitions, especially of new terms should remain simple despite the urge to make them complicated. Furthermore, the boundaries of Big Data, Data Science, Statistics and Data Mining definitions are not so discernible and include common principles and tools and, importantly, the same aim: extraction of valuable information.

Why?

What is the reason for extracting information from data? There is a brilliant quote by Jean Baudrillard “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise” In this context, proper data science is [ generally ]  neither basic science nor long term research; it is considered an extremely valuable resource for the creation of business. Mining large amounts of both structured and unstructured data to identify patterns that can directly help an organization in terms of costs, in creating customer profiles, increasing efficiencies, recognizing new market opportunities and enhancing the organization’s competitive advantage.

When?

Through history, an extensive list of names have been given to a well known duality: information=power;  from the middle ages census to the Royal Navy strategies based on statistical analysis. Concerning the current understanding of Data Science, its name has moved away from being a synonym for Data Analysis in the early 20th century to being associated, from the nineteen-nineties, with Knowledge Discovery (KD). One of the very best compilations of data science history and publications over the last 60 years can be found in this Forbes article.

Throughout history, the various methods and tools used have changed, developing as both the mathematical, extraction and software and hardware capabilities have increased in recent years. The consequent “sudden” eruption in Data Science jobs,  which identifies the market’s real interest in those potential benefits that knowledge extraction offers, is visually described with the following graph taken from Linkedin analytics:

Courtesy LinkedIn Corp.

Who?

If you are a lawyer or a doctor everybody knows more or less your level of education at university and the nature of your daily tasks. What is then a “Data Scientist”? The clear paths that could lead to a Data Science career are not so defined and are difficult to identify. The so called “Sexiest Job of the 21st century” (according to the Harvard Business Review), needs a common definition and even specific university degrees.  The data jockeys that have always been employed in Wall Street are no longer alone. Meanwhile the scope and variety of data now available is a non-stop, growing, force resulting in operational, statistical and even hacking backgrounds being welcome to extract value from it. More information about data scientist careers and the main disciplines can be found in this excellent article from naturejobs.com.

In order to understand Data Science job titles, we recommend you also have a look at this article by Vincent Granville from DataScienceCentral. It’s a living tongue twister: data mining activity done by a data scientist regarding data scientist job titles. Summing it up, it is pretty similar to the following recipe: Take a mixer from the kitchen; add the words “Data” “Analytics” “Scientist”; switch it on; include some institutional label “director” “Junior” “Manager”. An additional optional topping could be your university degree “engineer” “mathematician”. There you have one of the possible names of current data scientist.

Which?

Which data is “datascience-able”? As we described in our previous post about Data Science, there is huge potential in almost every imaginable field that could provide sufficient quality data for analysis. Although, even where the date is available, there are challenges faced,  generally connected with data storing and managing capabilities. These challenges are covered in detail in the Innaxis blogpost, “The benefits and challenges of Big Data”. One of the remarkable and exciting things about Data Science is that there is additional knowledge to extract from data sets that at first sight are not expected to provide anything beyond the obvious potential from the so called “direct” datasets. The reality is it’s hard to know which data sets will add value before testing them with Data Science. When discovered, hidden patterns and unseen correlations are really adding more valuable knowledge to entities than direct cause-and-effect relationships. They represent being one step ahead, which is crucial in the highly competitive world in which we are living.

By Héctor Ureta – Collaborative R&D Aerospace Engineer at Innaxis

 

 

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Wh- Questions about Data Science

The five Wh’s of Data Science – What, Why, When, Who and Which.

While preparing the upcoming October workshop in Data Science, Innaxis has gathered wh- questions and simple answers about the “new reality” of data science. We also provide links to pages where more information about these important questions have been provided.

What?

The basic answer to what is Data Science could be “a set of fundamental principles that support and guide the principled extraction of information and knowledge from data”. Definitions, especially of new terms should remain simple despite the urge to make them complicated. Furthermore, the boundaries of Big Data, Data Science, Statistics and Data Mining definitions are not so discernible and include common principles and tools and, importantly, the same aim: extraction of valuable information.

Why?

What is the reason for extracting information from data? There is a brilliant quote by Jean Baudrillard “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise” In this context, proper data science is [ generally ]  neither basic science nor long term research; it is considered an extremely valuable resource for the creation of business. Mining large amounts of both structured and unstructured data to identify patterns that can directly help an organization in terms of costs, in creating customer profiles, increasing efficiencies, recognizing new market opportunities and enhancing the organization’s competitive advantage.

When?

Through history, an extensive list of names have been given to a well known duality: information=power;  from the middle ages census to the Royal Navy strategies based on statistical analysis. Concerning the current understanding of Data Science, its name has moved away from being a synonym for Data Analysis in the early 20th century to being associated, from the nineteen-nineties, with Knowledge Discovery (KD). One of the very best compilations of data science history and publications over the last 60 years can be found in this Forbes article.

Throughout history, the various methods and tools used have changed, developing as both the mathematical, extraction and software and hardware capabilities have increased in recent years. The consequent “sudden” eruption in Data Science jobs,  which identifies the market’s real interest in those potential benefits that knowledge extraction offers, is visually described with the following graph taken from Linkedin analytics:

Courtesy LinkedIn Corp.

Who?

If you are a lawyer or a doctor everybody knows more or less your level of education at university and the nature of your daily tasks. What is then a “Data Scientist”? The clear paths that could lead to a Data Science career are not so defined and are difficult to identify. The so called “Sexiest Job of the 21st century” (according to the Harvard Business Review), needs a common definition and even specific university degrees.  The data jockeys that have always been employed in Wall Street are no longer alone. Meanwhile the scope and variety of data now available is a non-stop, growing, force resulting in operational, statistical and even hacking backgrounds being welcome to extract value from it. More information about data scientist careers and the main disciplines can be found in this excellent article from naturejobs.com.

In order to understand Data Science job titles, we recommend you also have a look at this article by Vincent Granville from DataScienceCentral. It’s a living tongue twister: data mining activity done by a data scientist regarding data scientist job titles. Summing it up, it is pretty similar to the following recipe: Take a mixer from the kitchen; add the words “Data” “Analytics” “Scientist”; switch it on; include some institutional label “director” “Junior” “Manager”. An additional optional topping could be your university degree “engineer” “mathematician”. There you have one of the possible names of current data scientist.

Which?

Which data is “datascience-able”? As we described in our previous post about Data Science, there is huge potential in almost every imaginable field that could provide sufficient quality data for analysis. Although, even where the date is available, there are challenges faced,  generally connected with data storing and managing capabilities. These challenges are covered in detail in the Innaxis blogpost, “The benefits and challenges of Big Data”. One of the remarkable and exciting things about Data Science is that there is additional knowledge to extract from data sets that at first sight are not expected to provide anything beyond the obvious potential from the so called “direct” datasets. The reality is it’s hard to know which data sets will add value before testing them with Data Science. When discovered, hidden patterns and unseen correlations are really adding more valuable knowledge to entities than direct cause-and-effect relationships. They represent being one step ahead, which is crucial in the highly competitive world in which we are living.

By Héctor Ureta – Collaborative R&D Aerospace Engineer at Innaxis

 

 

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