- Bluetooth pairing
- Voice control
- Full Cortana integration
- Caller ID announcements when receiving a call
So all-in-all I am very happy with my choice. More test results after some road tests in the coming days.
So all-in-all I am very happy with my choice. More test results after some road tests in the coming days.
My wife and I are avid campers. We spend at least 6 weeks a year in various campgrounds in Europe and The United States. Our preferred way of camping is with a tent and we like to stay in ‘rural’ campsites. In the USA that means campground in national and state parks, in Europe we avoid the big campsites and look for smaller privately owned sites.
We have a small tent that has room for three, so we can fit 2 beds and our luggage easily. Up until last year we slept on self-inflatables and that worked out fine until I noticed to get some trouble with my lower back (I am 51 years old). It would hurt when I got up in the morning and immediately start hurting again when we went back to bed on the 2nd or 3rd day.
So I started looking for alternatives and came across the Thermarest LuxuryLite Cot. More specifically the extra-large variant since I am quite large (193 cm) and because this version is also extra wide, it would allow me to sleep on my side, which is my preferred position.
Now, you need to know that this cot is expensive! I ordered it at CAMPZ.NL and the price-tag was over 225 euro’s – which is a lot, but it came with a 100 day return guarantee, so I would be able to test it thoroughly.
Did it work? The answer is wholeheartedly ‘Yes’ – I used the cot during a 4 week camping trip and spend almost 24 nights on this bed (other nights were spend in a hotel for the necessary showers and laundry facilities) – in over 6 different campsites.
Now it would be strange if I would say that not spending that time in my own bed did not affect me, but overall I slept great and had no back pain to speak of.
The cot consists of a sturdy cloth, 2 foldable sticks that slide into the long side and a set of smaller sticks and ‘feet’ that allow you to build the cot. It is almost impossible to explain in text, so I refer to the YouTube video for an explanation how to set this thing up ( http://youtu.be/orRtuiTgQ40 )
The cot comes in a small bag, that holds all of the components easily and the complete package is surprisingly light (less than 2 kg). Setting up takes some effort and strength in your upper arms.
After the first week I decided to change the way that the struts and feet are located – I used to evenly spread them across the length of the bed, but I discovered that putting 2 close together to support my head and neck, leave a bigger gap and then put in the rest (I suggest you look at the video, it will make more sense than my textual description here), was a better setup.
After 6 weeks the bed is still in perfect condition, but I do have some concerns about the holes in the cloth where you fix the ‘feet’; due to the tension that is put on the holes, I expect that this is an area that will suffer from the setup process. Also I noticed that this is not a very sturdy bed, getting in and out of it needs to be done with some care – you can absolutely forget to do anything else then sleep in this bed.
All in all I am happy with my purchase and hope we can enjoy camping for a long time.
Imagine you have an automatically and real-time updated agenda – it continuously adapts your schedule to meetings taking longer, predicts and updates in real-time your travel-time to the next meetings and will adapt your schedule because it ‘knows’ that typically any meeting with your best client always takes 30 minutes longer than you originally plan it for.
A proof of concept conducted by the Atos Scientific Community looked at this aspect of predictability and took the data of the traffic in the city of Berlin to see if it was possible to do real time traffic forecasting (RTTF). The result is in a recently published white paper.
“RTTF enables a prediction (within 1 minute) of sensor data streams for the immediate future (up to four hours) and provides traffic condition classification for the upcoming time period based on the forecasted data.”
“The forecast provides a suitable time span for proactively managing upcoming incidents even before they appear.”
The team took a radical different approach to the challenges of today’s traffic management. Instead of proposing another reactive traffic management IT system with some smart analytics, the team targeted successfully a proactive traffic management approach which provides analytics solutions to predict critical events in advance before they appear. Using historic data and artificial neuron network technology, predictions are created for the intermediate future and utilized to determine the traffic status of the upcoming next four hours. Based on that information, actions can be taken proactively to mitigate or avoid future upcoming events. Utilizing the software and bringing in data scientists with an understanding of the context was the next step. This helped in defining the right parameters and a pattern based strategy (PBS) in place.
“Being able to identify patterns out of the existing data, model them into patterns and come up with a system that can provide reliable predictions is a remarkable achievement in itself, but the true value of PBS is being able to apply such capabilities to strategy definition and decision making.”
Working with the subject matter experts the team identified multiple models that were then consequently implemented in the software. The models are important, they avoid that you are trapped into simplification; when a car is driving slowly, it can be because of a traffic jam, but it can also be an older person driving more carefully.
By introducing the concept of ‘flow’ – the number of vehicles passing a sensor each hour – the team could identify 4 different states, which were in themselves also parameterized by looking at road capacity, speed limits, etc. This information is then fed into a look-up table based complex event processing engine in order to predict, within 1 minute, the traffic situation at given locations.
Because in real-life the historic data is continuously refreshed with the actual events of the past time, the system will be able to predict in real-time the situation on the road.
The proof of concept clearly showed that a self-learning system, combined with a complex event processing unit and the help of some subject matter expert data scientist can accurately predict the future – the white paper shows this in some great details.
“Real Time Traffic Forecasting is an excellent example of how data sources and identified patterns can be exploited to gain insights and to develop proactive strategies to deal with upcoming events and incidents. It enables a short term view into the future which is long enough to act on predicted incidents rather than react on occurring ones”
For me this proof of concept shows the benefits of data analytics in everyday life, and I am looking forward to this future.
This blog post was previously published at http://blog.atos.net/blog/2013/12/12/watch-this-space-your-future-now-available-in-real-time/
Detroit Electric car charging (Photo credit: Wikipedia)
I do not like batteries and I do not much like cars. So when my colleagues in the Atos Scientific Community talk about these two things in 1 whitepaper you can understand it is something I cannot ignore.
Batteries are empty when you need them most (look at any horror movie and you know what I mean). Cars are expensive and they pollute our planet.
There is a multitude of battery formats and you have to go to the store to buy them – or you need to charge them and that takes a lot of time (iPhone anybody?).
So, electric cars, cars fuelled by batteries, is something that I find an unlikely combination.
Charging them is time consuming and a charger is not available everywhere. The use of changeable batteries might seems like a good idea, but is it really convenient and what about formats and availability?
Exactly this dilemma of ‘Electromobility’ is described in a whitepaper of the Atos Scientific Community:
“The success of Electromobility depends on addressing two major challenges: User acceptance and the availability of supporting infrastructure and services.”
After looking into the subject it became clear that next to user acceptance and the support infrastructure, we can also see this as a huge area of new revenue possibilities and innovation.
Leasing and pay-for-use in mobility are much better business cases for both the vendor and the consumer. It also ‘fuels’ (sorry, I could not resist making that joke…), the innovation process because you take out the big capex investment for the end user.
“New business models like battery leasing, simple easy to use charging infrastructure and the involvement of all stakeholders are the key to a positive business case.”
Instead of buying a new car every 5 or 6 years, the consumer (and companies) can choose to either own a car or lease one according to his needs of today.
With the right infrastructure he can choose to just change battery or even change the whole car. And through some clever analysis the supplier can even predict the end user behavior.
This prediction leads to a more tailored offering and in itself will drive further innovation. Such analysis, combined with extending the eco system for example into insurance companies, food and beverage providers, holiday brokers and other leisure providers can create whole new commercial eco systems.
“Electromobility needs multi-sided flexible business platforms with open interfaces to create new value.”
This is of course all very much in the future and will mean we need to change our habits, both on the provider and the consumer side of driving a car.
But change is inevitable and with the right standardization to support ease of use, and a well-integrated system for payments and loyalty schemes, I might be persuaded to buy into such a solution for visiting my mother.
For now we just need to start to understand what is at stake, what is possible and which actions we need to take. For this the upcoming whitepaper is an excellent first step.
Real estate brokers already know this for years; the most important aspect of their profession is location, location and location.
Given the recent boom in providing maps to mobile phones, this aspect of our lives is not just important for real estate brokers anymore. Anybody with a decent smart phone has access to a variety of mapping material, aiding in navigation and, through the addition of a GPS-receiver, location based services.
The impact of this capability to use digital maps is described in a whitepaper by the Atos Scientific Community, which states:
Magellan Blazer12 GPS Receiver. (Photo credit: Wikipedia)
“For centuries, humankind has devoted efforts to cartography (map making) as a means to represent the Earth in order to achieve a better understanding of the world and to solve practical problems (navigation, exploration, planning). The advent of electronic computers and peripheral devices caused another revolution in cartography during the 20th century. However, until the late 1990’s geo-information did not take a pervasive step into other fields. In the 2000’s, geo-information has become popular and is widely used by billions of people around the world for everyday purposes: we look for directions, plan our holidays or check the evolution of news events with the support of geo-information."
And while we are all just getting accustomed to this digitally enhanced 2D world, a new way of looking at this information is emerging: 3D location based services.
Using complex mathematics and new type of sensors, we will be able to enrich our flat maps with depth, height and rotation. This opportunity opens a new dimension (pun intended) in the way we can use the digital maps for more purposes than just finding our way home.
Applications in building-management, tsunami warning systems, climate and weather control as well as looking at relations between human wellbeing and location based data (such as pollution), are now in use.
The new elements coming because of 3D are emerging, so we can track people in buildings, assets in datacenters and generally combine information to create meaningful relationships in data visualization.
Tracing and combining data in 3 dimensions is not only more accurate, it also give more insight in relationships between the subjects. Only now that we are getting more insight in the associated math and have the (real-time)processing capability to calculate complex equations, we are getting insight in the correlations and how they help us interpret the real word around us.
The paper describes multiple examples and explains how the extra info can be expected to enrich our way of using the extra information for location based data.
The key is to connect the available 3D information to this extra information. This combination not only will enrich our lives, but also opens a new business opportunity for information brokers to provide and combine data that without this technology would possibly be meaningless by itself. In order for this additional data to be completely integrated in our daily lives we certainly need more study and technical breakthroughs. Most of this is done in the background, but some research and results are needed in understanding the way that people interpret 3D information.
Google Earth and other similar technology is showing me nice pictures, but does it provide extra value in 3D (on a flat screen…)?
In the end the 3D element will, certainly, greatly influence the way we interact with our world and our understanding how things relate to each other. Understanding relationships, in my opinion, always makes us a better human.
That combined with the new business opportunities, drives both personal and economic growth, so I consider this a good thing.