Tag Archives: modeling
You might be really pleased with the camera technology in your latest smartphone, which can recognize your face and take slow-mo video in ultra-high definition. But these technological feats are just the start of a larger revolution that is underway.
The latest camera research is shifting away from increasing the number of mega-pixels towards fusing camera data with computational processing. By that, we don’t mean the Photoshop style of processing where effects and filters are added to a picture, but rather a radical new approach where the incoming data may not actually look like at an image at all. It only becomes an image after a series of computational steps that often involve complex mathematics and modeling how light travels through the scene or the camera.
This additional layer of computational processing magically frees us from the chains of conventional imaging techniques. One day we may not even need cameras in the conventional sense any more. Instead we will use light detectors that only a few years ago we would never have considered any use for imaging. And they will be able to do incredible things, like see through fog, inside the human body and even behind walls.
Single Pixel Cameras
One extreme example is the single pixel camera, which relies on a beautifully simple principle. Typical cameras use lots of pixels (tiny sensor elements) to capture a scene that is likely illuminated by a single light source. But you can also do things the other way around, capturing information from many light sources with a single pixel.
To do this you need a controlled light source, for example a simple data projector that illuminates the scene one spot at a time or with a series of different patterns. For each illumination spot or pattern, you then measure the amount of light reflected and add everything together to create the final image.
Clearly the disadvantage of taking a photo in this is way is that you have to send out lots of illumination spots or patterns in order to produce one image (which would take just one snapshot with a regular camera). But this form of imaging would allow you to create otherwise impossible cameras, for example that work at wavelengths of light beyond the visible spectrum, where good detectors cannot be made into cameras.
These cameras could be used to take photos through fog or thick falling snow. Or they could mimic the eyes of some animals and automatically increase an image’s resolution (the amount of detail it captures) depending on what’s in the scene.
It is even possible to capture images from light particles that have never even interacted with the object we want to photograph. This would take advantage of the idea of “quantum entanglement,” that two particles can be connected in a way that means whatever happens to one happens to the other, even if they are a long distance apart. This has intriguing possibilities for looking at objects whose properties might change when lit up, such as the eye. For example, does a retina look the same when in darkness as in light?
Single-pixel imaging is just one of the simplest innovations in upcoming camera technology and relies, on the face of it, on the traditional concept of what forms a picture. But we are currently witnessing a surge of interest for systems that use lots of information but traditional techniques only collect a small part of it.
This is where we could use multi-sensor approaches that involve many different detectors pointed at the same scene. The Hubble telescope was a pioneering example of this, producing pictures made from combinations of many different images taken at different wavelengths. But now you can buy commercial versions of this kind of technology, such as the Lytro camera that collects information about light intensity and direction on the same sensor, to produce images that can be refocused after the image has been taken.
The next generation camera will probably look something like the Light L16 camera, which features ground-breaking technology based on more than ten different sensors. Their data are combined using a computer to provide a 50 MB, re-focusable and re-zoomable, professional-quality image. The camera itself looks like a very exciting Picasso interpretation of a crazy cell-phone camera.
Yet these are just the first steps towards a new generation of cameras that will change the way in which we think of and take images. Researchers are also working hard on the problem of seeing through fog, seeing behind walls, and even imaging deep inside the human body and brain.
All of these techniques rely on combining images with models that explain how light travels through through or around different substances.
Another interesting approach that is gaining ground relies on artificial intelligence to “learn” to recognize objects from the data. These techniques are inspired by learning processes in the human brain and are likely to play a major role in future imaging systems.
Single photon and quantum imaging technologies are also maturing to the point that they can take pictures with incredibly low light levels and videos with incredibly fast speeds reaching a trillion frames per second. This is enough to even capture images of light itself traveling across as scene.
Some of these applications might require a little time to fully develop, but we now know that the underlying physics should allow us to solve these and other problems through a clever combination of new technology and computational ingenuity.
This article was originally published on The Conversation. Read the original article.
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As Dorothy famously said in The Wizard of Oz, there’s no place like home. Home is where we go to rest and recharge. It’s familiar, comfortable, and our own. We take care of our homes by cleaning and maintaining them, and fixing things that break or go wrong.
What if our homes, on top of giving us shelter, could also take care of us in return?
According to Chris Arkenberg, this could be the case in the not-so-distant future. As part of Singularity University’s Experts On Air series, Arkenberg gave a talk called “How the Intelligent Home of The Future Will Care For You.”
Arkenberg is a research and strategy lead at Orange Silicon Valley, and was previously a research fellow at the Deloitte Center for the Edge and a visiting researcher at the Institute for the Future.
Arkenberg told the audience that there’s an evolution going on: homes are going from being smart to being connected, and will ultimately become intelligent.
Intelligent home technologies are just now budding, but broader trends point to huge potential for their growth. We as consumers already expect continuous connectivity wherever we go—what do you mean my phone won’t get reception in the middle of Yosemite? What do you mean the smart TV is down and I can’t stream Game of Thrones?
As connectivity has evolved from a privilege to a basic expectation, Arkenberg said, we’re also starting to have a better sense of what it means to give up our data in exchange for services and conveniences. It’s so easy to click a few buttons on Amazon and have stuff show up at your front door a few days later—never mind that data about your purchases gets recorded and aggregated.
“Right now we have single devices that are connected,” Arkenberg said. “Companies are still trying to show what the true value is and how durable it is beyond the hype.”
Connectivity is the basis of an intelligent home. To take a dumb object and make it smart, you get it online. Belkin’s Wemo, for example, lets users control lights and appliances wirelessly and remotely, and can be paired with Amazon Echo or Google Home for voice-activated control.
Speaking of voice-activated control, Arkenberg pointed out that physical interfaces are evolving, too, to the point that we’re actually getting rid of interfaces entirely, or transitioning to ‘soft’ interfaces like voice or gesture.
Drivers of change
Consumers are open to smart home tech and companies are working to provide it. But what are the drivers making this tech practical and affordable? Arkenberg said there are three big ones:
Computation: Computers have gotten exponentially more powerful over the past few decades. If it wasn’t for processors that could handle massive quantities of information, nothing resembling an Echo or Alexa would even be possible. Artificial intelligence and machine learning are powering these devices, and they hinge on computing power too.
Sensors: “There are more things connected now than there are people on the planet,” Arkenberg said. Market research firm Gartner estimates there are 8.4 billion connected things currently in use. Wherever digital can replace hardware, it’s doing so. Cheaper sensors mean we can connect more things, which can then connect to each other.
Data: “Data is the new oil,” Arkenberg said. “The top companies on the planet are all data-driven giants. If data is your business, though, then you need to keep finding new ways to get more and more data.” Home assistants are essentially data collection systems that sit in your living room and collect data about your life. That data in turn sets up the potential of machine learning.
Colonizing the Living Room
Alexa and Echo can turn lights on and off, and Nest can help you be energy-efficient. But beyond these, what does an intelligent home really look like?
Arkenberg’s vision of an intelligent home uses sensing, data, connectivity, and modeling to manage resource efficiency, security, productivity, and wellness.
Autonomous vehicles provide an interesting comparison: they’re surrounded by sensors that are constantly mapping the world to build dynamic models to understand the change around itself, and thereby predict things. Might we want this to become a model for our homes, too? By making them smart and connecting them, Arkenberg said, they’d become “more biological.”
There are already several products on the market that fit this description. RainMachine uses weather forecasts to adjust home landscape watering schedules. Neurio monitors energy usage, identifies areas where waste is happening, and makes recommendations for improvement.
These are small steps in connecting our homes with knowledge systems and giving them the ability to understand and act on that knowledge.
He sees the homes of the future being equipped with digital ears (in the form of home assistants, sensors, and monitoring devices) and digital eyes (in the form of facial recognition technology and machine vision to recognize who’s in the home). “These systems are increasingly able to interrogate emotions and understand how people are feeling,” he said. “When you push more of this active intelligence into things, the need for us to directly interface with them becomes less relevant.”
Could our homes use these same tools to benefit our health and wellness? FREDsense uses bacteria to create electrochemical sensors that can be applied to home water systems to detect contaminants. If that’s not personal enough for you, get a load of this: ClinicAI can be installed in your toilet bowl to monitor and evaluate your biowaste. What’s the point, you ask? Early detection of colon cancer and other diseases.
What if one day, your toilet’s biowaste analysis system could link up with your fridge, so that when you opened it it would tell you what to eat, and how much, and at what time of day?
Roadblocks to intelligence
“The connected and intelligent home is still a young category trying to establish value, but the technological requirements are now in place,” Arkenberg said. We’re already used to living in a world of ubiquitous computation and connectivity, and we have entrained expectations about things being connected. For the intelligent home to become a widespread reality, its value needs to be established and its challenges overcome.
One of the biggest challenges will be getting used to the idea of continuous surveillance. We’ll get convenience and functionality if we give up our data, but how far are we willing to go? Establishing security and trust is going to be a big challenge moving forward,” Arkenberg said.
There’s also cost and reliability, interoperability and fragmentation of devices, or conversely, what Arkenberg called ‘platform lock-on,’ where you’d end up relying on only one provider’s system and be unable to integrate devices from other brands.
Ultimately, Arkenberg sees homes being able to learn about us, manage our scheduling and transit, watch our moods and our preferences, and optimize our resource footprint while predicting and anticipating change.
“This is the really fascinating provocation of the intelligent home,” Arkenberg said. “And I think we’re going to start to see this play out over the next few years.”
Sounds like a home Dorothy wouldn’t recognize, in Kansas or anywhere else.
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