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VibroSense tracks home appliance usage via deep learning and lasers

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Advances in technology have made many household appliances more energy efficient, and even given outdated old ones some energy-saving smarts, but addressing the power usage of each individual device across the home is still a tall order. Researchers at Cornell University have been working on more of a one-size-fits-all solution, developing a vibration-sensing device that can keep tabs on appliance usage through machine learning and lasers. The team points to smart homes of the future as its inspiration for developing the VibroSense device, imagining scenarios where the house itself knows when a washing machine has completed its cycle, when a microwave has finished heating food or a faucet is dripping. While replacing each appliance with smart versions or attaching specific sensors to them could be one way to tackle this, the Cornell team sees a more efficient way forward. "In order to have a smart home at this point, you'd need each device to be smart, which is not realistic; or you'd need to install separate sensors on each device or in each area," says Cheng Zhang, assistant professor of information science and senior author of the study.


Cornell's VibroSense makes appliances 'smart' by tracking their vibrations

Engadget

We've all been there at some point: you forget to go to the dryer when it completes a cycle, letting your clothes sit and collect wrinkles. That's just one example of inefficient home appliance use, and it's probably one that most people would like to avoid if they could. A new device developed by a team of researchers at Cornell University promises to let you do just that. Best of all, you wouldn't need to replace your existing appliances. The team created a device called VibroSense that can identify and categorize home appliances by the specific vibrations they produce in your home as those vibrations travel from room to room.


Machine learning predicts honeybee swarms

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When honeybees are ready to establish a new colony, they initiate a coordinated procedure called swarming. For beekeepers, swarming provides an opportunity to capture the departing bees and establish a new hive. To forecast a swarm, beekeepers regularly inspect their hives for the presence of larger honeycomb cells that host developing future queens. But those regular inspections are laborious. Now Martin Bencsik of Nottingham Trent University in the UK and his colleagues are automating the process by using machine learning.


Balluga mattress has built-in air conditioning and stops snoring

Daily Mail - Science & tech

If lack of a good night's sleep has left you dreaming of drifting off to the land of nod, a new vibrating smart bed may be just what you need. The Balluga bed is made up of air-filled balls, covered in foam, and is crammed full of tech to monitor sleep and regulate temperature. Developed by British designers, its makers claim the smart bed is the solution to sleepless nights, mattress-related back ache and even snoring partners. The bed has key tech features (pictured) which the makers say will help the user to drift off to sleep. According to the bed's Kickstarter page, many of the tech features can be controlled using a phone.


Wearable medical tech is about to become crucial for staying alive

#artificialintelligence

Medical treatment today primarily takes the form of drugs and therapy. But a third option is slowly emerging: on-body, digital devices that can treat both mental and physical conditions. Such "wearable" therapy offers unique advantages in that it is often more targeted, cheaper, personalised and has fewer negative side effects. Mobile and wearable devices such as phones or fitness trackers are now routinely used for preventive health. They monitor physiological data and behaviour, increase self-awareness and encourage behaviour change.