Smart Home Technologies Are Saving Money and Lives: Reaching out in new directions, signal processing-supported smart technologies are rapidly changing-and improving-everyday life

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Smart Home Technologies Are Saving Money and Lives: Reaching out in new directions, signal processing-supported smart technologies are rapidly changing-and improving-everyday life

By: 
John Edwards

Smart home technologies, designed to make users happier, healthier, and wealthier, are rapidly becoming a mainstay of everyday life. In most cases, signal processing is essential to the devices’ operation and performance.

These days, a variety of intelligent automated devices can be found in nearly every home. The trend is accelerating so rapidly that it now appears inevitable that smart technology will soon be integrated into virtually every facet of daily life.

With smart technologies already widely used in applications as diverse as home security, environmental control, and home entertainment, researchers are now heading in new directions, leading to applications as diverse as home appliance monitoring, improved power delivery, and advanced remote medical support.

Good Vibrations

For anyone who has ever neglected to turn off an electric kettle, water faucet, air conditioner, or other household appliance before leaving home—potentially leading to unfortunate consequences—Cornell University researchers are offering a potential safeguard. The team’s new VibroSense device is a smart home technology that can track 17 types of appliances using subtle vibrations found in walls, ceilings, and floors; it is also a deep learning network that models vibration data to create different signatures for each appliance.

Many home appliances generate vibrations identifiable by unique signatures that propagate through a home’s walls, ceilings, and other infrastructure elements, observes Cheng Zhang, a Cornell assistant professor of computing and information science as well as director of the university’s SciFi Lab. “These unique characteristics … can be captured by a laser vibrometer and recognized by AI (artificial intelligence) technology—in our case, a deep learning algorithm—to distinguish the sound source.”

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