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Roomba for Rivers – IEEE Spectrum

Roomba for Rivers – IEEE Spectrum


Big Picture presents technology through the lens of photographers.

Every month, IEEE Spectrum selects the most stunning technological images recently captured by photographers around the world. We choose images that reflect important achievements, trends, or simply catch the eye. We post all images on our website and one also appears in our monthly print publication.

Enjoy the latest images and if you have any suggestions leave a comment below.

Roomba for rivers

Humanity is in love with water. The beauty and usefulness of the earth’s oceans, rivers, lakes and streams explains why 40 percent of us live within 100 kilometers of the planet’s coastline. But we don’t always respect and properly care for the things we love. Like many of our habitats, the world’s waterways have become dumping grounds for our garbage. Picking up the trash that pollutes these scenic areas is a full-time job. But few communities have the resources or the political will to pay for cleanup costs. That may change now that French robotics company Interactive Autonomous Dynamic Systems (IADYS) has unveiled a jellyfish robot. The machine, which can operate autonomously or at the behest of a remote operator, picks up debris and debris (such as plastic bottles, oil spills and algae) that float on the water, as well as detritus located up to 10 meters deep. surface. The Jellyfishbot is equipped with sensors that not only allow it to navigate autonomously, but also measure water quality in terms of salinity, temperature, turbidity and the abundance of organisms, including cyanobacteria and phytoplankton. Hooray for robotic labor!

Sebastian Gallnow/Getty Images

blown away by the wind

Anyone who has ever been in charge of lawn or garden care knows that dandelions are an indomitable enemy. As with the mythological hydra, you pluck one of the blades of grass, only to watch helplessly as several others take its place. Finally, scientists have studied dandelion’s winning ways to take on the traits of the weed for their own purposes. A team of researchers from the University of Washington have developed tiny sensors that mimic the shape and aerodynamic properties of fluffy dandelion seed spores. Nature has designed spores in such a way that they can catch gusts of wind and travel up to a kilometer before landing and putting down roots that will eventually produce a new flower. Building on a strategy that allowed the dandelion to thrive despite our herbicide efforts, scientists have successfully deployed their solar-powered sensors over a wide area to monitor the environment without the time, effort, and expense that manual placement would require.

Mark Stone/Washington University

AI in the Fusion menu

At first glance, the item depicted here looks like a motorcycle tire. But in reality it is a tokamak, a thermonuclear reactor. Researchers at DeepMind and the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland are using this one to bring us closer to using fusion energy to generate electricity. To pull this off, a tokamak would need a carefully calibrated arrangement of magnetic coils that generate the device’s magnetic fields. These fields control the fusion reactions in the plasma and hold the material when it reaches a temperature close to that inside the Sun. In their quest to perfectly position these coils, the team relied on a type of AI called deep reinforcement learning.

deep mind

Green finger AI

Why is AI not good? Scientists have known for years that the oils and sugars in algae can be refined and made into a renewable replacement for the petroleum products that have caused global climate change since the Industrial Revolution began. They also knew that we would need a hell of a lot if we were to reduce our dependence on fossil fuels. Many schemes have been developed to increase the production of algae flowers; so far, they have all failed to meet the enormous demand for energy to provide light and heat (or cooling) to our homes and businesses, mechanical motion in vehicles, and the labor-saving work done by machines of myriad shapes and sizes. AI stepped into this gap. A team of researchers at Texas A&M University is using artificial intelligence to more reliably industrialize algae farming in order to deliver on their original promises. They created two machine learning models that speed up algae cultivation. One predicts how light will travel through algal blooms; the other predicts the point at which algae growth will become self-limiting because the parts of the flower closest to the light source begin to block the rays from reaching the rest of the organism. Harvesting some algae just before they reach counterproductive concentrations supports the growth of blue-greens at a rate that has hitherto been unsustainable.

Texas A&M University


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