As reported by Engadget: If you want to surf, but are too lazy to paddle or look for waves, the Wakejet Cruise from Swedish outfitRadinnis for you! The company says it "marries the agility and speed of wakeboarding with the freedom of surfing," but that doesn't mean you can take the electric-powered craft lightly. It cruises along at a rather insane 28mph for a full half-hour on a single charge -- or up to an hour if you're willing to go slower. That's about the same speed as a water skier, meaning that unlikeseated watercraft, it'll require your full attention, along with some skill and athleticism.
You control the speed with a hand-held remote, and can recharge the built-in battery in about an hour when you're done. The carbon and kevlar board also has a mobile app, built-in GPS and magnetic safety switches. Radinn's wakejet is hardly the first product like this, but with the relatively short recharge time and long range, it's probably the most practical -- and expensive! It's on pre-order with a Q2 2015 delivery for a mere $20,000 or so, except that other one-percenters already snapped up the first run. Luckily, the next batch is available in Q3.
As reported by Gizmodo: The first road-legal autonomous truck made a splashy debut earlier this month. The Freightliner Inspiration Truck is shiny and new, but it will not be good for everyone. Autonomous trucks will destroy jobs, Scott Santens points out at Medium, killing the truck stop as we know it.
Even if you reserve no particular nostalgia for truck stops, the effects will be devastating for local economics. Autonomous trucks will obviously replace drivers, an estimated 3.5 million of them, but they will make the business that cater to drivers obsolete, too. Santens writes:
Those 3.5 million truck drivers driving all over the country stop regularly to eat, drink, rest, and sleep. Entire businesses have been built around serving their wants and needs. Think restaurants and motels as just two examples. So now we’re talking about millions more whose employment depends on the employment of truck drivers. But we still can’t even stop there.
Those working in these restaurants and motels along truck-driving routes are also consumers within their own local economies. Think about what a server spends her paycheck and tips on in her own community, and what a motel maid spends from her earnings into the same community. That spending creates other paychecks in turn. So now we’re not only talking about millions more who depend on those who depend on truck drivers, but we’re also talking about entire small town communities full of people who depend on all of the above in more rural areas. With any amount of reduced consumer spending, these local economies will shrink.
But autonomous trucks have obvious benefits, too. Trucking is a decently paid job but also a dangerous one. It takes truckers far from their families for long stretches of time. It encourages poor lifestyle habits. There are over 300,000 crashes involving heavy trucks every year.
Autonomous trucks will almost inevitably start taking over our roads, but it is worth pausing to consider their unintended effects. A few weeks ago, Martin Ford, author of Rise of Robots,pointed out the ripple effects of autonomous cars in Gizmodo. Not everyone will be a winner in the self-driving future.
As reported by ESA: When extreme weather comes our way, real-time information from space can help us to decide if closing a bridge is the right thing to do.
ESA is working with the UK’s University of Nottingham to monitor the movements of large structures as they happen using satellite navigation sensors.
Satnav sensors and wind meters
The team fixed highly sensitive satnav receivers for detecting movements as small as 1 cm at key locations on the Forth Road Bridge in Scotland.
Measurements from these sensors were continuously transmitted in real time via satellite to a processing centre at the university and made available via a web-based interface as part of GeoSHM, the project for Global Navigation Satellite System and Earth Observation for Structural Health Monitoring.
This realtime information was complemented by historical Earth observation satellite data to give a better overall picture of possible influences on bridge safety through gradual changes in the surrounding ground and any movements of critical structures.
After analysing Earth observation images of the Forth Road Bridge dating back seven years, the team found no displacements of the towers or the surrounding soil.
Not all bridges are as stable, however: satellite imagery from China has revealed subsidence caused by underground engineering and groundwater extraction around bridge sites in Shanghai and Wuhan.
Over the past 50 years, traffic on the Forth Road suspension bridge has increased from the expected 30 000 vehicles per day to a daily average of 40 000, with 60 000 crossing on peak weekdays.
As a result of this increased load, the bridge has stressed structural members and unexpected deformations. Also, extreme weather conditions such as high winds cause frequent bridge closures, and having only one lane open in each direction results in upwards of £650 000 in lost revenues per day.
Web-based interface
Bridgemaster Barry Colford observed: “This information is extremely useful for understanding how much the bridge can move under extreme weather conditions. This allows us to decide to close the bridge based on precise deformation information.”
"For example, I knew that the bridge can move significantly under high winds but for the first time I know that bridge moved 3.5 m laterally and 1.83 m vertically under a wind speed of 41 m/s.
“Other information provided by the GeoSHM system is also important to define reliable alarm thresholds for issuing the right alerts at the right time.”
The global market for the installation of GeoSHM on existing and currently planned long-span bridges is worth in excess of $1.5 billion. The UK market alone is estimated to be worth in excess of £200 million and growing. China is expected to be the largest market.
While GeoSHM is designed mainly for monitoring bridges with a main span greater than 400 m, it also has potential for shorter bridges, such as Hammersmith Bridge and the Millennium Bridge in the UK.
“Eventually, GeoSHM could be deployed for monitoring offshore wind turbines, masts, towers, dams, viaducts and high-rise buildings, for example,” said Xiaolin Meng, GeoSHM team leader.
"The combination of long-term monitoring of ground levels using Earth observation data and short-term satnav positioning creates a potent information service,” commented Beatrice Barresi, ESA’s GeoSHM project manager.
The capsule, containing more than 3,000lb (1,360kg) of experiments and equipment, aimed for an early afternoon splashdown in the Pacific, off the southern California coast.
The capsule arrived at the orbiting lab last month, bearing much-needed groceries and other goods for the six station residents.
The California-based SpaceX company is Nasa’s only means of getting supplies to the 260-mile-high station, ever since last year’s loss of an Orbital Sciences Corp craft in a Virginia launch explosion.
More recently, a Russian supply ship went into an uncontrollable spin after liftoff and was destroyed upon re-entry earlier this month, its entire contents undelivered.
SpaceX will attempt to launch another shipment on 26 June from Cape Canaveral, Florida.
This was the sixth of 15 scheduled cargo resupply missions that California-based SpaceX is taking on under the terms of a NASA contract. SpaceX launched the Dragon with more than 4,300 pounds of cargo on a Falcon 9 rocket on April 14, making for a 37-day stay at the space station. One of the items on board was the first zero-G espresso machine to go into outer space.
As reported by Yahoo Autos:The very word "gigafactory" makes Tesla Motors' lithium-ion cell fabrication and battery assembly plant sound quite large.
But how huge it actually is can be hard to comprehend.
Context is provided by diagrams that compare its planned footprint to, say, the largest building for assembling jetliners on Boeing's Washington state campus.
So a new video may help add perspective. Shot just this week, it shows flyover footage of the factory under construction in northern Nevada.
Tesla Motors gigafactory - size comparisons [source: EV Obsession]
Brought to us via theTransport Evolvedwebsite, the video was taken not from an airplane but by a remote-controlled drone.
Accompanied by somber music, the 2-minute clip shows the rectangular two-story factory building from a number of angles.
The building appears to have the second-story roof largely completed.
According to the YouTube page, it was posted by a user named "Quick Laptop Cash" (ahem). It's described as follows:
The first 4k ultra-high-definition video of the Tesla Gigafactory. Located 15 minutes east of Reno, Nevada, the Gigafactory is growing at a steady pace and helping fuel the strong economic recovery in Northern Nevada.
The description continues as follows:
To ensure safety this video was recorded while no workers were present and from over 1 mile away with a DJI Phantom 3 Professional Drone utilizing GPS. The drone was in constant visual contact as well as maintaining an altitude of not more than 400 feet above ground level.
Rendering of Tesla battery gigafactory outside Reno, Nevada, Sep 2014
The YouTube page is followed by a promotional link to a site that purports to inform about and list homes in the Reno, Tahoe, and Sparks areas of Nevada.
Interstingly, it appears to have been created and posted by local boosters grateful for the jobs and opportunity the huge plant is expected to provide.
When completed, the gigafactory will provide batteries not only for the Tesla Model 3 electric car expected to launch in 2017 or 2018, but also the Tesla Powerwall home energy-storage battery and similar products intended for commercial and industrial use.
The video ends with a note of thanks to many parties: Tesla CEO Elon Musk, Tesla itself, Nevada governor Brian Sandoval, the construction workers building the factory, EDAWN, and Nevada lawmakers.
"You are creating jobs and helping to redefine our once-struggling local economy" by building the $5 billion gigafactory, it says in closing.
As reported by The Verge: Swiss-German IT firm GeOps has collaborated with the University of Freiburg on an interactive map of the world's major mass transit systems, incorporating public data feeds (like the MTA's) offered by train and bus operators to show everything moving in real time. Over 200 systems from around the globe are represented here, and it's absolutely mesmerizing to watch the colorful dots slowly amble their way across the grid.
Of course, not all mass transit authorities offer truly real-time data — GeOps notes that much of the map is based on schedule information, though it incorporated live data where it could. Still, it's incredible to watch.
As reported by MIT Technology Review: Many of the devices around us may soon acquire powerful new abilities to understand images and video, thanks to hardware designed for the machine-learning technique called deep learning. Companies like Google have made breakthroughs in image and face recognition through deep learning, using giant data sets and powerful computers (see “10 Breakthrough Technologies 2013: Deep Learning”). Now two leading chip companies and the Chinese search giant Baidu say hardware is coming that will bring the technique to phones, cars, and more. Chip manufacturers don’t typically disclose their new features in advance. But at a conference on computer vision Tuesday, Synopsys, a company that licenses software and intellectual property to the biggest names in chip making, showed off a new image-processor core tailored for deep learning. It is expected to be added to chips that power smartphones, cameras, and cars. The core would occupy about one square millimeter of space on a chip made with one of the most commonly used manufacturing technologies. Pierre Paulin, a director of R&D at Synopsys, told MIT Technology Review that the new processor design will be made available to his company’s customers this summer. Many have expressed strong interest in getting hold of hardware to help deploy deep learning, he said. Synopsys showed a demo in which the new design recognized speed-limit signs in footage from a car. Paulin also presented results from using the chip to run a deep-learning network trained to recognize faces. It didn’t hit the accuracy levels of the best research results, which have been achieved on powerful computers, but it came pretty close, he said. “For applications like video surveillance it performs very well,” he said. The specialized core uses significantly less power than a conventional chip would need to do the same task. The new core could add a degree of visual intelligence to many kinds of devices, from phones to cheap security cameras. It wouldn’t allow devices to recognize tens of thousands of objects on their own, but Paulin said they might be able to recognize dozens.
That might lead to novel kinds of camera or photo apps. Paulin said the technology could also enhance car, traffic, and surveillance cameras. For example, a home security camera could start sending data over the Internet only when a human entered the frame. “You can do fancier things like detecting if someone has fallen on the subway,” he said. Jeff Gehlhaar, vice president of technology at Qualcomm Research, spoke at the event about his company’s work on getting deep learning running on apps for existing phone hardware. He declined to discuss whether the company is planning to build support for deep learning into its chips. But speaking about the industry in general, he said that such chips are surely coming. Being able to use deep learning on mobile chips will be vital to helping robots navigate and interact with the world, he said, and to efforts to develop autonomous cars. “I think you will see custom hardware emerge to solve these problems,” he said. “Our traditional approaches to silicon are going to run out of gas, and we’ll have to roll up our sleeves and do things differently.” Gehlhaar didn’t indicate how soon that might be. Qualcomm has said that its coming generation of mobile chips will include software designed to bring deep learning to camera and other apps (see “Smartphones Will Soon Learn to Recognize Faces and More”).
Ren Wu, a researcher at Chinese search company Baidu, also said chips that support deep learning are needed for powerful research computers in daily use. “You need to deploy that intelligence everywhere, at any place or any time,” he said. Being able to do things like analyze images on a device without connecting to the Internet can make apps faster and more energy-efficient because it isn’t necessary to send data to and fro, said Wu. He and Qualcomm’s Gehlhaar both said that making mobile devices more intelligent could temper the privacy implications of some apps by reducing the volume of personal data such as photos transmitted off a device. “You want the intelligence to filter out the raw data and only send the important information, the metadata, to the cloud,” said Wu.
