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A team of faculty members and students at the University of Washington have developed the first phone that can operate without a battery to power its functions. The phone is made with commercially available components on a printed circuit board. (Photo via University of Washington, you can read the research paper here)


Communication is an essential part of life, and the telephone has likely been the greatest innovation in enabling communication between two remote locations, but ever since the need to speak on telephones went mobile, reliance on batteries can range from a minor inconvenience to a catastrophe. The phone developed by researchers at the University of Washington is a promising development in mobile communication and navigates around the possible perfect storm of an emergency scenario and a dead cell phone. It uses ambient power from surrounding radio signals, as well as from light because it has tiny photodiodes which capture light and convert it into an electrical current.


The user places a call by pressing capacitive touch buttons on the circuit board (which have the same layout as a regular phone), and according to the research team’s video, the phone transmits digital packets back to the cellular network of the base station from which it draws power, and they combine to form a phone number that is dialed using Skype. According to the team’s research paper, in its testing, the phone picked up power from radio frequency signals transmitted by a base station 31 feet away from the phone and was able to place a Skype Call to a base station that was 50 feet away. The team believes that their recent innovation, “ a major leap in the capability of battery-free devices and a step towards a fully functional battery-free cellphone.”


At this stage in its development, the battery-free phone’s prototype has limited functionality, but it only consumes about 3.5 microWatts of power which is sufficiently supplied by ambient radio waves and light, for the purposes of this research. In Jennifer Langston’s article for UW News, co-author and electrical engineering doctoral student, Bryce Kellogg, is quoted as saying, “...the amount of power you can actually gather from ambient radio or light is on the order of 1 or 10 microwatts. So real-time phone operations have been really hard to achieve without developing an entirely new approach to transmitting and receiving speech.”


According to Langston, the team plans on improving the operating range and encrypting conversations, as well as trying to stream video on a battery-free cell phone by adding a visual display using low-power E-ink screens. This will obviously necessitate more power, and therefore a new approach to supplying the power needed based on the estimates of available power provided by Kellogg. As it stands, the University of Washington team has provided an intriguing proof-of-concept, as well as future directions for exploration and refinement, so now the world must wait to see if their revolutionary invention sparks an even greater change in the culture of mobile communication.


The team’s research was funded by the National Science Foundation and Google Faculty Research Awards.


Watch the video below to see the team demonstrate the operation of their battery-free phone.




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Google creates a neural network that’s capable of multitasking called Multimodal. A diagram of how Google’s new neural network works (Photo via Google)


My immediate thought… Neural Network Raspberry Pi?


Multitasking is something we do every day whether or not we realize it. While some of us are better at it than others, we all still have this capability. Neural networks don’t, however. Normally, they’re trained to do one task, whether that’s adding animation to video games or translating languages. Try to give it another task and the network can’t do its first job very well. Tech giant Google is looking to change this with their latest system, MultiModal.


Modeled after the human brain, their new system can handle eight tasks at one time and pull them off pretty well. Some of the tasks the system is now able to do are detect objects in images, recognize speech, translate between four pairs of languages along with deciphering grammar and syntax and provide captions. The system did all of these tasks at the same time, which is impressive for a neural network.


So, how does it do it? The neural network from Google Brain, the company’s deep learning team, is made up of subnetworks that specialize in certain tasks relating to audio, images or text. It also has a shared model equipped with an encoder, input/output mixer, and decoder. From this, the system learned how to perform these eight tasks at the same time. During testing, the system didn’t break any records and still showed some errors, but its performance was consistently high. It achieved an accuracy score of 86 percent meaning its image recognition abilities were only 9 percent worse than specialized algorithms.  Still, it managed to match the abilities of the best algorithms in use five years ago.


While there’s still work to be done to improve the system, MultiModal is already showing its benefits. Normally, deep-learning systems need large amounts of data for training to complete its task. With Google’s new system, it learns from gathering data from a completely different task. For instance, the network’s ability to parse sentences for grammar improved when trained on a database of images, which has nothing to do with sentence parsing.


Not wanting to keep the system to themselves, Google released the MultiModal code as part of its Tensor Flow open source project. Now, other engineers can experiment with the neural network and see what they can get it to do. The company hopes sharing the source code will help facilitate quicker researcher in order to improve the neural network.


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