Not Moonbounce, Rather Building- And Pedestrian-Bounce

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Not Moonbounce, Rather Building- And Pedestrian-Bounce

In late January, an Alphabet-owned Waymo self-driving car was cruising near an elementary school in Santa Monica, California, when a young child suddenly darted into the street. Waymo’s LiDAR sensors detected the student, who had just emerged from behind a parked SUV, but it was too late. Despite slamming on the brakes and slowing from 17 to six mph, the driverless car struck the child, knocking them to the pavement. Luckily, reports show that the child only suffered minor injuries, but that’s likely little comfort to parents whose children live in the growing number of cities where driverless cars operate.

In this case, the Waymo detected the child once they came into view—but what if it could have “seen” them from around the corner? That is the general idea behind new research emerging out of the University of Pennsylvania, where a team of engineers have developed a sensor system that uses radio waves to help robots detect objects (or people) hidden behind walls.

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EMC Committee Publishes New Guide for Reporting Harmful Interference

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The RSGB EMC Committee has recently published a new “Guide for Reporting Harmful Interference to Ofcom". The leaflet contains some do’s and don’ts on the style and content of the report that has to be submitted. It also provides some examples on wording that can be used. This is the 18th leaflet the EMC Committee has released that offers advice on dealing with interference issues. 

More Information - https://rsgb.org/main/technical/emc/

Neural Codec Called 'Milestone' for Digital Voice

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It's being heralded as a milestone in the long-overdue evolution of speech quality for land-mobile radio systems - the use of an adaptive neural network that replaces traditional signal processing.

A digital voice milestone was announced at the recent acoustics and speech conference in California, where the Free DV Project's David Rowe, VK5DGR, co-presented a paper describing a neural network that replaces traditional signal processing with machine learning.

“the first known real-world deployment of a neural codec – an important milestone that the Ham community can be proud of.”
— Free DV Project's David Rowe, VK5DGR

Rowe and programmer Jean-Marc Valin presented the details to attendees at the IEEE Signal Processing Society conference, where David said it was well-received.

“provides unprecedented speech quality and robustness for VHF/UHF land mobile radio applications.”
— David Rowe, VK5DGR

Instead of using the fixed algorithms of traditional digital voice, the FreeDV Radio Encoder, known as RADE V1, employs fully adaptive machine learning to produce a higher-quality result, developed using open-source software.

The FreeDV Project - https://freedv.org/