WaveSense to Use Geo Radars For Autonomous Vehicles

Company believes that this can improve the safety of self-driving cars
22 August 2018   1553

WaveSense executive director Tarik Bolat announced that radars, lidars and cameras do not provide full-fledged safety of self-driving cars. In rain, snow and fog, sensors may not recognize road signs or markings and make mistakes when constructing a route. To solve this problem, he proposed to work the way through the maps of underground objects.

It is proposed to use georadars for orientation on the underground map. They are equipped with 12-element antenna arrays to transmit high-frequency magnetic pulses. Waves penetrate to a depth of three meters below the earth's surface and are reflected from underground objects: pipes, stones and tree roots. The received signals are checked against the underground WaveSense card and help the autopilot system to determine the location of the car.

The technology was developed at the Lincoln Laboratory at the Massachusetts Institute of Technology. It was intended for military equipment used in regions with bad roads without road signs.

The effectiveness of the system was proved in 2016. Georadar was installed on a sports truck, and in a snowstorm the car remained on its lane with an error of not more than 4 cm.

The head of WaveSense admitted that the technology is not capable of completely replacing radars, lidars and cameras. It is proposed to be used as an addition to existing sensors operating under conditions of poor visibility. With all its advantages, the WaveSense system is difficult to deploy, as each road needs individual scanning.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   202

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.