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Prasar Bharati CEO insists on youth-targeted programmes

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By NewsGram Staff Writer

Kolkata: On Tuesday, Jawahar Sircar, the chief of India’s public service broadcaster Prasar Bharati, requested Akashvani Kolkata to start some youth-targeted programmes in order to popularize the All India Radio’s services in West Bengal. It is important to target the youth as they are the future of the nation.

He also put forward an idea to reflect on what the listeners want from radio services. Only when this idea is implemented can the listeners can be drawn back to the radio.

“One of the suggestions that I have for Akashvani Kolkata is to go for crowd-sourcing. By putting out a few questions on social media, we can get an idea on what programmes are in demand and what listeners would like to be introduced to. The trend can be realised through this,” Sircar said.

AIR logoBy NewsGram Staff Writer

Jawahar Sircar commissioned the DRM (Digital Radio Mondiale) transmitter for the Kolkata-A radio service, one of the five channels of Akashvani Kolkata. The DRM transmitter allows the service to be broadcasted in digital as well as analogue mode. This further enables it to reach out to the south Bengal districts as well.

While launching the service, the Prasar Bharati chief also stressed on the five channels maintaining a distinct identity and showcasing a mix of both Bengali and other language songs. This is to ensure a wider range of listeners.

“Since it’s not just Bengali’s who reside here, we must also consider the other languages that are in use. Gradually, we must start thinking about what listeners want,” he said.

He further gave the example of AIR FM Rainbow which intends to broadcast programmes for the youth and thus lure in more listeners in that category.

“Get back the youth and use the internet,” he stated.

(With inputs from IANS)

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AI Helps Find Source Of Radio Bursts 3 Billion Light Years Away From Earth

The researchers developed the new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally.

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Space, radio
'AI helps track down mysterious cosmic signals', Pixabay

Scientists say they have used artificial intelligence (AI) to discover 72 new fast radio bursts from a mysterious source about three billion light years away from Earth.

The initiative may advance the search to find signs of intelligent life in the universe, said researchers from the University of California, Berkeley in the US.

Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies.

However, the source of these emissions is still unclear, according to the research published in The Astrophysical Journal.

Theories range from highly magnetised neutron stars blasted by gas streams from a nearby supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization.

 

earth, radio
While most fast radio bursts are one-offs, the source here, FRB 121102, is unique in emitting repeated bursts. Wikimedia Commons

 

“This work is exciting not just because it helps us understand the dynamic behaviour of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms,” said Andrew Siemion from the University of California – Berkele.

 

Researchers are also applying the successful machine-learning algorithm to find new kinds of signals that could be coming from extraterrestrial civilisations.

While most fast radio bursts are one-offs, the source here, FRB 121102, is unique in emitting repeated bursts.

This behaviour has drawn the attention of many astronomers hoping to pin down the cause and the extreme physics involved in fast radio bursts.

The AI algorithms dredged up the radio signals from data were recorded over a five-hour period in 2017, by the Green Bank Telescope in West Virginia in the US.

Radio
The researchers developed the new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally. (IANS)

An earlier analysis of the 400 terabytes of data employed standard computer algorithms to identify 21 bursts during that period.

“All were seen within one hour, suggesting that the source alternates between periods of quiescence and frenzied activity,” said Berkeley postdoctoral researcher Vishal Gajjar.

Also Read: HCL Launches AI Based ‘HCL Turbo’

The researchers developed the new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally.

This brings the total number of detected bursts from FRB 121102 to around 300 since it was discovered in 2012, researchers said. (IANS)