Fighting Today’s Targeted Email Scams

Email phishing is far more sophisticated than it used to be—and even you could fall for it

10 min read
Illustration: QuickHoney
Illustration: QuickHoney

Say you receive an email saying, “We have kidnapped your child. To verify that we are telling the truth, just call your child’s cellphone. To get your child back, you need to send us $10,000 within one hour. We will send instructions in a separate email. Do not tell anybody—or else.”

Chances are you’d pick up the phone and call your child. Imagine the chill along your spine when a stranger answers, “We have your child.”

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New Transceiver Receives Power and Data Simultaneously

The beam-steering approach aims to make 5G relays and IoT devices battery-less

3 min read
Side by side images of a gold side of a transceiver with gold squares and the soldering side with an RF integrated circuit.

Prototype of a 64-element millimeter-wave-band phased-array transceiver.

Tokyo Institute of Technology

The quest to transmit electric power wirelessly and over distance has been a goal of electrical engineers since the end of the 19th century when Nicola Tesla tried his hand at it, to no avail. In the 1970s, NASA and the U.S. Department of Energy engineers achieved some notable successes in wireless power transfer (WPT) in the kilowatt-kilometer range—their efforts spurred on by the energy crises of the time. Interest waned, however, as energy became plentiful again.

Now, with the advent of 5G and its ability to transmit at high frequencies in the millimeter wave band range, new opportunities and approaches are opening up for WPT. Researchers at the Tokyo Institute of Technology have developed a prototype 64-element millimeter-waveband phased-array transceiver that can send and receive data while simultaneously receiving power. The aim is to employ the transceiver initially as a 5G relay, and later to integrate into Internet of Things (IoT) devices. This would enable such devices to shed their batteries, plugs, and cables, says lead researcher Atsushi Shirane. The result would be devices that are smaller, more practical, and capable of speedier communications, with potentially reduced maintenance costs.

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Measuring AI’s Carbon Footprint

New tools track and reduce emissions from machine learning

3 min read
An abstract triangle mosaic background made of circuit board and leaf stock photos
Istock photo

Machine-learning models are growing exponentially larger. At the same time, they require exponentially more energy to train, so that they can accurately process images or text or video. As the AI community grapples with its environmental impact, some conferences now ask paper submitters to include information on CO2 emissions. New research offers a more accurate method for calculating those emissions. It also compares factors that affect them, and tests two methods for reducing them.

Several software packages estimate the carbon emissions of AI workloads. Recently a team at Université Paris-Saclay tested a group of these tools to see if they were reliable. “And they’re not reliable in all contexts,” says Anne-Laure Ligozat, a co-author of that study who was not involved in the new work.

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As 5G evolves into 6G networks, it will be critical that it adopt the most energy-efficient technologies to reduce carbon emissions and our dependence on non-renewable resources.

In terms of increased sustainability, 6G will need to aim directly at lessening its overall environmental impact, including water consumption, raw material sourcing, and waste handling. But it is also important to consider the indirect impact of 6G networks can have on sustainability by conserving resources and minimizing waste in either existing use-cases or novel use-cases.

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