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Tiny crop-health sensors could help cut the cost of groceries

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Tiny crop-health sensors could help cut the cost of groceries


Tiny crop-health sensors could help cut the cost of groceries
The sensor system can rapidly switch between edge detection – imaging the outline of an object, such as a fruit – and extracting detailed infrared information, without the need for creating large volumes of data and using bulky external processors. Credit: Lincoln Clark, ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS)

A compact, lightweight sensor system with infrared imaging capabilities developed by an international team of engineers could be easily fitted to a drone for remote crop monitoring.

This flat-optics technology has the potential to replace traditional optical lens applications for environmental sensing in a range of industries.

This innovation could result in cheaper groceries as farmers would be able to pinpoint which crops require irrigation, fertilization and pest control, instead of taking a one-size-fits-all approach, thereby potentially boosting their harvests.

The sensor system can rapidly switch between edge detection—imaging the outline of an object, such as a fruit—and extracting detailed infrared information, without the need for creating large volumes of data and using bulky external processors.

The capability to switch to a detailed infrared image is a new development in the field and could allow farmers to collect more information when the remote sensor identifies areas of potential pest infestations.

This research by engineers at the City University of New York (CUNY), the University of Melbourne, RMIT University and the ARC Center of Excellence for Transformative Meta-Optical Systems (TMOS) is published in Nature Communications in a paper titled “Reconfigurable image processing metasurfaces with phase-change materials.”

How does the sensor system work?

The prototype sensor system, which comprises a filter made with a thin layer of a material called vanadium dioxide that can switch between edge detection and detailed infrared imaging, was engineered by TMOS Chief Investigator Professor Madhu Bhaskaran and her team at RMIT in Melbourne.

“Materials such as vanadium dioxide add a fantastic tuning capability to render devices ‘smart'”, she said.

“When the temperature of the filter is changed, the vanadium dioxide transforms from an insulating state to a metallic one, which is how the processed image shifts from a filtered outline to an unfiltered infrared image.”

“These materials could go a long way in futuristic flat-optics devices that can replace technologies with traditional lenses for environmental sensing applications—making them ideal for use in drones and satellites, which require low size, weight and power capacity.

RMIT holds a granted US patent and has a pending Australian patent application for its method of producing vanadium dioxide films, which may be suitable for a broad range of applications.

Lead author Dr. Michele Cotrufo said the system’s ability to switch between processing operations, from edge detection to capturing detailed infrared images, was significant.

“While a few recent demonstrations have achieved analog edge detection using metasurfaces, most of the devices demonstrated so far are static. Their functionality is fixed in time and cannot be dynamically altered or controlled,” said Corufo, who conducted his research at CUNY.

“Yet, the ability to dynamically reconfigure processing operations is key for metasurfaces to be able to compete with digital image processing systems. This is what we have developed.”

Next steps

Co-author Shaban Sulejman from the University of Melbourne said the design and materials used make the filter amenable to mass-manufacturing.

“It also operates at temperatures compatible with standard manufacturing techniques, making it well-placed to integrate with commercially available systems and therefore move from research to real-world usage rapidly.”

TMOS Chief Investigator Ann Roberts, also from the University of Melbourne, said flat optics technologies had the potential to transform countless industries.

“Traditional optical elements have long been the bottleneck preventing the further miniaturization of devices. The ability to replace or complement traditional optical elements with thin-film optics breaks through that bottleneck.”

More information:
Michele Cotrufo et al, Reconfigurable image processing metasurfaces with phase-change materials, Nature Communications (2024). DOI: 10.1038/s41467-024-48783-3

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RMIT University


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Tiny crop-health sensors could help cut the cost of groceries (2024, June 7)
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A type of cyberattack that could set your smartphone on fire using its wireless charger

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A type of cyberattack that could set your smartphone on fire using its wireless charger


A type of cyberattack that could set your smartphone on fire using its wireless charger
Attack overview: A victim uses Commercial-Off-The-Shelf Qi-compatible wireless chargers and power receivers. An intermediary-connected attacking device on the power adapter manipulates the output voltage and current to: 1) manipulate the magnetic field to interfere with the charged device. 2) interactively communicate with the charger and control the charging process. This setup enables foreign object destruction, wireless power toasting, and voice assistant manipulation attacks. Credit: arXiv (2024). DOI: 10.48550/arxiv.2402.11423

A team of security experts at the University of Florida working with security audit company CertiK has found that a certain class of cyberattacks could cause a smartphone to catch fire via its wireless charger. The team has posted a paper describing their research and results on the arXiv preprint server.

Inductive chargers are devices that can be used to charge a smartphone or other device without the need for plugging in a cable. Such devices work by making use of electromagnetic fields to transfer energy from one device to another through induction. In order for a smartphone to be charged properly on such a device, it must communicate with the charger through a Qi communication-based feedback control system. And in order for a wireless charger to work, it must be connected to an AC outlet.

But the charger, like a phone, cannot plug directly into the wall; it plugs instead into an adapter. And this, the researchers suggest, is where the system’s vulnerabilities lie. They have found through testing that by attaching an intermediary device to the adapter, disruptions can be made to the Qi communication-based feedback control system, resulting in signals that can override controls that stop overcharging, which can lead to overheating, and in some cases a fire. They call such an attack a “VoltSchemer.”

The research team has come up with three types of attacks that can occur with a VoltSchemer. According to the researchers, “A charger can be manipulated to control voice assistants via inaudible voice commands, damage devices being charged through overcharging or overheating, and bypass Qi-standard specified foreign-object-detection mechanism to damage valuable items exposed to intense magnetic fields.”

The researchers tested multiple types of wireless chargers and phones and found they were all vulnerable. They have notified manufacturers and expect that changes will be made to overcome these vulnerabilities to protect consumers from VoltSchemer attacks.

More information:
Zihao Zhan et al, VoltSchemer: Use Voltage Noise to Manipulate Your Wireless Charger, arXiv (2024). DOI: 10.48550/arxiv.2402.11423

Journal information:
arXiv


© 2024 Science X Network

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A type of cyberattack that could set your smartphone on fire using its wireless charger (2024, February 23)
retrieved 24 June 2024
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US bans Russia’s Kaspersky antivirus software

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US bans Russia's Kaspersky antivirus software


The US Commerce Department said it would prohibit the sale of Kaspersy's software in the United States
The US Commerce Department said it would prohibit the sale of Kaspersy’s software in the United States.

The United States on Thursday banned Russia-based cybersecurity firm Kaspersky from providing its popular antivirus products in the country on national security grounds, the US Commerce Department announced.

“Kaspersky will generally no longer be able to, among other activities, sell its software within the United States or provide updates to software already in use,” the agency said in a statement announcing the action, which it said is the first of its kind.

The announcement came after a lengthy investigation which found that Kaspersky’s “continued operations in the United States presented a national security risk due to the Russian Government’s offensive cyber capabilities and capacity to influence or direct Kaspersky’s operations,” it added.

Kaspersky did not immediately respond to a request for comment on the decision.

“Russia has shown time and again they have the capability and intent to exploit Russian companies, like Kaspersky Lab, to collect and weaponize sensitive US information,” US Commerce Secretary Gina Raimondo said in a statement.

The Commerce Department’s actions demonstrate to America’s adversaries that it would not hesitate to act when “their technology poses a risk to the United States and its citizens,” she added.

The move is the first such action taken since a Trump-era executive order gave the Commerce Department the power to investigate whether certain companies pose a national security risk.

While the multinational firm is headquartered in Moscow, it has offices in 31 countries around the world, servicing more than 400 million users and 270,000 corporate clients in more than 200 countries, the Commerce Department said.

As well as banning the sale of Kaspersky’s antivirus software, the Commerce Department also added three entities linked to the firm to a list of companies deemed to be a national security concern, “for their cooperation with Russian military and intelligence authorities in support of the Russian government’s cyber intelligence objectives.”

The Commerce Department said it “strongly encouraged” users to switch to new vendors, although its decision does not ban them from using the software should they choose to do so.

Kaspersky is allowed to continue certain operations in the United States, including providing antivirus updates, until September 29 this year, “in order to minimize disruption to US consumers and businesses and to give them time to find suitable alternatives,” it added.

© 2024 AFP

Citation:
US bans Russia’s Kaspersky antivirus software (2024, June 21)
retrieved 24 June 2024
from https://techxplore.com/news/2024-06-russia-kaspersky-antivirus-software.html

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Using 3D printer students design attachment for a quieter leaf blower

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Using 3D printer students design attachment for a quieter leaf blower


leaf blower
Credit: Pixabay/CC0 Public Domain

Nate Greene, an engineer at Towson’s Stanley Black & Decker, calls the innovation “extremely atypical.”

A group of students from Johns Hopkins University signed onto a class project and were tasked with building a new product for the multinational tool company. And they actually did it.

Using a campus 3D printer, a team of four seniors at Hopkins designed a new attachment for leaf blowers, capable of quieting some of the harshest decibels of a blower’s sound.

“The university’s focus is so theoretical,” said Greene, who advised the students on their design. “So to find a group that understands the right ways to apply that theory right off the bat … The team has been not only good at the content they’re working on but good at just working through changing projects.”

The attachment is a cylindrical nozzle, which allows most of the air from the blower to pass through but directs some of it into thin, helical channels, dampening the high-pitched whine typical of the neighborhood nuisance.

The students chose to target the most annoying part of the blower’s sound, said Madison Morrison, one of the mechanical engineering majors who helped create the design.

“We knew that if we could improve the noise quality—even though, obviously, with a blower system, it’s hard to completely eliminate noise—it’d be at least a more pleasant experience for your neighbors trying to sleep in or yourself even as the user,” she said.

On the cusp of their graduation, the students filed for a patent, and the invention is on its way to manufacturing at Stanley, expected to hit store shelves in about 2026, Greene said.

“The design is super unique. We haven’t seen really anything like this in the industry,” Greene said. “Because it is so novel, the team’s been able to file a patent. The design is patent-pending, which is a huge step.”

The students developed their prototype for a specific DeWalt electric blower, Greene said. Now, the company is evaluating whether their attachment could work for other blowers, too.

When the students began working on the project last August, their mission was simply to quiet the blower. They weren’t sure how they’d accomplish it.

At the outset, they had different options, including taking an “active” or “passive” approach, said Michael Chacon, one of the students on the project. The former would be akin to noise-canceling headphones, which generate competing sound waves to cancel out noise. The latter would be similar to a gun silencer, which doesn’t cancel out the sound but dampens it. They chose the latter, hoping it would be easier to generate, prototype and install on the blower.

Once they decided they’d create an attachment, made of a type of plastic, they began testing different designs in the 3D printer. Their first iteration was shorter in length and had different shaped channels but already showed promise, Morrison said.

“We were like, ‘Wow, this design has so much potential,'” Morrison said. “Going from that drawing board to: This is in my hand and kind of works? Honestly, that is such a great feeling.”

By the end of the project, they’d created more than 40 different prototypes in blue, red, green orange and pink using campus 3D printers. Among the considerations was balancing the performance of the blower with the performance of the noise cancellation, Morrison said.

“If you just slap a muffler on here, well, you’re probably not going to blow many leaves,” Morrison said.

Under the program, Stanley Black & Decker will have the patent, Greene said. The students will not profit from the design but will be listed as inventors, an invaluable resume-builder for young mechanical engineers, Greene said.

For their part, the students were thrilled that their idea might go to market.

“It’s really exciting to see that something that we made in this class is actually likely going to go to market,” said one of the students, Andrew Palacio. “It would have been really easy for this project to not go anywhere. I think it’s pretty rare for even some of the better projects that students make to actually become a product.”

Even if they don’t have yards, the students might find themselves buying leaf blowers a few years from now, said one of the students, Leen Alfaoury.

“You could put it on one of those museum clear glass stands,” she joked.

2024 The Baltimore Sun. Distributed by Tribune Content Agency, LLC.

Citation:
Using 3D printer students design attachment for a quieter leaf blower (2024, June 8)
retrieved 24 June 2024
from https://techxplore.com/news/2024-06-3d-printer-students-quieter-leaf.html

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Scientists propose AI method that integrates habitual and goal-directed behaviors

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Scientists propose AI method that integrates habitual and goal-directed behaviors


Simplicity versus adaptability: Understanding the balance between habitual and goal-directed behaviors
The Bayesian behavior framework. Credit: Nature Communications (2024). DOI: 10.1038/s41467-024-48577-7

Both living creatures and AI-driven machines need to act quickly and adaptively in response to situations. In psychology and neuroscience, behavior can be categorized into two types—habitual (fast and simple but inflexible), and goal-directed (flexible but complex and slower).

Daniel Kahneman, who won the Nobel Prize in Economic Sciences, distinguishes between these as System 1 and System 2. However, there is ongoing debate as to whether they are independent and conflicting entities or mutually supportive components.

Scientists from the Okinawa Institute of Science and Technology (OIST) and Microsoft Research Asia in Shanghai have proposed a new AI method in which systems of habitual and goal-directed behaviors learn to help each other.

Through computer simulations that mimicked the exploration of a maze, the method quickly adapts to changing environments and also reproduced the behavior of humans and animals after they had been accustomed to a certain environment for a long time.

The study, published in Nature Communications, not only paves the way for the development of systems that adapt quickly and reliably in the burgeoning field of AI, but also provides clues to how we make decisions in the fields of neuroscience and psychology.

The scientists derived a model that integrates habitual and goal-directed systems for learning behavior in AI agents that perform reinforcement learning, a method of learning based on rewards and punishments, based on the theory of “active inference,” which has been the focus of much attention recently.

In the paper, they created a computer simulation mimicking a task in which mice explore a maze based on visual cues and are rewarded with food when they reach the goal.

They examined how these two systems adapt and integrate while interacting with the environment, showing that they can achieve adaptive behavior quickly. It was observed that the AI agent collected data and improved its own behavior through reinforcement learning.

What our brains prefer

After a long day at work, we usually head home on autopilot (habitual behavior). However, if you have just moved house and are not paying attention, you might find yourself driving back to your old place out of habit.

When you catch yourself doing this, you switch gears (goal-directed behavior) and reroute to your new home. Traditionally, these two behaviors are considered to work independently, resulting in behavior being either habitual and fast but inflexible, or goal-directed and flexible but slow.

“The automatic transition from goal-directed to habitual behavior during learning is a very famous finding in psychology. Our model and simulations can explain why this happens: The brain would prefer behavior with higher certainty. As learning progresses, habitual behavior becomes less random, thereby increasing certainty. Therefore, the brain prefers to rely on habitual behavior after significant training,” Dr. Dongqi Han, a former Ph.D. student at OIST’s Cognitive Neurorobotics Research Unit and first author of the paper, explained.

For a new goal that AI has not trained for, it uses an internal model of the environment to plan its actions. It does not need to consider all possible actions but uses a combination of its habitual behaviors, which makes planning more efficient.

This challenges traditional AI approaches which require all possible goals to be explicitly included in training for them to be achieved. In this model, each desired goal can be achieved without explicit training but by flexibly combining learned knowledge.

“It’s important to achieve a kind of balance or trade-off between flexible and habitual behavior,” Prof. Jun Tani, head of the Cognitive Neurorobotics Research Unit stated. “There could be many possible ways to achieve a goal, but to consider all possible actions is very costly, therefore goal-directed behavior is limited by habitual behavior to narrow down options.”

Building better AI

Dr. Han got interested in neuroscience and the gap between artificial and human intelligence when he started working on AI algorithms. “I started thinking about how AI can behave more efficiently and adaptably, like humans. I wanted to understand the underlying mathematical principles and how we can use them to improve AI. That was the motivation for my Ph.D. research.”

Understanding the difference between habitual and goal-directed behaviors has important implications, especially in the field of neuroscience, because it can shed light on neurological disorders such as ADHD, OCD, and Parkinson’s disease.

“We are exploring the computational principles by which multiple systems in the brain work together. We have also seen that neuromodulators such as dopamine and serotonin play a crucial role in this process,” Prof. Kenji Doya, head of the Neural Computation Unit explained.

“AI systems developed with inspiration from the brain and proven capable of solving practical problems can serve as valuable tools in understanding what is happening in the brains of humans and animals.”

Dr. Han would like to help build better AI that can adapt their behavior to achieve complex goals.

“We are very interested in developing AI that have near human abilities when performing everyday tasks, so we want to address this human-AI gap. Our brains have two learning mechanisms, and we need to better understand how they work together to achieve our goal.”

More information:
Dongqi Han et al, Synergizing habits and goals with variational Bayes, Nature Communications (2024). DOI: 10.1038/s41467-024-48577-7

Citation:
Simplicity versus adaptability: Scientists propose AI method that integrates habitual and goal-directed behaviors (2024, June 14)
retrieved 24 June 2024
from https://techxplore.com/news/2024-06-simplicity-scientists-ai-method-habitual.html

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