If a suicide bomber lurks in the public with an explosive device, bomb-sniffing dogs can often detect the explosive chemicals from the tiniest whiff—these canine superheroes can sense the presence of the explosive triacetone triperoxide (TATP) if just a few molecules are present, on the scale of parts per trillion.
Researchers at the University of Rhode Island are striving to make a comparable device for detecting TATP in its vapor form. Their new detection system, which pairs a conductance sensor with a traditional thermodynamic sensor, confirms the presence of TATP at the level of parts per billion (ppb). Their work is described in a study published on 2 October in IEEE Sensors Letters.
When detectives and other forensics specialists arrive at a crime scene, there is a pressing need to survey the area quickly. Environmental disturbances such as wind or an incoming tide could ruin valuable evidence, and even the investigators themselves are at risk of contaminating the crime scene. Could a fleet of evidence-surveying drones be of help?
Pompílio Araújo, a criminal expert for the Federal Police of Brazil, is responsible for recording crime scenes exactly as found. In his other role as a researcher at the Intelligent Vision Research Lab at Federal University of Bahia, he is trying to make his first job easier by developing drones that can—very quickly—home in on a piece of evidence and record it from multiple angles.
Many drivers are familiar with the irritation of being stuck in traffic on a sweltering summer day. Two researchers at the University of Michigan are working to make uncomfortable situations like this a bit more bearable, by developing a system that will automatically control the climate within a car to optimize both the passengers’ comfort level and the efficiency of the HVAC system.
Over the past few years, Mohamed Abouelenien and Mihai Burzo have been developing approaches to analyze and detect various human behaviors, including lying, feeling stressed, remaining alert at the wheel, and expressing affection, among others. Their latest effort has been to develop a system for cars and homes that automatically detects a person’s thermal discomfort and adjusts accordingly, without any human input.
A new antenna that uses saltwater and plastic instead of metal to shape radio signals could make it easier to build networks that use VHF and UHF signals.
Being able to focus the energy of a radio signal towards a given receiver means you can increase the range and efficiency of transmissions. If you know the location of the receiver, and are sure that it’s going to stay put, you can simply use an antenna that is shaped to emit energy mostly in one direction and point it. But if the receiver’s location is uncertain, or if it’s moving, or if you’d like to switch to a different receiver, then things get tricky. In this case, engineers often fall back on a technique called beam-steering or beamforming, and doing it at at a large scale is one of the key underlying mechanisms behind the rollout of 5G networks.
Beam-steering lets you adjust the focus of antenna without having to move it around to point in different directions. It involves adjusting the relative phases of a set of radio waves at the antenna: these waves interfere constructively and destructively, cancelling out in unwanted directions and reinforcing the signal in the direction you want to send it. Different beam patterns, or states, are also possible—for example, you might want a broader beam if you are sending the same signal to multiple receivers in a given direction, or a tighter beam if you are talking to just one.
Now, researchers have developed an advanced liquid-based antenna system that relies on a readily available ingredient: saltwater.
To be sure, this is not the first liquid antenna: these antennas, which use fluid to transmit and receive radio signals, can be useful in situations where VHF or UHF frequencies are required (frequencies between 30 megahertz and 3 gigahertz). They tend to be small, transparent, and more reconfigurable than conventional metal antennas. For these reasons, they are being explored in for some internet of things (IoT) and 5G applications.
Liquid antennas that depend on salty water have even more benefits, since the substance is readily available, low-cost and eco-friendly. Several saltwater-based antennas have been developed to date, but these designs are limited in how easily the beam can be steered and reconfigured.
However, in a recent publication in IEEE Antennas and Wireless Propagation Letters, Lei Xing and her colleagues at the College of Electronic and Information Engineering at Nanjing University of Aeronautics and Astronautics in China have proposed a new saltwater-based antenna that achieves 12 directional beam-steering states and one omnidirectional state. Its circular configuration allows for complete 360-degree beam-steering and works for frequencies between 334 to 488 MHz.
The proposed design consists of a circular ground plane, with 13 transparent acrylic tubes that can be filled with (or emptied of) salt water on demand. One tube is located in the center to act as a driven monopole (the radio signal is fed in via a copper disk at the base of the tube). Surrounding it are 12 so-called parasitic monopoles. When only the driven monopole is excited, this creates an omnidirectional signal. But the 12 remaining monopoles, when filled with water, work together to act as reflectors and give the broadcasted signal direction.
“The most challenging part of designing this antenna is how to effectively and efficiently control the water parasitic monopoles,” Xing explains. To do so, her team developed a liquid control system using micropumps, which she says can be applied to other liquid antennas or antenna arrays.
“The attractive feature of using water monopoles is that both the water height and activating status can be dynamically tuned through microfluidic techniques, which has a higher degree of design flexibility than metal antennas,” explains Xing. “More importantly, the antenna can be totally ‘turned off’ when not in use.”
When the antenna is switched completely off and drained, it is nearly undetectable by radar. In contrast, this effect is hard to achieve with metal antennas.
The new antenna’s operating range of 334 MHz to 488 MHz makes it a promising candidate for very-high frequency applications such as IoT and maritime applications, says Xing. One limitation of saltwater-based antennas, she notes, is that that the permittivity of saline water (a measure of how it interacts with electric fields) is sensitive to temperature variation. Xing says she plans to continue to explore various liquid-based designs for antennas moving forward.
This proposed crowdsensing approach for tracking drones allows participants to make some side cash
Because they’re so useful for so many things, drones will undoubtedly become a more common sight in the next few years. And as the number of drones in the sky increases, the need to track these mini-flying machines as they move from one spot to another will become more important.
In a recent study in IEEE Transactions on Mobile Computing, a team of scientists in China proposed an intriguing way to track unfamiliar drones through crowdsensing. Their approach leverages participants’ smartphones to detect the Wi-Fi signals of drones.
Tracking drones would be especially helpful in situations where the devices were being used for ill-intentioned purposes, such as for peeping in at someone or to transport illegal substances. But as Zhiguo Shi of Zhejiang University notes, “Detecting drones, especially in urban environments, is not easy. Traditional approaches are of huge cost, since the corresponding equipment, such as radars, cameras, and microphone arrays, are very expensive.”
His team sought to find a cheaper method. They realized that most drones use Wi-Fi technology to communicate with ground control stations. At the same time, virtually all smartphones can detect Wi-Fi signals and phones are abundant, especially in urban settings.
A simple attack can knock off a system’s timing by 48 years
When it comes to synchronizing large and important networks, for instance in the energy or financial sectors, every microsecond counts. Different protocols have been designed and implemented to achieve such precision. One of the most effective approaches is called IEEE 1588-2008 or the Precision Time Protocol (PTP). But while PTP can in theory help networks synchronize their actions to within a microsecond, a team of computer scientists recently demonstrated that PTP also makes it possible—in multiple ways—to hack such a system.
In a network using PTP, one central clock, referred to as a “master” clock, is responsible for coordinating and communicating time to “slave” clocks across the network (these controversial terms were recently removed from the popular programming language Python, but continue to be used in many fields). The master clock accomplishes this by sending time-stamped data packets to the slaves. The protocol itself measures and compensates for time delays over the network.
A team of researchers from IBM and Marist College recently sought to test PTP from a cybersecurity standpoint, probing for weaknesses. They identified a remarkably simple but effective way to hack a PTP network—throwing the timing of the slave clocks off by a whopping 2,149.5 minutes after just a 37-second attack. They describe this approach, as well as several others, in a study published 23 May in IEEE Transactions on Instrumentation and Measurement.
After analyzing more than 100,000 paintings, this AI concludes that the most beautiful images are not necessarily memorable
Whether it’s the enigmatic playfulness of Mona Lisa’s smile or the swirling soft colors of a Monet painting, there are qualities of fine art that attract audiences, like a moth to a flame. What is it about these pieces that has captivated people throughout centuries? Researchers are now using machine-learning algorithms to tease apart these intricacies and explore the relationship between the aesthetics, sentimental value, and memorability of fine art.
Eva Cetinic is an art enthusiast and researcher at the Rudjer Boskovic Institute in Croatia. While she believes that art is indescribable in many ways, she wanted to challenge her own perspective by exploring how machine learning might quantify art. “The rise of artificial intelligence forces us to re-think what values are specifically human, and the understanding of art is a particularly fruitful playground for this kind of investigation,” she explains.
To start, Cetinic and her colleagues analyzed more than 100,000 images from WikiArt. Their results, published 5 June in IEEE Access, hint at common themes of what we find beautiful and captivating.
Security researchers will soon begin selling a tool that can tell when a thief is trying to steal your credit card information
While consumers enjoy the ease at which a sale can be made with one swipe of a credit card, it also takes just one swipe for credit card theft to occur. Inside what looks like a regular point-of-sale device at the cashier’s counter or an ATM machine could be a skimmer, a gadget that records personal credit card information as you insert your card.
Hoping to put a fatal end to this type of credit card fraud, several years ago a group of researchers at the University of Florida created a device, aptly named the Skim Reaper, that detects skimmers. After some fine-tuning, sales of the Skim Reaper are poised to begin in the next few weeks.
The demand for such a tool has the potential to be very high, at a time when credit fraud is increasing at an alarming rate. A 2018 report found that 60 million U.S. payment cards were compromised over a 12-month period, 75 percent of which were compromised at a point-of-sale device.
Participants were less accurate and became more tired when completing a task with the HoloLens, compared to the naked eye
With the right device, some programming, and the flick of a switch, augmented reality (AR) can change the world—or at least change what we see a few centimeters in front of our eyes. But while the industry rapidly expands and works hard to improve the AR experience, it must also overcome an important natural barrier: the way in which our eyes focus on objects.
A recent study shows that our eyes are not quite up to the task of simultaneously focusing on two separate objects—one real and one not—in close proximity to one another.
The results, published 6 May in IEEE Transactions on Biomedical Engineering, suggest that accomplishing an AR-assisted task that’s close at hand (within two meters) and requires a high level of precision may not be feasible with existing technology. This could be unwelcome news for researchers attempting to design certain AR-assisted programs.
An accelerator unit improves both the performance and efficiency of a system by taking over one simple task
Without you noticing (much), your computer is working hard in the background to organize its memory system. On top of its many tasks, a CPU must do something called “garbage collection,” whereby it identifies and deletes redundant or irrelevant data from applications to free up additional memory space.
Garbage collection is meant to spare programmers from having to manually address this unnecessary data, but the automated process that CPUs are tasked with consumes a lot of computational power—up to 10 percent or more of the total time a CPU spends on an application.
While completing his PhD at the University of California, Berkeley, Martin Maas, who now works at Google, designed a new type of device that relieves the CPU from its garbage collection duties. The design is described in a paper published 23 April in IEEE Micro.
With the new system, every student is scored based on how likely they are to finish their courses
It’s easy enough for students to sign up for online university courses, but getting them to finish is much harder. Dropout rates for online courses can be as high as 80 percent. Researchers have tried to help by developing early warning systems that predict which students are more likely to drop out. Administrators could then use these predictions to target at-risk students with extra retention efforts. And as these early warning systems become more sophisticated, they also reveal which variables are most closely correlated with dropout risk.
In a paper published 16 April in IEEE Transactions on Learning Technologies, a team of researchers in Spain describe an early warning system that uses machine learning to provide tailored predictions for both new and recurrent students. The system, called SPA (a Spanish acronym for Dropout Prevention System), was developed using data from more than 11,000 students who were enrolled in online programs at Madrid Open University (UDIMA) over the course of five years.
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