Sometimes, we try to capture a QR code with a good digital camera on a smartphone, but the reading eventually fails. This usually happens when the QR code itself is of poor image quality, or if it has been printed on surfaces that are not flat—deformed or with irregularities of unknown pattern—such as the wrapping of a courier package or a tray of prepared food.

Now, a team from the University of Barcelona and the Universitat Oberta de Catalunya has designed a methodology that facilitates the recognition of QR codes in physical environments where reading is more complicated. The paper is published in the journal Pattern Recognition Letters.

The new system does not depend absolutely on the underlying topography, and is applicable to QR codes that can be found on tubular surfaces (bottles), food trays, etc. It is the first technological proposal capable of combining a generalist methodology and two-dimensional barcodes to facilitate the recognition of digital information.

The study’s first author is Professor Ismael Benito from the UB’s Faculty of Physics and Department of Electronic and Biomedical Engineering and the UOC’s Department of Computer Science, Multimedia and Telecommunications Studies. All the authors have participated in different positions in the creation of ColorSensing, SL, a UB spin-off company in the field of smart labeling.

Why are some QR codes difficult to read?

QR codes are a variation of the typical barcode, capable of collecting information in computer language—in a two-dimensional matrix of black and white pixels—when scanned with a scanning device. They facilitate access to data of interest, save time and resources such as paper, and have revolutionized the way users access information in the digital realm.

However, it is sometimes difficult to scan a barcode correctly. According to Benito, from the UB’s Department of Electronic and Biomedical Engineering and former technology director of ColorSensing, this happens, “first of all, because of the quality of the image. Although many people today have access to good digital cameras, they cannot always capture the QR image well.

“Secondly, the print quality of the QR code and the colors used—with good contrast—is sometimes not satisfactory. Finally, if the printing surface is not flat enough and not parallel to the capture plane, it is also difficult to capture the information in the code.”

“For example, all these factors come into play when we try to capture a Bicing QR with the mobile app: the surface is not flat—it is a cylinder—and if we try to capture the QR too close, the deformation of the surface becomes evident and the reading fails—5–10 centimeters; if we move too far away, the QR becomes too small and the capture is not good—1 meter; if we are in an intermediate range, the apparent distortion of the surface is reduced and the quality is suitable for capturing it—30–50 centimeters,” explains Benito.

An algorithm that exploits properties of QR codes

The study, which is part of Ismael Benito’s doctoral thesis at the UB, presents a new algorithm that takes advantage of the QR’s own characteristics—i.e., the code’s internal patterns—to extract the underlying surface on which the code is positioned.

The texture of this surface is recovered by a generalist adjustment based on mathematical functions known as splines, which allow the topography of the surface to be adjusted locally. Benito points out that “they are functions that adapt locally to the ups and downs of the surface, and form a technique that was originally widely used in fields such as geology or photographic editing to adjust or generate deformations in surfaces.”

There are still many technological challenges to improving the whole process of QR code recognition.

In the case of commercial applications activated by the user’s code reader, the expert explains that “the main challenge is to be able to provide correct and reliable readings. We are also working hard to ensure that the codes cannot be attacked by modification techniques, for example, with a fake URL that can capture data with small modifications to the code.”

“In the case of industry, where captures are done in controlled environments, the main challenge is to reduce the speed of capture,” says Benito.

Gray foxes have been a staple of Virginia’s—and the Southeast’s—landscape for decades. In recent years, there’s been a growing concern that they might be undergoing a population decline in the commonwealth.

In collaboration with the Virginia Department of Wildlife Resources, Virginia Tech researchers are working on understanding exactly what is happening with gray foxes—if the population decline is habitat related, caused by pressure from coyotes, competition with red foxes, or if there’s a population decline at all.

“All of this is little understood and the comments we’ve received are anecdotal,” said Marcella Kelly, professor in the College of Natural Resources and Environment and primary investigator on the project. “It’s important for us to figure out because we’ve been hearing mixed messages about gray foxes. Some people don’t think they’re declining, and some are convinced the population is declining.”

Gray foxes are native to Virginia and are ecologically important to the environment.

“Gray foxes are valuable as seed dispersers because they eat fruits and berries, but they largely help with population control of small mammals,” said Victoria Monette, a Ph.D. student in the Department of Fish and Wildlife Conservation.

“Take mice, for example. Mice are known to be vectors of diseases, such as Lyme disease. Gray foxes help keep these populations in check. If there is a population decline in gray foxes, it can have indirect impacts on people.”

Based on the results of the research, the Department of Wildlife Resources, in collaboration with the research team, could create a management plan for gray foxes.

To study the gray fox population, the researchers placed hundreds of trail cameras across Virginia to gather data on current population levels.

After analyzing Virginia’s diverse environment, Monette determined that trail cameras associated with the project should be spaced out at least every 5 kilometers. To do this, it required the tried-and-true method of canvassing—knocking on people’s doors one by one to gain permission to place a trail camera on the property.

As of Sept. 20, 369 cameras have been placed with 74% of cameras set on private properties, 19% on national forest lands, 4% on The Nature Conservancy-managed properties, and 3% on Department of Wildlife Resources-managed properties.

While data is still coming in, of the 192 locations where cameras have been retrieved, gray foxes have been detected at 25, or 13% , of the locations.

The researchers are expecting to close out the 2024 field season with more than 400 cameras set throughout 40 counties in the Virginia Appalachian mountain region.

Roughly 50 additional cameras have been set up by private landowners participating as citizen scientists, with participants from Virginia Master Naturalists, Virginia Working Lands, Virginia Outdoors Foundation, Virginia Forest Landowners Group, The Nature Conservancy, Virginia Tech Agricultural Research and Extension Centers, Banshee Reeks Nature Preserve, the Nature Foundation at Wintergreen, the Blue Ridge Discovery Center, and more.

It’s important for past data to be studied to get an idea of what the population was previously to have a baseline comparison. The research team will look at studies and remote camera footage going back more than two decades to get a sense of the population of gray foxes in the past.

This project, which encompasses the traditional scientific method while incorporating citizen-science elements, has been a valuable component in Monette’s pursuit of her Ph.D.

“Through my recent experiences, I’ve gained a deeper understanding of the importance of engaging with the public in research, something I hadn’t fully considered before,” Monette said.

“This project has shown me how crucial it is to involve and communicate with a broader audience, sparking my passion for public outreach alongside academic research. I’ve realized how much people care about the work we’re doing, and it’s been incredibly rewarding to share results with them and foster interest in conservation. Moving forward, I’m excited to pursue a Ph.D. that combines my love for research, teaching, and meaningful community engagement.”

The research team will share results of the project after data past and present has been analyzed.

Ka-band metasurface antennas, with their low-cost, low-profile design and superior beam-steering capabilities, show significant potential in the field of satellite communications. However, the constraints of limited satellite resources and significant atmospheric losses at Ka-band frequencies require these antennas to achieve wide-angle beam scanning capabilities and high antenna gain, adding considerable complexity to their design.

In order to achieve the design of a multifunctional and highly efficient meta-antenna, the design optimization will involve numerous parameters, greatly increasing the use of computational resources and optimization time. Addressing the critical issue of balancing multiple optimization objectives, such as gain and scanning angle, while improving optimization speed, remains a key challenge in the design process.

To address these challenges of meta-antenna design, researchers from the University of Electronic Science and Technology of China, Tongji University, and City University of Hong Kong have joined forces in an extensive collaboration.

Leveraging their long-term expertise in the field of meta-optics, they proposed a Ka-band meta-antenna design method based on a Physics-Assisted Particle Swarm Optimization (PA-PSO) algorithm. Using this method, they designed and fabricated a Ka-band meta-antenna. The study is published in the journal Opto-Electronic Science.

The antenna proposed in the paper is designed using the PA-PSO algorithm. Compared to the traditional PSO algorithm, the optimization direction of particles in the PA-PSO algorithm is guided by extremum conditions derived from the variational method. This not only reduces computation time but also decreases the likelihood of finding suboptimal designs.

The final optimized results indicate that the relative strength achieved by the PA-PSO algorithm is 94.62806, which is comparable to the relative strength of 94.62786 achieved by the traditional PSO algorithm. However, the computational cost of the PA-PSO algorithm is significantly lower; it reaches the optimal state after only 650 iterations, whereas the traditional PSO algorithm requires 4100 iterations.

This means the computation time of the PA-PSO algorithm is less than one-sixth of that for the PSO algorithm. Therefore, the PA-PSO method can guide particle swarms more efficiently, reducing computation time, making it an important tool for addressing complex multivariate and multi-objective optimization challenges.

Based on the phase distribution optimized by the PA-PSO algorithm, the team designed and fabricated a hexagonal meta-antenna sample with a focal length of 22 mm, diagonal length of 110 mm, and a thickness of only 1.524 mm.

The antenna has an f-number of only 0.2, a beam scanning angle of ±55°, a maximum gain of 21.7 dBi, and a gain flatness of within 4 dB. This innovative hexagonal meta-antenna, with its wide scanning angle, compact design, and high transmission gain, exhibits enormous potential for applications in satellite communication, radar systems, 5G networks, and the Internet of Things, among many other fields.

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