Aurelia Butler

A novel 256-element wirelessly powered transceiver array for non-line-of-sight 5G communication, featuring efficient wireless power transmission and high-power conversion efficiency, has been designed by scientists at Tokyo Tech. The innovative design can enhance the 5G network coverage even to places with link blockage, improving flexibility and coverage area, and potentially making high-speed, low-latency communication more accessible.
Millimeter wave 5G communication, which uses extremely high-frequency radio signals (24 to 100 GHz), is a promising technology for next-generation wireless communication, exhibiting high speed, low latency, and large network capacity. However, current 5G networks face two key challenges. The first one is the low signal-to-noise ratio (SNR). A high SNR is crucial for good communication. Another challenge is link blockage, which refers to the disruption in signal between transmitter and receiver due to obstacles such as buildings.
Beamforming is a key technique for long-distance communication using millimeter waves which improves SNR. This technique uses an array of sensors to focus radio signals into a narrow beam in a specific direction, akin to focusing a flashlight beam on a single point. However, it is limited to line-of-sight communication, where transmitters and receivers must be in a straight line, and the received signal can become degraded due to obstacles. Furthermore, concrete and modern glass materials can cause high propagation losses. Hence, there is an urgent need for a non-line-of-sight (NLoS) relay system to extend the 5G network coverage, especially indoors.
To address these issues, a team of researchers led by Associate Professor Atsushi Shirane from the Laboratory for Future Interdisciplinary Research of Science and Technology at Tokyo Institute of Technology(Tokyo Tech) designed a novel wirelessly powered relay transceiver for 28 GHz millimeter-wave 5G communication. Their study has been published in the Proceedings of the 2024 IEEE MTT-S International Microwave Symposium.
Explaining the motivation behind their study, Shirane says, “Previously, for NLoS communication, two types of 5G relays have been explored: an active type and a wireless-powered type. While the active relay can maintain a good SNR even with few rectifier arrays, it has high power consumption. The wirelessly powered type does not require a dedicated power supply but needs many rectifier arrays to maintain SNR due to low conversion gain and uses CMOS diodes with lower than ten percent power conversion efficiency. Our design addresses their issues while using commercially available semiconductor integrated circuits (ICs).”
The proposed transceiver consists of 256 rectifier arrays with 24 GHz wireless power transfer (WPT). These arrays consist of discrete ICs, including gallium arsenide diodes, and baluns, which interface between balanced and unbalanced (bal-un) signal lines, DPDT switches, and digital ICs. Notably, the transceiver is capable of simultaneous data and power transmission, converting 24 GHz WPT signal to direct current (DC) and facilitating 28 GHz bi-directional transmission and reception at the same time. The 24 GHz signal is received at each rectifier individually, while the 28 GHz signal is transmitted and received using beamforming. Both signals can be received from the same or different directions and the 28 GHz signal can be transmitted either with retro-reflecting with the 24 GHz pilot signal or in any direction.
Testing revealed that the proposed transceiver can achieve a power conversion efficiency of 54% and a conversion gain of -19 decibels, higher than conventional transceivers while maintaining SNR over long distances. Additionally, it achieves about 56 milliwatts of power generation which can be increased even further by increasing the number of arrays. This can also improve the resolution of the transmission and reception beams. “The proposed transceiver can contribute to the deployment of the millimeter-wave 5G network even to places where the link is blocked, improving installation flexibility and coverage area,” remarks Shirane about the benefits of their device. More

A new way to teach artificial intelligence (AI) to understand human line drawings — even from non-artists — has been developed by a team from the University of Surrey and Stanford University.
The new model approaches human levels of performance in recognising scene sketches.
Dr Yulia Gryaditskaya, Lecturer at Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) and Surrey Institute for People-Centred AI (PAI), said:
“Sketching is a powerful language of visual communication. It is sometimes even more expressive and flexible than spoken language.
“Developing tools for understanding sketches is a step towards more powerful human-computer interaction and more efficient design workflows. Examples include being able to search for or create images by sketching something.”
People of all ages and backgrounds use drawings to explore new ideas and communicate. Yet, AI systems have historically struggled to understand sketches.
AI has to be taught how to understand images. Usually, this involves a labour-intensive process of collecting labels for every pixel in the image. The AI then learns from these labels.

Instead, the team taught the AI using a combination of sketches and written descriptions. It learned to group pixels, matching them against one of the categories in a description.
The resulting AI displayed a much richer and more human-like understanding of these drawings than previous approaches. It correctly identified and labelled kites, trees, giraffes and other objects with an 85% accuracy. This outperformed other models which relied on labelled pixels.
As well as identifying objects in a complex scene, it could identify which pen strokes were intended to depict each object. The new method works well with informal sketches drawn by non-artists, as well as drawings of objects it was not explicitly trained on.
Professor Judith Fan, Assistant Professor of Psychology at Stanford University, said:
“Drawing and writing are among the most quintessentially human activities and have long been useful for capturing people’s observations and ideas.
“This work represents exciting progress towards AI systems that understand the essence of the ideas people are trying to get across, regardless of whether they are using pictures or text.”
The research forms part of Surrey’s Institute for People-Centred AI, and in particular its SketchX programme. Using AI, SketchX seeks to understand the way we see the world by the way we draw it.

Professor Yi-Zhe Song, Co-director of the Institute for People-Centred AI, and SketchX lead, said:
“This research is a prime example of how AI can enhance fundamental human activities like sketching. By understanding rough drawings with near-human accuracy, this technology has immense potential to empower people’s natural creativity, regardless of artistic ability.”
The findings will be presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024. It takes place in Seattle from 17-21 June 2024. More

A recently developed wirelessly powered 5G relay could accelerate the development of smart factories, report scientists from Tokyo Tech. By adopting a lower operating frequency for wireless power transfer, the proposed relay design solves many of the current limitations, including range and efficiency. In turn, this allows for a more versatile and widespread arrangement of sensors and transceivers in industrial settings.
One of the hallmarks of the Information Age is the transformation of industries towards a greater flow of information. This can be readily seen in high-tech factories and warehouses, where wireless sensors and transceivers are installed in robots, production machinery, and automatic vehicles. In many cases, 5G networks are used to orchestrate operations and communications between these devices.
To avoid relying on cumbersome wired power sources, sensors and transceivers can be energized remotely via wireless power transfer (WPT). However, one problem with conventional WPT designs is that they operate at 24 GHz. At such high frequencies, transmission beams must be extremely narrow to avoid energy losses. Moreover, power can only be transmitted if there is a clear line of sight between the WPT system and the target device. Since 5G relays are often used to extend the range of 5G base stations, WPT needs to reach even further, which is yet another challenge for 24 GHz systems.
To address the limitations of WPT, a research team from Tokyo Institute of Technology has come up with a clever solution. In a recent study, whose results have been presented in the2024 IEEE Symposium on VLSI Technology & Circuits, they developed a novel 5G relay that can be powered wirelessly at a lower frequency of 5.7 GHz. “By using 5.7 GHz as the WPT frequency, we can get wider coverage than conventional 24 GHz WPT systems, enabling a wider range of devices to operate simultaneously,” explains senior author and Associate Professor Atsushi Shirane.
The proposed wirelessly powered relay is meant to act as an intermediary receiver and transmitter of 5G signals, which can originate from a 5G base station or wireless devices. The key innovation of this system is the use of a rectifier-type mixer, which performs 4th-order subharmonic mixing while also generating DC power.
Notably, the mixer uses the received 5.7 GHz WPT signal as a local signal. With this local signal, together with multiplying circuits, phase shifters, and a power combiner, the mixer ‘down-converts’ a received 28 GHz signal into a 5.2 GHz signal. Then, this 5.2 GHz signal is internally amplified, up-converted to 28 GHz through the inverse process, and retransmitted to its intended destination.
To drive these internal amplifiers, the proposed system first rectifies the 5.7 GHz WPT signal to produce DC power, which is managed by a dedicated power management unit. This ingenious approach offers several advantages, as Shirane highlights: “Since the 5.7 GHz WPT signal has less path loss than the 24 GHz signal, more power can be obtained from a rectifier. In addition, the 5.7 GHz rectifier has a lower loss than 24 GHz rectifiers and can operate at a higher power conversion efficiency.” Finally, this proposed circuit design allows for selecting the transistor size, bias voltage, matching, cutoff frequency of the filter, and load to maximize conversion efficiency and conversion gain simultaneously.
Through several experiments, the research team showcased the capabilities of their proposed relay. Occupying only a 1.5 mm by 0.77 mm chip using standard CMOS technology, a single chip can output a high power of 6.45 mW at an input power of 10.7 dBm. Notably, multiple chips could be combined to achieve a higher power output. Considering its many advantages, the proposed 5.7 GHz WPT system could thus greatly contribute to the development of smart factories. More

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 PhD 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 PhD 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

Griffith University researchers have developed innovative, eco-friendly quantum materials that can drive the transformation of methanol into ethylene glycol.
Ethylene glycol is an important chemical used to make polyester (including PET) and antifreeze agents, with a global production of over 35 million tons annually with strong growth.
Currently, it’s mainly produced from petrochemicals through energy-intensive processes.
Methanol (CH3OH) can be produced sustainably from CO2, agricultural biomass waste, and plastic waste through various methods such as hydrogenation, catalytic partial oxidation, and fermentation. As a fuel, methanol also serves as a circular hydrogen carrier and a precursor for numerous chemicals.
Led by Professor Qin Li, the Griffith team’s method uses solar-driven photocatalysis to convert methanol into ethylene glycol under mild conditions.
This process uses sunlight to drive chemical reactions, which minimises waste and maximises the use of renewable energy.
While previous attempts at this conversion have faced challenges — such as the need for toxic or precious materials — Professor Li and the research team have identified a greener solution.

“Climate change is a major challenge facing humanity today,” Professor Li said.
“To tackle this, we need to focus on zero-emission power generation, low-emission manufacturing, and a circular economy. Methanol stands out as a crucial chemical that links these three strategies.
“What we have created is a novel material that combines carbon quantum dots with zinc selenide quantum wells.”
“This combination significantly enhances the photocatalytic activity more than four times higher than using carbon quantum dots alone, demonstrating the effectiveness of the new material,” Lead author Dr Dechao Chen said.
The approach has also shown high photocurrent, indicating efficient charge transfer within the material, crucial for driving the desired chemical reactions.
Analyses confirmed the formation of ethylene glycol, showcasing the potential of this new method. It’s worth noting that the by-product of this reaction is green hydrogen.

This discovery opens up new possibilities for using eco-friendly materials in photocatalysis, paving the way for sustainable chemical production.
As a new quantum material, it also has the potential to lead to further advancements in photocatalysis, sensing, and optoelectronics.
“Our research demonstrates a significant step towards green chemistry, showing how sustainable materials can be used to achieve important chemical transformations,” Professor Li said.
“This could transform methanol conversion and contribute significantly to emissions reduction.”
The findings ‘Colloidal Synthesis of Carbon Dot-ZnSe Nanoplatelet Vander Waals Heterostructures for Boosting PhotocatalyticGeneration of Methanol-Storable Hydrogen’ have been published in the journal Small. More

Getting a molecule to do what you want it to do is not always easy. As an example, an organic molecule will absorb only certain wavelengths of light based on its arrangement of electrons, which can be difficult to fine tune. Even so, the ability to make substances that respond to only specific ranges of the spectrum could lead to important new applications.
There is currently significant interest in the design of new organic semiconducting materials for high-tech applications such as solar cells and transistors. In particular, molecules that can absorb near-infrared light but not visible light, and so are colorless, have applications in everything from chemotherapy to photodetectors. Some such compounds have already been developed but so far there has been no systematic process for making these molecules.
In a study recently published in Advanced Science, researchers from SANKEN at Osaka University were able to systematically design a large, complex molecule that does not absorb visible light, meaning that it is completely colorless and transparent, but do absorb near-infrared radiation. This was accomplished by carefully constructing molecules that have suitable arrangements of electrons.
The absorbance of light by an organic compound is based on electrons moving between regions around atoms known as orbitals. In this work, the researchers show a methodical approach to constructing molecules having orbitals that allow some ranges of light to be absorbed but not others.
“The main challenge was finding a rational approach to constructing molecules with the desired electronic transitions,” says lead author of the study Soichi Yokoyama. “To do so, we focused on large structures having many delocalized electrons, using theoretical calculations to guide our selections.”
These compounds were based on a so-called donor-acceptor-donor system and utilized a naphthobisthiadiazole group as the acceptor combined with either pyrrole or indenopyrrole donor groups along with boron bridges. This specialized structure allowed electrons to spread out over wider areas of the molecules, producing just the right type of light absorption. The new molecule was exhaustively characterized and were found not to absorb in the visible region of the spectrum but to absorb near-infrared light, as planned.
“A somewhat similar molecule absorbing near-infrared radiation was reported some time ago,” explains Yutaka Ie, senior author, “but this compound also absorbed visible light and so appeared blue. Our goal was to find a molecule that showed no color at all, to allow specific applications. A combination of an extended polyene structure and orbital symmetry were key.”
The molecule was found to act as semiconductors and the pyrrole-based compound could also be used to construct a phototransistor responsive to near-infrared light. Many uses for organic compounds that show unique optoelectronic properties and specific light absorption characteristics are yet to be explored. This work is expected to pave the way for the future design of transparent, colorless molecules that respond to near-infrared light and lead to many new applications. More

Using computer-assisted neural networks, Researchers at the Karlsruhe Institute of Technology (KIT) and the University of Duisburg-Essen have been able to accurately identify affective states from the body language of tennis players during games. For the first time, they trained a model based on artificial intelligence (AI) with data from actual games. Their study, published in the journal Knowledge-Based Systems, demonstrates that AI can assess body language and emotions with accuracy similar to that of humans. However, it also points to ethical concerns.
For their study, “Recognizing affective states from the expressive behavior of tennis players using convolutional neural networks,” sports sciences, software development and computer science researchers from KIT and the University of Duisburg-Essen developed a special AI model. They used pattern-recognition programs to analyze video of tennis players recorded during actual games.
Success Rate of 68.9 Percent
“Our model can identify affective states with an accuracy of up to 68.9 percent, which is comparable and sometimes even superior to assessments made by both human observers and earlier automated methods,” said Professor Darko Jekauc of KIT’s Institute of Sports and Sports Science.
An important and unique feature of the study is the project team’s use of real-life scenes instead of simulated or contrived situations to train their AI system. The researchers recorded video sequences of 15 tennis players in a specific setting, focusing on the body language displayed when a point was won or lost. The videos showed players with cues including lowered head, arms raised in exultation, hanging racket, or differences in walking speed; these cues could be used to identify the players’ affective states.
After being fed with this data, the AI learned to associate the body language signals with different affective reactions and to determine whether a point had been won (positive body language) or lost (negative body language). “Training in natural contexts is a significant advance for the identification of real emotional states, and it makes predictions possible in real scenarios,” said Jekauc.
Humans and Machines Recognize Negative Emotions Better Than Positive Ones
Not only does the research show that AI algorithms may be able to surpass human observers in their ability to identify emotions in the future, it also revealed a further interesting aspect: both humans and AI are better at recognizing negative emotions. “The reason could be that negative emotions are easier to identify because they’re expressed in more obvious ways,” said Jekauc. “Psychological theories suggest that people are evolutionarily better adapted to perceive negative emotional expressions, for example because defusing conflict situations quickly is essential to social cohesion.”

Ethical Aspects Need Clarification Before Use
The study envisions a number of sports applications for reliable emotion recognition, such as improving training methods, team dynamics and performance, and preventing burnout. Other fields, including healthcare, education, customer service and automotive safety, could also benefit from reliable early detection of emotional states.
“Although this technology offers the prospect of significant benefits, the potential risks associated with it also have to be taken into account, especially those relating to privacy and misuse of data,” Jekauc said. “Our study adhered strictly to existing ethical guidelines and data protection regulations. And with a view to future applications of such technology in practice, it will be essential to clarify ethical and legal issues ahead of time.” More

Porous covalent organic frameworks (COFs) are a class of highly ordered, porous materials consisting of organic molecules that are linked by covalent bonds to form a network. They enable the construction of functional materials with molecular precision. Similar to metal organic frameworks (MOFs), which were discovered around 25 years ago and have already reached market maturity, COFs possess highly promising structural, optical and electronic properties for numerous applications, for example in gas and liquid storage, catalysis, sensor technology and energy applications.

Previous research on COFs has generally focussed on the construction of rigid frameworks with static material properties. Dr Florian Auras and his team at the Chair of Molecular Functional Materials at TUD have now developed a design strategy for dynamic two-dimensional COFs that can open and close their pores in a controlled manner, similar to a sponge. “The main aim of the study was to equip these frameworks, which are normally very precisely ordered but rigid, with exactly the right degree of flexibility so that their structure can be switched from compact to porous. By adding solvent to the molecular sponge, we can now temporarily and reversibly change the local geometry as well as optical properties such as colour or fluorescence,” says Florian Auras, explaining his research approach.
The ability to switch the structural and optoelectronic properties of the materials back and forth in a targeted manner makes the materials particularly interesting for future applications in electronics and information technology. “Our research results form the basis for our further research into stimuli-responsive polymers, particularly with the aim of realising switchable quantum states. When working on COFs, I am always fascinated by how precisely their properties can be manipulated by controlling the molecular structure,” adds Auras. More