Aurelia Butler

A new bio-inspired sensor can recognise moving objects in a single frame from a video and successfully predict where they will move to. This smart sensor, described in a Nature Communications paper, will be a valuable tool in a range of fields, including dynamic vision sensing, automatic inspection, industrial process control, robotic guidance, and autonomous driving technology.
Current motion detection systems need many components and complex algorithms doing frame-by-frame analyses, which makes them inefficient and energy-intensive. Inspired by the human visual system, researchers at Aalto University have developed a new neuromorphic vision technology that integrates sensing, memory, and processing in a single device that can detect motion and predict trajectories.
At the core of their technology is an array of photomemristors, electrical devices that produce electric current in response to light. The current doesn’t immediately stop when the light is switched off. Instead, it decays gradually, which means that photomemristors can effectively ‘remember’ whether they’ve been exposed to light recently. As a result, a sensor made from an array of photomemristors doesn’t just record instantaneous information about a scene, like a camera does, but also includes a dynamic memory of the preceding instants.
‘The unique property of our technology is its ability to integrate a series of optical images in one frame,’ explains Hongwei Tan, the research fellow who led the study. ‘The information of each image is embedded in the following images as hidden information. In other words, the final frame in a video also has information about all the previous frames. That lets us detect motion earlier in the video by analysing only the final frame with a simple artificial neural network. The result is a compact and efficient sensing unit.’
To demonstrate the technology, the researchers used videos showing the letters of a word one at a time. Because all the words ended with the letter ‘E’, the final frame of all the videos looked similar. Conventional vision sensors couldn’t tell whether the ‘E’ on the screen had appeared after the other letters in ‘APPLE’ or ‘GRAPE’. But the photomemristor array could use hidden information in the final frame to infer which letters had preceded it and predict what the word was with nearly 100% accuracy.
In another test, the team showed the sensor videos of a simulated person moving at three different speeds. Not only was the system able to recognize motion by analysing a single frame, but it also correctly predicted the next frames.
Accurately detecting motion and predicting where an object will be are vital for self-driving technology and intelligent transport. Autonomous vehicles need accurate predictions of how cars, bikes, pedestrians, and other objects will move in order to guide their decisions. By adding a machine learning system to the photomemristor array, the researchers showed that their integrated system can predict future motion based on in-sensor processing of an all-informative frame.
‘Motion recognition and prediction by our compact in-sensor memory and computing solution provides new opportunities in autonomous robotics and human-machine interactions,’ says Professor Sebastiaan van Dijken. ‘The in-frame information that we attain in our system using photomemristors avoids redundant data flows, enabling energy-efficient decision-making in real time.’ More

Wireless wearable biosensors have been a game changer in personalized health monitoring and healthcare digitization because they can efficiently detect, record, and monitor medically significant biological signals. Chipless resonant antennae are highly promising components of wearable biosensors, as they are affordable and tractable. However, their practical applications are limited by low sensitivity (inability to detect small biological signals) caused by low quality (Q) factor of the system.
To overcome this hurdle, researchers led by Professor Takeo Miyake from Waseda University, Professor Yin Sijie from Beijing Institute of Technology, and Taiki Takamatsu from Japan Aerospace Exploration Agency, have developed a wireless bioresonator using “parity-time (PT) symmetry” that can detect minute biological signals. Their work has been published in Advanced Materials Technologies.
In this study, the researchers designed a bioresonator consisting of a magnetically coupled reader and sensor with high Q factor, and thus, increased sensitivity to biochemical changes. The reader and sensor both comprise an inductor (L) and capacitor (C) that are parallel-connected to a resistor (R). In the sensor, the resistor is a chemical sensor called a “chemiresistor” that converts biochemical signals into changes in resistance. The chemiresistor contains an enzymatic electrode with an immobilized enzyme. Minute biochemical changes at the enzymatic electrode (in response to changes in the levels of biomolecules such as blood sugar or lactate) are thus converted into electrical signals by the sensor, and then amplified at the reader.
Explaining the technical concept behind their novel biosensor, Miyake says, “We modeled the characteristics of the PT-symmetric wireless sensing system by using an eigenvalue solution and input impedance, and experimentally demonstrated the sensitivity enhancement at/near the exceptional point by using parallel inductance-capacitance-resistance (LCR) resonators.The developed amplitude modulation-based PT-symmetric bioresonator can detect small biological signals that have been difficult to measure wirelessly until now. Moreover, our PT-symmetric system provides two types of readout modes: threshold-based switching and enhanced linear detection. Different readout modes can be used for different sensing ranges.”
The researchers tested the system (here containing a glucose-specific enzyme) on human tear fluids and found that it could detect glucose concentrations ranging from 0.1 to 0.6 mM. They also tested it with a lactate-specific enzyme and commercially available human skin and found that it could measure lactate levels in the range of 0.0 to 4.0 mM through human skin tissue, without any loss of sensitivity. This result further indicates that the biosensor can be used as an implantable device. Compared to a conventional chipless resonant antenna-based system, the PT-symmetric system achieved a 2000-fold higher sensitivity in linear and a 78% relative change in threshold-based detection respectively.
Sharing his vision for the future, Miyake concludes, “The present telemetry system is robust and tunable. It can enhance the sensitivity of sensors to small biological signals. We envision that this technology can be used for developing smart contact lenses to detect tear glucose and/or implantable medical devices to detect lactate for efficient monitoring of diabetes and blood poisoning.” More

The typical image of a robot is one composed of motors and circuits, encased in metal. Yet the field of molecular robotics, which is being spearheaded in Japan, is beginning to change that.
Much like how complex living organisms are formed, molecular robots derive form and functionality from assembled molecules. Such robots could have important applications, such as being used to treat and diagnose diseases in vivo.
The first challenge in building a molecular robot is the same as the most basic need of any organism: the body, which holds everything together. But manufacturing complex structures, especially at the microscopic level, has proven to be an engineering nightmare, and many limitations on what is possible currently exist.
To address this problem, a research team at Tohoku University has developed a simple method for creating molecular robots from artificial, multicellular-like bodies by using molecules which can organize themselves into the desired shape.
The team, including Associate Professor Shin-ichiro Nomura and postdoctoral researcher Richard Archer from the Department of Robotics at the Graduate School of Engineering, recently reported their breakthrough in the American Chemical Society’s publication, Langmuir.
“Our work demonstrated a simple, self-assembly technique which utilizes phospholipids and synthetic surfactants coated onto a hydrophobic silicone sponge,” said Archer.
When Nomura and his colleagues introduced water into the lipid coated sponge, the hydrophilic and hydrophobic forces enabled the lipids and surfactants to assemble themselves, thereby allowing water to soak in. The sponge was then placed into oil, spontaneously forming micron sized, stabilized aqueous droplets as the water was expelled from the solid support. When pipetted on the surface of water, these droplets quickly assembled into larger planar macroscopic structures, like bricks coming together to form a wall.
“Our developed technique can easily build centimeter size structures from the assembly of micron sized compartments and is capable of being done with more than one droplet type,” adds Archer. “By using different sponges with water containing different solutes, and forming different droplet types, the droplets can combine to form heterogeneous structures. This modular approach to assembly unleashes near endless possibilities.”
The team could also turn these bodies into controllable devices with induced motion. To do so, they introduced magnetic nanoparticles into the hydrophobic walls of the multi-compartment structure. Archer says this multi-compartment approach to robot design will allow flexible modular designs with multiple functionalities and could redefine what we imagine robots to be. “Future work here will move us closer to a new generation of robots which are assembled by molecules rather than forged in steel and use functional chemicals rather than silicon chips and motors.” More

Aston University has worked with international partners to develop a software package to help scientists answer key questions about genetic factors associated with shared characteristics among different species.
Called CALANGO (comparative analysis with annotation-based genomic components), it has the potential to help geneticists investigate vital issues such as antibacterial resistance and improvement of agricultural crops.
This work ,”CALANGO: a phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species,” has been published in the journal Patterns. It is the result of a four year collaboration between Aston University, the Federal University of Minas Gerais in Brazil and other partners in Brazil, Norway and the US.
Similarities between species may arise either from shared ancestry (homology) or from shared evolutionary pressures (convergent evolution). For example, ravens, pigeons and bats can all fly, but the first two are birds whereas bats are mammals.
This means that the biology of flight in ravens and pigeons is likely to share genetic aspects due to their common ancestry. Both species are able to fly nowadays because their last common ancestor — an ancestor bird — was also a flying organism.
In contrast, bats have the ability to fly via potentially different genes than the ones in birds, since the last common ancestor of birds and mammals was not a flying animal.

Untangling the genetic components shared due to common ancestry from the ones shared due to common evolutionary pressures requires sophisticated statistical models that take common ancestry into account.
So far, this has been an obstacle for scientists who want to understand the emergence of complex traits across different species, mainly due to the lack of proper frameworks to investigate these associations.
The new software has been designed to effectively incorporate vast amounts of genomic, evolutionary and functional annotation data to explore the genetic mechanisms which underly similar characteristics between different species sharing common ancestors.
Although the statistical models used in the tool are not new, it is the first time they have been combined to extract novel biological insights from genomic data.
The technique has the potential to be applied to many different areas of research, allowing scientists to analyse massive amounts of open-source genetic data belonging to thousands of organisms in more depth.
Dr Felipe Campelo from the Department of Computer Science in the College of Engineering and Physical Sciences at Aston University,said: “There are many exciting examples of how this tool can be applied to solve major problems facing us today. These include exploring the co-evolution of bacteria and bacteriophages and unveiling factors associated with plant size, with direct implications for both agriculture and ecology.”
“Further potential applications include supporting the investigation of bacterial resistance to antibiotics, and of the yield of plant and animal species of economic importance.”
The corresponding author of the study, Dr Francisco Pereira Lobo from the Department of Genetics, Ecology and Evolution at the Federal University of Minas Gerais in Brazil, said: “Most genetic and phenotypic variations occur between different species, rather than within them. Our newly developed tool allows the generation of testable hypotheses about genotype-phenotype associations across multiple species that enable the prioritisation of targets for later experimental characterization.”
Further information: https://labpackages.github.io/CALANGO/ More

Wind speed and direction provide clues for forecasting weather patterns. In fact, wind influences cloud formation by bringing water vapor together. Atmospheric scientists have now found a novel way of measuring wind — by developing an algorithm that uses data from water vapor movements. This could help predict extreme events like hurricanes and storms.
A study published by University of Arizona researchers in the journal Geophysical Research Letters provides, for the first time, data on the vertical distribution of horizontal winds over the tropics and midlatitudes. The researchers got the water vapor movement data by using two operational satellites of the National Oceanic and Atmospheric Administration, or NOAA, the federal agency for weather forecasting.
Wind brings everything else in the atmosphere together, including clouds, aerosols, water vapor, precipitation and radiation, said Xubin Zeng, co-author of the study and the director of the Climate Dynamics and Hydrometeorology Collaborative at UArizona. But it has remained somewhat elusive.
“We never knew the wind very well. I mean, that’s the last frontier. That’s why I’m excited,” Zeng said.
Thanks to more advanced algorithms, Zeng said, the researchers were able to do the estimation of horizontal winds not just at one altitude, but at different altitudes at the same location.
“This was not possible a decade ago,” Zeng said.

Wind measurement typically is done in three different ways, Zeng explained. The first is through the use of radiosonde, an instrumental package suspended below a 6-foot-wide balloon. Sensors on the radiosonde measure wind speed and direction, and take measurements of atmospheric pressure, temperature and relative humidity. The downsides of radiosonde balloons, Zeng said, is the cost. Each launch could cost around $400 to $500, and some regions, such as Africa and the Amazon rainforest, have limited radiosonde stations. The other limitation is that radiosondes are not available over oceans, Zeng said.
Another way to measure wind is using cloud top, which is the height at which the upper visible part of the cloud is located, Zeng said. By tracking cloud top movement using geostationary satellite data, weather experts monitor wind speed and direction at one height. But Zeng said cloud tops exist most of the time below 2 miles or above 4 1/2 miles above Earth’s surface, depending on whether the clouds are low or high. This means wind information is usually not available in the middle, between 2 and 4 1/2 miles.
Lidar, which stands for light detection and ranging, is a method that precisely measures wind movements at different elevations, and it provides very good data, Zeng said. But with lidar, measurements can be acquired only in one vertical “curtain,” with measured wind typically in the east-west direction, he added.
Nowadays, Zeng said, to study topics like air quality and volcano ash dispersion, which are directly influenced by wind, experts use weather forecasting models to ingest measurements from different sources rather than using direct measurements of wind. But model outputs are not good enough when there is rainfall, Zeng said.
In their study, Zeng and his team avoided using data from models. They instead used data from the movement of water vapor recorded by the two NOAA satellites. The satellites moved in the same direction separated by a 50-minute interval, and they detected the water vapor movement through infrared radiation.
While our eyes cannot detect the minute movements of water vapor in the atmosphere, lead study author Amir Ouyed, a member of Zeng’s research group, used machine-learning algorithms that do better image processing to track water vapor.
“For decades, people were saying, ‘You have to move the cloud top or water vapors enough so that you can see the difference of the pattern.’ But now, we don’t need to do that,” Zeng said.
“The resolution of the data is coarse, with a pixel size of 100 kilometers. It’s a demonstration of the feasibility for our future satellite mission we are pursuing where we hope to provide the 10-kilometer resolution,” Zeng said.
Zeng and his collaborators at other institutions are planning to pursue a new satellite wind mission in which they envision combining water vapor movement data and measurements from wind lidar to provide better wind measurements overall. More

How could the fictitious Marvel movie character Ant-Man produce high energy out of his small body? The secret lies in the “transistors” on his suit that amplify weak signals for processing. Transistors that amplify electrical signals in the conventional way lose heat energy and limit the speed of signal transfer, which degrades performance. What if it were possible to overcome such limitation and make a high-performance suit that is light and small but without loss of heat energy?
A POSTECH team of Professor Kyoung-Duck Park and Yeonjeong Koo from the Department of Physics and a team from ITMO University in Russia led by Professor Vasily Kravtsov jointly developed a “nano-excitonic transistor” using intralayer and interlayer excitons in heterostructure-based semiconductors, which addresses the limitationsof existing transistors.
“Excitons” are responsible for light emission of semiconductor materials and are key to developing a next-generation light-emitting element with less heat generation and a light source for quantum information technology due to the free conversion between light and material in their electrically neutral states. There are two types of excitons in a semiconductor heterobilayer, which is a stack of two different semiconductor monolayers: the intralayer excitons with horizontal direction and the interlayer excitons with vertical direction.
Optical signals emitted by the two excitons have different lights, durations, and coherence times. This means that selective control of the two optical signals could enable the development of a two-bit exciton transistor. However, it was challenging to control intra- and interlayer excitons in nano-scale spaces due to the non-homogeneity of semiconductor heterostructures and low luminous efficiency of interlayer excitons in addition to the diffraction limit of light.
The team in its previous research had proposed technology for controlling excitons in nano-level spaces by pressing semiconductor materials with a nano-scale tip. This time, for the first time ever, the researchers were able to remotely control the density and luminance efficiency of excitons based on polarized light on the tip without directly touching the excitons. The most significant advantage of this method, which combines a photonic nanocavity and a spatial light modulator, is that it can reversibly control excitons, minimizing physical damage to the semiconductor material. Also, the nano-excitonic transistor that utilizes “light” can help process massive amounts of data at the speed of light while minimizing heat energy loss.
Artificial intelligence (AI) has made inroads into our lives more quickly than we ever expected, and it requires huge volumes of data for learning in order to provide good answers that are actually helpful for users. The ever-increasing volume of information should be collected and processed as more and more fields utilize AI. This research is expected to propose a new data processing strategy befitting an era of data explosion. Yeonjeong Koo, one of the co-first authors of the research paper, said, “The nano-excitonic transistor is expected to play an integral role in realizing an optical computer, which will help process the huge amounts of data driven by AI technology.
The research, recently published in international journal ACS Nano, was supported by the Samsung Science and Technology Foundation and National Research Foundation of Korea. More

The flow of matter, from macroscopic water currents to the microscopic flow of electric charge, underpins much of the infrastructure of modern times. In the search for breakthroughs in energy efficiency, data storage capacity, and processing speed, scientists search for ways in which to control the flow of quantum aspects of matter such as the “spin” of an electron – its magnetic moment – or its “valley state”, a novel quantum aspect of matter found in many two dimensional materials. A team of researchers at the Max Born Institute in Berlin have recently discovered a route to induce and control the flow of spin and valley currents at ultrafast times with specially designed laser pulses, offering a new perspective on the ongoing search for the next generation of information technologies.
Ultrafast laser control over the fundamental quantum degrees of freedom of matter represents the outstanding foundational challenge to be met in establishing future information technologies beyond the semi-conductor electronics that defines our present time. Two of the most promising quantum degrees of freedom in this respect are the spin of the electron and the “valley index,” the latter an emergent degree of freedom of two dimensional materials related to the quasiparticle momentum. Both spintronics and valleytronics offer many potential advantages over classical electronics in terms of data manipulation velocity and energy efficiency. However, while spin excitations suffer from a dynamical loss of character arising from the spin-orbit induced spin precession, the valley wavefunction represents a “data bit” whose stability is threatened only by intervalley scattering, a feature controllable be sample quality. Valleytronics thus presents a potentially robust platform for going beyond classical electronics.
At the heart of any future valleytronics or spintronics technologies will, in addition to quantum excitations encoding data bits, reside the control and creation of valley- and spin-currents. However, while sustained attention has been paid to the task of tailoring lightforms on ultrafast time scales to selectively excite valley quasiparticles, the precise creation and control of valley-currents and spin-currents — vital for any future valleytronics technology — has remained beyond the realm of ultrafast light control. In a study recently published in Science Advances, a team of researchers from the Max Born Institute in Berlin have shown how a hybrid laser pulse combining two polarization types allows complete control over ultrafast laser-light-induced currents.
Control over the charge state by circularly polarized light is now well established, the famous “spin-valley locking” of the transition metal dichalcogenides that has its origin in the valley selective response to circularly polarized light. This can be viewed as arising from a selection rule involving the magnetic quantum numbers of the d-orbitals that comprise the gap edge states. While circularly polarized light excites valley charge it does not, however, create a valley current. This situation arises as for each quasi-momentum in the valley
Control over the charge state by circularly polarized light is now well established, the famous “spin-valley locking” of the transition metal dichalcogenides that has its origin in the valley selective response to circularly polarized light. This can be viewed as arising from a selection rule involving the magnetic quantum numbers of the d-orbitals that comprise the gap edge states. While circularly polarized light excites valley charge it does not, however, create a valley current. This situation arises as for each quasi-momentum in the valley kvalley that is excited a corresponding -kvalley also is excited: the Bloch velocities thus cancel and there is no net valley current.
Full control over light induced valley currents, their magnitude and direction, thus requires going beyond the spin-valley locking paradigm of circularly polarized light. Creation of a valley excited state that does result in a net valley and spin current must therefore involving breaking the local kvalley , -kvalley degeneracy. As the laser vector potential couples directly to crystal quasi-momentum, k – >k — A (t)/c, the most effective way in which this can be done is through a linearly polarized single cycle pulse with duration comparable to that of the circularly polarized pulse: such a pulse will evidently be in the “THz window” of 1 THz to 50 THz. The hencomb lightform generates a substantial residual (i.e. persisting after the laser pulse) current. This results from a non-cancellation of the Bloch velocities of excited quasi-momentum, as the distribution of excited charge is now shifted off the high symmetry K point by exactly the polarization vector of the THz pulse,. More