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By Lynne Gauthier, UMass Lowell and Jiabin Shen, UMass Lowell 

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to [email protected].

Why are some people able to visualize scenarios in their minds, with colors and details, and some people are not? – Luiza, age 14, Goiânia, Brazil

Imagine you are in a soccer match, and it’s tied. Each team will begin taking penalty kicks. The crowd is roaring, and whether or not your team wins the game depends on your ability to hit the shot. As you imagine this scene, are you able to picture the scenario with colors and details?

Scientists are hard at work trying to understand why some people can visualize these kinds of scenarios more easily than others can. Even the same person can be better or worse at picturing things in their mind at different times.

As neuroscientists in the fields of physical therapy and psychology, we think about the ways people use mental imagery. Here is what researchers do know so far.

The brain and mental imagery

Mental imagery is the ability to visualize things and scenarios in your mind, without actual physical input.

For example, when you think about your best friends, you may automatically picture their faces in your head without actually seeing them in front of you. When you daydream about an upcoming vacation, you may see yourself on the sunny beach.

People who dream about taking a penalty kick could visualize themselves like they are watching a video of it in their mind. They may even experience the smell of the turf or hear the sounds that fans would make.

Scientists believe your primary visual cortex, located in the back of your brain, is involved in internal visualization. This is the same part of the brain that processes visual information from the eyes and that lets you see the world around you.

Another brain region, located in the very front of the brain, also contributes to mental imagery. This structure, called the prefrontal cortex, is in charge of executive functions – a group of high-level mental skills that allow you to concentrate, plan, organize and reason.

Scientists have found such skills to be, at least to some extent, related to one’s mental imagery ability. If someone is good at holding and manipulating large amounts of information in mind, this person can play with things like numbers or images in their mind on the go.

Experiencing and remembering

Most of the same brain areas are active both while you’re actually experiencing an event and also when you’re visualizing it from a memory in your head. For example, when you behold the beauty of the Grand Canyon, your brain creates a memory of the image. But that memory is not simply stored in a single place in the brain. It’s created when thousands of brain cells across different parts of the brain fire together. Later, when a sound, smell or image triggers the memory, this network of brain cells fires together again, and you may picture the Grand Canyon in your head as clearly as if it were in front of you.

Benefits of mental imagery

The ability to mentally visualize can be helpful.

Notice the look of concentration on a gymnast’s face before competition. The athlete is likely visualizing themselves executing a perfect rings routine in their mind. This visualization activates the same brain regions as when they physically perform on the rings, building their confidence and priming their brain for better success.

Athletes can use visualization to help them acquire skills more quickly and with less wear and tear on their bodies. Engineers and mechanics can use visualization to help them fix or design things.

Mental visualization can also help people relearn how to move their bodies after a brain injury. However, with additional practice, those who do not use visualization will eventually catch up.

Nature-nurture interactions

All is not lost if you have difficulty visualizing. It is possible that the ability to visualize in your mind is a combined effect of both how your individual brain works and your life experiences.

For example, taxi drivers in London need to navigate very complicated streets and, scientists found, experience changes to their brain structures over the course of their careers. In particular, they develop larger hippocampuses, a brain structure related to memory. Scientists believe that the training the taxi drivers went through – having to visualize a map of complex streets across London in daily driving – made them better at mental imagery via changes in their hippocampus.

And watching someone else do a physical action activates the same brain areas as creating your own internal mental imagery. If you want to be able to do something, watching a video of someone else doing it can be just as helpful as visualizing yourself doing it in your head. So even if you struggle with mental visualization, there are still ways to reap its benefits.

Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to [email protected]. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.

About the Authors:

Lynne Gauthier, Associate Professor of Physical Therapy and Kinesiology, UMass Lowell and Jiabin Shen, Assistant Professor of Psychology, UMass Lowell

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Nicholas Card, University of California, Davis 

Brain-computer interfaces are a groundbreaking technology that can help paralyzed people regain functions they’ve lost, like moving a hand. These devices record signals from the brain and decipher the user’s intended action, bypassing damaged or degraded nerves that would normally transmit those brain signals to control muscles.

Since 2006, demonstrations of brain-computer interfaces in humans have primarily focused on restoring arm and hand movements by enabling people to control computer cursors or robotic arms. Recently, researchers have begun developing speech brain-computer interfaces to restore communication for people who cannot speak.

As the user attempts to talk, these brain-computer interfaces record the person’s unique brain signals associated with attempted muscle movements for speaking and then translate them into words. These words can then be displayed as text on a screen or spoken aloud using text-to-speech software.

I’m a reseacher in the Neuroprosthetics Lab at the University of California, Davis, which is part of the BrainGate2 clinical trial. My colleagues and I recently demonstrated a speech brain-computer interface that deciphers the attempted speech of a man with ALS, or amyotrophic lateral sclerosis, also known as Lou Gehrig’s disease. The interface converts neural signals into text with over 97% accuracy. Key to our system is a set of artificial intelligence language models – artificial neural networks that help interpret natural ones.

Casey Harrell, who has ALS, works with a brain-computer interface to turn his thoughts into words.
Nicholas Card

Recording brain signals

The first step in our speech brain-computer interface is recording brain signals. There are several sources of brain signals, some of which require surgery to record. Surgically implanted recording devices can capture high-quality brain signals because they are placed closer to neurons, resulting in stronger signals with less interference. These neural recording devices include grids of electrodes placed on the brain’s surface or electrodes implanted directly into brain tissue.

In our study, we used electrode arrays surgically placed in the speech motor cortex, the part of the brain that controls muscles related to speech, of the participant, Casey Harrell. We recorded neural activity from 256 electrodes as Harrell attempted to speak.

Decoding brain signals

The next challenge is relating the complex brain signals to the words the user is trying to say.

One approach is to map neural activity patterns directly to spoken words. This method requires recording brain signals corresponding to each word multiple times to identify the average relationship between neural activity and specific words. While this strategy works well for small vocabularies, as demonstrated in a 2021 study with a 50-word vocabulary, it becomes impractical for larger ones. Imagine asking the brain-computer interface user to try to say every word in the dictionary multiple times – it could take months, and it still wouldn’t work for new words.

Instead, we use an alternative strategy: mapping brain signals to phonemes, the basic units of sound that make up words. In English, there are 39 phonemes, including ch, er, oo, pl and sh, that can be combined to form any word. We can measure the neural activity associated with every phoneme multiple times just by asking the participant to read a few sentences aloud. By accurately mapping neural activity to phonemes, we can assemble them into any English word, even ones the system wasn’t explicitly trained with.

To map brain signals to phonemes, we use advanced machine learning models. These models are particularly well-suited for this task due to their ability to find patterns in large amounts of complex data that would be impossible for humans to discern. Think of these models as super-smart listeners that can pick out important information from noisy brain signals, much like you might focus on a conversation in a crowded room. Using these models, we were able to decipher phoneme sequences during attempted speech with over 90% accuracy.

From phonemes to words

Once we have the deciphered phoneme sequences, we need to convert them into words and sentences. This is challenging, especially if the deciphered phoneme sequence isn’t perfectly accurate. To solve this puzzle, we use two complementary types of machine learning language models.

The first is n-gram language models, which predict which word is most likely to follow a set of n words. We trained a 5-gram, or five-word, language model on millions of sentences to predict the likelihood of a word based on the previous four words, capturing local context and common phrases. For example, after “I am very good,” it might suggest “today” as more likely than “potato”. Using this model, we convert our phoneme sequences into the 100 most likely word sequences, each with an associated probability.

The second is large language models, which power AI chatbots and also predict which words most likely follow others. We use large language models to refine our choices. These models, trained on vast amounts of diverse text, have a broader understanding of language structure and meaning. They help us determine which of our 100 candidate sentences makes the most sense in a wider context.

By carefully balancing probabilities from the n-gram model, the large language model and our initial phoneme predictions, we can make a highly educated guess about what the brain-computer interface user is trying to say. This multistep process allows us to handle the uncertainties in phoneme decoding and produce coherent, contextually appropriate sentences.

Real-world benefits

In practice, this speech decoding strategy has been remarkably successful. We’ve enabled Casey Harrell, a man with ALS, to “speak” with over 97% accuracy using just his thoughts. This breakthrough allows him to easily converse with his family and friends for the first time in years, all in the comfort of his own home.

Speech brain-computer interfaces represent a significant step forward in restoring communication. As we continue to refine these devices, they hold the promise of giving a voice to those who have lost the ability to speak, reconnecting them with their loved ones and the world around them.

However, challenges remain, such as making the technology more accessible, portable and durable over years of use. Despite these hurdles, speech brain-computer interfaces are a powerful example of how science and technology can come together to solve complex problems and dramatically improve people’s lives.

About the Author:

Nicholas Card, Postdoctoral Fellow of Neuroscience and Neuroengineering, University of California, Davis

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Mohamed-Slim Alouini, King Abdullah University of Science and Technology and Mariette DiChristina, Boston University 

About one-third of the global population, around 3 billion people, don’t have access to the internet or have poor connections because of infrastructure limitations, economic disparities and geographic isolation.

Today’s satellites and ground-based networks leave communications gaps where, because of geography, setting up traditional ground-based communications equipment would be too expensive.

High-altitude platform stations – telecommunications equipment positioned high in the air, on uncrewed balloons, airships, gliders and airplanes – could increase social and economic equality by filling internet connectivity gaps in ground and satellite coverage. This could allow more people to participate fully in the digital age.

One of us, Mohamed-Slim Alouini, is an electrical engineer who contributed to an experiment that showed it is possible to provide high data rates and ubiquitous 5G coverage from the stratosphere. The stratosphere is the second lowest layer of the atmosphere, ranging from 4 to 30 miles above the Earth. Commercial planes usually fly in the lower part of the stratosphere. The experiment measured signals between platform stations and users on the ground in three scenarios: a person staying in one place, a person driving a car and a person operating a boat.

My colleagues measured how strong the signal is in relation to interference and background noise levels. This is one of the measures of network reliability. The results showed that the platform stations can support high-data-rate applications such as streaming 4K resolution videos and can cover 15 to 20 times the area of standard terrestrial towers.

Early attempts by Facebook and Google to commercially deploy platform stations were unsuccessful. But recent investments, technological improvements and interest from traditional aviation companies and specialized aerospace startups may change the equation.

The goal is global connectivity, a cause that brought the platform stations idea recognition in the World Economic Forum’s 2024 Top 10 Emerging Technologies report. The international industry initiative HAPS Alliance, which includes academic partners, is also pushing toward that goal.

An experimental aircraft like this solar-powered airship could someday play a role in providing internet access to rural areas or disaster zones.
Thales Alenia Space via Wikimedia Commons, CC BY-SA

Fast, cost effective, flexible

Platform stations would be faster, more cost effective and more flexible than satellite-based systems.

Because they keep communications equipment closer to Earth than satellites, the stations could offer stronger, higher-capacity signals. This would enable real-time communications speedy enough to communicate with standard smartphones, high-resolution capabilities for imaging tasks and greater sensitivity for sensing applications. They transmit data via free-space optics, or light beams, and large-scale antenna array systems, which can send large amounts of data quickly.

Satellites can be vulnerable to eavesdropping or jamming when their orbits bring them over adversarial countries. But platform stations remain within the airspace of a single country, which reduces that risk.

High-altitude platform stations are also easier to put in place than satellites, which have high launch and maintenance costs. And the regulatory requirements and compliance procedures required to secure spots in the stratosphere are likely to be simpler than the complex international laws governing satellite orbits. Platform stations are also easier to upgrade, so improvements could be deployed more quickly.

Platform stations are also potentially less polluting than satellite mega-constellations because satellites burn up upon reentry and can release harmful metals into the atmosphere, while platform stations can be powered by clean energy sources such as solar and green hydrogen.

The key challenges to practical platform stations are increasing the amount of time they can stay aloft to months at a time, boosting green onboard power and improving reliability – especially during automated takeoff and landing through the lower turbulent layers of the atmosphere.

Beyond satellites

Platform stations could play a critical role in emergency and humanitarian situations by supporting relief efforts when ground-based networks are damaged or inoperative.

The stations could also connect Internet of Things (IoT) devices and sensors in remote settings to better monitor the environment and manage resources.

In agriculture, the stations could use imaging and sensing technologies to help farmers monitor crop health, soil conditions and water resources.

Their capability for high-resolution imaging could also support navigation and mapping activities crucial for cartography, urban planning and disaster response.

The stations could also do double duty by carrying instruments for atmospheric monitoring, climate studies and remote sensing of Earth’s surface features, vegetation and oceans.

From balloons to airplanes

Platform stations could be based on different types of aircraft.

Balloons offer stable, long-duration operation at high altitudes and can be tethered or free-floating. Airships, also known as dirigibles or blimps, use lighter-than-air gases and are larger and more maneuverable than balloons. They’re especially well suited for surveillance, communications and research.

Gliders and powered aircraft can be controlled more precisely than balloons, which are sensitive to variations in wind speed. In addition, powered aircraft, which include drones and fixed-wing airplanes, can provide electricity to communication equipment, sensors and cameras.

Next-generation power

Platform stations could make use of diverse power sources, including increasingly lightweight and efficient solar cells, high-energy-density batteries, green hydrogen internal combustion engines, green hydrogen fuel cells, which are now at the testing stage, and eventually, laser beam powering from ground- or space-based solar stations.

The evolution of lightweight aircraft designs coupled with advancements in high-efficiency motors and propellers enable planes to fly longer and carry heavier payloads. These cutting-edge lightweight planes could lead to platform stations capable of maneuvering in the stratosphere for extended periods.

Meanwhile, improvements in stratospheric weather models and atmospheric models make it easier to predict and simulate the conditions under which the platform stations would operate.

Bridging the global digital divide

Commerical deployment of platform stations, at least for post-disaster or emergency situations, could be in place by the end of the decade. For instance, a consortium in Japan, a country with remote mountainous and island communities, has earmarked US$100 million for solar-powered, high-altitude platform stations.

Platform stations could bridge the digital divide by increasing access to critical services such as education and health care, providing new economic opportunities and improving emergency response and environmental monitoring. As advances in technology continue to drive their evolution, platform stations are set to play a crucial role in a more inclusive and resilient digital future.

About the Authors:

Mohamed-Slim Alouini, Distinguished Professor of Electrical and Computer Engineering, King Abdullah University of Science and Technology and Mariette DiChristina, Dean and Professor of the Practice in Journalism, College of Communication, Boston University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Koki Ho, Georgia Institute of Technology and Mariel Borowitz, Georgia Institute of Technology 

Most space mission systems historically have used one spacecraft designed to complete an entire mission independently. Whether it was a weather satellite or a human-crewed module like Apollo, nearly every spacecraft was deployed and performed its one-off mission completely on its own.

But today, space industry organizations are exploring missions with many satellites working together. For example, SpaceX’s Starlink constellations include thousands of satellites. And new spacecraft could soon have the capabilities to link up or engage with other satellites in orbit for repairs or refueling.

Some of these spacecraft are already operating and serving customers, such as Northrop Grumman’s mission extension vehicle. This orbiting craft has extended the lives of multiple communications satellites.

These new design options and in-orbit capabilities make space missions look more like large logistics operations on Earth.

We’re researchers who have studied the space industry for years. We’ve studied how the space sector could learn lessons from companies like Amazon or FedEx about managing complex fleets and coordinating operations.

As companies develop satellite constellations as shown in this illustration, they’ll need to repair satellites in orbit.
NOIRLab/NSF/AURA/P. Marenfeld, CC BY-ND

Lessons from the ground transportation network

Space mission designers plan their routes in order to deliver their payloads to the Moon or Mars, or orbit efficiently within a set of cost, timeline and capacity constraints. But when they need to coordinate multiple space vehicles working together, route planning can get complicated.

Logistics companies on the ground solve similar problems every day and transport goods and commodities across the globe. So, researchers can study how these companies manage their logistics to help space companies and agencies figure out how to successfully plan their mission operations.

One NASA-funded study in the early 2000s had an idea for simulating space logistics operations. These researchers viewed orbits or planets as cities and the trajectories connecting them as routes. They also viewed the payload, consumables, fuel and other items to transport as commodities.

This approach helped them reframe the space mission problem as a commodity flow problem – a type of question that ground logistics companies work on all the time.

Lessons from ground logistics infrastructure

New capabilities for refueling and repairing spacecraft in orbit create new opportunities as well as challenges.

Namely, space operators don’t usually know which satellite will be the next one to fail or when that will happen. For these new technologies to be useful, space mission designers would need to come up with an infrastructure system. That could look like a fleet of service vehicles and depots in space that quickly respond to any unpredictable events.

Fortunately, space mission designers can learn from operations on the ground. City planners and emergency response organizations think through these types of challenges while determining where to locate hospitals or fire departments. They also consider these facilities’ capacities to respond to unpredictable calls.

We can draw an analogy between a ground logistics system design and an in-space servicing system design. This way, researchers can leverage theories developed for ground logistics to improve the space mission design practice.

One study published in November 2020 developed a framework for servicing spacecraft on orbit using what logistics experts call spatial queuing theory. Researchers most commonly use this modeling theory to analyze the performance of a ground logistics system.

Lessons from ground warehouse management

In the past, individual spacecraft carried out their missions independently, so if a satellite failed, its mission engineers had to develop and send a replacement.

Now, for missions with multiple satellites, such as the Iridium satellite constellation, operators often maintain one or more spares on orbit.

This becomes complicated for constellations made up of hundreds or thousands of spacecraft. Mission designers want to ensure they have enough spare satellites in orbit so they don’t have to interrupt the mission if one breaks. But sending too many spare satellites gets expensive.

When dealing with these types of large constellations, mission designers can learn from the methods Amazon and other ground companies use to manage their warehouses. Amazon puts these warehouses in specific places and stocks them with certain items to make sure the deliveries are handled efficiently.

Inventory management theories on the ground can help inform how space companies tackle these challenges.

A study published in November 2019 developed an approach that space companies could use to manage their spare strategies. This approach can help them decide where in orbit to allocate their spare satellites to meet their needs while minimizing any service interruptions.

International dimensions

Spacecraft operate in a complex and rapidly changing environment. Operators need to know where other missions are operating and what rules they should follow when refueling or repairing in space. In space, however, nobody has defined these rules yet.

Ships, aircraft and ground vehicles all have clear rules of the road to follow when interacting with other vehicles. For example, civilian ships and aircraft have to share their location with other vehicles and officials to help manage traffic.

Some researchers are examining what similar rules could look like for space. One study examined how developing rules based on a spacecraft’s size, age or other attributes might help future space operations run more smoothly. For example, one rule might be that the spacecraft that launched most recently should take responsibility for maneuvering when there’s another craft in its path.

With more satellites and spacecraft launching now than ever, companies and government agencies will need new technologies and policies to coordinate them. As space activity becomes more complex, researchers can continue to apply what they’ve learned on the ground to new missions in space.

About the Author:

Koki Ho, Associate Professor of Aerospace Engineering, Georgia Institute of Technology and Mariel Borowitz, Associate Professor of International Affairs, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.