Engineering researchers at the University of California, Los Angeles (UCLA) have developed an advanced three-dimensional (3D) image projection system capable of displaying 28 distinct depth layers simultaneously in a single optical exposure.

Led by Professor Aydogan Ozcan from the UCLA Samueli School of Engineering and the California NanoSystems Institute (CNSI), the project introduces a compact framework designed to enhance next-generation holographic displays, medical imaging, and virtual reality interfaces.

The research details a hybrid digital-optical architecture that resolves persistent issues of visual distortion and cross-talk in dense 3D imaging.

Traditional multi-layer volumetric projection methods often suffer from image degradation when multiple focal planes are closely spaced. As these layers converge, the light fields often bleed into one another, reducing depth clarity, causing severe blurriness, and diminishing visual sharpness for the viewer.

Deep learning and light programming

To overcome these limitations, the UCLA research team utilized deep learning to co-optimize a digital computational encoder alongside a passive, physical optical decoder.

The system functions by running target visual data through a digital neural network that incorporates explicit depth and coordinate instructions. This network compresses the multi-layered structural information into a singular, unified phase pattern that represents the entire 3D volume.

When light passes through the system, it hits a series of structurally optimized diffractive surfaces that act as an analog decoder. These surfaces physically manipulate the light wave, routing specific components of the image to their exact pre-assigned depth planes.

This precise “light programming” allows the system to suppress data leakage between adjacent layers, maintaining clean visual separation even when planes are separated by distances near the scale of a single light wavelength.

Scalability and experimental validation

Through numerical modeling, the team demonstrated that the framework scales effectively to accommodate complex volumetric scenes split into 28 independent axial slices.

Additionally, the system features a dynamic adjustment capability, allowing operators to alter the target depth positions of the projected images on demand without modifying the core physical architecture.

To confirm the practical viability of this digital-optical pipeline, the researchers built a physical, two-plane hardware prototype that uses a single-layer optical decoder operating in the visible light spectrum.

Experimental measurements confirmed that the projected light distribution closely matched both the target designs and computational simulations. The experimental setup significantly outperformed unassisted free-space optical systems, validating the real-world stability and accuracy of the design.

Future tech integration

The compact layout of this encoder-decoder architecture provides an energy-efficient foundation for high-resolution volumetric imaging. Beyond immediate integration into near-eye augmented reality (AR) and virtual reality (VR) headsets, the technology holds potential applications in multi-depth microscopy, real-time 3D medical visualization, and optical computing.

Moving forward, the research team aims to expand the capabilities of this framework by exploring multispectral operations to support full-color projections, multi-perspective holography, and the integration of physically fabricated multi-layer decoders suitable for commercial manufacturing form factors.

The research was first published in the journal Light: Science & Applications.

For years, the idea of a fully autonomous weapon system killing a human being on the battlefield existed as a warning, a thought experiment, and an unconfirmed rumor. It is no longer any of those things.

A Ukrainian drone manufacturer has confirmed to New Scientist that a test involving 10 fully autonomous AI-controlled drones — operating with no human oversight or connection — killed Russian soldiers on the front line of the Ukraine war approximately two years ago.

The disclosure, made at a press event hosted by the Ukrainian embassy, marks what weapons experts are calling a historic threshold in the history of warfare.

“Everything it sees will be killed”

The drone-maker behind the technology, Alexander Kokhanovskyy, described the operation in blunt terms. The drones were launched toward the front line near the cities of Bakhmut and Chasiv Yar during a Ukrainian counteroffensive push, programmed to cover between three kilometers (1.86 miles) and five kilometers (3.10 miles) over approximately 10 minutes, at which point they entered what Kokhanovskyy called “Terminator mode” — a state in which an onboard AI model independently searched for, identified, and attacked targets with no human input.

“We just launch it, and we know everything will be dead — everything that will be found there in this particular area will be dead,” Kokhanovskyy told New Scientist. “There is no connection to the drone at all, you cannot see the video, nothing. Everything it sees will be killed.”

Because the autonomous drones transmitted no live footage during the mission, there was no way to monitor them in real time. After the test concluded, human-piloted drones were sent into the area to manually survey results. What they found: “a couple of soldiers, one truck.”

Kokhanovskyy was not personally present at the test, which was carried out by an unnamed military unit. He was clear that it remained a one-off experiment and was never expanded into wider deployment. “It’s a test. We never implemented it more widely,” he said.

What makes this different from every prior incident

The distinction matters. Traditional armed drones transmit live video to a human operator who makes the decision to fire. A fully autonomous drone detects its target, decides whether it meets the engagement criteria, calculates an attack path, and executes the strike — entirely in software, with no human consulted at any point. In Terminator mode, that decision — whether a person lives or dies — was made by an algorithm.

A UN report in 2021 suggested that a Turkish Kargu-2 quadcopter may have been used to autonomously attack humans in Libya in 2020, but that account contained no confirmed casualties and gave no specific sourcing. The Ukraine disclosure, by a named manufacturer speaking on record at a diplomatic event, is the first confirmed account with stated outcomes.

What Kokhanovskyy wants

Ukraine’s current regulations prohibit fully autonomous lethal operation in the final stage of an engagement, requiring human verification before a strike is executed.

Kokhanovskyy acknowledged this constraint in the same conversation — while noting that his company’s upcoming ALITA counter-drone system, consisting of 16 launch pads and 64 drones, will require just two human operators to manage the entire battery. The system can travel toward incoming threats at 280mph, engaging everything from small drones to helicopters.

Even with humans required in the final step, Kokhanovskyy made his personal view clear: “I would love to operate without that restriction.”

That position is not universal inside Ukraine’s military. Major Danylo Polozhukhno, a senior figure in Ukraine’s 21st Separate Unmanned Systems Regiment who was unaware of the test, told New Scientist that his soldiers use semi-autonomous systems but always maintain a human in the loop.

An international team of space physicists and engineers has unveiled a highly ambitious planetary defense concept designed to protect Earth from the devastating impacts of extreme space weather.

The proposal, dubbed “StormWall,” outlines a space-based system that can artificially reinforce the planet’s natural defenses just before a major solar storm strikes.

According to researchers, applying serious physics to the problem demonstrates that humanity no longer has to simply brace for impact and accept whatever the Sun throws at the planet.

The fragility technological infrastructure

Geomagnetic storms occur when a massive surge of solar energy—often carried by coronal mass ejections—violently interacts with Earth’s magnetosphere, the magnetic bubble protecting the planet.

While these events trigger beautiful auroras, they pose a severe threat to modern civilization by inducing major electrical currents that can overload power grids, destroy multi-million-dollar transformers, damage satellites, and knock out global GPS and communication networks.

Historically, severe events such as the 1859 Carrington Event disrupted telegraph lines across continents. In today’s hyper-connected society, a similar extreme storm could cause trillions of dollars in economic damage and leave entire populations in prolonged darkness.

The physics of the ‘orbital airbag’

The core breakthrough of the StormWall concept lies in suppressing a process known as magnetic reconnection. During powerful solar storms, the magnetic fields carried by the solar wind can align with Earth’s magnetic field, linking the two. This cosmic link opens a pathway that allows solar energy to pour directly into near-Earth space.

To exploit this physical vulnerability, the StormWall system functions similarly to an automotive airbag. The proposed architecture involves a constellation of six spacecraft strategically deployed into geosynchronous orbit. These satellites would carry stores of safe solid or liquid alkaline materials, such as barium, lithium, sodium, or calcium.

When space weather forecasters detect a dangerous solar storm barreling toward Earth, ground operators would trigger the fleet to release the materials into space. Upon release, solar radiation would rapidly vaporize and ionize the elements, forming a thick cloud of electrically charged plasma.

This artificial plasma would drift toward the sun-facing frontier of the magnetosphere, effectively thickening the planet’s shield and forcing the harsh solar wind to slide around Earth rather than break through.

High rewards and earthwide benefits

To test the viability of the concept, the research team ran advanced computer simulations modeling the historic May 2024 geomagnetic storm. The data revealed that under normal conditions, the storm caused major magnetospheric disruptions, but when the simulated StormWall plasma shield was activated, the overall intensity of the geomagnetic storm was slashed by more than half.

Crucially, this geoengineering approach features minimal long-term environmental risk. The artificial plasma cloud is highly transient and would naturally dissipate within roughly six hours as it is swept into deep space by the solar wind, avoiding re-entry into Earth’s atmosphere.

Furthermore, because the magnetosphere blankets the entire globe, StormWall would act as a collective, democratic shield. The system cannot be modified to protect just one country or a specific corporate satellite fleet; it inherently shields the entire planet and everyone on it simultaneously.

The primary hurdle moving forward is reducing the steep manufacturing and deployment costs. Future phases of the project will focus on identifying the single most cost-effective element for mass release and optimizing orbital trajectories to minimize upkeep.