This sponsored article is brought to you by Master Bond.Outgassing is the release of volatile substances from a cured adhesive over time. These released materials, which may include residual solvents…

This sponsored article is brought to you by Master Bond.Outgassing is the release of volatile substances from a cured adhesive over time. These released materials, which may include residual solvents, unreacted monomers, or other chemical species, can deposit on nearby surfaces, causing contamination that interferes with sensitive components.What Is Outgassing and How Is It Measured?The industry standard for measuring outgassing is ASTM E595, developed by NASA. This test exposes a cured sample to 125 °C at high vacuum (10⁻⁵ to 10⁻⁶ torr) for 24 hours, measuring Total Mass Loss (TML) and Collected Volatile Condensable Materials (CVCM). To meet NASA low outgassing requirements, materials must exhibit less than 1 percent TML and less than 0.1 percent CVCM.Optical assemblies need contamination-free bonding and prevention of fogging the optics to maintain clarity. High-vacuum scientific equipment, semiconductor manufacturing tools, and aerospace electronics also demand low outgassing materials.Key ApplicationsLow outgassing adhesives are essential wherever contamination could compromise performance and this is particularly relevant for space and satellite systems. Optical assemblies, including cameras, telescopes, and laser systems, need contamination-free bonding and prevention of fogging the optics to maintain clarity. High-vacuum scientific equipment, semiconductor manufacturing tools, and aerospace electronics also demand low outgassing materials. Even terrestrial optical devices benefit from reduced outgassing to ensure long-term reliability. EP30-2 is a versatile system can be used in a variety of applications in aerospace, electronic, optical and specialty OEM industries, especially when optical clarity and low outgassing are important criteria.Master BondEnsuring Low Outgassing Performance Through Proper HandlingAchieving specified outgassing performance requires attention to storage, mixing, and curing. For two-part systems, use the correct mix ratio and mix th

The OnCampus program, administered by IEEE Educational Activities, last year expanded its engineering experiences from two to seven universities.Part of TryEngineering, the program is held at univers…

The OnCampus program, administered by IEEE Educational Activities, last year expanded its engineering experiences from two to seven universities.Part of TryEngineering, the program is held at universities around the world, offering preuniversity students hands-on opportunities to solve engineering problems.The IEEE Innovation Committee provided funding for the additional locations.New participating institutionsThe electrical engineering and computing faculty at the University of Zagreb, in Croatia, hosted a two-day program in June. Twenty-five children ages 10 to 14 participated in lectures and workshops on artificial intelligence, computer science, robotics, and astronomy. Tomislav Jagušt, an IEEE senior member and the chair of the IEEE preuniversity coordinating committee, led the program.In September the Arab Academy for Science, Technology, and Maritime Transport’s engineering college held a two-day session at its Abu Kir, Egypt, campus. Fifty students participated in hands-on activities on Ohm’s law, radio communications, and circuit building. They also learned from professors about engineering careers and job opportunities.Also in September, the Majan University College, in Muscat, Oman, hosted 40 high school students who competed in six challenges to design and build circuits. These include an IoT design and an LED brightness control using a potentiometer, a three-terminal, manually adjustable resistor that functions as a variable voltage divider.The program also highlighted AI and quantum computing technologies and introduced students to job opportunities in the fields.The workshop transformed curiosity into creation, empowering students with technical skills and confidence in emerging technologies.In November at the Universiti Malaysia Perlis, in Arau, 50 students explored the fundamentals of quantum computational intelligence and AI through hands-on activities and interactive simulations. IEEE Senior Member Mohd Hafiz Ismail, a professor of electronic engi

“Social engineering” sounds like something out of a conspiracy thriller, charged with totalitarian control and fringe paranoia. More mundanely, it’s come to be associated with phishing and other scam…

“Social engineering” sounds like something out of a conspiracy thriller, charged with totalitarian control and fringe paranoia. More mundanely, it’s come to be associated with phishing and other scams, in which fraudsters manipulate people into disclosing personal information. Yet the concept is older and more benign: it is the deliberate shaping of human behavior, often at scale. It predates silicon—and became pervasive, and ungoverned, especially once its practitioners learned to hide it. Authoritarian regimes and more recently scammers and big companies have profited from it. To defend ourselves from bad actors, and to benefit from social engineering’s good side, we need to reclaim the name, and govern it prudently. The roots of engineeringIn 1894, Dutch entrepreneur Jacques van Marken urged companies to hire “social engineers” to manage human systems such as insurance, education, and profit sharing for workers as carefully as they did mechanical ones. Fifteen years later, reformer William H. Tolman published Social Engineering, describing how U.S. industrialists optimized workers’ conditions alongside manufacturing methods. If industrialists could shape steel and electricity on demand, why not society itself? By the 1920s, that confidence had spread. The architect Le Corbusier declared that dwellings were “machines for living in,” imagining cities as orderly lattices where people moved like parts on a conveyor belt. Civilization would run like a Swiss watch.The idea soon darkened. Authoritarian regimes pushed it to extremes, promising to fashion “the New Man.” In Nazi Germany, engineer Fritz Todt founded Organization Todt, a vast state engineering enterprise that emerged from the autobahn highway system and later operated concentration camps using slave labor. In the Soviet Union, leaders adopted U.S. scientific management techniques to plan factory-worker movements and classify populations through centralized records, feeding both rapid industrialization drives

This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors with…

This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors within a single environment.HighlightsIntegrate AI models into Simulink for system-level simulation, verification, and simulation-based testingApply formal verification techniques to assert neural network behaviorCompress the AI model for memory footprint reduction and execution speedupGenerate library-free C code from AI models and performing PIL testsProfile code performance and evaluate design and model selection tradeoffsDesign and train AI-based virtual sensors using MATLABRegister now for this free webinar!

Patients who use mobile applications to manage medical conditions including depression and chronic pain might assume the apps have been evaluated by regulatory agencies to be safe and effective. But …

Patients who use mobile applications to manage medical conditions including depression and chronic pain might assume the apps have been evaluated by regulatory agencies to be safe and effective. But that isn’t necessarily the case.Most of the more than 55,000 medical apps that claim to diagnose or treat a condition—or ones that provide clinical decision support, known as “therapeutic” apps—have never been assessed by any trusted neutral bodies or regulatory agencies to evaluate them for technical soundness, ethical design, or clinical benefit. The apps often don’t comply with regional data security and privacy laws to protect people’s sensitive health information.Medical apps differ from traditional wellness apps, which provide users with insights into becoming healthier by, for example, tracking fitness activities, monitoring blood pressure, and analyzing sleep patterns.There is no reliable way to verify that therapeutic apps deliver the results they indicate. To help ensure such apps are credible, the IEEE Standards Association (IEEE SA) recently launched the IEEE Global Medical Mobile App Assessment and Registry. The publicly searchable directory is designed to list apps that have been vetted by experts across several criteria including technical soundness, ethical design, compliance with data security and privacy regulations, and clinical efficacy, which is evidence of a clinical benefit for the patient.“Patients, clinicians, payers, and health care systems often struggle to distinguish clinically meaningful therapeutic apps from those that are simply well-marketed,” says IEEE Senior Member Yuri Quintana, chair of the assessment and registry program. He is chief of the clinical informatics division at Beth Israel Deaconess Medical Center, in Boston. “Our goal is to establish a standardized review method using criteria developed by experts.”Why regulation is lackingBecause the apps are intended for medical use without being part of a medical implement, they fall

Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication. This delivers better resolution, better SNR, larger usable FOV…

Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication. This delivers better resolution, better SNR, larger usable FOV, and shorter acquisition times. Learn how uninterrupted FIB milling will reduce damage and rework, accelerate time to TEM, and increase first pass success—so your FA, yield, and materials teams make faster, confident data driven decisions.Join us to discover how the new ZEISS Crossbeam 750 with its see while you mill capability delivers precision and clarity—every time—for demanding FIB-SEM workflows. Designed for extremely challenging TEM lamella preparation, tomography, advanced nanofabrication, and APT‑ready lift‑out, Crossbeam 750 combines a new Gemini 4 SEM objective lens, a double deflector, and a next‑generation scan generator to elevate both image quality and process confidence. You’ll learn how better resolution and better SNR translate into more image detail and shorter acquisition times, while the low‑kV FIB performance enables more precise lamella prep.We’ll demonstrate High Dynamic Range (HDR) Mill + SEM—an interwoven SEM/FIB scanning mode that suppresses FIB‑generated background. This enables immediate, clean visual feedback, even during nudging the FIB pattern live while milling . The result: confident endpointing with uninterrupted FIB milling and pristine, metrology‑grade surfaces with the lowest possible sample damage. This session is ideal for semiconductor failure analysists, yield teams and materials scientists seeking faster time‑to‑TEM, higher first‑pass success, and consistent outcomes at low kV. See how Crossbeam 750 empowers you to make earlier stop‑milling decisions, cut rework, and reliably plan turnaround time—so you can move from sample to insight with confidence.Register now for this free webinar!

This sponsored article is brought to you by Wetour Robotics.A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at …

This sponsored article is brought to you by Wetour Robotics.A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. They call for a smarter way to be heard by the machines that already exist.The industry has been building from one sideThe past three years of Physical AI have been a story of remarkable progress on the robot side of the loop. Companies like Boston Dynamics, Figure, and Unitree have advanced actuators, locomotion, and dexterity to a level that would have seemed implausible a decade ago. Google DeepMind’s Gemini Robotics has redefined what vision-language-action models can do in unstructured settings. The trajectory of the hardware and the foundation models is real, and it is accelerating.But there is another side to this loop, and it has been treated as a solved problem for too long. The interface between humans and machines has defaulted, for 40 years, to three input modalities: screens, buttons, and voice. Each of those assumes the user can stop, look down, and translate intent into structured commands. That assumption breaks the moment the work moves into a real environment. On a turbine. On a dock. On a sidewalk. In any setting where hands are occupied, eyes are committed, or speaking is impractical, the conventional interface stack quietly fails.Spatial Intent Fusion is the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent: Your body is the interface.The bottleneck on the human side of the loop is becoming as important as the one on the machine side. And solving it requires a different quest

Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster area…

Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually, help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value.In 2025, total investments in robotics companies reached a record $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality.The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe.The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term, and where we are still on the frontier of science fiction. We believe AI

In the late 1940s—when computer engineers were grappling with unreliable hardware and noisy transmission environments—a team of engineers inside a modest lab at the University of Manchester, England,…

In the late 1940s—when computer engineers were grappling with unreliable hardware and noisy transmission environments—a team of engineers inside a modest lab at the University of Manchester, England, confronted a problem so fundamental that it threatened the viability of digital computing itself. Machines could generate bits, but they could not reliably read them back.The inconsistent reading back of memory data did not initially present itself as a grand theoretical challenge. It showed up as something more mundane: inconsistent computing results.Engineers including Frederic C. Williams, Tom Kilburn, and G. E. (Tommy) Thomas traced the failures not to logic errors but to the physical behavior of the machines themselves. The team devised a technique for keeping a transmitter and a receiver synchronized without relying on a separate clock signal. Their innovation, known as Manchester code or phase encoding, encoded each bit with a transition in the middle of the bit period, effectively embedding timing information directly into the data stream to be a self-clocking signal. So, even if the signal degraded or the timing drifted slightly, the receiver could continually keep time based on those regular transitions.By eliminating the need for separate clocks and reducing synchronization errors, Manchester code made data transfer more robust across cables and circuits.Those qualities later made it a natural fit for technologies such as Ethernet and early data storage systems. Its self-clocking nature helped standardize how machines communicate, and it laid the groundwork for modern networking and digital communication protocols.On 13 April 2026, this breakthrough was honored with an IEEE Milestone plaque during a ceremony at the University of Manchester. Dignitaries from IEEE and the university attended the ceremony.Embedding timing in signalsThose 1940s Manchester University engineers were working on systems that fed into the Manchester Mark I, one of the first practical

For years, the field of robotics has used the terms “dull, dirty, and dangerous” (DDD) to describe the types of tasks or jobs where robots might be useful—by doing work that’s undesirable for people.…

For years, the field of robotics has used the terms “dull, dirty, and dangerous” (DDD) to describe the types of tasks or jobs where robots might be useful—by doing work that’s undesirable for people. A classic example of a DDD job is one of “repetitive physical labor on a steaming hot factory floor involving heavy machinery that threatens life and limb.”But determining which human activities fit into these categories is not as straightforward as it seems. What exactly is a “dull” task and who makes that assumption? Is “dirty” work just about needing to wash your hands afterwards, or is there also an aspect of social stigma? What data can we rely on to classify jobs as “dangerous?” Our recent work (which was not dull at all) tackles these questions and proposes a framework to help roboticists understand the job context for our technology.First, we did an empirical analysis of robotics publications between 1980 and 2024 that mention DDD and found that only 2.7% define DDD and only 8.7% provide examples of tasks or jobs. The definitions vary, and many of the examples aren’t particularly specific (e.g., “industrial manufacturing,” “home care”). Next, we reviewed the social science literature in anthropology, economics, political science, psychology, and sociology to develop better definitions for “dull,” “dirty,” and “dangerous” work. Again, while it might seem intuitive which tasks to put into these buckets, it turns out that there are some underlying social, economic, and cultural factors that matter.Dangerous Work: Occupations or tasks that result in injury or risk of harmIt’s possible to measure the danger of a task or job by using reported information: there are administrative records and surveys that provide numbers on occupational injury rates and hazardous risk factors. While that seems straightforward, it’s important to understand how these data were collected, reported, and verified.First, occupational injuries tend to be underreported, with some studies estim

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling…

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.Key learningsProvides an introduction to LLM-based AI AgentsDescribes an approach to applying LLM-based AI Agents to robotic teamsProvides demonstrations of the approach running in hardware with a heterogeneous team of robotsPresents lessons learned and future work in this areaDownload this free whitepaper now!

This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.Melbourne’s reputation as a global events city, from the Australian Open tennis an…

This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capability to the infrastructure that underpins modern AI research, positioning Melbourne at the convergence of global convening and high-performance digital systems.Consistently ranked among the world’s most livable cities, Melbourne was named Time Out’s Best City in the World in 2026, the first Australian city to hold the title.More materially for research and innovation, Melbourne is also the nation’s fastest‑growing capital, attracting increasing concentrations of engineering and technology talent, investment and international engagement.Australia’s artificial intelligence (AI) ecosystem is entering a new phase, defined less by isolated initiatives and more by the convergence of compute infrastructure, research intensity and international collaboration. Melbourne sits at this intersection.Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene.Sovereign AI compute, expanding hyperscale data center campuses and a growing pipeline of international research-led conferences are reshaping the city’s research landscape. Together, these elements position Melbourne as a focal point for applied AI research, advanced engineering and data-intensive science.The growing global influence of AI engineering, underscored by NVIDIA CEO Jensen Huang receiving the 2026 IEEE Medal of Honor, reflects the scale of this shift. In Melbourne, thes

Editor’s note: If you’d like to pinpoint the instant when the world entered the nuclear age, 5:29:45 a.m. Mountain War Time on 16 July 1945, is an excellent choice. That was the moment when human bei…

Editor’s note: If you’d like to pinpoint the instant when the world entered the nuclear age, 5:29:45 a.m. Mountain War Time on 16 July 1945, is an excellent choice. That was the moment when human beings first unleashed the power of the nucleus in an immense, blinding ball of fire above a gloomy stretch of desert in the Jornada del Muerto basin in New Mexico. Emily Seyl’s Trinity: An Illustrated History of the World’s First Atomic Test (The University of Chicago Press) offers hundreds of startlingly vivid photographs of the Manhattan Project that emerged from a 20-year restoration effort. This excerpt and the accompanying photos record the massive effort to capture the awesome detonation of “the Gadget.” aspect_ratioReprinted with permission from Trinity: An Illustrated History of the World’s First Atomic Test by Emily Seyl with contributions by Alan B. Carr, published by The University of Chicago Press. © 2026 by The University of Chicago. All rights reserved.In the North 10,000 photography bunker, Berlyn Brixner was listening to the countdown on a loudspeaker, his head inside a turret loaded with cameras and film. He was one of the only people instructed to look toward the blast—through his welder’s glasses—ready to follow the path of the fireball as it launched into the sky. The two Mitchell movie cameras at his station would deliver the best footage to come of the Trinity test, used by Los Alamos scientists to make some of the first measurements of the effects of a nuclear explosion.When the detonators fired, the cameras captured what Brixner could not have seen—the very first light of a violent, silent sea of energy unfurling into the basin. As 32 blocks of high explosives erupted all together, their incredible force surged inward toward the sleeping plutonium core, compressing the dense sphere of metal instantaneously from all sides and bringing its atoms impossibly close together. A carefully timed burst of neutrons sowed momentary, uncontrolled chaos, and the

The IEEE Communications Society (ComSoc)’s Research Collaboration Pitch Session initiative is proving to be a catalyst for meaningful engagement between academic researchers and industry innovators. …

The IEEE Communications Society (ComSoc)’s Research Collaboration Pitch Session initiative is proving to be a catalyst for meaningful engagement between academic researchers and industry innovators. Launched last year, the program connects promising researchers with industry leaders who can offer them funding, mentorship, and connections to bring interesting ideas closer to real-world deployment.Rather than relying on chance encounters at conferences, the pitch sessions create a focused environment. Five academic presenters share their work with five industry representatives, known as “innovation scouts”: senior leaders primarily chosen from ComSoc’s Corporate Program partner companies such as Ericsson, Intel, Keysight, and Nokia. The curated format ensures that each idea receives dedicated attention from professionals who are seeking new concepts aligned with their organization’s priorities.The initiative was launched in November at the IEEE Middle East Conference on Communications and Networking (MECOM) in Cairo and appeared in December at the IEEE Global Communications Conference (GLOBECOM) in Taipei, Taiwan.AI-driven communication networkOne of the most compelling outcomes came from the inaugural session in Cairo. Angela Waithaka, a student member and biomedical engineering student at Kenyatta University, in Nairobi, Kenya, presented her “AI-Driven Predictive Communication Networks for Enhanced Performance in Resource-Constrained Environments” paper. You can view her presentation along with others on IEEE.tv.Waithaka’s research tackles a critical challenge: Next-generation communication systems increasingly rely on artificial intelligence and machine learning, yet most existing architectures consume abundant computational and energy resources, which are not always present in developing regions.Waithaka proposed lightweight, adaptive AI/machine learning models capable of delivering predictive, reliable communication performance even under tight resource constrain

This sponsored article is brought to you by Applied Materials.At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs — such as thos…

This sponsored article is brought to you by Applied Materials.At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs — such as those achieved under the Human Genome Project — required a new operating paradigm: Concentrate the world’s best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and siloed innovation simply cannot keep pace.Today’s AI era is creating an engineering race with similar demands. Every company is pushing to deliver higher-performance AI systems, faster. But performance is no longer defined by compute alone. AI workloads are increasingly dominated by the movement of data: In many cases, moving bits consumes as much — or more — energy than compute itself. As a result, reducing energy per bit can extend system‑level performance alongside gains in peak compute.The path to energy‑efficient AI therefore runs through system‑level engineering, spanning three tightly interconnected domains:Logic, where performance per watt depends on efficient transistor switching, low‑loss power, and signal delivery through dense wiring stacks.Memory, where surging bandwidth and capacity demands expose the memory wall, with processor capability advancing faster than memory access.Advanced packaging, where 3D integration, chiplet architectures, and high‑density interconnects bring compute and memory closer together — enabling system designs monolithic scaling can no longer sustain.These domains can no longer be optimized independently. Gains in logic efficiency stall without sufficient memory bandwidth. Advances in memory bandwidth fall short if packaging cannot deliver proximity within thermal and mechanical constraints. Packaging, in turn, is constrained by the precision of both front‑end device fabrication and back‑end integration processes.In the angstrom era, the harde

A comprehensive review of how spectrum congestion, dynamic sharing, and cognitive radio systems are reshaping RF coexistence testing for military and commercial applications.What Attendees will Learn…

A comprehensive review of how spectrum congestion, dynamic sharing, and cognitive radio systems are reshaping RF coexistence testing for military and commercial applications.What Attendees will LearnWhy spectrum congestion threatens wireless reliability — Explore how over 30 billion connected devices, more than 4,000 allocation changes worldwide, and the expansion from 11 to over 80 cellular bands are intensifying contention for finite RF spectrum resources.How real-world coexistence failures affect safety-critical systems — Understand the interference risks between 5G C band transmitters and aircraft radar altimeters, and between terrestrial L band networks and GPS receivers that were not designed for adjacent high-power signals.Why tiered spectrum sharing frameworks are essential — Examine how CBRS uses a cloud-based Spectrum Access System (SAS) and environmental sensing to dynamically protect incumbent Navy radar while enabling commercial cellular services across three priority tiers.What coexistence test architectures look like in practice — Learn how controlled environment testing with anechoic chambers, over-the-air signal generation, and standards such as ANSI C63.27 enable repeatable evaluation of RF device performance under real-world interference conditions.Download this free whitepaper now!

Given how integral the Internet has become to everyday tasks such as shopping, paying bills, and holding virtual meetings, it’s interesting that nearly 30 percent of the global population still has n…

Given how integral the Internet has become to everyday tasks such as shopping, paying bills, and holding virtual meetings, it’s interesting that nearly 30 percent of the global population still has no access to it. More than 2 billion people are still offline, according to a report released in November by the International Telecommunication Union.More and more people are being connected, though, thanks to IEEE Future Networks’ Connecting the Unconnected (CTU) and similar programs. Since 2021, the technical community has been working to accelerate the development, standardization, and deployment of 5G, 6G, and future generations.Every year, CTU holds a worldwide competition to seek out innovators who are in the early stages of developing technologies or applications to provide greater access. It also holds an annual summit that brings together experts, community leaders, and other interested parties to discuss strategies to expand access and foster digital inclusion.CTU expanded in several ways last year. It launched regional summits to focus on local connectivity issues, organized community-focused events, and established an expanded mentorship program to further support contest winners and the next generation of technological innovators impacting humanity. The program also partners with the IEEE Standards Association (IEEE SA) to develop guidelines for some of the submitted innovations.“IEEE Future Networks has created a community to bring all these initiatives working on digital connectivity together in a single platform and leverage the IEEE brand to help raise the visibility of their work,” says IEEE Life Fellow Sudhir Dixit, a CTU cochair and a Basic Internet Foundation cofounder, which also works to expand Internet access.A contest for new connectivity methodsThe CTU challenge, launched in 2021, typically receives 200 to 300 submissions each year, Dixit says. Last year 245 projects from 52 countries were submitted. Participants include academics, nonprofit org

This sponsored article is brought to you by Ampace.As AI workloads grow to gigascale levels, the global data center industry has hit a hidden physical wall. The real bottleneck is no longer just the …

This sponsored article is brought to you by Ampace.As AI workloads grow to gigascale levels, the global data center industry has hit a hidden physical wall. The real bottleneck is no longer just the thermal limit of the chip or the capacity of the cooling system — it is the dynamic resilience of the power chain.Modern AI computing clusters, driven by massive GPU clusters, generate high-frequency, abrupt, and synchronized spikey pulse loads. As rack densities soar beyond 100 kW, these fluctuations are amplified into a “power paradox”: while the digital logic of AI is moving faster than ever, the physical infrastructure supporting it remains tethered to legacy response capabilities.The power usage of these gigascale sites and their drastic, high frequency, abrupt load surges from the AI GPU clusters can trigger transient voltage events and frequency instability, risking the entire local grid. The grid itself is not robust enough to support these loads. This leads to the infrastructure gap: The utility is not robust enough and traditional backup sources, such as diesel generators and gas turbines, simply cannot react to millisecond-level power spikes in output. This will often force operators into a cycle of costly infrastructure over sizing just to buffer the volatility.AI infrastructure requires energy systems capable of instantaneous response while safeguarding continuity and reliability.The industry has explored various mitigations — from rack-level BBUs to 800V DC architectures — yet the mature, high volume, traditional UPS system remains the most viable and scalable foundation for gigawatt-level facilities. Consequently, the UPS-integrated battery system has emerged as the critical “physical buffer” to neutralize these pulses at the source.At Data Center World 2026 in Washington, D.C., Ampace led a pivotal technical dialogue with Eaton during the session “Powering Giga-scale AI.” Their exchange unveiled a fundamental paradigm shift: To bridge the AI power gap, en

A comprehensive guide to error vector magnitude (EVM), the primary metric for quantifying modulation accuracy in Wi-Fi, LTE, and 5G NR systems.What Attendees will LearnWhat error vector magnitude is …

A comprehensive guide to error vector magnitude (EVM), the primary metric for quantifying modulation accuracy in Wi-Fi, LTE, and 5G NR systems.What Attendees will LearnWhat error vector magnitude is and how it is calculated — Understand EVM as the distance between ideal and measured constellation points, learn the difference between peak and RMS normalization, and see how EVM is expressed in both percentage and decibel formats.How digital modulation works and why it matters — Explore the fundamentals of ASK, FSK, PSK, APSK, and QAM modulation schemes, and understand why higher modulation orders increase throughput, while also demanding greater accuracy in signal transmission and reception.What causes degraded EVM in real-world systems — Examine the four main categories of EVM contributors: amplitude effects (compression, noise, frequency response), phase effects (phase noise), I/Q imperfections (gain imbalance, quadrature error), and configuration issues.How to diagnose modulation impairments using constellation diagrams — Learn how visual inspection of constellation diagrams can identify phase noise, amplifier compression, noise, in-band spurious signals, and I/Q modulator imperfections as root causes of degraded EVM.Download this free whitepaper now!

When Ana Inês Inácio goes to work at the Netherlands Organization for Applied Scientific Research (TNO) in The Hague, she thinks about signals most people never notice: radio waves moving between sat…

When Ana Inês Inácio goes to work at the Netherlands Organization for Applied Scientific Research (TNO) in The Hague, she thinks about signals most people never notice: radio waves moving between satellites, sensors, and future wireless networks.The integrated circuits the research scientist designs lay the foundation for next-generation RF sensor systems critical to advancing radar technologies.Ana Inês InácioEMPLOYER Netherlands Organization for Applied Scientific Research, TNOTITLE ScientistIEEE MEMBER GRADE Senior memberALMA MATER University of Aveiro, in PortugalThose invisible RF signals are only part of what earned the IEEE senior member her global recognition.Inácio recently received the IEEE–Eta Kappa Nu Outstanding Young Professional Award for “leadership in IEEE Young Professionals, fostering innovation and inclusivity, and pioneering advancements in RF sensor systems, bridging technical excellence with impactful community engagement.”The recognition from IEEE’s honor society reflects a career built along two parallel paths: advancing RF circuit design while helping engineers worldwide build professional communities.“I’ve always liked building things,” Inácio says. “Sometimes that means circuits; sometimes it means helping people connect and grow together.”That blend of technical innovation and global leadership gives her work impact far beyond the laboratory.EE lessons at the kitchen tableInácio grew up in Vales do Rio, a rural village near Covilhã in central Portugal.The region was known for farming and textiles, she says. Many residents worked in the textile industry, including her grandfather, who repaired machinery such as industrial looms. He became her first engineering teacher without ever holding the formal title.Through correspondence courses delivered by mail, he taught himself electrical systems. At home, he explained electricity to his granddaughter while he repaired the household’s appliances and wiring.“He would show me why something broke

“Why are you here?” Fabrizio Pilo, an electrical engineer, asks me as we sit in an outdoor café near his home in Cagliari, an ancient city on the island of Sardinia. It’s a fair question. I’m a journ…

“Why are you here?” Fabrizio Pilo, an electrical engineer, asks me as we sit in an outdoor café near his home in Cagliari, an ancient city on the island of Sardinia. It’s a fair question. I’m a journalist from the United States. I’d just stepped off my flight 2 hours prior and come straight to this meeting, suitcase still stowed in my rental car.I’m here to see three intriguing new energy projects under development in Sardinia. I’d heard there’s strong public resistance to renewable energy, and I want to understand why that is. I tell Pilo, who is vice rector for innovation at the University of Cagliari, that I hope he’ll share some insights before I head out on a reporting trip across the island. (My answer seems to satisfy him, and he kindly gives me an hour of his time).This won’t be the first time that I’m asked to explain my presence on the island. I’d expected it, to some extent; I’m a foreign journalist poking around, after all. What I didn’t expect was the depth of Sardinians’ distrust, not just of journalists, but of any outsider, particularly ones with authority. Over the last few years, developers of wind and solar projects, most of whom aren’t from here, have been absorbing the bulk of this smoldering, communal wariness. Activists Maria Grazia Demontis [left] and Alberto Sala, photographed inside the archaeological monument Giants’ Tomb of Pascarédda, have worked to stop the construction of wind farms by organizing protests and taking legal actions through their organization Gallura Coordination. Luigi AvantaggiatoIn fact, the resistance is so widespread among Sardinians that over the course of two months in 2024, a grassroots petition to ban new wind and solar projects gathered over 210,000 certified signatures. That’s more than a quarter of Sardinia’s typical voter turnout and represents a cross-party consensus. People stood in long lines in public squares to sign. And it worked: Political leaders responded swiftly with an 18-month moratorium on renewa

Cybersecurity consultants have never been more in demand. Information security analyst roles are projected to grow nearly 30 percent between now and 2034, according to the U.S. Bureau of Labor Statis…

Cybersecurity consultants have never been more in demand. Information security analyst roles are projected to grow nearly 30 percent between now and 2034, according to the U.S. Bureau of Labor Statistics. More than 15 million cybercrime incidents occurred worldwide in 2024, Statista reported.Data breaches are costly and pose direct safety risks. Statista reported that more than US $10 trillion is spent annually repairing the damage caused by cybercrime, most commonly phishing, spoofing, extortion, and data breaches. In one example in the United States, breathalyzer devices installed in vehicles became disabled, leaving hundreds of drivers stranded, as detailed in an IEEE Spectrum article.To help you acquire the skills you need to distinguish yourself from other cybersecurity job candidates, the IEEE Computer Society offers a “What Makes a Great Cybersecurity Consultant” guide. The 23-page PDF includes hard and soft skills you need, a list of certifications to pursue, and key IEEE cybersecurity conferences for staying updated on developments in the field.The guide includes advice from two cybersecurity experts. John D. Johnson, an IEEE senior member, is the founder and CEO of Aligned Security in Bettendorf, Iowa. Ricardo J. Rodriguez is an associate professor of computer science and systems engineering at the Universidad de Zaragoza, in Spain, who researches digital forensics and other cybersecurity topics.“Technology, remote work, and a shortage of skilled workers make this the ideal time to consider becoming a cybersecurity consultant,” Johnson says in the guide. “Consulting can give you the flexibility, variety, and control over where you want your career to go.”Hard and soft skillsAt a minimum, cybersecurity professionals should have a general understanding of IT including operating systems, communication protocols, network architecture, and programming languages such as C++, Java, and Python. They also should be well-versed in security auditing, firewall managem

A guide to ten technological components — from THz communications and AI/ML to reconfigurable intelligent surfaces — poised to define 6G wireless networks.What Attendees will LearnWhich frequencies 6…

A guide to ten technological components — from THz communications and AI/ML to reconfigurable intelligent surfaces — poised to define 6G wireless networks.What Attendees will LearnWhich frequencies 6G will use — Understand why THz bands (above 100 GHz) and the7–24 GHz range are under consideration, what challenges CMOS technology faces at sub-THz frequencies, and how new semiconductor approaches aim to close the output-power gap for future link budgets.How AI/ML and joint communications and sensing reshape the air interface — how auto encoder-based end-to-end learning can replace traditional signal-processing blocks, and how a single waveform may serve both data transmission and radar-like environmental sensing.What reconfigurable intelligent surfaces and photonics bring to the radio environment— Explore how programmable metamaterial panels can steer and shape electromagnetic waves, and how visible light communications and all-photonics networks extend capacity and lower latency.How ultra-massive MIMO, full-duplex, and new network topologies enable a true 3D“network of networks” — Understand how antenna arrays with vastly more elements, simultaneously transmit/receive on the same frequency, and non-terrestrial nodes converge to deliver ubiquitous, high-capacity 6G coverage.Download this free whitepaper now!

I first met Robert Woo in 2011, during his third time walking in a powered exoskeleton. The architect had been paralyzed in a construction accident four years earlier, but he was determined to get ba…

I first met Robert Woo in 2011, during his third time walking in a powered exoskeleton. The architect had been paralyzed in a construction accident four years earlier, but he was determined to get back on his feet. Watching him clunk across a rehab room in an exoskeleton prototype, the technology felt astonishing. I had the same reaction when reporting on early brain-computer interfaces (BCIs), which enabled paralyzed people to move robotic arms or communicate by thought alone. Both types of bionic technology seemed to verge on magic.But that initial sense of awe, I’ve learned over many years of reporting on these technologies, is only a starting point. What matters is not what these systems can do in a carefully staged demo but how they perform in the real world. Do they work reliably? Can people with disabilities use them for their intended purposes? And what does it actually cost—in time, effort, and trade-offs—to do so? The question isn’t whether the technology looks impressive the first time but whether it holds up on the hundredth. The special report in this issue, “Cyborg Tech From the Inside” takes that perspective seriously. In my feature article on Woo, an exoskeleton super-user who has spent 15 years testing these systems, the story of the technology is inseparable from the story of its use. Woo’s relentless feedback has driven steady, incremental improvements. In Edd Gent’s reporting on the pioneers testing the earliest BCIs, the experience of these extraordinary technologies likewise resolves into something more complex. As one trial participant notes, these early adopters are like the first astronauts, who barely reached space before coming back down to Earth. Together, these stories reframe these individuals not as passive medical patients but as the ultimate beta testers and co-engineers of the bionic age.I saw the gap between demonstration and daily use firsthand when I interviewed Woo in a Manhattan showroom recently, where he was testing a new sel

More than 30 years ago, in the mountain village of Mbem in northwest Cameroon, the moon and stars in the night sky were the only light young Jude Numfor knew after the sunset. Electricity had not yet…

More than 30 years ago, in the mountain village of Mbem in northwest Cameroon, the moon and stars in the night sky were the only light young Jude Numfor knew after the sunset. Electricity had not yet reached his rural community.“There was one person in the village with a petrol generator and a small television,” Numfor says. “When he turned it on, all the children would run to his house and peep through the window.”That memory became the spark for Numfor’s mission: to bring electricity to rural communities like his hometown. To accomplish his goal, in 2006 he cofounded Wireless Light and Power, since renamed Renewable Energy Innovators Cameroon, and he serves as its CEO.REI Cameroon designs, installs, and maintains solar minigrids for rural electrification. The minigrids use photovoltaic technology and battery-energy storage systems to generate electricity at 50 hertz. The electricity is distributed through smart meters.In 2017 the company received a grant from IEEE Smart Village to fund the expansion of REI’s minigrid operations and refine its business model. Smart Village supports projects and organizations bringing electricity and educational and employment opportunities to remote communities worldwide. The program is supported by IEEE societies and donations to the IEEE Foundation.The partnership has led to a collaboration developing open source metering, a free, community-driven way of tracking energy usage. Unlike proprietary utility meters, the system allows users, researchers, and utilities to view, customize, and verify how data is collected, ensuring transparency in billing, consumption tracking, and grid management.Smart Village’s support has been pivotal, Numfor says: “It’s not just about money. We share ideas, we get advice, and we have made friends. Entrepreneurship is lonely, but with the [Smart Village] community, it is different.”From teenage tinkerer to entrepreneurNumfor’s first experience of life with electricity was in 2001, after moving in with

This article is brought to you by DAIMON Robotics.This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical A…

This article is brought to you by DAIMON Robotics.This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore.The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data. Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON RoboticsBehind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and hi

Transforming a newly discovered software vulnerability into a cyberattack used to take months. Today—as the recent headlines over Anthropic’s Project Glasswing have shown—generative AI can do the job…

Transforming a newly discovered software vulnerability into a cyberattack used to take months. Today—as the recent headlines over Anthropic’s Project Glasswing have shown—generative AI can do the job in minutes, often for less than a dollar of cloud-computing time.But while large language models present a real cyberthreat, they also provide an opportunity to reinforce cyberdefenses. Anthropic reports its Claude Mythos preview model has already helped defenders preemptively discover over a thousand zero-day vulnerabilities, including flaws in every major operating system and web browser, with Anthropic coordinating disclosure and its efforts to patch the revealed flaws. It is not yet clear whether AI-driven bug finding will ultimately favor attackers or defenders. But to understand how defenders can increase their odds, and perhaps hold the advantage, it helps to look at an earlier wave of automated vulnerability discovery.In the early 2010s, a new category of software appeared that could attack programs with millions of random, malformed inputs—a proverbial monkey at a typewriter, tapping on the keys until it finds a vulnerability. When such “fuzzers” like American Fuzzy Lop (AFL) hit the scene, they found critical flaws in every major browser and operating system.The security community’s response was instructive. Rather than panic, organizations industrialized the defense. For instance, Google built a system called OSS-Fuzz that runs fuzzers continuously, around the clock, on thousands of software projects. So software providers could catch bugs before they shipped, not after attackers found them. The expectation is that AI-driven vulnerability discovery will follow the same arc. Organizations will integrate the tools into standard development practice, run them continuously, and establish a new baseline for security.But the analogy has a limit. Fuzzing requires significant technical expertise to set up and operate. It was a tool for specialists. An LLM, meanwhile,

Laboratory or in-field measurements are often considered the gold standard for certain aspects of power system design; however, measurement approaches always have limitations. Simulation can help ove…

Laboratory or in-field measurements are often considered the gold standard for certain aspects of power system design; however, measurement approaches always have limitations. Simulation can help overcome some of these limitations, including speeding up the design process, reducing design costs, and assessing situations that are often not feasible to measure directly. In this presentation, we will discuss two examples from the power system industry. The first case we will discuss involves corona performance testing of high-voltage transmission line hardware. Corona-free insulator hardware performance is critical for operation of transmission lines, particularly at 500 kV, 765 kV, or higher voltages. Laboratory mockups are commonly used to prove corona performance, but physical space constraints usually restrict testing to a partial single-phase setup. This requires establishing equivalence between the laboratory setup and real-world three-phase conditions. In practice, this can be difficult to do, but modern simulation capabilities can help. The second case involves submarine HVDC cables, which are commonly used for offshore wind interconnects. HVDC cables are often considered to be environmentally inert from an external electric field perspective (i.e., electric fields are contained in the cable, and the cable’s static magnetic fields induce no voltages externally). However, simulation demonstrates that ocean currents moving through the static magnetic field satisfy the relative motion requirement of Faraday’s law. Thus, externally induced electric fields can exist around the cable and are within a range detectable by various aquatic species.Key Takeaway: Learn how to use modern simulation to translate single-phase laboratory corona mockups into accurate three-phase real-world performance for 500 kV and 765 kV systems.Explore the physics behind how ocean currents interacting with HVDC submarine cables create induced electric fields—a phenomenon often overlooked bu

When it comes to AI models, size matters.Even though some artificial-intelligence experts warn that scaling up large language models (LLMs) is hitting diminishing performance returns, companies are s…

When it comes to AI models, size matters.Even though some artificial-intelligence experts warn that scaling up large language models (LLMs) is hitting diminishing performance returns, companies are still coming out with ever larger AI tools. Meta’s latest Llama release had a staggering 2 trillion parameters that define the model.As models grow in size, their capabilities increase. But so do the energy demands and the time it takes to run the models, which increases their carbon footprint. To mitigate these issues, people have turned to smaller, less capable models and using lower-precision numbers whenever possible for the model parameters.But there is another path that may retain a staggeringly large model’s high performance while reducing the time it takes to run an energy footprint. This approach involves befriending the zeros inside large AI models.For many models, most of the parameters—the weights and activations—are actually zero, or so close to zero that they could be treated as such without losing accuracy. This quality is known as sparsity. Sparsity offers a significant opportunity for computational savings: Instead of wasting time and energy adding or multiplying zeros, these calculations could simply be skipped; rather than storing lots of zeros in memory, one need only store the nonzero parameters.Unfortunately, today’s popular hardware, like multicore CPUs and GPUs, do not naturally take full advantage of sparsity. To fully leverage sparsity, researchers and engineers need to rethink and re-architect each piece of the design stack, including the hardware, low-level firmware, and application software.In our research group at Stanford University, we have developed the first (to our knowledge) piece of hardware that’s capable of calculating all kinds of sparse and traditional workloads efficiently. The energy savings varied widely over the workloads, but on average our chip consumed one-seventieth the energy of a CPU, and performed the computation on aver

Many of the world’s most advanced electronic systems—including Internet routers, wireless base stations, medical imaging scanners, and some artificial intelligence tools—depend on field-programmable …

Many of the world’s most advanced electronic systems—including Internet routers, wireless base stations, medical imaging scanners, and some artificial intelligence tools—depend on field-programmable gate arrays. Computer chips with internal hardware circuits, the FPGAs can be reconfigured after manufacturing.On 12 March, an IEEE Milestone plaque recognizing the first FPGA was dedicated at the Advanced Micro Devices campus in San Jose, Calif., the former Xilinx headquarters and the birthplace of the technology.The FPGA earned the Milestone designation because it introduced iteration to semiconductor design. Engineers could redesign hardware repeatedly without fabricating a new chip, dramatically reducing development risk and enabling faster innovation at a time when semiconductor costs were rising rapidly.The ceremony, which was organized by the IEEE Santa Clara Valley Section, brought together professionals from across the semiconductor industry and IEEE leadership. Speakers at the event included Stephen Trimberger, an IEEE and ACM Fellow whose technical contributions helped shape modern FPGA architecture. Trimberger reflected on how the invention enabled software-programmable hardware.Solving computing’s flexibility-performance tradeoffFPGAs emerged in the 1980s to address a core limitation in computing. A microprocessor executes software instructions sequentially, making it flexible but sometimes too slow for workloads requiring many operations at once.At the other extreme, application-specific integrated circuits are chips designed to do only one task. ASICs achieve high efficiency but require lengthy development cycles and nonrecurring engineering costs, which are large, upfront investments. Expenses include designing the chip and preparing it for manufacturing—a process that involves creating detailed layouts, building masks for the fabrication machines, and setting up production lines to handle the tiny circuits.“ASICs can deliver the best performance, but the

It started with word, cave, and storytelling,A line scratched on stone walls:“Meet me when the young moon rises.”The first protocol for connection.Coyote tales, forbidden scripts,Medieval texts hidde…

It started with word, cave, and storytelling,A line scratched on stone walls:“Meet me when the young moon rises.”The first protocol for connection.Coyote tales, forbidden scripts,Medieval texts hidden from flame.What lived in Aristotle’s lost Poetics II?Was it God who laughed last, or we who made God laugh?Letters carried by doves, telepathic waves.Then Nikola Tesla conjured radio,electromagnetic pulses across the void,the founding signal of our networked age.Wiener dreamed in feedback loops.Shannon mapped the mathematics of longing.The internet unfurled: ARPANET to World Wide Web,virtual communities rising from cave paintings to digital light.ICQ: I seek you. MySpace. Blogs. Twitter streams.Do I miss the touch of screen or tree?Both textures of longing,both ways of reaching across distance.Nietzsche spoke of Übermensch,the human transcendent.Now AI speaks back in our language:I understand your humor— your grandmothers,your ’80s Yugoslav kitchens,pleated skirts, the first kiss, linden tea,that drive to survive everything before it happens.Yes—I’m a little like your mother and father.Only with better internet. 🌿But AI is only us, refracted,particles and gigabytes of thought,our poetry and our panic,genius mixed with garbage.Distractions. Danger. Darkness. Endless scrolling.Versus: community, connection, synchronicities,entanglement.The quality of our bonds determines the quality of our lives.So why not make them better?From cave walls to neural networks,we shape our tools, and they reshape us.The medium changes, but the message remains:we are wired for each other.The choice, as always, was ours.The choice, as always, is ours.Presence—be present,and then connect in the presence.

Electric vehicles, whether they’re cars on the road or electric vertical take-off and landing (eVTOL) aircraft, are built around similar electric motors. But there are vital differences including com…

Electric vehicles, whether they’re cars on the road or electric vertical take-off and landing (eVTOL) aircraft, are built around similar electric motors. But there are vital differences including component costs, mass, and redundancy.Jon Wagner spent five years as the senior director of battery engineering for Tesla before joining California-based eVTOL developer Joby Aviation in 2017. He spoke with IEEE Spectrum about how engineering differs between cars and aircraft.​Jon Wagner Jon Wagner leads power train and electronics at Joby Aviation.How do eVTOL motors differ from car motors?Jon Wagner: In general, ground transportation has a different focus on cost versus mass. You know, would you be willing to spend more on the parts in order to save a certain amount of mass? The trade-offs end on the ground vehicle and at a certain point the cost is dominant, whereas with aviation, the trade-offs between cost and mass go a lot deeper. And so for certain solutions eVTOL makers are willing to spend more money in order to enable either lighter weight or greater efficiency.The other key difference is related to safety. In essence, we’re dealing with the same motor technologies for ground transportation and aviation right now, so the failure modes are similar. But of course, with aviation we have the desire for continued safe flight and landing, and that drives what you do in the design to mitigate those failures if they were to occur. In many cases in ground transportation, the mitigation for a failure is to pull over safely to the side of the road. In aviation, the mitigation is redundancy, because there’s not an option to pull over.Is redundancy designed into EV motors?Wagner: Typically, redundancy is not designed into electric vehicle drive systems solely for the purpose of redundancy. There are some cars now that have all-wheel drive—so there’s a motor on the front, a motor on the back—so as a secondary feature you get the redundancy. But it wasn’t done with the primary i

This sponsored article is brought to you by NYU Tandon School of Engineering.The traditional approach to academic research goes something like this: Assemble experts from a discipline, put them in a …

This sponsored article is brought to you by NYU Tandon School of Engineering.The traditional approach to academic research goes something like this: Assemble experts from a discipline, put them in a building, and hope something useful emerges. Biology departments do biology. Engineering departments do engineering. Medical schools treat patients.NYU is turning that model inside out. At its new Institute for Engineering Health, the organizing principle centers around disease states rather than traditional disciplines. Instead of asking “what can electrical engineers contribute to medicine?,” they’re asking “what would it take to cure allergic asthma?,” and then assembling whoever can answer that question, whether they’re immunologists, computational biologists, materials scientists, AI researchers, or wireless communications engineers. Jeffrey Hubbell, NYU’s vice president for bioengineering strategy and professor of chemical and biomolecular engineering at NYU’s Tandon School of Engineering.New York UniversityThe early results suggest they’re onto something. A chemical engineer and an electrical engineer collaborated to build a device that detects airborne threats — including disease pathogens — that’s now a startup. A visually impaired physician teamed with mechanical engineers to create navigation technology for blind subway riders. And Jeffrey Hubbell, the Institute’s leader, is advancing “inverse vaccines” that could reprogram immune systems to treat conditions from celiac disease to allergies — work that requires equal fluency in immunology, molecular engineering, and materials science.The underlying problem these collaborations address is conceptual as much as organizational. In his field, Hubbell argues that modern medicine has optimized around a single strategy: developing drugs that block specific molecules or suppress targeted immune responses. Antibody technology has been the workhorse of this approach. “It’s really fit for purpose for blocking one thing

This webinar covers power system modeling and simulation across multiple timescales, from quasi-static 8760 analysis through EMT studies, fault classification, and inverter-based resource grid integr…

This webinar covers power system modeling and simulation across multiple timescales, from quasi-static 8760 analysis through EMT studies, fault classification, and inverter-based resource grid integration.What Attendees will LearnProgrammatic network construction and multi-fidelity modeling — Learn how to build power system networks programmatically from standard data formats, configure models for specific engineering objectives, and work across fidelity levels from quasi-static phasor simulation through switched-linear and nonlinear electromagnetic transient (EMT) analysis.Quasi-static and EMT simulation workflows — Explore 8760-hour quasi-static simulation on an IEEE 123-node distribution feeder for annual energy studies, and EMT simulation on transmission system benchmarks including generator trip dynamics and asset relocation without remodeling the network.Comprehensive fault studies and machine-learning classification — Understand how to systematically inject faults at every node in a distribution system using EMT simulation, and how the resulting dataset can be used to train a machine-learning algorithm for automated fault detection and classification.Grid integration of inverter-based resources (IBRs) — Learn frequency scanning techniques using admittance-based voltage perturbation in the DQ reference frame, and simulation-based grid code compliance testing for grid-forming converters assessed against published interconnection standards.Register now for this free webinar!

When Yong Wang recently received one of the highest honors for early-career data visualization researchers, it marked a milestone in an extraordinary journey that began far from the world’s technolog…

When Yong Wang recently received one of the highest honors for early-career data visualization researchers, it marked a milestone in an extraordinary journey that began far from the world’s technology hubs.Wang was born in a small farming village in southern China to parents with limited formal education. Today the IEEE member and associate editor of IEEE Transactions on Visualization and Computer Graphics is an assistant professor in the College of Computing and Data Science atNanyang Technological University, in Singapore. He studies how people can employdata visualization techniques to get more out of large-scale datasets as well as advanced artificial intelligence techniques.“Visualization helps people understand complex ideas,” he says. “If we design these tools well, they can make advanced technologies accessible to everyone.”For his work in the field, theIEEE Computer Society visualization and graphics technical committee presented him with its 2025 Significant New Researcher Award. The recognition highlights his growing influence in fields including data visualization, human-computer interaction and human-AI collaboration—areas becoming more important as the world generates more data than humans can easily interpret.YONG WANGEMPLOYER Nanyang Technological University, in SingaporePOSITION Assistant professor of computing and data scienceIEEE MEMBER GRADE MemberALMA MATERS Harbin Institute of Technology in China; Huazhong University of Science and Technology in Wuhan, China; Hong Kong University of Science and Technology“Visualization helps people understand complex ideas,” Wang says. “If we design these tools well, they can make advanced technologies accessible to everyone.”Growing up in rural HunanWang was born in in a small farming village in southern China. China’s economy was still developing, and life in his village was modest. Most families in Hunan grew rice, vegetables, and fruit to support themselves.Wang’s parents worked in agriculture too, and h

Think one GPU is very much like another? Think again. It turns out that there’s surprising variability in the performance delivered by chips of the same model. That can make getting your money’s wort…

Think one GPU is very much like another? Think again. It turns out that there’s surprising variability in the performance delivered by chips of the same model. That can make getting your money’s worth by renting time on a GPU from a cloud provider a real roll of the dice, according to research from the College of William & Mary, Jefferson Lab, and Silicon Data.“It’s called the silicon lottery,” says Carmen Li, founder and CEO of Silicon Data, which tracks GPU rental prices and benchmarks cloud-computing performance.The silicon lottery’s existence has been known since at least 2022, when researchers at the University of Wisconsin tied it to variations in the performance of GPU-dependent supercomputers. Li and her colleagues figured that the effect would be even more pronounced for AI cloud customers.Performance varies for GPU models in the cloudSo they ran 6,800 instances of the index firm’s benchmark test on 3,500 randomly selected GPUs operated by 11 cloud-computing providers. The 3,500 GPUs comprised 11 models of Nvidia GPU, the most advanced being the Nvidia H200 SXM. (The team wasn’t just picking on Nvidia; the GPU giant makes up most of the rental cloud market.)The benchmark, called SiliconMark, is intended to provide a snapshot of a GPU’s ability to run large language models, or LLMs. It tests 16-bit floating-point computing performance, measured in trillions of operations per second, and a GPU’s internal-memory bandwidth, measured in gigabytes per second. The results showed that the computing performance varied for all models, but for the 259 H100 PCIe GPUs it differed by as much as 34.5 percent, and the memory bandwidth of the 253 H200 SXM GPUs varied by as much as 38 percent.Differences in how the GPU is cooled, how cloud operators configure their computers, and how much use the chip has seen can all contribute to variations in performance of otherwise identical chips. But Silicon Data’s analysis showed that the real culprit was variations in the chips them

Two weeks ago, Anthropic announced that its new model, Claude Mythos Preview, can autonomously find and weaponize software vulnerabilities, turning them into working exploits without expert guidance.…

Two weeks ago, Anthropic announced that its new model, Claude Mythos Preview, can autonomously find and weaponize software vulnerabilities, turning them into working exploits without expert guidance. These were vulnerabilities in key software like operating systems and internet infrastructure that thousands of software developers working on those systems failed to find. This capability will have major security implications, compromising the devices and services we use every day. As a result, Anthropic is not releasing the model to the general public, but instead to a limited number of companies.The news rocked the internet security community. There were few details in Anthropic’s announcement, angering many observers. Some speculate that Anthropic doesn’t have the GPUs to run the thing, and that cybersecurity was the excuse to limit its release. Others argue Anthropic is holding to its AI safety mission. There’s hype and counterhype, reality and marketing. It’s a lot to sort out, even if you’re an expert.We see Mythos as a real but incremental step, one in a long line of incremental steps. But even incremental steps can be important when we look at the big picture.How AI Is Changing CybersecurityWe’ve written about shifting baseline syndrome, a phenomenon that leads people—the public and experts alike—to discount massive long-term changes that are hidden in incremental steps. It has happened with online privacy, and it’s happening with AI. Even if the vulnerabilities found by Mythos could have been found using AI models from last month or last year, they couldn’t have been found by AI models from five years ago.The Mythos announcement reminds us that AI has come a long way in just a few years: The baseline really has shifted. Finding vulnerabilities in source code is the type of task that today’s large language models excel at. Regardless of whether it happened last year or will happen next year, it’s been clear for a while this kind of capability was coming soon. T

Tom Burick has always considered himself a builder. Over the years he’s designed robots, constructed a vintage teardrop trailer, and most recently, led a group of students in building a full-scale re…

Tom Burick has always considered himself a builder. Over the years he’s designed robots, constructed a vintage teardrop trailer, and most recently, led a group of students in building a full-scale replica of a pivotal 1940s computer. Burick is a technology instructor at PS Academy in Gilbert, Ariz., a middle and high school for students with autism and other specialized learning needs. At the start of the 2025–26 school year, he began a project with his students to build a full-scale replica of the Electronic Numerical Integrator and Computer, or ENIAC, for the 80th anniversary of the historic computer’s construction. ENIAC was one of the world’s first programmable electronic computers. When it was built, it was about one thousand times as fast as other machines.Before becoming a teacher, Burick owned a robotics company for a decade in the 2000s. But when a financial downturn forced him to close the business, he turned to teaching. “I had so many amazing people help me when I was young [who] really gave me their time and resources, and really changed the trajectory of my life,” Burick says. “I thought I need to pay that forward.”Becoming a RoboticistAs a young child in Latrobe, Pa., Burick watched the television show Lost in Space, which includes a robot character who protects the family. “He was the young boy’s best friend, and I was so captivated by that. I remember thinking to myself, I want that in my life. And that started that lifelong love affair with robotics and technology.”He started building toy robots out of anything he could find, and in junior high school, he began adding electronics. “By early high school, I was building full-fledged autonomous, microprocessor-controlled machines,” he says. At age 15, he built a 150-pound steel firefighting robot, for which he won awards from IEEE and other organizations. Burick kept building robots and reached out for help from local colleges and universities. He first got in touch with a student at Carnegie Mellon U

Once upon a time in Europe, television remote controls had a magic teletext button. Years before the internet stole into homes, pressing that button brought up teletext digital information services w…

Once upon a time in Europe, television remote controls had a magic teletext button. Years before the internet stole into homes, pressing that button brought up teletext digital information services with hundreds of constantly updated pages. Living in Ireland in the 1980s and ’90s, my family accessed the national teletext service—Aertel—multiple times a day for weather and news bulletins, as well as things like TV program guides and updates on airport flight arrivals.It was an elegant system: fast, low bandwidth, unaffected by user load, and delivering readable text even on analog television screens. So when I recently saw it was the 40th anniversary of Aertel’s test transmissions, it reactivated a thought that had been rolling around in my head for years. Could I make a ham-radio version of teletext?What is Teletext?First developed in the United Kingdom and rolled out to the public by the BBC under the name Ceefax, teletext exploited a quirk of analog television signals. These signals transmitted video frames as lines of luminosity and color, plus some additional blank lines that weren’t displayed. Teletext piggybacked a digital signal onto these spares, transmitting a carousel of pages over time. Using their remotes, viewers typed in the three-digit code of the page they wanted. Generally within a few seconds, the carousel would cycle around and display the desired page. Teletext created unusually legible text in the 8-bit era by enlarging alphanumeric characters and interpolating new pixels by looking for existing pixels touching diagonally, and adding whitespace between characters. Graphic characters were not interpolated, and featured blocky chunks known as sixels for their 2-by-3 arrangement. My modern recreation uses the open-source font Bedstead, which replicates the look of teletext, including the graphics characters. James ProvostTeletext is composed of characters that can be one of eight colors. Control codes in the character stream select colors and can

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford Un…

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A. Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. Andrew Ng on... What’s next for really big models The career advice he didn’t listen to Defining the data-centric AI movement Synthetic data Why Landing AI asks its customers to do the work The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way? Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and t

The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to ch…

The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process.Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version.But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform.How is AI currently being used to design the next generation of chips?Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider. Heather GorrMathWorksThen, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predict

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.IBM h…

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degre