Feed aggregator

Наносупутник PolyITAN-1 КПІ ім. Ігоря Сікорського встановив рекорд за тривалістю польоту в космосі KPI4U-2 пт, 07/31/2026 - 16:56

India Electronics Development Fund (EDF) strengthened the country’s deep-tech ecosystem with an accumulated investment of Rs 1,335.77 crore (approx. $139.23 million) supporting 128 start-ups and tech enterprises. This milestone further demonstrates the government’s commitment to developing Indian capabilities in innovation across electronics, semiconductors, nanoelectronics, and IT. This development reiterates their aim to foster an indigenous self-reliant ecosystem that can eventually enable the creation of a globally competitive technology economy leveraging high innovation, leading edge technology, high-quality human talent to create differentiated technology solutions.​

The Electronics Development Fund (EDF) set up by the Ministry of Electronics and Information Technology (MeitY) in February 2016 is an Umbrella-Fund of Funds concept. In this model, the EDF invests into other professionally managed venture capital and angel investment funds, called “Daughter Funds.” These Daughter Funds invest into the high- growth potential start ups that build future technologies. As of September 30, 2025, a sum of 257.77 crore of direct equity in eight Daughter Funds by EDF and additional 1,335.77 crore capital invested by Daughter Funds in total of 128 start-ups/ ventures were catalysed in India.

The electronics development fund manages eight SEBI regulated Daughter Funds like Unicorn India Ventures Trust, Aaruha Technology Fund-I, Endiya Seed Co-creation Fund, Karsemven Fund, pi Ventures Fund I, YourNest India VC Fund II, Ventureast Proactive Fund-II and Exfinity Technology Fund Series II. The daughter venture funds pick out startups that can use cutting edge technologies and have high commercial viability.

Having invested in 128 start-ups spanning various frontier technologies, the Electronics Development Fund has become a significant lever for nurturing deep-tech innovation in the country. Over the coming years, the programme will play an even larger role in scaling the nation’s electronics ecosystem, create high-skill, high-value jobs, and advance India’s vision to lead the world in areas like electronics design, semiconductor technologies, and next generation digital innovations.

The post Electronics Development Fund Backs 128 Start-ups with $139.23 Million in Investments appeared first on ELE Times.

Hi everyone! After building and testing my whole 4 bits computer architecture on breadboards, I moved to custom PCB modules, and here is the result! The goal remains the same: keep the architecture simple, modular, and easy to understand, with each part working independently or together as a complete system. The project is divided into 5 independent modules connected by a 4-bit bus Clock module (4-bit counter), ALU + registers module + two 4-bit registers with an ALU capable of performing addition, subtraction, and equality comparison, Instruction register module, Display module and Manual programming module. The idea is to follow a simple RISC philosophy: minimal instructions, simple hardware blocks, and a design where every signal can be observed and understood. After the breadboard prototype phase, these PCB modules make the system cleaner, i'm very proud!! submitted by /u/Fuzzy_Function_1896 [link] [comments]

Розширення партнерства із закладами вищої освіти Пакистану KPI4U-2 пт, 07/31/2026 - 12:02

For its fiscal first-quarter 2027 (ended 27 June 2026), Qorvo Inc of Greensboro, NC, USA (which provides core technologies and RF solutions for mobile, infrastructure and defense applications) has reported revenue of $784.8m, down 2.9% on last quarter’s $808.3m and 4.2% on $818.8m a year ago...

New to hobby electronics. Total tourist. Decided to try my hand at free form circuit design. This is pretty sloppy. But hope to clean up designs as I go, add some elegance. Super fun creative process though, to think about a circuit design in 3D essentially. Simple 555 ic flashing lights circuit but designed as a spider crawling up a tree. Again, not aesthetically pleasing yet. But the circuit works and it was a lot of fun to put together. submitted by /u/Dr_Pavoreal [link] [comments]

The Supervisory Board of ams OSRAM AG of Premstaetten, Austria, and Munich, Germany has approved the reappointment of CEO Aldo Kamper and agreed to a new contract effective 1 October, and running through 30 September 2031. His previous term was set to expire on 31 March 2027. Kamper has been CEO since April 2023...

Just finished my final year engineering project: a custom hardware accelerator that runs YOLOv8n (an object detection AI model) directly on an FPGA, in real time. Instead of running the AI model in software on a CPU/GPU, this is custom digital logic (written in Verilog) that does the computation directly, faster and more power-efficient for edge devices like drones or embedded cameras. Some highlights: Configurable performance, can scale from 1x to 4x throughput depending on the application Uses FP16 precision, tested against lower precision options and it actually performed better for our use case The tricky part was moving data between processing stages without slowing everything down, that took way longer to solve than expected If anyone's into FPGAs, embedded AI, or just wants to see what a final year project like this looks like under the hood: https://github.com/waseemnabi08/yolov8n-cnn-accelerator-fpga submitted by /u/Waseeemnabi [link] [comments]

Finland-based laser developer and manufacturer Vexlum — which was spun off from Tampere University of Technology’s Optoelectronics Research Centre in 2017 and develops and manufactures high-power semiconductor vertical-external-cavity surface-emitting laser (VECSEL) technology systems — has announced the appointment of Dr Stefan Truppe as managing director of Vexlum UK, and the establishment of a dedicated laboratory in London, positioning the firm at the center of the UK’s rapidly growing quantum ecosystem...

India is stepping up investments in key EV technologies. The South Asian country is speeding up its investments in key electric vehicle (EV) technologies while planning to boost a self-dependent and globally relevant EV manufacturing ecosystem. Driven by various central government initiatives, state government programs, and private sector investments, India attracted commitments for around US$25.6 billion worth of investments across battery manufacturing, advanced battery chemistries, powertrains, charging infrastructure, and technologies for battery and electric powertrains. These investments will help scale up domestic manufacturing and reduce dependence on imported components.

The Government of India launched multiple schemes to boost domestic manufacturing of the Electric Vehicles (EV) ecosystem through production-linked Incentive (PLI) Schemes for Advanced Chemistry Cell (ACC) and battery storage, as well as the PM E-Drive program to promote charging. The PM E-Drive program aims to promote charging for automobiles and auto components. Along with manufacturers both domestically and overseas, these schemes lead to localization of EVs’ key technologies.

The post India Accelerates Core EV Technology Investments to Strengthen Domestic Manufacturing Ecosystem appeared first on ELE Times.

High-voltage batteries for e-mobility have become highly complex systems consisting of numerous components. The market for these batteries is becoming increasingly dynamic, and technologies are constantly evolving: innovative battery cells with new chemical compositions are being introduced. Automotive suppliers and manufacturers therefore require a tailored and flexible testing strategy for battery cell quality assurance, both in development and in production. Only in this way can suppliers compete in the market with more powerful batteries, higher levels of automation, and optimized workflows.

To address these challenges, GÖPEL electronic has developed a high-performance and quickly configurable battery cell tester that features a modular design and high flexibility. This allows for the quick and transparent determination of quality, charging efficiency, and reliability in up to five battery cells simultaneously. The tester complements the product portfolio, which includes, among other things, GÖPEL electronic’s EOL battery test system, which performs comprehensive test routines for the entire battery pack.

The new battery cell tester covers standard safety tests that evaluate the condition of the cells: The OCV (Open Circuit Voltage) value indicates the voltage of a battery without a load (open circuit) and serves as an indicator of its state of charge. For the ACIR (Alternating Current Internal Resistance) measurement, the tester uses a test method to determine the internal resistance of the battery cells. This internal resistance under alternating current provides information about the performance, state of aging, and quality of the battery cell.

The main advantage of the new, smaller tester from GÖPEL electronic lies in its fast and reproducible test results: By interfacing with automated equipment, highly efficient batch testing can be performed in a matter of seconds. This is ideal, for example, for incoming inspection or cell grouping.

The post Fast and Flexible Test Device for Safety and Functional Testing of Battery Cells appeared first on ELE Times.

Ather Energy has started rolling out Pothole+ Alerts for customers with Gen 2 and above scooters, including the Ather 450 Apex, 450X and Rizta Z. The feature leverages data generated by Ather’s connected scooter fleet to warn riders about potholes, broken road sections, uneven surfaces and speed breakers ahead while navigating. It also goes a step further, suggesting the smoothest route before a ride begins, beyond just the fastest one.

Announcing the rollout on X, Ather Co-founder and CEO Tarun Mehta said the feature had been nearly nine years in the making. “The challenge was never the idea. It was the data,” Mehta wrote. He explained that building the feature required a large connected fleet travelling the same roads over time, allowing Ather to generate the scale of fleet intelligence needed to accurately identify road conditions.

Every Ather scooter comes equipped with onboard compute and connectivity, enabling the company to continuously gather real-world riding data and deliver new capabilities through over-the-air software updates. According to Mehta, Pothole+ Alerts represents one of the first large-scale applications of this connected ecosystem, transforming millions of kilometres of ride data into a feature that benefits riders every day.

Integrated into the scooter’s navigation experience, Pothole+ Alerts provides advance notifications for potholes, broken road sections, uneven surfaces and speed breakers. Alerts are displayed on the dashboard and can also be delivered through the scooter’s speakers, the Ather Halo smart helmet or any compatible Bluetooth headset.

The rollout reflects Ather’s continued focus on building connected features powered by real-world riding data. Mehta added that Pothole+ Alerts is just the beginning, with the company planning to build more rider experiences on top of its growing road intelligence dataset.

The post Ather Energy Rolls Out Pothole+ Alerts, Built on Connected Fleet Intelligence appeared first on ELE Times.

Aeluma Inc of Goleta, CA, USA has signed a letter of intent (LOI) for up to $30m of proposed funding under the CHIPS and Science Act, which is administered by the US Department of Commerce. The award will support R&D on the firm’s scalable, non-InP (indium phosphide) semiconductor manufacturing platform for photonics...

AXT Inc of Fremont, CA, USA — which makes gallium arsenide (GaAs), indium phosphide (InP) and germanium (Ge) substrates and raw materials at plants in China — has entered into a definitive agreement with Lumentum Operations LLC of San Jose, CA, USA (which designs and makes photonic products for optical networks and lasers for industrial and consumer markets) for the supply and capacity reservation of indium phosphide wafer substrates...

Стипендія КМУ за видатні заслуги у сфері вищої освіти Віталію Дідковському KPI4U-2 чт, 07/30/2026 - 10:00

Here’s a question your grandfather could have answered: Would you rather have a million dollars today or a penny that doubles every day for a month? Most people take the million. The penny reaches $5 million by day 30.

Human intuition is simply bad at exponential math. We think in straight lines, and compounding curves surprise us every time. Now run that intuition in reverse.

An AI coding agent that’s right 98% of the time sounds impressive. And 98% is a generous assumption, probably better than reality for most tasks. But apply that accuracy across 34 steps with no verification in the loop, and you’ve crossed the coin-flip line. More likely wrong than right. The math is 0.98^34 = 0.505.

The surprise is the same one your grandfather felt. And in a safety-critical development environment, the consequences are not a missed investment opportunity.

Sophisticated agentic systems don’t run open-loop. They compile, test, lint, and self-verify at each step, and the public record shows it works.

Andreas Kling ported Ladybird’s LibJS engine from C++ to Rust using AI agents across hundreds of human-directed prompts, producing 25,000 lines of Rust with zero regressions across 65,359 tests and byte-for-byte identical bytecode output. The human was in the loop at every decision point, which is precisely why it worked.

The Bun JavaScript runtime went further. AI Weekly highlighted that Claude agents rewrote roughly one million lines of Zig to Rust autonomously across 6,755 commits, passing 99.8% of its existing test suite. It also left 13,044 unsafe Rust blocks, where a comparable handwritten project would have 73. A passing test suite doesn’t surface this safety debt—it stops a safety-critical certification in its tracks.

Both of these projects succeeded because verification ran inside the loop at every step. They also illustrate exactly where the limits are. In most software development contexts, the floor is an efficiency problem. Verification catches it, the agent retries, and the process converges. Expensive in tokens and time, but recoverable.

In safety-critical development, the calculus is different. This is where functional correctness testing and safety-critical qualification part ways. Bun passed its own test suite. Ladybird produced byte-for-byte identical bytecode. Those are impressive results. But they are not safety cases. ISO 26262, DO-178C, and IEC 62304 don’t recognize self-generated test results as qualified verification evidence.

Your braking system software doesn’t get partial credit for passing tests it wrote for itself. Your insulin pump firmware isn’t certified on a curve. The standards assume deterministic tools producing verifiable evidence—qualified tools, documented configurations, and traceable outputs. An agentic workflow that self-verifies is better than one that doesn’t. But in safety-critical development, it still isn’t enough.

What safety-critical compliance actually requires isn’t vague.

ISO 26262 mandates a documented safety plan, requirements with bidirectional traceability from hazard analysis through to verified implementation, and evidence that coding guidelines—typically MISRA C or CERT C—were enforced by a qualified tool using a qualified configuration.

DO-178C adds structural coverage requirements. At the highest criticality levels, every statement, every branch, and every condition and its complement must be exercised by tests that are themselves traced to requirements.

IEC 62304 requires a software development lifecycle with documented verification activities at each phase. In every case, the evidence must be generated as the work happens rather than reconstructed afterward—and not self-certified by the tool that produced the artifact being evaluated.

The open-loop pipeline isn’t an edge case; it’s what every team promises to fix after the next release. A requirements review is handed to a code generator, a documentation tool, and a traceability updater with testing saved for the end. That’s not an agentic worst case. That’s a pipeline. At 98% per-step accuracy across 34 stages, you’ve crossed the coin-flip line before you’ve run a single test.

The answer isn’t a better model. It’s the same answer safety-critical engineers have always given to unreliable processes. You don’t improve your way to acceptable; you gate your way there.

Static analysis enforces expected coding patterns and flags dangerous anti-patterns like uninitialized memory, undefined behavior, and violations of MISRA or CERT rules that exist precisely because they’ve caused failures before.

Unit tests verify that individual components behave as specified under known conditions. And coverage in safety-critical development isn’t a spot-checking exercise. DO-178C requires 100% MC/DC coverage at DAL A, and ISO 26262 requires the same at ASIL D. Every line. Every branch. Every condition.

Each gate resets the accumulated uncertainty back toward zero before the next stage compounds it further. That’s not a new idea. It’s how you build software that people’s lives depend on.

The question AI raises isn’t whether to use gates. It’s whether the gates you already have are positioned to catch what an AI agent introduces and whether you’ve thought carefully about where in the workflow the uncertainty is actually accumulating.

The gates were designed for a world where code has an author who made deliberate choices. A human developer who writes an uninitialized variable made a mistake. A human developer who skips a boundary check made a tradeoff. Static analysis flags both—the developer understands the finding in context, and the correction is made by someone who knows what the code is supposed to do. The evidence trail is intact. The intent is recoverable.

An AI agent doesn’t make mistakes in that sense. It produces outputs that are statistically consistent with its training: plausible, often correct, and occasionally wrong in ways that look right.

The static analysis tool will still flag the MISRA violation. The unit test will still fail on the boundary condition. But the developer reviewing the finding is now one step removed from the original intent because there wasn’t original intent in the human sense. There was a probability distribution. And when you ask the agent why it made that choice, the answer is not recoverable in any form a certification auditor can use.

The gates catch the artifact. They don’t reconstruct the argument. In a safety case, you need both, and one of them must have been generated as the decisions were made, not reverse engineered from the output afterward.

The consumer technology press calls it “hallucination,” which means the AI confidently states something wrong. This term captures the symptom, but not the mechanism.

In safety-critical engineering the mechanism is what matters. ISO/PAS 8800, the emerging automotive standard for AI safety that the broader embedded industry is watching closely as a template, uses the term “functional insufficiency”: an unexpected error under specific conditions not adequately represented during development. As EDN noted, for engineers building software for medical devices, industrial automation, rail, aerospace, and defense, dismissing this document as “just for cars” would be a missed opportunity.

The distinction matters. Hallucination implies the system invented something from nothing. Functional insufficiency describes something more precise. The system performed exactly as its training data suggested it should, and the training data didn’t cover this case.

You can’t fix a hallucination by improving the model. You can’t fix a functional insufficiency that way either. What you can do is bound it, monitor it, and build an architecture that prevents it from propagating into a safety-critical decision unchecked.

None of this is an argument against AI in safety-critical development. These industries already have the architectural foundations to manage it responsibly. That argument is already lost, and it should be. AI tools are accelerating development, surfacing defects earlier, and handling the kind of repetitive verification work that exhausts engineers and introduces its own error rate.

The question was never whether AI would enter these industries. It’s here. The question is whether the engineering discipline surrounding it will keep pace.

Compound uncertainty doesn’t care about your intentions or your vendor’s benchmark scores. A 98% accurate agent in a 34-step open-loop workflow has already crossed the coin-flip line. Those numbers don’t improve because the use case is important or the schedule is tight.

Compound uncertainty in multi-step workflows. Even with 95% per-step accuracy, overall success rate declines sharply as the number of workflow steps (N) increases—not because model performance degrades, but because the workflow itself compounds error. Source: Parasoft

What does improve the outcome is treating AI in safety-critical development the way these industries have always treated unreliable components: with gates, evidence, and documented reasoning that survives an audit.

The standards that govern medical devices, aviation software, and automotive systems were written for a deterministic world. But the principles they encode—rigorous verification, traceable decisions, complete coverage, and structured safety arguments—turn out to be exactly the right response to a world where probabilistic behavior slipped into the development process before anyone checked its credentials.

ISO/PAS 8800 is the automotive industry’s first formal attempt to extend those principles into AI-specific territory. Other domains are watching. The framework outlined in the embedded world—manage uncertainty, bound it, argue it, and monitor it—applies whether you’re building firmware for a ventilator or a flight control system or an autonomous vehicle.

You will never eliminate functional insufficiency from an AI system. However, you can build an architecture that catches it before it becomes a safety event. That’s not a limitation of technology. It’s just engineering.

Arthur Hicken is a senior software evangelist at Parasoft.

Ricardo Camacho is director of product strategy for embedded and safety critical compliance at Parasoft.

Related Content

• AI Safety Moves to the Forefront
• Specifying Objectives is Key to AI Safety
• Can We Trust AI in Safety Critical Systems?
• Safe Automated Driving Starts with Architecture
• The impact of AI/ML on qualifying safety-critical software

The post Compound uncertainty: AI’s hidden risk in safety-critical development appeared first on EDN.

submitted by /u/Quietgoer [link] [comments]

Multi-national industrial and energy company METLEN Energy & Metals S.A. of Athens, Greece — which operates the only vertically integrated bauxite, alumina and primary aluminium production unit in the European Union (EU) with privately owned port facilities — has announced a long-term commercial agreement for the supply of about 25% of the annual gallium production from its planned production facility in Greece...

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has appointed Andy W. Mattes to its board of directors...