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STMicroelectronics announced the acceleration of global development and adoption of physical AI systems, including humanoid, industrial, service and healthcare robots. ST is integrating its comprehensive portfolio for advanced robotics into the reference set of components compatible with the NVIDIA Holoscan Sensor Bridge (HSB). In parallel, high-fidelity NVIDIA Isaac Sim models of ST components are being integrated into both companies’ robotics ecosystems to support faster, more accurate sim-to-real research and development. The first deliverables available to developers today include the integration of Leopard’s depth camera enabled by ST with the NVIDIA HSB and the high-fidelity model of an ST IMU into NVIDIA’s Isaac Sim ecosystem.

“ST is well engaged within the robotics community, providing robust support and a well-established ecosystem,” said Rino Peruzzi, Executive Vice President, Sales & Marketing, Americas & Global Key Account Organization at STMicroelectronics. “Our collaboration with NVIDIA aims to unleash the next wave of cutting-edge robotics innovation with developer and customer experience streamlined at every step, from the inception of AI algorithms to the seamless integration of sensors and actuators. This will accelerate the evolution of sophisticated AI-driven physical platforms.”

“Accelerating the development of next-generation autonomous systems requires high-fidelity simulation and seamless hardware integration to bridge the gap between virtual training and real-world deployment,” said Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA. “The integration of STMicroelectronics’ sensor and actuator technologies with NVIDIA Isaac Sim, Holoscan Sensor Bridge and Jetson platforms provides developers with a unified foundation to build, simulate and deploy physical AI at scale.”

Simplifying sensor and actuator integration with the Holoscan Sensor Bridge

With the NVIDIA HSB, developers can unify, standardise, synchronise, and streamline data acquisition and logging from multiple ST sensors and actuators, a critical foundation for building high-fidelity NVIDIA Isaac models, accelerating learning, and minimising the sim-to-real gap.

The goal is to simplify the process of connecting ST sensors and actuators to NVIDIA Jetson platforms through pre-integrated solutions for the combination of STM32 MCUs, advanced sensors (including IMUs, imagers, and ToF devices) and motor‑control solutions, particularly for humanoid robot designs. Leopard Imaging’s stereo depth camera for robots is the perfect example. Using ST imaging, depth and motion-sensing technologies, it is expected to support a broad wave of designs across Physical AI OEMs, academic research groups and the industrial robotics community.

Reducing cost, complexity, and challenges with high-fidelity modelling for Omniverse Isaac

Advanced robotics developers face high development costs, in addition to modelling challenges. High‑fidelity simulations with extensive randomisation demand substantial GPU and CPU resources and large datasets. Selecting which parameters to randomise, and over what ranges, requires deep domain expertise. Poor choices can result in unrealistic scenarios or inefficient training. Finally, excessive variability can confuse models, slow convergence, and degrade real‑world performance when randomisation no longer reflects plausible conditions.

ST and NVIDIA’s objective is to provide accurate, hardware-calibrated models for the comprehensive portfolio of ST components, matching the requirements of advanced robotics. Following the availability of the first model of an IMU, ST is working to bring developers models of ToF sensors, actuators and other ICs derived from benchmark data collected on real ST hardware, using ST tools to capture accurate parameters and realistic behaviour, resulting in models optimised to NVIDIA’s Isaac Sim ecosystem. NVIDIA HSB is being integrated into ST’s toolchain collaboratively.

As a result, ST and NVIDIA envision that more accurate models will significantly improve robot learning. With models that closely mirror real-world device behaviour, robots can learn from simulations that better reflect actual conditions, shortening training cycles and lowering the cost of building and refining humanoid robotics applications.

The post STMicroelectronics accelerates global adoption and market growth of Physical AI with NVIDIA appeared first on ELE Times.

In booth 1401 at the Optical Fiber Communications Conference and Exhibition (OFC 2026) at the Los Angeles Convention Center (17–19 March), materials, networking and laser technology firm Coherent Corp of Saxonburg, PA, USA is highlighting the breadth and scalability of its indium phosphide (InP) innovations, showcasing a broad portfolio of lasers, modulators, photodiodes and subsystems for powering next-generation data-center architectures...

Across markets such as AI, high-performance computing (HPC), and automotive, the demand for computational power continues to accelerate. This demand spans everything from compact edge devices to massive data center servers. Traditionally, that capacity was delivered by monolithic systems-on-chip (SoCs) implemented on a single silicon die. While manufacturing trade-offs can ease some pressures, a large die still limits optimization, forcing designers to balance power and performance across the entire chip rather than fine-tuning each function individually.

The problem is structural. Monolithic SoCs have reached physical and economic limits. As shown in Figure 1, reticle size is fixed, yields decline as die size grows, and the cost of large devices is prohibitively high.

Figure 1 Multi-die architectures are emerging as monolithic scaling reaches its limits. Source: Arteris Inc.

Multi-die systems offer a practical path forward. By breaking a large SoC into smaller chips, teams gain better yields, leverage proven components, and combine diverse process technologies in a single package. Additionally, chiplets can be reused across product lines, improving scalability and reducing cost.

The semiconductor industry has long envisioned chiplets as modular and interoperable, backed by fully proven standards. Companies are not waiting for that vision to materialize fully. They are already moving ahead with chiplet adoption while standards remain in flux.

Why chiplets, and why now?

Until recently, the world’s largest semiconductor companies were the predominant users of chiplet technology. These companies could control every aspect of the design, integration, and packaging processes.

Mid-size and startup companies also long for this future to be realized. However, lacking the resources of industry giants, they must adapt and take incremental steps today, even as the whole framework evolves.

Disaggregating a monolithic design into chiplets offers multiple advantages. By mounting these components on a common silicon substrate, the resulting multi-die systems can be manufactured at the most appropriate technology node.

For example, memory at 28 nm, a high-performance processor at 7 nm, and a cutting-edge CPU at 2 nm. Combining all dies into a single package creates a multi-die system that outperforms a monolithic design.

Standards: Ideal vs. actual

One of the issues is that the standards needed to make chiplets broadly interchangeable are not yet fully baked. They still need to be implemented, validated, and tested across different pieces of silicon before designers can count on them.

Even when two companies follow the exact specification, small details such as sideband signals or initialization steps can differ enough to cause unexpected failures. Until compatibility is proven at scale, design teams need to remain pragmatic in their approach to developing multi-die systems.

The ideal case is often described as chiplets that fit together like Lego bricks, highlighting the requirement that they are straightforward to combine and verified so that they work reliably together. Achieving that vision will ultimately depend on widely adopted industry standards that enable dies from different sources to function as one system.

Initiatives such as AMBA CHI Chip-to-Chip (C2C), Bunch of Wires (BoW), and Universal Chiplet Interconnect Express (UCIe) are helping to define the physical and protocol layers for die-to-die (D2D) links. Yet many challenges remain in areas such as system-level verification, latency optimization, power efficiency, security, and ensuring that chiplets from different vendors perform cohesively, as shown in Figure 2.

Figure 2 Multi-die SoC adoption is expanding across multiple markets. Source: Arteris Inc.

Companies can turn to multi-die systems

Progress can’t be delayed until standards are finalized, so design teams are advancing with innovation. Some of the ways system architects are tackling multi-die design are as follows:

• Design for modularity: Partition compute, memory, and IO into reusable blocks. Utilize silicon-proven network-on-chip (NoC) interconnect IP that supports multiple device-to-device (D2D) protocols and topologies.
• Build with interoperability in mind: Utilize tools and IP that are co-validated with major electronic design automation (EDA), physical layer (PHY), and foundry partners to align chiplet workflows and ensure IP, tool, and foundry compatibility.
• Automate integration: Hand-stitching chiplets together is a time-consuming and error-prone nightmare. Employ tools that automate HW/SW interface definition and assembly, which is essential for fast iteration and derivative design creation.
• Use coherency only where it matters: Certain functions, such as CPU and GPU clusters, may require coherent chiplets and D2D interfaces that necessitate the use of a coherent NoC. By comparison, functions like AI/ML accelerators may be satisfied by non-coherent chiplets and D2D interfaces. These are simpler and more power-efficient and can be addressed with a non-coherent NoC.
• Reuse what works: Adopt chiplet templates that can scale across product families and incorporate proven monolithic dies alongside new multi-die IP in derivative designs.
• Accept that the ecosystem is co-evolving: Standards are years away from full maturity. And companies are just beginning to explore building modular, standard-aware designs, laying the groundwork for the ecosystem’s future.

Build now, don’t wait

Multi-die system development teams should adopt modular design principles, utilize proven IP blocks with flexible D2D support, implement automated integration tools, and embrace ecosystem-aware development flows. Designers should also collaborate with like-minded innovators, partners, and customers to deliver tomorrow’s complex systems today.

Chiplets design solutions show how multi-die architectures can be built and deployed now. They enable companies to address today’s performance and scalability needs while laying the groundwork for seamless interoperability in the future.

Andy Nightingale, VP of Product Management and Marketing at Arteris, has over 39 years of experience in the high-tech industry, including 23 years in various engineering and product management roles at Arm.

 

Special Section: Chiplets Design

• What the special section on chiplets design has to offer

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Socomec has announced the launch of its new advanced MASTERYS GP4 UPS and ATyS a M Automatic Transfer Switch. This further strengthens their portfolio of reliable power management solutions. With over 25 years in the industry, the launch reinforces the company’s focus on innovative, efficient technologies for modern infrastructure.

Mr. Meenu Singhal, Regional Managing Director, Socomec Innovative Power Solutions, said,
“The launch of the MASTERYS GP4 UPS and ATyS a M Automatic Transfer Switch strengthens our portfolio with solutions that drive operational continuity and efficiency. From data centres and IT rooms to commercial buildings, organisations require resilient power infrastructure to ensure uninterrupted operations and protect critical systems. These products help optimise power supply while supporting reliable performance. We remain focused on innovation and committed to delivering dependable, future-ready power solutions for our customers.”

MASTERYS GP4 UPS, Designed for Critical Power Environments:

The Socomec MASTERYS GP4 200–250 kVA UPS is a high-performance uninterruptible power supply designed to ensure reliable power continuity for mission-critical environments. Built with advanced power protection technology and high-efficiency SiC technology, it delivers superior energy efficiency, consistent power quality, and reliable performance for data centres, industrial operations, and commercial infrastructure requiring uninterrupted operations.

•Reliable power protection: Ensures uninterrupted power for critical infrastructure such as data centres, IT rooms, industrial processes, and commercial facilities, helping maintain operational continuity during grid disturbances. •Advanced double-conversion technology: Provides stable and high-quality power output while minimising energy losses and supporting lower CO₂ emissions. • High efficiency and robust design: Combines high efficiency levels with a resilient architecture, leveraging advanced Sic technology to reduce downtime and support continuous operations in demanding environments. •Optimised for modern digital infrastructure: Designed to meet the growing power reliability needs of expanding digital ecosystems and industrial facilities.

 

ATyS a M Automatic Transfer Switch: Compact, Reliable Source Switching:

Socomec’s ATyS a M Automatic Transfer Switch enables automatic and seamless switching between two power sources, such as the main utility supply and a backup generator, ensuring uninterrupted power for commercial buildings, industrial facilities and other critical installations where continuous operations are essential.

•Automatic Source Transfer: Automatically switches between the main power source and backup supply, ensuring continuity of operations during power interruptions. •Compact Modular Design: More compact than similar solutions, enabling easier integration within electrical panels and helping save valuable installation space. •Quick & Easy Commissioning: Integrated pre-configured controller automatically manages parameters and source transfers, reducing setup time and risk of manual error. •Proven Reliability for Low-Voltage Installations: Designed and tested according to international standards, supporting reliable switching for commercial and industrial facilities.

 

Socomec offers support in design and commissioning, ensuring high-performing and sustainable electrical installations that are compliant. These solutions improve continuous power supply and strengthen resilience across data centres, industrial facilities, commercial buildings, and other critical infrastructure.

The post Socomec Expands Power Solutions Portfolio in India, Launches MASTERYS GP4 UPS and ATyS a M Automatic Transfer Switch appeared first on ELE Times.

Ігор Івіцький: "КПІ навчив мене думати як вчений, а бізнес дозволив масштабувати силу цього мислення" Інформація КП ср, 03/18/2026 - 11:00

In booth #1439 at the Optical Fiber Communications Conference and Exhibition (OFC 2026) at the Los Angeles Convention Center (17–19 March), Lumentum Holdings Inc of San Jose, CA, USA (which designs and makes photonics products for optical networks and lasers for industrial and consumer markets) is showcasing technology and demonstrating products designed to meet the accelerating demands of next-generation AI and data-center infrastructure...

In booth #1439 at the Optical Fiber Communications Conference and Exhibition (OFC 2026) at the Los Angeles Convention Center (17–19 March), Lumentum Holdings Inc of San Jose, CA, USA (which designs and makes photonics products for optical networks and lasers for industrial and consumer markets) has announced an optical interconnect solution designed to support next-generation AI infrastructure using vertical-cavity surface-emitting laser (VCSEL) technology. This solution offers a scalable optical platform for next-generation rack-level architectures to address the bandwidth, power, and integration challenges of AI scale-up networks...

EDOM Technology continues to expand its edge computing and intelligent system integration capabilities. Built on NVIDIA IGX Thor Development Kit, EDOM enables industries to adopt safety-critical computing platforms, accelerating the deployment of intelligent devices and autonomous computing systems while strengthening the physical AI competitiveness of Taiwan and the broader Asia-Pacific region.

With extensive system integration expertise, EDOM provides end-to-end hardware and software architecture planning, complemented by value-added engineering services that help customers streamline product development and deployment. Beyond computing platform advisory, EDOM also provides specialised services for safety-critical and sensor-rich edge environments, including functional safety architecture consulting, peripheral sensor and control module selection and integration, as well as ecosystem partner solution enablement. Through these capabilities and close collaboration with ecosystem partners, EDOM helps strengthen the industry value chain, lower technical integration barriers, and accelerate customers’ time to market.

The NVIDIA IGX Thor Platform is powered by NVIDIA Blackwell GPU architecture and supports NVIDIA Multi-Instance GPU (MIG) technology, enabling multiple AI workloads to run concurrently for improved resource utilisation. Designed to deliver high-performance edge AI computing, the platform achieves up to 5,581 FP4 TFLOPS of AI compute performance. Integrated with an Arm Neoverse CPU architecture, NVIDIA IGX Thor balances high-throughput AI inference with real-time data processing requirements, making it well-suited for deployment in a safety-critical environment.

NVIDIA IGX Thor Developer Kit also supports functional safety architecture implementation, incorporating compute monitoring and protection mechanisms to enhance long-duration operational stability. It also provides high-speed I/O connectivity and diverse expansion interfaces, facilitating integration with a wide range of sensing devices and industrial control modules. These capabilities address key edge computing requirements, including low latency, high reliability, and multi-sensor data integration.

As intelligent industry applications extend from cloud computing environments to physical devices, NVIDIA IGX Thor is well-suited for deployment in smart healthcare, industrial automation, and autonomous robotics. In smart manufacturing and industrial inspection scenarios, it supports real-time quality monitoring, predictive maintenance, and intelligent production line management. In healthcare environments, it enables high-precision imaging analysis and clinical decision support workloads. For autonomous mobile machines and service robots, its multi-sensor data fusion and real-time inference capabilities enhance navigation accuracy and safe obstacle avoidance.

Looking ahead, EDOM will continue to deepen its physical AI ecosystem integration services by combining hardware value-added integration expertise with an open partner collaboration model. Working alongside technology providers, system integrators, and application developers, EDOM aims to accelerate the deployment of edge AI computing solutions across diverse industry scenarios. By supporting customers from proof-of-concept validation through commercial rollout, EDOM enables the realisation of high-value AI-driven solutions and advances the evolution of next-generation smart industry value chains.

The post EDOM Technology Strengthens Its Role in Integrating the Physical AI Ecosystem appeared first on ELE Times.

Industry leaders at ACREX India 2026 highlight that the Indian HVAC sector is poised for significant expansion, with the market expected to grow at 15% annually and potentially double within five years.

The industry is shifting toward local manufacturing and AI-driven predictive maintenance to capture massive growth potential in residential and infrastructure sectors. With residential AC penetration at just 10%, leaders are prioritising sustainability through humidity-optimised, super-efficient systems that can cut energy use by 60%. Ultimately, the sector is evolving beyond equipment sales to focus on the entire system lifecycle, emphasising energy efficiency, indoor air quality, and environmental impact.

Organised by ISHRAE, ACREX India 2026 served as a global hub where more than 400 exhibitors representing 40 nations and over 30,000 attendees gathered for South Asia’s premier HVAC and intelligent building exhibition. During the event, prominent industry leaders such as LG, Carrier, Daikin, Voltas, Danfoss, Schneider Electric, Panasonic, Johnson Controls and Tecumseh presented their latest advancements in next-generation cooling technology.

Speaking at the event, Mr Mukundan Menon, Managing Director, Voltas Limited, said, “The Indian HVAC industry is at the cusp of significant expansion, with the sector expected to grow at nearly 15% annually, potentially doubling within the next five years. Currently, about 15 million residential AC units are sold in India each year, and this is projected to reach around 30 million units by 2030. On the commercial side, India continues to build rapidly, creating strong opportunities across data centres, district cooling and infrastructure development. The recent GST reduction on ACs from 28% to 18% is a welcome policy step that will stimulate demand, with the first visible impact expected during the summer of 2026.”

Emphasizing the strong transformation and long-term growth potential of the HVAC industry in India, Mr Ravichandran Purushothaman, President, Danfoss Industries Private Limited, said, “Over the past three years, the HVAC industry in India has nearly doubled in size, with a significant shift toward local manufacturing, reflecting the momentum of Atmanirbhar Bharat and the government’s focus on reducing import dependence in the cooling sector. Looking ahead, the opportunity is substantial. As per the India Cooling Action Plan, cooling demand in India is expected to grow eightfold by 2038. On the commercial side, rapid expansion in semiconductor facilities, advanced manufacturing and data centres is driving demand for high-performance cooling solutions. At the same time, the industry is focusing on energy-efficient, water-efficient and carbon-efficient technologies, increasing localisation in electronics and strengthening new skill capabilities.”

Mr Jayanta Kumar Das, Society President, ISHRAE, said, “ACREX India brings the entire HVAC ecosystem onto one platform, enabling companies to showcase innovations and engage with the broader industry community. As cooling demand grows rapidly in India, the focus must move beyond equipment to the entire lifecycle of systems, where installation, operation and maintenance account for nearly 90% of the total cost. Through nationwide training programs, research and industry partnerships across 55+ locations, ISHRAE is working to strengthen skills, encourage innovation and support the sector’s mission of delivering more cooling with lower energy consumption and reduced environmental impact.”

Mr Amod Dikshit, Chairman, ACREX India, said, “India’s rapid infrastructure expansion and growing dependence on cooling across sectors such as data centres, district cooling, airports, hospitals, hotels and metro rail projects is creating a significant opportunity for the HVAC industry over the next decade. As this demand accelerates, the industry is focusing on localising the production of key sub-assemblies, strengthening capabilities and advancing more energy-efficient technologies. Cooling already accounts for nearly 40% of India’s electricity demand, which makes efficiency and sustainability critical priorities. With the Bureau of Energy Efficiency progressively upgrading standards every two years by about 7–10%, the industry continues to move towards more energy-efficient solutions.”

Ricardo Maciel, CEO of Tecumseh, said, “India is one of the world’s fastest-growing HVAC and refrigeration markets, fueled by urbanisation, food security, and expanding cold chain infrastructure. At ACREX, we highlighted our commitment to the Indian market through advanced, sustainable technology and strengthened local manufacturing. By combining global engineering with local production, we are meeting the market’s growing demand for energy efficiency, helping customers lower operating costs while supporting India’s long-term sustainability goals. Tecumseh introduced the new TC3 premium-efficiency compressor platform, ranging from 3 to 12 cc, delivering more than 30% energy savings compared to current platforms.”

Mr. Sanjeev Seth, Sr Vice President and Business Head, Systems Air Conditioning Division, LG Electronics India Limited, said, “India’s HVAC sector is on a strong growth trajectory, driven by rapid urbanisation, infrastructure expansion and increasing demand for efficient climate control. At the same time, AI is beginning to revolutionise HVAC system management in India, as customers seek higher energy efficiency and reduced downtime. Technologies such as predictive maintenance, cloud-based remote monitoring and intelligent controls are improving reliability and optimising energy consumption in VRF and chiller systems. This integration of digital technologies will significantly boost system performance and operational efficiency. At ACREX India 2026, LG Electronics India showcased its latest intelligent HVAC innovations designed for India’s evolving cooling landscapes. “

Mr. Abhishek Verma, Head – Products Marketing & Planning, Panasonic Life Solutions India Pvt. Ltd., said, “The air conditioning industry in India continues to offer strong growth potential. With residential AC penetration at around 8%, the segment has significant headroom for expansion and is expected to grow at a CAGR of nearly 15%, while the commercial AC market is also witnessing robust demand. As climate needs evolve, energy efficiency and indoor air quality are becoming key priorities. At Panasonic, we are advancing AI-driven cooling technologies to enhance energy efficiency without compromising comfort. At ACREX India, Panasonic showcased these intelligent and sustainable cooling solutions for India’s evolving needs.”

Globally, the HVAC industry is entering a significant expansion phase, with the market projected to reach nearly $445 billion by 2033, while HVAC systems already account for close to 40% of total building energy consumption worldwide. In response, the industry is rapidly shifting toward sustainable and energy-efficient technologies. Innovations such as AI-enabled smart HVAC systems, Variable Refrigerant Flow (VRF) technologies, natural refrigerants, district cooling systems, and advanced data centre cooling solutions are transforming the sector.

The post Indian HVAC Market Poised to Double in Five Years with 15% Annual Growth appeared first on ELE Times.

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Hey everyone, I am currently developing a custom tracker using the lighthouse trackers from a VR headset (HTC vive). The end goal is tracking small robots indoors for ~$10-15 per unit. For that I built a custom PCB in the simplest way possible, as I am still quite a beginner in electronics. I am using 2 BPW-34 photodiodes - they have no IR filter built in, so i'm using floppy disk film as a cheap IR bandpass which works surprisingly well. To amplify and filter the signal i used an op-amp as somehow better options such as the TS4231 were not sourceable easily for me. It seems like most of these chips are sold out or hard to get by. But even with just that a very basic tracking that captures the laser pulses from the lighthouse worked! For the future I will try to use at least 3 sensors to be able to maybe position objects in space as well. https://youtu.be/bWUpHzh0yHs submitted by /u/monkeydance26 [link] [comments]

КПІшники — переможці інженерного челенджу від Brave1 kpi вт, 03/17/2026 - 16:47

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Sivers Semiconductors AB of Kista, Sweden (which supplies RF beam-former ICs and lasers for AI data-center, SATCOM, defense and telecom applications) has announced a strategic partnership with optical communication device, module and subsystem maker O-Net Technologies (Group) Co Ltd of Shenzhen, China and Enablence Technologies Inc of Ottawa, Ontario, Canada (which designs and manufactures optical components) to develop an advanced external light source (ELS) module with Sivers laser arrays to support co-packaged optics (CPO) roll-out in AI data centers and high-performance computing (HPC) systems. O-Net will serve as the ODM partner, integrating Sivers’ laser arrays and Enablence’s NxN Star Coupler to deliver a scalable ELS module for scale-out and scale-up optical systems...

In the first part of this Design Idea, we saw how a cheap op-amp can give remarkably precise readings from an RTD. We also found that it was a false economy, owing to incurable thermal drift. This concluding part fixes that problem by using a more costly OP177 precision op-amp, which is still much cheaper than an RTD sensor alone.

Wow the engineering world with your unique design: Design Ideas Submission Guide

Now that we don’t need to worry about drift, we can balance the output of the input stage against a passive network: R8 to R11 in Figure 1. That figure also shows the (ideal, theoretical) error curve once the positive-feedback resistor R5 has been trimmed for minimum errors around 0 to 100°C, which is the same as part 1’s Figure 2.

Figure 1 Using a precision op-amp lets us set the output reference level with a passive network because thermal drifts and mismatches are no longer a problem. LED1 acts both as a rail-splitter and a power indicator, while LED2 gives a simple low-battery warning. The calculated error curve assumes perfect components and shows the limits to precision for this circuit.

While this performs identically to part 1’s circuit, there are some practical differences. The OP177 needs at least a 6 V rail to work (but can handle ±15 V), so a 9 V battery (e.g., MN1604) makes a good power source. The rail must be split, which is where LED1 comes in. R1 passes current through the voltage reference D1 and the components bridging it. That current fed through LED1 both lights it and offsets the common rail by a couple of volts from the negative one, which is plenty considering the small voltage swings involved. Bright, pure green devices dropped about 2.3 V; normal ones gave less but were rather dim. R1 was chosen to guarantee the correct operation of D1 down to a battery voltage below 5.6 V, which was where my op-amps actually failed.

Flat battery: you will be warned

The other addition (R12–15, Q1/2, LED2) should ensure that you never run the battery that far down! It’s a simple low-battery indicator that lights LED2 when the voltage across R1 falls below a critical level. As built, that tripped when the battery fell to ~6.5 V. A suitable micropower comparator—didn’t have one handy—would have been neater and not temperature-sensitive, though D2 helps with that. This circuit block isn’t shown on subsequent schematics, but could easily be added.

Next question: since we now have split rails, why not bias A1 with an offset so that its output refers to the common rail, giving 0 V at 0°C? It’s more elegant, because “negative” temperatures now give a negative output, it saves a resistor (cheapskate), and is shown in Figure 2. But that passive network, though discarded for now, will come in handy later.

Figure 2 Adding a biasing network to A1 allows its output to be at 0 mV (common) when sensing 0°C, and to swing negative for lower temperatures.

The necessary biasing is provided by R8–10. Because R4 is now bridged by ~23k, A1’s gain is increased slightly. R5’s new value gives the same error performance as before.

Increasing the measurement span

 So far, we’ve only looked at a comparatively narrow temperature band. Taking that to extremes is instructive. Figure 3 plots the errors from -200 up to +600°C. (The Callender–Van Dusen equations apply up to 661°C—the melting point of aluminum.) At the end of part 1, we saw that R5 determines the errors. Now, we can see how varying it can give a different balance of errors over a wider span. Figure 3’s plots are normalized for zero error at 0 and 100°C, which are still valid as calibration points and are used to calculate the ideal slope. No components apart from R5 are affected, though the settings of R6 and R8 will change.

Figure 3 Plotting the errors for various values of R5—the positive feedback resistor—shows that we can optimize performance for minimal errors around 0–100°C (magenta) or accept a wider error band over a greater temperature range. The red curve is within 0.1°C from -130 to +420°C and 1°C from <-200 to >+640°C.

Indicating in Fahrenheit

With a few changes, Figure 2’s circuit can give a direct 1 mV/°F reading, should you need that. The gain must be increased by a nominal 9/5 and an extra offset of 32° provided. Figure 4 shows what’s needed.

Figure 4 Changing three components gives an output of 1 mV/°Fahrenheit.

This works well, but needs care in calibration. Using the 100/138.5Ω (0/100°C) RTD sim from part 1 would mean some iteration: set 32°F, set 212°F, and repeat . . . Paralleling the 100Ω resistor with 1k3354 drops it to 93.0334Ω, the resistance of a (theoretically perfect) 100Ω-PtRTD at 0°F or -17.7778°C. (Yes, more decimals than you’ll need; it never hurts.) The sim then switches cleanly between 0 and +179.0°F—not 180°, because of the RTD’s response curve, which also introduces a minute error at the low point. Hopefully, the actual RTD will be precise enough to minimize the time spent with crushed ice and condensing steam needed for the final trim.

Kelvins

Figure 1’s circuit gives an output (from A1) of ~260 mV at 0°C. If the RTD’s curve were linear and A1’s gain ideal, that would be 273.15 mV, the final readout still being at 1 mV/K. Applying a small offset to A1 fixes things so that we can read absolute temperatures directly. Figure 5 details the necessary changes, with the offset coming indirectly from the voltage across LED1. Again, R5 is shown trimmed for maximum accuracy in the 273.15–373.15K region—and those values are what your meter must show in millivolts when using the 100/138.5Ω sim gadget.

Figure 5 A small negative offset allows the basic circuit to give an output directly proportional to absolute temperature.

Something simple, with split supplies

A final version of the circuit can eliminate the DMM. OP177s will run on supplies from 6 to >30 V, so should you need to add an RTD to kit having suitably split rails, Figure 6—the simplest variant, and little more than part 1’s figure 1 made practical—may be ideal.  

Figure 6 Stripping out all the frills and fancies leaves a basic circuit that is ideal for running off split rails and compatible with most ADCs.

R3 is now increased to 68k for a gain of ~69, giving ~10.9 mV/°C, R5 being adjusted accordingly. The 0°C datum is now at ~2.43 V, so even with ±5 V rails, readings can span from -120 to +150°C (±0.1°C error) before A1 saturates. A higher positive rail would allow far higher temperature readings; the lower limit—always above zero volts—merely depends on the allowable error at that point.

This assumes that the output will be read directly by an ADC, probably using a 5 V reference, and that the host system can adjust the zero and the span, which is why no calibration trimmers are shown: that host needs only simple arithmetic rather than the math of a full CVD calculation. For different spans, try the equations for calculating R5 as shown at the end of part 1.

The final build

These circuits were all breadboarded and checked, but I ended up building something slightly different for actual use. This variant starts with the Kelvin approach and then adds the passive reference network discarded from Figure 1, allowing instant switching between the two temperature scales.

Figure 7 Referring the output from the amplifying stage to either a positive reference or to common allows the indication to be switched between Celsius and Kelvin.

For readings in Celsius, the reference network is fed from Vref; for Kelvins, the top of the network is grounded, which both zeroes the reference and keeps the extra resistance seen by R10 constant. This may or may not be useful because on most DMMs the Kelvin range will lose a decimal point compared with the Celsius, so that reading in °C and adding 273.15 gives better accuracy, but it was a fun thing to try.

No DMMs were harmed in the making of this DI.

—Nick Cornford built his first crystal set at 10, and since then has designed professional audio equipment, many datacomm products, and technical security kit. He has at last retired. Mostly. Sort of.

Related Content

• https://www.edn.com/newer-shinier-dmm-rtds-part-1/
• DIY RTD for a DMM
• Dropping a PRTD into a thermistor slot—impossible?
• Improved PRTD circuit is product of EDN DI teamwork
• Fake contacts, bounced to order
• Calculation of temperature from PRTD resistance

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India’s electric mobility transition is entering a decisive phase. While early discourse focused on vehicle innovation and battery chemistry, the spotlight has now shifted toward charging infrastructure, specifically, how intelligent systems can make it scalable, reliable, and grid-compatible. Artificial Intelligence (AI) and Machine Learning (ML) are no longer experimental add-ons; they are becoming the operational backbone of modern EV charging ecosystems.

From predictive maintenance and grid-responsive load management to dynamic pricing and battery safety modelling, Indian charging operators are embedding AI at every layer of infrastructure. Industry leaders such as Tata Power, Statiq, ChargeZone, and Bolt.Earth, Intellicar (Fabric IoT), and Coulomb AI are redefining what it means to deploy “smart” infrastructure in a high-growth, power-sensitive market like India.

AI-Driven Infrastructure Planning & Site Selection

Charging infrastructure planning in India can no longer rely on static demographic assumptions or simple traffic counts. With capital expenditure per fast-charging site running high, predictive intelligence has become central to ensuring ROI viability. AI-driven site selection models now ingest multi-layered datasets including vehicular density heatmaps, dwell-time patterns, telematics feeds, grid capacity data, and urban expansion forecasts to simulate demand even before physical deployment.

Such geospatial optimisation is particularly critical for India’s highway corridors and Tier-II cities, where deployment miscalculations can significantly impact utilisation rates. By integrating predictive analytics with grid feasibility mapping, operators are achieving measurable improvements in charger usage efficiency and long-term sustainability.

Predictive Maintenance & Reliability Enhancement

Reliability remains the most decisive performance metric in charging infrastructure. A single non-operational charger can undermine customer trust and disrupt fleet operations. AI-powered predictive maintenance is addressing this challenge by transforming chargers into continuously monitored, self-reporting assets.

Modern charging stations now incorporate IoT sensors that track temperature fluctuations, voltage irregularities, connector wear, vibration signatures, and cooling system performance. These data streams feed machine learning models capable of detecting anomaly patterns weeks before a component failure occurs.

Operators such as ChargeZone are leveraging AI-driven network management systems to monitor thousands of charging points simultaneously, ensuring SLA compliance and minimising revenue loss from unexpected outages. The result is not just improved uptime but a tangible reduction in ‘charge anxiety’ among users.

Smart Charging & Dynamic Load Management

India’s distribution grids were not originally designed for high-density EV loads. Uncoordinated charging can create localised transformer stress and peak demand spikes. AI-driven smart charging systems are mitigating this risk by dynamically balancing load in real time.

By analysing grid capacity constraints, renewable energy availability, historical consumption curves, and user charging behaviour, AI systems intelligently stagger charging sessions without compromising user convenience. Time-of-Use (ToU) optimisation algorithms further encourage off-peak charging, reducing stress on urban feeders.

Pratik Kamdar, Co-founder & CEO of Neuron Energy: “AI and advanced software are emerging as the backbone of the modern EV ecosystem… [enabling] features such as real-time monitoring, predictive diagnostics, and faster charging capabilities that are increasingly prioritised by customers”.

Battery-integrated charging hubs deployed by ChargeZone further demonstrate how AI can shave peak demand and buffer grid volatility, a critical capability as EV adoption accelerates.

Dynamic Pricing & Revenue Optimisation

The economics of EV charging depend heavily on utilisation efficiency and tariff structuring. Traditional flat-rate pricing models often fail to respond to fluctuating grid conditions or consumer demand patterns. AI-powered dynamic pricing engines are now enabling real-time tariff modulation.

By factoring in grid stress indicators, occupancy rates, historical usage behaviour, and localised demand forecasts, AI models optimise pricing structures that balance revenue maximisation with consumer fairness.

Raghav Bharadwaj, CEO of Bolt.earth: On operational optimisation: “EV charging cannot be treated like a pure software startup… Every station’s economics must be optimised from day one. We measure success by uptime, utilisation, and energy delivered” (Source: Industry Perspectives).

Machine learning also supports customer segmentation, allowing differentiated pricing for fleet operators, subscription users, and retail consumers; thereby strengthening long-term business sustainability.

Vehicle-to-Grid (V2G) Technology

Vehicle-to-Grid technology introduces a paradigm shift in which EVs function as distributed energy storage assets capable of feeding electricity back into the grid. While regulatory frameworks in India are still evolving, AI is already playing a central role in enabling safe and optimised bidirectional charging.

AI algorithms determine optimal discharge windows, forecast grid demand spikes, and ensure battery health parameters remain within safe thresholds during V2G cycles. Without such intelligent orchestration, bidirectional charging could accelerate battery degradation.

As India moves toward distributed energy markets, AI-enabled V2G systems could unlock new revenue streams for EV owners and fleet operators alike.

Battery Safety & Thermal Management

Fast charging environments introduce elevated thermal risks, making battery safety paramount. AI-driven Battery Management Systems (BMS) are now capable of predicting thermal runaway scenarios before they escalate into critical failures.

Using chemistry-specific modelling and real-time telemetry data, machine learning algorithms estimate State-of-Charge (SoC) with accuracy exceeding 95% while simultaneously forecasting degradation patterns. This is particularly important given India’s mix of lithium iron phosphate (LFP) and nickel manganese cobalt (NMC) chemistries across vehicle categories.

Such advancements are not only improving safety but also extending battery lifecycle economics, a critical factor in total cost of ownership calculations.

User Experience Enhancement

Beyond engineering efficiency, AI is reshaping the end-user journey. Intelligent routing systems now guide drivers to available chargers based on real-time occupancy predictions. Machine learning models calculate accurate charge time estimations by factoring in battery health, ambient temperature, and charger capacity.

Anshuman Divyanshu, CEO – EVSE, Exicom: “Ease comes from thoughtful design. Chargers and apps should feel intuitive… Selecting a connector, activating a session, pairing with an app, and making a payment. These steps shouldn’t feel like a technical exercise. A properly designed charger should operate like familiar everyday technology“.

Meanwhile, Statiq integrates predictive booking systems that mitigate congestion during peak hours. AI personalisation engines recommend preferred stations based on historical behaviour, payment patterns, and travel routes, creating a frictionless digital experience.

Cloud Computing & Edge AI Integration

The scalability of AI-driven charging infrastructure depends on a hybrid architecture that balances edge responsiveness with cloud intelligence. Edge computing processes latency-sensitive operations such as load modulation and fault isolation in real time, while cloud platforms handle macro-level optimisation, fleet analytics, and model retraining.

Arvind Gopalakrishnan, CTO & CIO at SUN Mobility: “We are leveraging AI to build robust, data-driven platforms that optimise EV charging, routing, and energy distribution across urban and intercity networks… enabling real-time decision-making and improving grid efficiency”.

Cybersecurity frameworks are also increasingly AI-driven, employing anomaly detection algorithms to identify spoofing attempts and data breaches in highly connected charging ecosystems.

The Road Ahead: 2026–2030

As India moves toward deeper electrification, AI is poised to become the central nervous system of charging infrastructure. Self-healing networks, autonomous fleet charging depots, AI-integrated smart city command centres, and revenue-generating distributed energy marketplaces are no longer distant possibilities; they are emerging realities.

Khushboo Shrivastava, CEO of Coulomb AI, concludes, “The competitiveness of future charging networks will not be defined by hardware density alone, but by algorithmic intelligence. AI is what transforms infrastructure into an ecosystem.”

In the coming decade, India’s EV charging expansion will be defined less by the number of chargers deployed and more by the intelligence embedded within them. The evolution from hardware-centric infrastructure to software-defined energy ecosystems has already begun.

By: Shreya Bansal, Sub-Editor

The post Smart EV Charging in India: How AI and ML Are Optimising Grid, Pricing and Reliability appeared first on ELE Times.

Henkel has announced the launch of its Customer Application Centre in Bengaluru, reinforcing its commitment to India’s rapidly expanding electronics manufacturing sector. The new facility will serve as a collaborative innovation hub where Henkel experts and customers can co-develop, test, and validate advanced adhesive and thermal management solutions for next-generation electronics manufacturing.

The new facility represents one of Henkel’s most significant application engineering commitments in the India Middle East and Africa (IMEA) region, and is designed to address a critical gap in India’s electronics value chain: the absence of localized, world-class application testing and validation infrastructure that allows manufacturers to develop, qualify, and scale advanced materials solutions without the time and cost of sending work overseas.

India’s electronics manufacturing sector has grown nearly sixfold over the past decade. The momentum is accelerating, driven by the rapid build-out of data centre and AI computing infrastructure, 5G and fibre network expansion, electric vehicle charging systems, industrial automation, and advanced medical devices. Each of these sectors depends critically on high-performance adhesives, thermal management materials, and protective coatings, and each demands faster, more localised application engineering support than India’s ecosystem has traditionally been able to provide.

Bengaluru was a natural choice. The city’s concentration of semiconductor design talent, electronics R&D centres, and global OEM engineering teams makes it the single most important node in India’s electronics innovation ecosystem. Locating the centre here puts Henkel’s application expertise directly alongside the engineers and manufacturers who need it most.

“India’s electronics manufacturing ecosystem is at an inflexion point, and Bengaluru is at the centre of it,” said S. Sunil Kumar, Country President – India, Henkel. “What manufacturers across our focus sectors increasingly need is not just world-class materials, but a local partner who can co-develop, test, and validate those materials under real production conditions, and help them move from concept to market faster. That is precisely what this centre is designed to do. It is our most tangible expression yet of Henkel’s long-term commitment to India’s electronics future.”

The 5,000 sq. ft. facility, of which approximately 2,400 sq. ft. is dedicated laboratory and testing space, is built to replicate actual electronics manufacturing conditions, allowing customers to evaluate and optimise materials and processes before committing to production scale. Around 60-65% of the investment has gone into advanced lab and testing equipment, with 20-25% directed at customer co-development infrastructure.

The facility serves five high-growth sectors: telecom and 5G infrastructure, data centres and AI computing, power electronics and EV systems, industrial automation, and medical electronics. Its key capabilities span advanced thermal management testing, precision dispensing systems, electrical characterisation tools, and rapid-cure chambers, supporting the full journey from prototyping and material validation through to production readiness.

The centre directly supports India’s Make-in-India and Production-Linked-Incentive objectives by bringing application engineering, process optimisation, and reliability validation onshore. A substantial share of activities that Indian electronics manufacturers previously had to route through overseas facilities, or simply defer, can now be conducted locally, compressing development cycles and accelerating time to market.

Henkel application experts will work side-by-side with customer engineering teams at the facility, co-developing solutions tailored to specific device architectures and manufacturing requirements. This collaboration model is central to the centre’s design and is what distinguishes it from a conventional testing laboratory.

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Navitas Semiconductor Corp of Torrance, CA, USA — which provides GaNFast gallium nitride (GaN) and GeneSiC silicon carbide (SiC) power semiconductors — has announced its latest DC–DC power delivery board (PDB) powered by GaNFast technology, enabling direct conversion from 800V to 6V in one power stage. This eliminates the traditional 48V intermediate bus converter (IBC) stage within the compute server trays, maximizing system efficiency, reliability and valuable real-estate, to deliver a simple power delivery solution to support advanced NVIDIA AI infrastructure...

Milestone Systems has announced significant advancements to its XProtect video management software (VMS) and BriefCam video analytics. The XProtect App Platform, a new containerised application platform for VMS, and a new BriefCam analytics engine are designed to deliver increased reliability, greater customisation, more efficient hardware utilisation, and full readiness for Generative AI and analytics, empowering security teams to stay ahead as demands evolve.

Cameras and sensors collect more data than ever before. Today, the challenge has shifted from capturing information to understanding it – and turning it into actionable insight. Surfacing the most urgent threats and the most valuable operational insights requires AI and analytics tools built for the scale of modern video.

Even as capabilities advance, integrating new functionality still requires time, expertise, and coordination. Even routine software updates introduce operational risk. The possibility of system downtime often forces security teams to delay the very innovations that would make their operations more effective.

For solution developers creating the next generation of VMS applications, building and distributing solutions across thousands of customer environments adds another layer of complexity.

Milestone has built its new solutions to address these challenges – without requiring customers to replace what already works.

Building the Future of Video Management with the XProtect App Platform

Milestone’s new XProtect App Platform is a component that brings the latest VMS applications – including solutions like AI, analytics, access control, and more – into a surveillance system without friction.

The XProtect App Platform amplifies existing infrastructure by enabling customers to unlock insight from new AI tools, customise their systems quickly and safely, and install updates without downtime.

Built on a Linux-based, containerised architecture, the platform runs alongside existing XProtect installations and extends what the system can do without changing how it operates. Because each application and service runs in its own container, isolated from the core VMS and from other apps, customers can install apps and updates without requiring a full system restart or disrupting live operations. 

Delivering next-generation analytics that scale with BriefCam’s new engine

BriefCam’s engine has been redesigned to deliver scalable analytics capabilities – with significant improvements to real-time processing, scalability, and workflow efficiency. Thanks to better resource utilisation, users will see an improvement of 38%* in real-time throughput. All processing can be run on-premise, with no cloud dependencies.

The new engine enables investigators to translate witness statements into searches using plain language instead of filters, identify key moments to reduce review time and turn fragmented video into a connected narrative, and train BriefCam with custom categorisations to match their organisational needs.

Andrew Burnett, Chief Technology Officer, Milestone Systems, said: 

“The rapid growth of AI in video security has created an urgent need for platforms that can keep pace. Together with our partners and customers, we are co-creating the next generation of our technology on our open platform foundation. The XProtect App Platform and the new BriefCam engine are two major steps forward – giving organisations the flexibility to adapt quickly and confidently, as well as powerful on-premise intelligence that doesn’t compromise data sovereignty or operational control.”

Innovation across the ecosystem: App Centre and Developer Portal

The XProtect App Platform runs applications from the Milestone App Centre — the home for applications developed by both Milestone and our technology partners. The App Centre enables customers to browse, test, and install verified applications that extend the capabilities of their XProtect VMS. This makes it easier to discover new functionality, add AI analytics, or test emerging innovations without risk to live operations.

To support this, Milestone is introducing a new set of tools for developers and technology partners across the ecosystem. The Milestone Developer Portal consolidates everything developers need to build applications for the open platform in one place — from idea to development to release — providing a single, simple path to reach Milestone customers worldwide. The portal will be generally available by the end of 2026.

The XProtect App Platform and the new BriefCam engine are available now for early access customers. General availability is currently planned for late 2026.

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