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The ubiquity of the 4 to 20mA current loop in analog process monitoring and control creates possibilities for peculiar designs of circuits for unusual accessory functions.  Figure 1 shows an example.  It does precision conversion of 4—20mA to 0—20mA.  That’s useful for accommodating analog inputs that wouldn’t like a 4mA zero offset.

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

Figure 1 This current conversion circuit’s function is define by the following equation: Iout = (IinR1 – 1.24v)/R2 = 1.25(Iin – 4mA).

The core of the circuit is the Vin = IR1 = 1.24v to 6.20v developed by the 4mA – 20mA input working into R1 and sensed by the Vref input of Z1. The principle in play is discussed here.

A potentially annoying shortcoming of the Figure 1 design, however, is its current sink output that’s referred not to ground but to the V+ source node, which needs to be at least 8v.  Figure 2 offers an accurate and straightforward fix: an active current mirror as described here. The input max overhead voltage is 8v.

Figure 2 This circuit adds an active current mirror to its predecessor to drive a grounded load.

Stephen Woodward‘s relationship with EDN’s DI column goes back quite a long way. Over 200 submissions have been accepted since his first contribution back in 1974.  They have included best Design Idea of the year in 1974 and 2001.

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• Simple but accurate 4 to 20 mA two-wire transmitter for PRTDs
• Positive analog feedback linearizes 4 to 20 mA PRTD transmitter

The post 4mA-20mA to 0mA-20mA converter’s current mirror drives grounded load appeared first on EDN.

Задля здолання бар'єрів на шляху якісного теплопостачання Інформація КП ср, 06/10/2026 - 13:21

Guerrilla RF Inc (GRF) of Greensboro, NC, USA — which provides radio-frequency integrated circuits (RFICs) and monolithic microwave integrated circuits (MMICs) for wireless applications — has announced an expanded focus on the fast-growing tactical radio market. Backed by a broad portfolio that ranges from low-noise small-signal devices to high-power RF amplifiers, Guerrilla RF now offers more than 60 high-performance RFIC solutions designed to strengthen communications links by enabling highly sensitive receivers and efficient, high-power transmit paths that protect link margin and maintain mission-critical connectivity...

Discrete device designer and manufacturer Nexperia B.V. of Nijmegen, the Netherlands (which operates wafer fabs in Hamburg, Germany, and Hazel Grove Manchester, UK) has released 1200V silicon carbide (SiC) MOSFETs in QDPAK packaging, extending its growing wide-bandgap (WBG) portfolio with a top-side-cooled surface-mount package optimized for high-power-density and thermally demanding applications...

ROHM has developed the TSC3PAK (14.00mm x 18.58mm x 3.50mm) package for silicon carbide (SiC) MOSFETs. Mass production began in June...

SemiQ Inc of Lake Forest, CA, USA — which designs, develops and manufactures silicon carbide (SiC) power semiconductors and 150mm SiC epitaxial wafers for high-voltage applications — has expanded its QSiC Dual3 family of half-bridge MOSFET modules, adding high-thermal-performance options with AlN (aluminium nitride) substrates and pre-applied TIM (thermal interface material), as well as new 1700V devices...

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has introduced its fifth-technology generation, demonstrating a substantial performance leap in efficiency for next-generation 1200V and 750V automotive and industrial applications...

In 2025, Innodisk launched the “AI beyond the edge” initiative at a forum that also hosted Intel, Nvidia, and Qualcomm, which shared details of their latest developments in edge AI. But what does “AI beyond the edge” really mean?

Don Yu, special assistant to the GM at Innodisk, said that “AI beyond the edge” is about enabling systems that operate autonomously, remain connected, and scale across real-world environments. He also mentioned two complementary domains as part of this initiative.

First, industry AI—built for smart manufacturing, automation, transportation, healthcare, retail, and smart cities—enhances on-site responsiveness through real-time recognition, predictive maintenance, and intelligent workflow optimization.

Second, enterprise AI—designed for data centers, on-premise AI, and advanced models such as large language models (LLMs) and visual language models (VLMs)—supports secure, intelligent decision-making across corporate, financial, medical, and public sectors. “That allows small and mid-size businesses (SMBs) to have their own AI engines locally instead of relying on the cloud,” Yu said.

But despite all the promise, deployment of edge AI has been a challenge so far. So, how are these edge AI initiatives faring so far, EDN asked Yu. And what is Innodisk doing to overcome these challenges in effectively implementing edge AI at scale?

Edge AI deployment challenges

Innodisk chairman Randy Chien acknowledges that the exponential rise of generative AI and LLMs has fundamentally changed the design equation at the edge. More specifically, as AI workloads grow in complexity, companies are facing increasing pressure in system integration, hardware-software coordination, and the ability to scale solutions across diverse deployment environments.

“Anticipating this shift early on, Innodisk has built on its strong hardware foundation by structuring its product portfolio into modular building blocks across memory, storage, camera modules, and a wide range of embedded peripherals,” Yu said. “On this foundation, the company has positioned itself as an AI architect, combining these building blocks to meet diverse industry requirements with tailored edge AI systems.”

So, edge AI developers can implement these solutions as individual modules or as fully integrated systems, depending on their application needs. Take the example of the APEX series of edge AI systems, which brings together key building blocks, including AI accelerators, DRAM modules, flash storage, industrial MIPI and GMSL camera modules, and embedded peripherals for networking and industrial I/O.

“The platform enables flexible system configuration based on specific use cases, while supporting customization to meet diverse deployment requirements,” Yu said.

Figure 1 Individual modules are fully integrated systems tailored according to edge AI application needs. Source: Innodisk

Yu added that Innodisk is heavily investing in firmware and software development to bolster its design ecosystem. Take vision-related AI, for instance, where Innodisk provides fully ported drivers for industrial camera modules, supporting both VLMs and computer-vision applications to streamline deployment and minimize integration friction.

Innodisk also provides specialized software toolkits to accelerate system integration. For example, it has introduced IQ Studio to support the development of Qualcomm-powered edge AI systems. IQ Studio is an open-source developer portal that provides essential board support packages (BSPs), reference code, and benchmarking tools.

How modular solutions aid system integrators

These modular solutions—segmented across five layers of compute, memory, storage, sensing and connectivity, and software—are aimed at addressing design challenges before the last mile of AI deployment in vertical markets. This cohesive system-level approach addresses common development challenges for system integrators and solution providers, enabling them to focus on developing their applications rather than managing integration.

Figure 2 Modular solutions handle integration complexity, which allows system integrators to focus on developing their applications. Source: Innodisk

Moreover, there is a wide range of pre-validated solutions that significantly shorten system integration development cycles. Case in point: AI on Arm series of computer-on-modules (COMs) are designed to be deployment-ready. “They can be directly integrated into customer systems with minimal development effort,” Yu said. “Additionally, they can be paired with Innodisk carrier boards and peripherals to support different system configurations.”

Figure 3 COM modules can be paired with carrier boards and peripherals to support different system configurations. Source: Innodisk

These deployment-ready solutions provide system integrators with practical reference points and inspiration for application design when applied in real-world scenarios. Take the APEX-X200 edge AI platform, for instance, which Innodisk showcased at Nvidia GTC 2026. This on-device inference platform analyzes X-ray and CT images in real time, generating draft medical reports and clinical insights through AI-assisted healthcare workflows.

APEX-X200, powered by an Intel Core Ultra 9 processor, also integrates an Nvidia RTX PRO 6000 Blackwell Server Edition GPU with 24,064 CUDA cores and 752 Tensor cores. Furthermore, it supports up to 96 GB of industrial-grade DDR5 memory and a 1 TB PCIe Gen5 x4 NVMe SSD.

Innodisk has also developed perception systems for heavy machinery and large vehicles in collaboration with its subsidiary Aetina. It integrates the Nvidia Jetson AGX Orin platform with up to eight GMSL2 camera modules alongside capture cards and extenders that support cable lengths up to 30 meters.

Figure 4 The edge AI-based perception system facilitates surround-view stitching, blind-spot detection, and driver-monitoring functions. Source: Innodisk

These perception systems enable surround-view stitching, blind-spot detection, and driver-monitoring functions, supporting real-time environmental awareness and helping identify potential risks such as fatigue or distraction under complex operating conditions. “It’s also an example of a modular architecture that supports future system upgrades without requiring major redesign efforts,” Yu said.

Eyeing U.S. and Europe

Innodisk, headquartered in New Taipei City, Taiwan, has global ambitions with more than 1,000 field-proven edge AI deployments worldwide. In Europe and the Unites States, it’s operating in close collaboration with regional distributors and partners in edge AI segments such as industrial automation, healthcare, aviation, and professional workstations.

Innodisk considers industry events a key tool for bolstering its presence in these crucial markets. It has showcased its edge AI solutions at Nvidia GTC 2026 in the United States, ICE Barcelona in Spain, and Embedded World 2026 and CloudFest 2026 in Germany.

Next, to support global deployment requirements, the company ensures its products comply with regional regulations. Its edge AI solutions meet CE and UKCA requirements for Europe and the U.K. and FCC regulations for the United States.

Also, in Europe, where cybersecurity requirements have become increasingly mandatory, Innodisk attained IEC 62443-4-1 certification in late 2025, embedding security throughout the product development lifecycle rather than treating it as a separate feature. It’s critical because the EU Cyber Resilience Act (CRA) is expected to be fully enforced by 2027.

Related Content

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• Speak Up to Shape Next-Gen Edge AI
• Edge AI powers the next wave of industrial intelligence
• How Advanced Packaging is Unleashing Possibilities for Edge AI
• Edge AI Is Forcing a Rethink of Predictive Maintenance Architecture

The post Edge AI deployment made easy for system integrators appeared first on EDN.

Im so happy that it even works! Took me about an hour. submitted by /u/Such_Network1389 [link] [comments]

Discrete device designer and manufacturer Nexperia B.V. of Nijmegen, the Netherlands (which operates wafer fabs in Hamburg, Germany, and Hazel Grove Manchester, UK) and power electronics firm Semikron Danfoss GmbH of Nuremberg, Germany have signed a memorandum of understanding (MoU) to explore a strategic collaboration on silicon carbide (SiC)-based power modules for automotive traction inverter applications. The collaboration aims to combine Nexperia’s expertise in SiC semiconductor technology with Semikron Danfoss’ capabilities in power module packaging and integration. Together, the firms intend to evaluate how a joint approach can enable high-performance, scalable solutions for next-generation electric vehicles...

Відновлювана енергетика як одна з підвалин енергетичної стійкості України Інформація КП вт, 06/09/2026 - 15:22

Engineers can build safer, higher-performing electric vehicles and energy storage systems with TI’s new BQ79826Z-Q1 battery monitor

News highlights:

• The industry’s first 26-cells-in-series channel battery monitor delivers best-in-class sensing accuracy, reducing system costs by supporting more cells per device than competing solutions.
• Integrated smart EIS engine enables early warning of thermal runaway from inside battery cells, helping ensure safety in EVs and ESSs.
• Supports engineers to create safer, higher-performing automotive and industrial applications, the BQ79826Z-Q1 is the latest addition to TI’s portfolio of BMS devices.

Texas Instruments (TI) today introduced the industry’s highest-cell-count battery monitor with an integrated electrochemical impedance spectroscopy (EIS) engine, bringing predictive intelligence, comprehensive data, and real-time diagnostics to battery monitoring in electric vehicles (EV) and Energy Storage System (ESS) applications.

The BQ79826Z-Q1 battery monitor enhances safety and extends battery life by detecting potential failures from within battery cells. The single chip delivers the highest cell count monitoring in its class, tracking up to 44% more channels than previous generations. With this increase in channels, the device significantly decreases the number of components required in a battery pack, reducing system complexity and cost without compromising reliability.

“The electrification of transportation and the rapid expansion of energy storage are redefining what battery performance must deliver, and as a leader in battery management technology, TI is uniquely positioned to meet that challenge,” said Wenjia Liu, vice president and general manager, battery management systems (BMS) at TI.

Delivering safety and performance with EIS technology

Just as an electrocardiogram (EKG) monitors the heart, EIS monitors a battery. It delivers continuous, real-time insight that reveals the battery’s health and warns of issues before they become critical. Integrated EIS technology enables the BQ78926Z-Q1 to detect fault conditions earlier from inside the cells helping maintain safety and notifying passengers of potential vehicle hazards such as thermal runaway.

These same benefits extend to ESSs, where reliable battery monitoring is critical to meeting the growing power demands of artificial intelligence data centers. As effective storage solutions become increasingly vital in the grid-to-gate ecosystem, EIS gives engineers real-time visibility into the state of charge and state of health of each battery cell, regardless of system size.

Maximizing efficiency with industry-leading cell count

The performance of an EV or ESS is fundamentally affected by the quality and efficiency of its batteries. The BQ79826Z-Q1 supports up to 26 cells per device, eight more than any competing solution, setting a new industry standard. Fewer monitoring devices mean a lower bill of materials, simplified architecture, and reduced board space requirements, translating to meaningful cost savings per channel without sacrificing quality or reliability.

When paired with the BQ79881-Q1 pack monitor and optional TI communications bridge, these devices create a powerful chipset that works across different module sizes, battery chemistries, and mechanical designs, giving engineers the flexibility to design once and deploy everywhere. This scalability reduces engineering overhead and accelerates time to market for automotive and energy storage designers.

Calculating charge readings with the best-in-class accuracy

With a voltage accuracy of <2mV across a full temperature range of –40°C to +125°C, higher resolution analog-to-digital converters, and ultra-low noise, the BQ78926Z-Q1 enables more accurate state-of-charge calculations, directly addressing one of the biggest concerns for EV drivers: range anxiety. Utilizing EIS technology, this device enables more accurate temperature and state-of-charge estimation, helping designers achieve longer battery life and faster charging without compromising battery health. With an EIS measurement time that is five times faster than previous solutions, this device delivers the highest functional safety voltage reading per cell. Compliance with Automotive Safety Integrity Level D and International Organization for Standardization 26262 gives designers a smarter, more efficient path to safer, longer-lasting batteries.

The post TI Launches a High-Cell-Count Battery Monitor featuring EIS appeared first on ELE Times.

How to design a simpler filter (or filter-like circuit) with a varying time constant dependent on what kind of waveform is fed to it.

Discussions with some former coworkers have focused on how to design a filter or circuit with filter-like performance that has the characteristic of a slower time constant on on increasing-signal waveforms and a faster time constant on decreasing-signal ones. Such a circuit was proposed in Reference 1, which made use of the Analog Devices AD534 chip.

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

Along with the “squirming baby” example in Reference 1, another example using such a filter might be a scale at a deli counter, filtering weight as a slice or two is added to the order. When weighing is complete and the slices are removed from the scale, the reading should conversely decrease quickly.

Could there be a different, simplified circuit that might find use in accomplishing the same effect? Thus this Design Idea.

Simplification using an op amp

One way to simplify is to use the same input voltage level as the output, which precludes requiring an input isolation circuit. See Figure 1 for an example.

Figure 1 This simplified derivative-controlled low pass filter has its output at V.

Starting with the circuit in Reference 1 as a foundation, the simplified circuit requires an R1C2 combination to act as the derivative function. The input signal requires a filter, R3C1 as the filter time constant. This derivative signal should be wired to a transistor switch, Q1, a 2N2907A, which discharges that capacitor at a faster rate, R4C1. A non inverting amplifier, ¼ of an LM324N, acts to provide isolation of the derivative input to the transistor switch. This is accomplished by ensuring that the Q1 emitter to base junction is zero, therefore not conducting at steady state.

Figures 2-4 show the actual circuit being tested, and the results.

Figure 2 The circuit in this Design Idea was breadboarded and lab-tested, not just simulated.

Figure 3 In this graph of test results, the red trace is the input, with the output at C1 in blue. Note that the output is at the same level as the input, but the time constants are different.

Figure 4 Conversely, in this graph of test results, the red trace is the output and the blue trace shows the derivative action.

Further simplification

Removing the op amp is possible if the emitter to base junction is biased below the cut-in voltage. Reference 2 has an extensive discussion on the subject, based on the Shockley diode equation. The emitter base junction is the diode in question. There is a point where the forward bias current quite low, assumed to be 1% of the maximum load current. The voltage at that point is considered to be the cut-in voltage; for silicon devices it is assumed to be 0.6V.

For this application, R1 is lowered to 500Ω, which results in a 0.238V difference across the forward-biased Q1 junction, below the cut-in voltage at steady state.

Figure 5 This schematic shows a further simplification of the previous circuit.

Figure 6 In this graph of test results for the further simplified version of the circuit, the red trace is again the input, with the output at C1 in blue.

Figure 7 Conversely, in this graph of test results for the further simplified version of the circuit, the red trace shows the voltage across R1, with the blue trace referencing the C1 voltage. Note the voltage difference in this case.

Conclusion

This circuit will not work for small changes in the input voltage, a topic which is discussed in Reference 1. The values used in these circuits are arbitrary; they can be scaled based on filtering requirements.

References

1. Sheingold, Daniel H., Transducer Interfacing Handbook, Analog Devices, Inc., Norwood, MA., 1980.
2. Millman, J.; Taub, H., Pulse, Digital, and Switching Waveforms, McGraw-Hill, New York, NY., 1965.

Robert Heider is a retired engineer with over 50 years’ experience with emphasis on the design of advanced process controls and process development.

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• Simple low-pass filters tunable with a single potentiometer
• Component tolerance sensitivities of single op-amp filter sections
• Filter impedance control

The post Derivative-controlled low pass filter, simplified appeared first on EDN.

Following publication of the UK Government’s AI Hardware Plan, to address a critical gap in UK AI infrastructure the Compound Semiconductor Applications (CSA) Catapult is to transition over this summer to become the Semiconductor Catapult, developed from its existing capability to focus on R&D for energy-efficient, deployable systems to accelerate the journey from AI research to real-world deployment, for data centers and industry...