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As India approaches Union Budget 2026–27, multiple industrial sectors—from power and automation to digital infrastructure and electric mobility—find themselves at a critical inflexion point. With the country balancing rapid industrialisation alongside sustainability and energy-transition goals, industry leaders are calling for continued capital expenditure, targeted incentives, and policy stability to strengthen infrastructure depth and global competitiveness.

At the core of these recommendations is the need to reinforce India’s power and grid ecosystem. According to Meenu Singhal, Regional Managing Director, Socomec Group, Greater India, sustained capex allocation, grid modernisation, and deeper indigenisation of critical power equipment will be essential to support rising industrial and digital demand. Industry stakeholders are urging the government to prioritise scalable manufacturing clusters, digitally enabled grid infrastructure, and structural reforms that improve reliability and execution efficiency.

Strategic schemes such as capex support mechanisms, fiscal incentives for local manufacturing, and policies favouring large-scale infrastructure implementation are seen as vital to closing capability gaps across transmission and distribution networks. Equally important, experts stress, is policy consistency and an enabling tax framework that continues to attract both domestic and global capital into the power sector, reinforcing India’s long-term vision of energy security and sustainable growth.

Automation as a Manufacturing Multiplier

Beyond core infrastructure, industrial automation has emerged as a key lever for enhancing India’s manufacturing competitiveness as the economy advances towards the $5-trillion milestone. Sanjeev Srivastava, Business Head – Industrial Automation SBP at Delta Electronics India, highlights that smart factories, AI-driven automation, and closer human–machine collaboration will define the next phase of industrial transformation.

Industry players believe that stronger Budget support in the form of smart manufacturing incentives, R&D-linked tax benefits, and skill-development programmes can significantly accelerate the adoption of next-generation automation technologies. Such measures would help manufacturers improve productivity, reduce operating costs, and strengthen India’s position on the global manufacturing and automation curve.

Also read industry’s recommendations on the Union Budget 2026 at: PCB Duty Cuts to Manufacturing Zones: Top Industry Recommendations for Budget 2026

Digital Infrastructure and Data Centres

As India moves deeper into the 5G, cloud, and AI era, mission-critical digital infrastructure is increasingly being viewed as the backbone of every industry. Pankaj Singh, Head – Data Centre & Telecom Business Solutions at Delta Electronics India, notes that the upcoming Budget presents an opportunity to prioritise energy-efficient and resilient data-centre ecosystems.

Industry recommendations include stronger incentives for modular and containerised data-centre deployments to enable faster rollout of scalable core and edge facilities. There is also a growing emphasis on supporting advanced cooling technologies—such as liquid-to-liquid and liquid-to-air coolant distribution systems—to manage the high thermal loads associated with AI-driven workloads. When complemented with sustainability-linked benefits and Make-in-India incentives for locally manufactured power, cooling, and automation equipment, these measures could encourage OEMs to invest with greater confidence in building a future-ready, low-carbon digital backbone.

Strengthening the EV Manufacturing Base

Meanwhile, India’s electric mobility ecosystem is entering a decisive phase, where long-term resilience and supply-chain stability are becoming as critical as adoption numbers. Bhaktha Keshavachar, Co-Founder & CEO of Chara Technologies, points out that while policy efforts have successfully focused on vehicle adoption and battery localisation, recent global disruptions have exposed vulnerabilities stemming from India’s dependence on imported rare-earth magnet motors.

As Budget 2026 approaches, industry voices are calling for formal recognition and fiscal support for magnet-free motor technologies within existing incentive frameworks. These solutions offer predictable costs, reduced supply-chain risk, and the development of indigenous intellectual property—particularly for high-volume segments such as two-wheelers, three-wheelers, and commercial fleets.

Targeted incentives for rare-earth-free motor manufacturing, stakeholders argue, would not only de-risk India’s EV ambitions but also position the country as a global hub for affordable, resilient, and export-ready EV powertrain solutions.

The Road Ahead

Taken together, these pre-Budget recommendations underline a shared industry priority: building resilient, scalable, and future-ready industrial ecosystems through focused policy support. Whether in power infrastructure, automation, digital systems, or electric mobility, Budget 2026 is widely seen as a pivotal opportunity to reinforce India’s transition towards sustainable growth, technological leadership, and global manufacturing competitiveness.

The post From Power Grids to EV Motors: Industry Flags Key Budget 2026 Priorities for India’s Next Growth Phase appeared first on ELE Times.

On the bench is a Behringer EP2500 pro audio amplifier. It's having a blown output stage and a shorted rectifier diagnosed and repaired. In play is a TTI signal generator and a Tek 468 scope, as well as a DIY dim bulb tester. I've been slowly acquiring all this gear over the past few years. Recently got hold of a proper electronics work bench with shelf a I've for the instruments. This has made life so much easier with all of the extra space it's freed up. It's great to be using all this stuff for real work, not just playing around! submitted by /u/pspkiller91 [link] [comments]

At SPIE Photonics West 2026 in San Francisco, CA, USA (20–22 January), Polar Light Technologies AB — which stems from research by founder professor Per-Olof Holtz and his team at Linköping University (with support from Sweden’s innovation agency Vinnova) — has announced that it has produced its first series of nano-scale LEDs. The firm says that this illustrates the flexibility of its patented pyramidal architecture, developed without requiring the traditional etching process...

Polar Light Technologies AB — which stems from research by founder professor Per-Olof Holtz and his team at Linköping University (with support from Sweden’s innovation agency Vinnova) — has closed a €5m+ funding round led by J2L Holding AB, with participation from STOAF, Almi Invest and Butterfly Ventures . The additional funds will advance the rollout of Polar Light’s initial products, all based on its unique micro-LED architecture. This uses pyramidal structures grown without etching, enabling full RGB on a single epiwafer...

Here are some of the PCBs I've made myself for an 8 bit computer project I'm working on. The boards, except the A register board, are double sided. Unfortunately no plated throughholes but there are functional vias with a piece of wire. Will definitely be posting more update about the entire project as I'm slowly finishing it. submitted by /u/p3623 [link] [comments]

submitted by /u/_binda77a [link] [comments]

Materials, networking and laser technology firm Coherent Corp of Saxonburg, PA, USA and quantum technology company Quside have reached a milestone in hardware-based security with the demonstration of a mass-manufacturable quantum entropy source. This shows that fast, verifiable quantum entropy — an essential foundation for secure digital systems — can be embedded at scale, supporting next-generation security architectures across a wide range of applications...

Artificial Intelligence (AI), like any other technology, comes with its own set of boons and banes. According to the Stanford AI Index 2024, India ranks first globally in AI skill penetration with a score of 2.8, ahead of the US (2.2) and Germany (1.9). AI talent concentration in India has grown by 263% since 2016, positioning the country as a major AI hub. India also leads in AI Skill Penetration for Women, with a score of 1.7, surpassing the US (1.2) and Israel (0.9).

India in its AI Era

According to a PIB report, India is one of the top five fastest-growing AI talent hubs, alongside Singapore, Finland, Ireland, and Canada. The demand for AI professionals in India is projected to reach 1 million by 2026. Taking this advancement into mind, all eyes will be on the Budget for 2026, judging what the government intends to propose to boost the AI landscape in India.

“India’s rapid advancement in the AI era places the spotlight on the upcoming Union Budget as a decisive moment for building a future-ready workforce. With over 40% of India’s IT and gig workforce already utilising AI tools, and India projected to account for the world’s AI talent by 2027, there is clear momentum; yet, significant gaps remain.

Although the employability of the workforce has improved, some part of the young workforce possesses deep AI skills, and many companies complain about the difficulties in hiring the right people. The provision of more funds for AI workforce training programs, along with the National Education Policy’s emphasis on the incorporation of applied AI, data science, and digital technologies into the curriculum, is important. We anticipate policies that foster close cooperation between the industry and the universities, provide incentives for certifying the basic knowledge gained through practical training, and allow more students to have access to hands-on labs and internships. Not only will these measures lift the entry-level skills of the labour force, but they will also make it certain that the young population of India is capable of turning to the global market as the main supplier of leaders in the coming years,” says Tarun Anand, Founder & Chancellor, Universal AI University.

Shortcomings of AI: Cybersecurity Threats

While AI has great potential and proposed advanced opportunities in various sectors, it comes with its own set of shortcomings. The issues of cybersecurity have escalated significantly in the AI era. Subsequently, it will be important to note what the new budget has in store to build on cybersecurity, as AI will continue to dominate the Indian landscape.

“As India approaches the 2026 Union Budget, the cybersecurity sector does so with clarity: compliance is no longer optional, and policy must now accelerate infrastructure transitions that enterprises cannot manage alone. In 2025, India faced nearly 265 million cyberattacks, with AI-driven ransomware democratizing threats at an unprecedented scale.

First, cybersecurity data centre infrastructure must be formally recognised as a critical national asset. Expanding the PLI framework to include cybersecurity data centres would strengthen India’s cyber sovereignty and reduce reliance on offshore infrastructure.

Second, the Budget should enable public–private partnerships to bolster SME cyber resilience. Manufacturing and mid-market enterprises are increasingly targeted by ransomware-as-a-service, yet lack access to enterprise-grade security. Government-backed subsidies routed through certified MSP networks would protect the Make in India ecosystem while democratizing DPDP compliance at scale.

Third, India must invest decisively in cybersecurity talent infrastructure. With a shortage of over 80,000 professionals, Budget 2026 should fund structured partnerships between government, academic institutions, and industry certifiers. This would create a domestic talent pipeline comparable to Singapore’s model. While we currently train over 2,000 professionals annually, government backing could scale this to more than 10,000 within three years.

The DPDP execution phase, starting in November 2026, will ultimately determine whether cyber resilience scales equitably across the country or remains concentrated in metro markets. Through targeted investments in infrastructure, partnerships, and education, the 2026 Budget has the opportunity to shape that outcome decisively,” says Rajesh Chhabra, General Manager, India and South East Asia, Acronis

By: Shreya Bansal, Sub-Editor

The post India’s Next Big Concern in the AI Era: Cybersecurity for Budget 2026 appeared first on ELE Times.

As high-performance computing (HPC) workloads become increasingly complex, generative artificial intelligence (AI) is being progressively integrated into modern systems, thereby driving the demand for advanced memory solutions. To meet these evolving requirements, the industry is developing next-generation memory architectures that maximize bandwidth, minimize latency, and enhance power efficiency.

Technology advances in DRAM, LPDDR, and specialized memory solutions are redefining computing performance, with AI-optimized memory playing a pivotal role in driving efficiency and scalability. This article examines the latest breakthroughs in memory technology and the growing impact of AI applications on memory designs.

Advanced memory architectures

Memory technology is advancing to meet the stringent performance requirements of AI, AIoT, and 5G systems. The industry is witnessing a paradigm shift with the widespread adoption of DDR5 and HBM3E, offering higher bandwidth and improved energy efficiency.

DDR5, with a per-pin data rate of up to 6.4 Gbps, delivers 51.2 GB/s per module, nearly doubling DDR4’s performance while reducing the voltage from 1.2 V to 1.1 V for improved power efficiency. HBM3E extends bandwidth scaling, exceeding 1.2 TB/s per stack, making it a compelling solution for data-intensive AI training models. However, it’s impractical for mobile and edge deployments due to excessive power requirements.

Figure 1 The above diagram chronicles memory scaling from MCU-based embedded systems to AI accelerators serving high-end applications. Source: Winbond

With LPDDR6 projected to exceed 150 GB/s by 2026, low-power DRAM is evolving toward higher throughput and energy efficiency, addressing the challenges of AI smartphones and embedded AI accelerators. Winbond is actively developing small-capacity DDR5 and LPDDR4 solutions optimized for power-sensitive applications around its CUBE memory platform, which achieves over 1 TB/s bandwidth with a significant reduction in thermal dissipation.

With anticipated capacity scaling up to 8 GB per set or even higher, such as 4Hi WoW, based on one reticle size, which can achieve >70 GB density and bandwidth of 40TB/s, CUBE is positioned as a viable alternative to traditional DRAM architectures for AI-driven edge computing.

In addition, the CUBE sub-series, known as CUBE-Lite, offers bandwidth ranging from 8 to 16 GB/s (equivalent to LPDDR4x x16/x32), while operating at only 30% of the power consumption of LPDDR4x. Without requiring an LPDDR4 PHY, system-on-chips (SoCs) only need to integrate the CUBE-Lite controller to achieve bandwidth performance comparable to full-speed LPDDR4x. This not only eliminates the high cost of PHY licensing but also allows the use of mature process nodes such as 28 nm or even 40 nm, achieving performance levels of 12-nm node.

This architecture is particularly suitable for AI SoCs or AI MCUs that come integrated with NPUs, enabling battery-powered TinyML edge devices. Combined with Micro Linux operating systems and AI model execution, it can be applied to low-power AI image sensor processor (ISP) edge scenarios such as IP cameras, AI glasses, and wearable devices, effectively achieving both system power optimization and chip area reduction.

Furthermore, SoCs without LPDDR4 PHY and only CUBE-light controller can achieve smaller die sizes and improved system power efficiency.

The architecture is highly suitable for AI SoCs—MCUs, MPUs, and NPUs—and TinyML endpoint AI devices designed for battery operation. The operating system is Micro Linux combined with an AI model for AI SoCs. The end applications include AI ISP for IP cameras, AI glasses, and wearable devices.

Figure 2 The above diagram chronicles the evolution of memory bandwidth with DRAM power usage. Source: Winbond

Memory bottlenecks in generative AI deployment

The exponential growth of generative AI models has created unprecedented constraints on memory bandwidth and latency. AI workloads, particularly those relying on transformer-based architectures, require extensive computational throughput and high-speed data retrieval.

For instance, deploying LLamA2 7B in INT8 mode requires at least 7 GB of DRAM or 3.5 GB in INT4 mode, which highlights the limitations of conventional mobile memory capacities. Current AI smartphones utilizing LPDDR5 (68 GB/s bandwidth) face significant bottlenecks, necessitating a transition to LPDDR6. However, interim solutions are required to bridge the bandwidth gap until LPDDR6 commercialization.

At the system level, AI edge applications in robotics, autonomous vehicles, and smart sensors impose additional constraints on power efficiency and heat dissipation. While JEDEC standards continue to evolve toward DDR6 and HBM4 to improve bandwidth utilization, custom memory architectures provide scalable, high-performance alternatives that align with AI SoC requirements.

Thermal management and energy efficiency constraints

Deploying large-scale AI models on end devices introduces significant thermal management and energy efficiency challenges. AI-driven workloads inherently consume substantial power, generating excessive heat that can degrade system stability and performance.

• On-device memory expansion: Mobile devices must integrate higher-capacity memory solutions to minimize reliance on cloud-based AI processing and reduce latency. Traditional DRAM scaling is approaching physical limits, necessitating hybrid architectures integrating high-bandwidth and low-power memory.
• HBM3E vs CUBE for AI SoCs: While HBM3E achieves high throughput, its power requirements exceed 30 W per stack, making it unsuitable for mobile and edge applications. Here, memory solutions like CUBE can serve as an alternative last level cache (LLC), reducing on-chip SRAM dependency while maintaining high-speed data access. The shift toward sub-7-nm logic processes exacerbates SRAM scaling limitations, emphasizing the need for new cache solutions.
• Thermal optimization strategies: As AI processing generates heat loads exceeding 15 W per chip, effective power distribution and dissipation mechanisms are critical. Custom DRAM solutions that optimize refresh cycles and employ TSV-based packaging techniques contribute to power-efficient AI execution in compact form factors.

DDR5 and DDR6: Accelerating AI compute performance

The evolution of DDR5 and DDR6 represents a significant inflexion point in AI system architecture, delivering enhanced memory bandwidth, lower latency, and greater scalability.

DDR5, with 8-bank group architecture and on-die error correction code (ECC), provides superior data integrity and efficiency, making it well-suited for AI-enhanced PCs and high-performance laptops. With an effective peak transfer rate of 51.2 GB/s per module, DDR5 enables real-time AI inference, seamless multitasking, and high-speed data processing.

DDR6, still in development, is expected to introduce bandwidth exceeding 200 GB/s per module, a 20% reduction in power consumption along with optimized AI accelerator support, further pushing AI compute capabilities to new limits.

Figure 3 CUBE, an AI-optimized memory solution, leverages through-silicon via (TSV) interconnects to integrate high-bandwidth memory characteristics with a low-power profile. Source: Winbond

The convergence of AI-driven workloads, performance scaling constraints, and the need for power-efficient memory solutions is shaping the transformation of the memory market. Generative AI continues to accelerate the demand for low-latency, high-bandwidth memory architectures, leading to innovation across DRAM and custom memory solutions.

As AI models become increasingly complex, the need for optimized, power-efficient memory architectures will become increasingly critical. Here, technological innovation will ensure commercial realization of cutting edge of AI memory solutions, bridging the gap between high-performance computing and sustainable, scalable memory devices.

Jacky Tseng is deputy director of CMS CUBE product line at Winbond. Prior to joining Winbond in 2011, he served as a senior engineer at Hon-Hai.

Special Section: AI Design

• The AI design world in 2026: What you need to know
• AI workloads demand smarter SoC interconnect design
• AI’s insatiable appetite for memory

The post The AI-tuned DRAM solutions for edge AI workloads appeared first on EDN.

Cree LED Inc of Durham, NC, USA (a Penguin Solutions brand) and Blizzard Lighting LLC have reached a mutually beneficial settlement resolving a patent infringement dispute involving Cree LED’s patents related to LED components commonly used in LED displays. As part of the settlement, Cree LED has granted Blizzard a limited license to certain Cree LED patents covering LED components...

Про цікавий воркшоп у Політехнічному музеї kpi ср, 01/21/2026 - 11:32

submitted by /u/john_galt_42069 [link] [comments]

Досягнуто прогресу у створенні гнучкого OLED-дисплея kpi ср, 01/21/2026 - 10:22

Used some nickel plated 3x10mm copper, cheap wire, and some banana connector from work submitted by /u/Plane_Argument [link] [comments]

submitted by /u/Fit-Spring1143 [link] [comments]

NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and developed and previously manufactured high-power industrial blue lasers — has completed its acquisition of Lyocon S.r.l., an Italian laser-technology company specializing in the design, manufacturing and integration of high-power blue laser systems for industrial applications, with established operations, customers and recurring revenues. The transaction was completed through Nuburu Subsidiary Inc...

NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and developed and previously manufactured high-power industrial blue lasers — has announced a significant advancement in its strategic partnership with Tekne S.p.A., following the execution of (i) a comprehensive industrial and commercial network contract (contratto di rete) through NUBURU’s defense subsidiary Nuburu Defense LLC, (ii) a €13m shareholder convertible loan, and (iii) the completion of an initial 2.9% equity investment in Tekne...

Editor’s note:

In this DI, high school student Tommy Liu builds a vehicle-mounted particulate matter monitoring system that siphons power from harvested wind energy from vehicle motion and an integrated supercapacitor.

Particulate matter (PM2.5) monitoring is a key public-health metric. Vehicle- and drone-mounted sensors can expand coverage, but many existing systems are too costly for broad deployment. This Design Idea (DI) presents a prototype PM2.5 sensing and power-generation front end for a low-cost, batteryless, vehicle-mounted node.

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

Two constraints drive the circuit design: 

1. Minimizing power to enable batteryless operation 
2. Harvesting and regulating power from a variable source

Beyond choosing a low-power sensor and MCU, the firmware duty-cycles aggressively: the PM2.5 sensor is fully powered down between samples, and the MCU enters deep sleep. A high-side MOSFET switch disconnects the sensor supply and avoids the ground bounce risk of low-side switching.

Low-cost micro wind turbines can harvest energy from vehicle motion, but available power is limited at typical road speeds, and the output voltage varies with airflow. A supercapacitor provides energy buffering, while a DC-DC buck converter clamps and regulates the rail for reliable sensor/MCU operation.

The circuits were built and tested, and the results highlight current limitations and next steps for improvement.

PM2.5 Sensor and MCU Circuit 

Figure 1 shows the sensing schematic: a PM2105 PM2.5 sensor, an ESP32-C3 module, and an FQP27P06 high-side PMOS switch.

Figure 1 Sensing circuit schematic with a PM2105 PM2.5 sensor, an ESP32-C3 module, and an FQP27P06 high-side PMOS switch.

Calculating the power budget

A PM2105 (cubic sensor and instrument) was chosen for low operating current (53 mA) and fast data acquisition (4 s). To size the batteryless budget, we measured total sensing-circuit power (PM2105 plus ESP32-C3) using an alternating on-and-standby test pattern (Figure 2). 

Figure 2 Sensing circuit power consumption in operating and standby mode.

Power peaks during the first ~4 s after sensor power-up and during sensor operation. This startup transient occurs as the sensor ramps the laser intensity and fan speed to stabilize readings. With a 5-V supply, the measured average power is ~650 mW for the first 4 s and ~500 mW for the remaining on interval. In standby, power drops to ~260 mW, with most consumption from the MCU.

Because the PM2105 settles in ~4 s, the firmware samples for ~4 s, then switches the sensor off and puts the MCU into deep sleep until the next sample time.

Operating and deep sleep modes

The MCU is based on Espressif Systems’ ESP32-C3, a low-power SoC. It controls the sensor, acquires PM2.5 data, and transmits it to the vehicle gateway, router, or portable hotspot. Both devices support I2C and UART, but UART was used to tolerate longer cable runs in a vehicle.

To fully remove PM2105 power between samples, an FQP27P06 PMOS high-side switch disconnects VCC (Figure 1). A low-side switch would also cut power, but digital switching currents can create ground IR drop and ground bounce. In sensing systems, ground noise is typically more damaging than supply ripple. FQP27P06 was selected for low on-resistance and high current capability.

In deep sleep mode, the MCU GPIOs float (high impedance). A 33 kΩ pull-down and an inverter force the PMOS gate to a defined OFF state during sleep. Because the ESP32-C3 uses 3.3 V GPIO, the high-side gate drive needs level shifting. A TI SN74LV1T04 provides both inversion and level shifting in one device.

Batteryless power generation  Wind turbine

Vehicle motion provides airflow, making a micro wind turbine a convenient harvester. A small brushed DC motor and rotor act as the turbine (Figure 3). Assuming vehicle speeds of ~15 to 65 mph, a representative average headwind speed is ~30 to 40 mph.

Figure 3 Micro wind turbine comprising a DC motor and rotor.

At 35 mph, the turbine under test delivered ~3.2 V and ~135 mW into 41 Ω, selected to approximate the average MCU and sensor load. That output is insufficient for a regulated 5-V rail and the ~650-mW startup peak.

Supercapacitor

To bridge this gap, a 10-F supercapacitor stores energy and buffers the turbine from the sensing load. Because turbine output varies with speed and the MCU and sensor maximum voltage must remain below 5.5 V, the turbine cannot be connected directly to the sensing circuit. We used an LM2596 adjustable buck-converter module set to 5 V to keep the voltage within limits. 

Figure 4 shows the power-generation schematic. A series Schottky diode (D1) protects the buck stage if the turbine reverses polarity during reverse rotation.

Figure 4 Power-generation system where a series Schottky diode (D1) protects the buck stage if the turbine reverses polarity during reverse rotation.

During sensor operation, the supercapacitor supplies load current. The supercapacitor droop per sample is:

where I is the average operating current, and T is the operating time per sample.

When the sensing circuit is on, the turbine voltage can fall below 5 V, for example, ~3.2 V at 35 mph, and the LM2596 output correspondingly drops. Because LM2596 is an asynchronous (diode-rectified) buck converter, reverse current is blocked when the converter output falls below the supercapacitor voltage, preventing the supercapacitor from discharging back into the converter. 

After sampling, the sensor is powered down, and the MCU enters deep sleep. With the load reduced, the turbine voltage rises. At 35 mph, the turbine produces ~9 V while charging a 10 F supercapacitor through the LM2596 with no additional load. 

The buck output regulates at 5 V and charges the supercapacitor. Near 5 V, the measured charge rate is ~2.3 mV/s. Therefore, the time to recover the ~50 mV droop from a sample is:

This supports ~30 s sampling at ~35 mph. Vehicle speed variation will affect the achievable sampling rate, but for public health PM2.5 monitoring, update intervals on the order of 1 minute are often sufficient. 

Results and future work

Figure 5 shows the prototype sensing PCB with the PM2105, ESP32-C3 circuitry, and a 10-F supercapacitor on the same board. Figure 6 shows the LM2596 buck module configured for a 5-V output.

Figure 5 Prototype sensing circuit board with the PM2105, ESP32-C3 circuitry, and a 10-F supercapacitor.

Figure 6 LM2596 DC-DC down-converter configured for a 5-V output.

A steady wind supply provided continuous airflow at ~35 mph, verified by an anemometer, directed at the turbine blade. The MCU powered up the sensor and acquired a PM2.5 sample every 30 s. Before the test, the supercapacitor was precharged to 5 V using USB power. During the run, the system was powered only by the supercapacitor and the wind turbine.

Over a 1-hour run, the system reported PM2.5 data at a 30-s sampling interval. Figure 7 shows an excerpt of the collected PM data.

Figure 7 Excerpt of the collected PM data (sensor not calibrated). 

Next, the system will be mounted on a test vehicle for road testing. One limitation is the micro wind turbine’s low output power. Once the supercapacitor is charged to 5 V, the system can sustain operation, but initial charging using only the turbine is slow. With a 10-F supercapacitor, the initial charge time can be on the order of ~30 minutes. Reducing capacitance shortens charge time, but larger capacitance helps ride through low-speed driving and stops.

In this prototype, PM data were logged locally and downloaded over USB after the test was completed. In deployment, Wi-Fi transmission typically increases MCU energy per sample. The connection and transmission can add up to ~1 s of active time. These factors increase the required harvested power. Future work focuses on increasing harvested power using a higher-power motor, an improved rotor, or multiple turbines in parallel. The goal is a self-starting system that charges the supercapacitor within a few minutes at typical road speeds.

Acknowledgement

I gratefully acknowledge Professor Shijia Pan, the founder of the PANS Lab (Pervasive Autonomous Networked Systems Lab) at the University of California, Merced, and my Ph.D. mentor Shubham Rohal for their mentorship, guidance, and technical feedback throughout this project. In addition, I gratefully acknowledge Philip for the generous donation of the test equipment used in this work.

Tommy Liu is currently a senior at Monta Vista High School (MVHS) with a passion for electronics. A dedicated hobbyist since middle school, Tommy has designed and built various projects ranging from FM radios to simple oscilloscopes and signal generators for school use. He aims to pursue Electrical Engineering in college and aspires to become a professional engineer, continuing his exploration in the field of electronics.

Related Content

• Building a low-cost, precision digital oscilloscope—Part 1
• Building a low-cost, precision digital oscilloscope – Part 2
• Let’s clear the air: analog and power management of environmental sensor networks
• A groovy apparatus for calibrating miniature high sensitivity anemometers

References

1. Espressif Systems. (2025, September 4). Datasheet of ESP32-C3 Series (Version 2.2). https://documentation.espressif.com/esp32-c3_datasheet_en.html (Espressif Documentation))
2. Cubic Sensor and Instrument Co., Ltd. (2022, March 21). PM2105L Laser Particle Sensor Module Specification (Version 0.1).
   https://www.en.gassensor.com.cn/Uploads/Blocks/Cubic-PM2105L-Laser-Particle-Sensor-Module-Specification.pdf
3. Texas Instruments. (2023, March). LM2596 SIMPLE SWITCHER® Power Converter 150-kHz 3-A Step-Down Voltage Regulator datasheet (Rev. G).
   https://www.ti.com/lit/gpn/lm2596 (Texas Instruments)
4. Rohal, Shubham, Zhang, Joshua, Montgomery-Yale, Farren, Lee, Dong Yoon, Schertz, Stephen, & Pan, Shijia. (2025, May 6–9). Self-Adaptive Structure Enabled Energy-Efficient PM2.5 Sensing. 13th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems (ENSsys ’25). https://doi.org/10.1145/3722572.3727928

The post Sensing and power-generation circuits for a batteryless mobile PM2.5 monitoring system appeared first on EDN.

LIGENTEC SA of Lausanne, Switzerland – which provides silicon nitride (SiN) and silicon-on-insulator (SOI) platforms with heterogeneous integration of thin-film lithium niobate (TFLN) and III-V materials – and analog/mixed-signal and specialty foundry X-FAB Silicon Foundries SE of Tessenderlo, Belgium have expanded their collaboration to further strengthen their integrated photonics offering and streamline customer access to advanced photonic technologies. The announcement marks the next step in aligning their portfolios to address growing demand across communication, computing, quantum and sensing markets...

One of the original and most important reasons Message Queuing Telemetry Transport (MQTT) became the de facto protocol for Internet of Things (IoT) is its ability to connect and control devices that are not directly reachable over the Internet.

In this article, we’ll discuss MQTT in an unconventional way. Why does it exist at all? Why is it popular? If you’re about to implement a device management system, is MQTT the best fit, or are there better alternatives?

Figure 1 This is how incoming connections are blocked. Source: Cesanta Software

In real networks—homes, offices, factories, and cellular networks—devices typically sit behind routers, network address translation (NAT) gateways, or firewalls. These barriers block incoming connections, which makes traditional client/server communication impractical (Figure 1).

However, as shown in the figure below, even the most restrictive firewalls usually allow outgoing TCP connections.

Figure 2 Even the most restrictive firewalls usually allow outgoing TCP connections. Source: Cesanta Software

MQTT takes advantage of this: instead of requiring the cloud or the user to initiate a connection into the device, the device initiates an outbound connection to a publicly visible MQTT broker. Once this outbound connection is established, the broker becomes a communication hub, enabling control, telemetry, and messaging in both directions.

Figure 3 This is how devices connect out but servers never connect in. Source: Cesanta Software

This simple idea—devices connect out, servers never connect in—solves one of the hardest networking problems in IoT: how to reach devices that you cannot address directly.

To summarize:

• The device opens a long-lived outbound TCP connection to the broker.
• Firewalls/NAT allow outbound connections, and they maintain the state.
• The broker becomes the “rendezvous point” accessible to all.
• The server or user publishes messages to the broker; the device receives them over its already-open connection.

Publish/subscribe

Every MQTT message is carried inside a binary frame with a very small header, typically only a few bytes. These headers contain a command code—called a control packet type—that defines the semantic meaning of the frame. MQTT defines only a handful of these commands, including:

• CONNECT: The client initiates a session with the broker.
• PUBLISH: It sends a message to a named topic.
• SUBSCRIBE: It registers interest in one or more topics.
• PINGREQ/PINGRESP: They keep alive messages to maintain the connection.
• DISCONNECT: It ends the session cleanly.

Because the headers are small and fixed in structure, parsing them on a microcontroller (MCU) is fast and predictable. The payload that follows these headers can be arbitrary data, from sensor readings to structured messages.

So, the publish/subscribe pattern works like this: a device publishes a message to a topic (a string such as factory/line1/temp). Other devices subscribe to topics they care about. The broker delivers messages to all subscribers of each topic.

Figure 4 The model shows decoupling of senders and receivers. Source: Cesanta Software

As shown above, the model decouples senders and receivers in three important ways:

• In time: Publishers and subscribers do not need to be online simultaneously.
• In space: Devices never need to know each other’s IP addresses.
• In message flow: Many-to-many communication is natural and scalable.

For small IoT devices, the publish/subscribe model removes networking complexity while enabling structured, flexible communication. Combined with MQTT’s minimal framing overhead, it achieves reliable messaging even on low-bandwidth or intermittent links.

Request/response over MQTT

MQTT was originally designed as a broadcast-style protocol, where devices publish telemetry to shared topics and any number of subscribers can listen. This publish/subscribe model is ideal for sensor networks, dashboards, and large-scale IoT systems where data fan-out is needed. However, MQTT can also support more traditional request/response interactions—similar to calling an API—by using a simple topic-based convention.

To implement request/response, each device is assigned two unique topics, typically embedding the device ID:

Request topic (RX): devices/DEVICE_ID/rx used by the server or controller to send a command to the device.

Response topic (TX): devices/DEVICE_ID/tx used by the device to send results back to the requester.

When the device receives a message on its RX topic, it interprets the payload as a command, performs the corresponding action, and publishes the response on its TX topic. Because MQTT connections are persistent and outbound from the device, this pattern works even for devices behind NAT or firewalls.

This structure effectively recreates a lightweight RPC-style workflow over MQTT. The controller sends a request to a specific device’s RX topic; the device executes the task and publishes a response to its TX topic. The simplicity of topic naming allows the system to scale cleanly to thousands or millions of devices while maintaining separation and addressing.

With it, it’s easy to implement remote device control using MQTT. One of the practical choices is to use JSON-RPC for the request/response.

Secure connectivity

MQTT includes basic authentication features such as username/password and transport layer security (TLS) encryption, but the protocol itself offers very limited isolation between clients. Once a client is authenticated, it can typically subscribe to wildcard topics and receive all messages published on the broker. Also, it can publish to any topic, potentially interfering with other devices.

Because MQTT does not define fine-grained access control in its standard, many vendors implement non-standard extensions to ensure proper security boundaries. For example, AWS IoT attaches per-client access control lists (ACLs) tied to X.509 certificates, restricting exactly which topics a device may publish or subscribe to. Similar policy frameworks exist in EMQX, HiveMQ, and other enterprise brokers.

In practice, production systems must rely on these vendor-specific mechanisms to enforce strong authorization and prevent devices from accessing each other’s data.

MQTT implementation on a microcontroller

MCUs are ideal MQTT clients because the protocol is lightweight and designed for low-bandwidth, low-RAM environments. Implementing MQTT on an MCU typically involves integrating three components: a TCP/IP stack (Wi-Fi, Ethernet, or cellular), an MQTT library, and application logic that handles commands and telemetry.

After establishing a network connection, the device opens a persistent outbound TCP session to an MQTT broker and exchanges MQTT frames—CONNECT, PUBLISH, and SUBSCRIBE—using only a few kilobytes of memory. Most implementations follow an event-driven model: the device subscribes to its command topic, publishes telemetry periodically, and maintains the connection with periodic ping messages. With this structure, even small MCUs can participate reliably in large-scale IoT systems.

An example of a fully functional but tiny MQTT client can be found in the Mongoose repository: mqtt-client.

WebSocket server: An alternative

If all you need is a clean way for your devices to talk to your back-end, MQTT can feel like bringing a whole toolbox just to tighten one screw. JSON-RPC over WebSocket keeps things minimal: devices open a WebSocket, send tiny JSON-RPC method calls, and get direct responses. No brokers, no topic trees, and no QoS semantics to wrangle.

The nice part is how naturally it fits into a modern back-end. The same service handling the WebSocket connections can also expose a familiar REST API. That REST layer becomes the human- and script-friendly interface, while JSON-RPC over WebSocket stays as the fast “device side” protocol.

The back-end basically acts as a bridge: REST in, RPC out. This gives you all the advantages of REST—a massive ecosystem of tools, gateways, authentication systems, monitoring, and automation—without forcing your devices to speak.

Figure 5 This is how REST to JSON-RPC over WebSocket bridge architecture looks like. Source: Cesanta Software

This setup also avoids one of MQTT’s classic security footguns, where a single authenticated client can accidentally gain visibility or access to messages from the entire fleet just by subscribing to the wrong topic pattern.

With a REST/WebSocket bridge, every device connection is isolated, and authentication happens through well-understood web mechanisms like JWTs, mTLS, API keys, OAuth, or whatever your infrastructure already supports. It’s a much more natural fit for modern access control models.

Beyond typical MQTT setup

This article offers a fresh look at IoT communication, going beyond the typical MQTT setup. It explains why MQTT is great for devices behind NAT/firewalls (devices only connect out to the broker) and highlights that the protocol’s lack of fine-grained access control can create security headaches. It also outlines an alternative solution: JSON-RPC over a single persistent WebSocket connection.

For a practical application demo of these MQTT principles, see the video tutorial that explains how to implement an MQTT client on an MCU and build a web UI that displays MQTT connection status, provides connect/disconnect control, and lets you publish MQTT messages to any topic.

In this step-by-step tutorial, we use STM32 Nucleo-F756ZG development board with Mongoose Wizard—though the same method applies to virtually any other MCU platform—and a free HiveMQ Public Broker. This tutorial is suitable for anyone working with embedded systems, IoT devices, or STM32 development stack, and looking to integrate MQTT networking and a lightweight web UI dashboard into their firmware.

Sergey Lyubka is co-founder and technical director of Cesanta Software Ltd. He is known as the author of the open-source Mongoose Embedded Web Server and Networking Library (https://mongoose.ws), which has been on the market since 2004 and has over 12k stars on GitHub.

Related Content

• Avoiding MQTT pitfalls
• Connecting correctly in MQTT
• MQTT essentials – Scenarios and the pub-sub pattern
• Device to Cloud: MQTT and the power of topic notation
• How to Control a Servo Motor Using Your Smartphone with the MQTT Protocol and the Raspberry Pi

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