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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...

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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

The post How to implement MQTT on a microcontroller appeared first on EDN.

ANRITSU CORPORATION showcases next-generation wireless solutions at MWC 2026 in Barcelona (Hall 5 Stand D41). The company’s portfolio includes software-centric tools for early 6G standardisation, pioneering 6G measurement and AI-powered test solution, unified RF Multiband and NTN Validation Platform, Field Simulation Test for digital twin development, cloud-based automotive validation for ADAS and SDV, sustainable IoT power consumption evaluation, and intelligent assurance for mobile, fixed, and private infrastructures. Together, these innovations confirm Anritsu’s vision to deliver trustworthy, sustainable, and high-performance connectivity, helping operators, manufacturers, and industry verticals unlock new value as networks evolve beyond intelligence.

As a global leader in communications test and measurement, Anritsu continues to empower innovators with tools that support the evolving demands of connectivity, automation, and network intelligence.

6G Test Platform for Early 6G Standardisation and Validation
Anritsu’s Virtual Signalling Tester is a software-based signalling tester for advanced 6G validation. Its Virtual ST-based solution supports L1/Physical layer test, and protocol test/Application test, capable of testing at the MAC layer, DIQ and RF Interface (with SDR). It’s equipped with an arbitrary waveform output function, which is being considered for 6G, making it highly useful for early validation during the 6G standardisation phase.

Pioneering 6G Measurement and AI-Driven Test Solution
Anritsu demonstrates a groundbreaking 6G measurement solution, designed to redefine wireless testing with AI at the core of the workflow. This next-generation solution harnesses advanced data management and AI-powered analytics to simplify complex test processes, reduce engineering workload, and accelerate development cycles. AI-assisted test sequence generation improves accuracy, optimises resource usage, and accelerates development cycles by learning from historical test patterns and real-world performance data. This intelligent approach ensures customers can meet the rapidly evolving demands of 6G technology, digital automation, and scalable network intelligence.

Unified 6G Multiband and NTN Validation Platform
The MT8000A Radio Communication Test Station now integrates support for the Upper Mid-Band (up to 16 GHz), enabling comprehensive RF front-end testing across FR1, FR2, and FR3 bands within a single modular platform. This unified architecture streamlines validation workflows for 6G devices, allowing simultaneous multi-band characterisation, inter-band handover testing, and advanced signal integrity analysis. In addition, the platform introduces next-generation NTN (Non-Terrestrial Network) measurement capabilities, including Direct-to-Cell and NR-NTN protocols delivered as a software upgrade. Engineers can leverage real-time emulation of satellite and aerial link conditions, protocol stack verification, and seamless integration with existing test automation environments. These enhancements empower engineers to efficiently validate NTN features, optimise RF performance, and maintain a competitive edge as wireless technologies evolve toward 6G.

Reproducing Real Networks: FST for Digital Twin Development
Anritsu demonstrates its unique Field Simulation Test (FST) solution, designed to capture real-world radio environments and accurately reproduce complex network propagation conditions in the laboratory. This innovative approach allows engineers to replicate issues observed in live networks and verify propagation scenarios. Moreover, the collection of propagation data supports the development of Digital Twin environments for research into next-generation wireless technologies, such as ISAC and CSI compression.

Future-Ready Automotive Testing: Cloud-Based Validation for Connected SDV Use Cases
Anritsu, in collaboration with Valeo, is demonstrating a Virtualised Automotive Testing Solution designed to accelerate Software development and reduce testing costs for connected and autonomous vehicles. By integrating Anritsu’s virtual connectivity solution for 5G and C-V2X connectivity with Valeo’s virtualised hardware and ECU simulation platform, this joint solution enables comprehensive validation of Connected SDV functions, eliminating the need for physical vehicles or test tracks. The approach delivers faster time-to-market, improved safety, and global scalability through cloud integration, transforming automotive testing into a cost-effective, future-ready process.

Power Consumption Testing for Smarter, Sustainable IoT Devices
As industries accelerate toward smarter, more connected solutions, the demand for low-power, sustainable devices is reshaping the landscape of IoT technology. Anritsu introduces a cutting-edge power consumption test environment, empowering engineers to evaluate sensors, wearables, and smart home systems under real-world operating conditions. This real-world approach provides actionable insights into energy usage, battery life, and optimisation potential, enabling precise measurement and analysis that drive smarter design choices and longer-lasting products. Leveraging the advanced capabilities of the Anritsu MT8000A platform, Qoitech’s Otii power measurement suite, and SmartViser’s expertise in intelligent test automation and orchestration, this solution sets a new benchmark for energy efficiency in IoT device development.

From Complexity to Clarity to Confidence. Applied AI for Autonomous Networks
Anritsu’s Service Assurance platform is a unified solution that transforms network complexity into a competitive advantage. By embedding intelligence directly into the service assurance workflow, Anritsu allows operators to surface the signals that matter most. Data from across the network is correlated, converting fragmented insights into actionable guidance that engineers and operations teams can trust.

This continuous AI-driven understanding of service health accelerates decision-making and forms the foundation for autonomous network transformation. The result is a clear, unified operational picture that empowers teams to act with confidence and deliver superior customer experiences.

Across our AI-powered assurance portfolio, Anritsu’s purpose-built intelligence delivers measurable business outcomes: lower costs, higher satisfaction, and faster resolution.

The post Anritsu Unveils Visionary 6G Solutions at MWC 2026 appeared first on ELE Times.

As the nation gears up for the Union Budget 2026, slated to be presented in Parliament on February 1, electronics industry associations are stepping up efforts to push India’s electronics manufacturing ecosystem to its next phase of growth.

Among the key recommendations, the Electronic Industries Association of India (ELCINA) has proposed the establishment of 10 world-class, product-specific Electronics Manufacturing Zones (EMCs). The association has urged the government to upgrade the existing EMC 1.0 and EMC 2.0 cluster models to globally accepted infrastructure standards. According to ELCINA, such an approach would help ensure regional balance, improve local facilitation, and enhance India’s competitiveness and export potential.

Strengthening the manufacturing value chain further, the India Cellular & Electronics Association (ICEA) has recommended a reduction in customs duty on microphone, receiver, and speaker assemblies for mobile phones from the current 15% to 10%. The association believes that this duty rationalisation would create cumulative cost advantages, improve global competitiveness, and encourage additional investments in domestic component manufacturing.

ICEA has also suggested reducing duties on Printed Circuit Board Assemblies (PCBAs) and Flexible PCB Assemblies (FPCAs) from 15% to 10%, a move aimed at supporting localisation and scale in electronics production.

Testing & Certification 

For any industry, standards play a major role, whether for exports or inbound use; without certification, no product can see the light of day. Recognising this to be at the forefront of product development, ELCINA recommends introducing a Testing & Certification Support Scheme to provide financial support or reimbursement of testing and certification charges to MSMEs. 

Also, to make the services accessible for small entities with minimal investments, the body recommends establishing regional accredited testing centers in collaboration with private labs, industry associations, and technical institutions as per BIS and other international standards.  This would successfully ensure one of the vitals for strengthening the domestic manufacturing and R&D ecosystem. 

Investment Fund for SMEs

As the Union Budget 2026 approaches, ELCINA has also highlighted the long-standing challenge of limited access to low-cost finance for SMEs in the electronics system design and manufacturing (ESDM) sector, noting that funding constraints continue to hamper their ability to scale and invest in advanced technologies. To address this, the association has recommended the creation of a dedicated Technology Acquisition Fund to support technology transfer and licensing, enabling Indian firms to move up the value chain and transition towards a product-led ecosystem. 

ELCINA has also proposed a professionally managed, government-backed venture fund to support high-value-added manufacturing in electronic components, PCBs, and modules, along with targeted tax incentives, including investment- and dividend-stage exemptions for at least five years, to attract private equity and high-net-worth investors and help build globally competitive Indian champions.

Classification of Displays 

In the same vein, the India Cellular and Electronics Association (ICEA) has raised concerns over the lack of clarity in the customs classification of display assemblies used across automobiles, medical devices, industrial electronics, and other applications. Although these displays are technologically identical to flat panel display modules used in mobile phones and televisions, they are often classified under different HSN codes based on end-use, resulting in inconsistent customs treatment and operational uncertainty across field formations. 

To address this, ICEA has recommended uniform classification of all display assemblies under HSN 8524, regardless of application, a step it says would ensure global alignment, reduce classification disputes, and enable smoother integration of display manufacturing across product segments as domestic capacity scales up under the Electronics Components and Manufacturing Scheme (ECMS).

Conclusively, the industry’s pre-Budget recommendations point to a clear priority: strengthening India’s electronics manufacturing depth through targeted policy, fiscal, and regulatory interventions. With focused action on infrastructure, duties, finance, and classification clarity, Budget 2026 has the opportunity to accelerate India’s shift from assembly-led growth to globally competitive electronics manufacturing. 

The post PCB Duty Cuts to Manufacturing Zones: Top Industry Recommendations for Budget 2026 appeared first on ELE Times.

Singulus Technologies AG of Kahl am Main, Germany (which makes production equipment for the optical disc and solar sectors) has received a follow-on order for a TIMARIS deposition system for the production of micro-LEDs. Following the commissioning of a first system of this type by the customer, the firm is now expanding its existing production capacities in the USA...