Збирач потоків

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.

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.

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.

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.

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.

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. 

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

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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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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.

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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. 

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CEA-Leti presented new research at SPIE Photonics West highlighting major progress in the integration of quantum cascade lasers (QCLs) with silicon photonic platforms for mid-infrared (MIR) applications.

The paper titled, “Advanced Architectures for Hybrid III-V/Silicon Quantum Cascade Lasers: Toward Integrated Mid-Infrared Photonic Platforms,” compares three complementary hybrid laser architectures that collectively advance the practicality, flexibility, and scalability of MIR photonics.

Toward ‘Smaller, More Robust, and More Manufacturable MIR Systems’

Mid-infrared light plays a critical role in technologies such as gas sensing, chemical spectroscopy, biomedical diagnostics, and security, because many molecules exhibit strong absorption signatures in this spectral region. Despite the technology’s importance, MIR photonic systems remain large, costly, and difficult to manufacture at scale. Integrating MIR light sources directly onto silicon photonic platforms offers a path toward smaller, more robust, and more manufacturable systems—bringing mid-infrared photonics closer to the level of integration in the near-infrared.

Three Architectures, Three Integration Strategies 

In its Photonics West presentation, CEA-Leti demonstrated and compared three distinct hybrid III-V/silicon QCL architectures, each addressing a different integration challenge:

Hybrid Distributed Feedback QCL on Silicon-on-Nothing-on-Insulator with Adiabatic Coupling

• This approach enables robust single-mode emission around 4.3 µm with efficient optical power transfer from the III-V active region into silicon waveguides. High-index-contrast silicon photonics provides precise feedback and light routing, making this architecture well-suited for scalable photonic integrated circuits targeting spectroscopy and chemical sensing.

Hybrid QCL with an External Silicon Distributed Bragg Reflector Cavity

• In this configuration, optical gain and optical feedback are decoupled: the III-V material provides amplification, while wavelength selection and feedback are implemented in silicon using distributed Bragg reflector (DBR) cavities. This separation offers enhanced design flexibility and opens a clear path toward tunable and multifunctional MIR sources for advanced spectroscopic and sensing systems.

Ultra-Compact QCL Micro-Sources Based on Photonic Crystals & Micro-Rings

• Miniature light sources in these devices achieve footprints below 100 µm² by leveraging strong optical confinement and resonant effects. The resulting extreme miniaturization enables dense on-chip integration and supports new system architectures where size, power consumption, and integration density are critical.

From Passive Platform to Active Host

Collectively, the results show that silicon photonics can play an active role in mid-infrared laser systems. By combining adiabatic optical coupling, silicon-based feedback and cavity engineering, and ultra-compact laser concepts, CEA-Leti establishes several viable integration pathways rather than a single, one-size-fits-all solution. The work highlights how different architectures trade off stability, flexibility, and footprint, providing designers with a practical toolkit for MIR photonic systems.

“By combining quantum cascade lasers with silicon photonics, we are bringing mid-infrared sources closer to the level of integration and scalability that silicon platforms have already achieved in the near-infrared,” said Alexis Hobl, presenter and lead author of the paper.

Looking Ahead

Future work will focus on further improving optical coupling efficiency, fabrication robustness, and thermal and electrical management, as well as integrating additional on-chip photonic functions such as filters, multiplexers, and interferometric circuits. Demonstrating wafer-scale reproducibility and packaging-ready designs will be key milestones on the path toward fully integrated mid-infrared photonic systems.

Acknowledgements: L’Institut des Nanotechnologies de Lyon (INL), III-V Lab, and Fraunhofer Applied Solid State Physics IAF contributed to this project.

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The Indian Open Source for Mobile Communication Networks (IOS-MCN) Consortium has developed a new open source, publicly releasing its software platform to allow organisations to build and run their own Private 5G networks. Designed for factories, campuses, research institutions and startups, the platform would allow users to deploy Private 5G networks that promise faster, more reliable and secure connectivity than Wi-Fi or public mobile networks at a lower cost.

The release, named Agartala 0.4.0, is a continuation of IOS-MCN’s convention of using Indian city names to reflect its national, open-source character. This is the fourth open-source milestone that is mature enough for pilot deployments of India’s homegrown Private 5G platform. Validation tests prove end-to-end latency of under 10 milliseconds and downlink throughput of up to 600 Mbps per gNB, making it suitable for early pilots and enterprise trials.

The IOS-MCN is being developed by a consortium led by IISc Bengaluru, IIT Delhi, and C-DAC, with funding from the Ministry of Electronics & Information Technology (MeitY), Government of India. Agartala 0.4.0 marks a decisive step towards industry-grade, deployable Private 5G networks built on an open-source platform.

Brings all key parts of a private 5G network into one integrated, easy-to-deploy software platform:

• Advanced Mobility: Xn, F1, and N2 handovers, cell reselection, and robust RRC idle mode handling
• Voice & Multimedia: VoNR and ViNR with full IMS integration
• ORAN-Compliant Disaggregated RAN with unified RAN support
• Network Slicing for enterprise and mission-critical use cases
• Service Management & Orchestration (SMO) with a unified dashboard
• RIC Framework: E2 interface implementation with near-RT and non-RT RIC support
• Precision Timing: PTP LLS C1 and C3
• Operational Excellence: PM counters across RAN and Core, extensive crash and assert fixes, and expanded test coverage

Agartala 0.4.0 positions IOS-MCN for early pilot deployments led by ecosystem partners, with installations planned to commence in the second half of 2026 across multiple sectors.

Niral Networks, which builds indigenous telecom and networking solutions, has proposed an Intelligent Village pilot. It stated, “Agartala 0.4.0 provides the foundational technology that enables Private 5G deployments for rural and semi-urban environments. We will leverage the IOS-MCN stack to deliver real-world analytics and digital inclusion solutions through initiatives like the Intelligent Village Pilot and Smart Village Connectivity Program.”

Techphosis, which develops secure technology solutions for defence and strategic applications, has proposed a Network-in-a-Box pilot for defence applications. It said: “Agartala 0.4.0 demonstrates the readiness of open-source, ORAN-compliant platforms for defence use cases. The Network-in-a-Box pilot, planned for the second half of 2026, aims to validate rapid, secure, portable and reliable Private 5G deployments for defence communications.”

Together, these proposed pilots, spanning several sectors, underscore IOS-MCN Agartala 0.4.0’s readiness to transition from platform development to industry validation and deployment starting in 2026.

The post IOS-MCN develops India’s Open Source Platform to Build Private 5G network led by IISc Bengaluru, IIT Delhi, CDAC, MeitY appeared first on ELE Times.

The idea for a low-cost UPS monitoring system at IIIT-H did not begin in a laboratory or a funding proposal. It began with a familiar frustration – raised by Prakash Nayak, a campus IT staffer who was tired of equipment failures with no clear explanation.

Power outages were happening. Servers were restarting. Despite the installation of UPS units everywhere, no one could say with certainty what the UPS systems were actually doing when the lights went out. That real-world problem became the starting point for a research project that has now resulted in a ₹2,000 IoT-based device capable of tracking UPS behaviour during outages with near-second precision.

The research was documented in a paper titled “Low-cost IoT-based Downtime Detection for UPS and Behaviour Analysis,” by authors Sannidhya Gupta, Prakash Nayak, and Prof. Sachin Chaudhari. It also received the Best Paper award at the 18th International Conference on COMmunication System and NETworkS (COMSNETS-2026) Workshop on AI of Things, recently held in Bangalore.

When monitoring costs more than the problem
“Frequent power outages in developing regions cause equipment damage, operational downtime, and data loss,” says Sannidhya Gupta, noting that while UPS systems are meant to provide protection, “affordable options for monitoring their performance remain limited.” Commercial UPS monitoring tools – typically SNMP cards that collect and organise information about managed devices over IP networks – were an option, but an impractical one. According to the paper, “Commercial solutions are expensive, manufacturer-specific, and reliant on network infrastructure”. With prices exceeding ₹20,000 per unit, the campus IT team simply could not justify deploying them at scale. Worse, these tools often failed at the moment they were most needed. “These systems are unable to record data when the UPS itself loses power,” the authors point out, making post-outage diagnosis nearly impossible.

A device that watches, not interferes
Responding directly to the IT team’s request for something affordable and reliable, the team designed a non-intrusive current-monitoring device. Instead of tapping into UPS internals, it clamps onto the input and output lines, observing how current flows before, during, and after outages. “UPS input and output currents are sensed non-intrusively to detect outages, switchovers, and recovery behaviour,” the researchers explain. Additionally, the device is battery-backed, allowing it to keep recording even when both mains power and internet connectivity are lost.

From theory to campus corridors
In order to test out the system, it was deployed across four UPS installations on campus, including one unit already suspected by IT staff to be malfunctioning. Over a month, the devices collected around 3.7 million data points, automatically detecting 61 outage events. The data confirmed what the IT team had suspected but could never prove. “One UPS repeatedly showed no clear charging behaviour after outages,” reports Prakash, indicating a system that could briefly support loads but failed to properly recharge its batteries.

Smart algorithms, Simple assumptions
The backend analytics automatically labels each event into phases – normal operation, outage, stabilisation, and battery charging – without manual configuration. “All thresholds are expressed as fractions of a locally estimated baseline,” the authors note, adding that this allows the system to adapt to different installations automatically. The results were precise: no missed outages, no false alarms, and timing errors typically within three seconds.

Real-time monitoring, Ten times cheaper 
A web-based dashboard now gives IT staff something they never had before: visibility. Instead of guessing whether a UPS is healthy, administrators can now see it. Plus, they have access to historical analysis of UPS behaviour. Built using off-the-shelf components, the device costs about ₹2,000 – roughly one-tenth the price of commercial monitoring cards. “Its affordability, power independence, and portability make it a practical option for cost-constrained environments,” concludes Sannidhya.

Research grounded in reality
What sets this work apart is not just the technology, but its origin. This was research born out of a real operational pain point, brought directly by the people responsible for keeping systems running. “It is important to note that IT staff, Mr. Prakash, is part of the research paper we have published. He is also part of the patent we have recently filed on this. This highlights the value of treating campus operations teams as co-creators of research problems rather than mere end users – a mindset that leads to more relevant and impactful outcomes,” states Prof. Chaudhari. In a landscape where academic research is often criticised for being disconnected from reality, this project offers a counter example of how researchers take note when a problem statement is identified, and build something that changes how systems are understood and managed.

The post How A Real-World Problem Turned Into Research Impact at IIIT-H appeared first on ELE Times.

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Bowing to user backlash, Microsoft eventually relented and implemented a one-year Windows 10 support-extension scheme. But (limited duration) lifelines are meaningless if they’re DOA.

Back in November, within my yearly “Holiday Shopping Guide for Engineers”, the first suggestion in my list was that you buy you and yours Windows 11-compatible (or alternative O/S-based) computers to replace existing Windows 10-based ones (specifically ones that aren’t officially Windows 11-upgradable, that is). Unsanctioned hacks to alternatively upgrade such devices to Windows 11 do exist, but echoing what I first wrote last June (where I experimented for myself, but only “for science”, mind you), I don’t recommend relying on them for long-term use, even assuming the hardware-hack attempt is successful at all, that is:

The bottom line: any particular system whose specifications aren’t fully encompassed by Microsoft’s Windows 11 requirements documentation is fair game for abrupt no-boot cutoff at any point in the future. At minimum, you’ll end up with a “stuck” system, incapable of being further upgraded to newer Windows 11 releases, therefore doomed to fall off the support list at some point in the future. And if you try to hack around the block, you’ll end up with a system that may no longer reliably function, if it even boots at all.

Fortunately, all of my Windows-based computers are Windows 11-compatible (and already upgraded, in fact), save for two small form factor systems, one (Foxconn’s nT-i2847, along with its companion optical drive), a dedicated-function Windows 7 Media Center server:

(mine are white, and no, the banana’s not normally a part of the stack):

and the other, an XCY X30, largely retired but still hanging around to run software that didn’t functionally survive the Windows 10-to-11 transition:

And as far as I can recall, all of the CPUs, memory DIMMs, SSDs, motherboards, GPUs and other PC building blocks still lying around here waiting to be assembled are Windows 11-compliant, too.

My wife’s laptop, a Dell Inspiron 5570 originally acquired in late 2019, is a different matter:

Dell’s documentation initially indicated that the Inspiron 5570 was a valid Windows 11 upgrade candidate, but the company later backtracked due to partner Microsoft’s increasingly-over-time stingy CPU and TPM requirements. Our secondary strategy was to delay its demise by a year by taking advantage of one of Microsoft’s Windows 10 Extended Support Update (ESU) options. For consumers, there initially were two paths, both paid: spending $30 or redeeming 1,000 Microsoft Rewards points, although both ESU options covered up to 10 devices (presumably associated with a common Microsoft account). But in spite of my repeated launching of the Windows Update utility over a several-month span, it stubbornly refused to display the ESU enrollment section necessary to actualize my extension aspirations for the system:

My theory at the time was that although the system was registered under my wife’s personal Microsoft account, she’d also associated it with a Microsoft 365 for Business account for work email and such, and it was therefore getting caught by the more complicated corporate ESU license “net”. So, I bailed on the ESU aspiration and bought her a Dell 16 Plus as a replacement, instead:

That I’d done (and to be precise, seemingly had to do) this became an even more bitter already-swallowed pill when Microsoft subsequently added a third, free consumer ESU option, involving backup of PC settings in prep for the delayed Windows 11 migration to still come a year later:

And then the final insult to injury arrived. At the beginning of October, a few weeks prior to the Windows 10 baseline end-of-support date, I again checked Windows Update on a lark…and lo and behold, the long-missing ESU section was finally there (and I then successfully activated it on the Inspiron 5570). Nothing had changed with the system, although I had done a settings backup a few weeks earlier in a then-fruitless attempt to coax the ESU to reactively appear. That said, come to think of it, we also had just activated the new system…were I a conspiracy theorist (which I’m not, but just sayin’), I might conclude that Microsoft had just been waiting to squeeze another Windows license fee out of us (a year earlier than otherwise necessary) first.

To that last point, and in closing, a reality check. At the end of the day, “all” we did was to a) buy a new system a year earlier than I otherwise likely would have done, and b) delay the inevitable transition to that new system by a year. And given how DRAM and SSD prices are trending, delaying the purchase by a year might have resulted in an increased cash outlay, anyway. On the other hand, the CPU would have likely been a more advanced model than we ended up, too. So…

A “First World”, albeit baffling, problem, I’m blessed to be able to say in summary. How did your ESU activation attempts go? Let me (and your fellow readers) know in the comments: thanks as always in advance!

—Brian Dipert is the Principal at Sierra Media and a former technical editor at EDN Magazine, where he still regularly contributes as a freelancer.

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The post Windows 10: Support hasn’t yet ended after all, but Microsoft’s still a fickle-at-best friend appeared first on EDN.

Rolec’s handCASE (IP 66/IP 67) handheld enclosures for machine control, robotics, and defense electronics can now be specified with a choice of lids and battery options.

These rugged diecast aluminum enclosures are ideal for industrial and military applications in which devices must survive challenging environments but also be comfortable to hold for long periods.

Robust handCASE can be specified with or without a battery compartment (4 × AA or 2 × 9 V). Two versions are available: S with an ergonomically bevelled lid, and R with a narrow-edged lid to maximize space. Both tops are recessed to protect a membrane keypad or front plate. Inside there are threaded screw bosses for PCBs or mounting plates.

The enclosures are available in three sizes: 3.15″ × 7.09″ × 1.67″, 3.94″ × 8.66″ × 1.67″ and 3.94″ × 8.66″ × 2.46″. As standard, Version S features a black (RAL 9005) base with a silver metallic top, while Version R is fully painted in light gray (RAL 7035).

Custom colors are available on request. They include weather-resistant powder coatings (F9) with WIWeB approvals and camouflage colors for military applications. These coatings are also available in a wet painted finish. They meet all military requirements, including the defense equipment standard VG 95211.

Options and accessories include a shoulder strap, a holding clip and wall bracket, and a corrosion-proof coating in azure blue (RAL 5009).

Rolec can supply handCASE fully customized. Services include CNC machining, engraving, RFI/EMI shielding, screen and digital printing, and assembly of accessories.

For more information, view the Rolec website: https://Rolec-usa.com/en/products/handcase#top

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