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VolAnti is an open source acoustic drone detector: it listens to the sound of the propellers instead of the radio signal. That is why it also works against fibre-optic FPV drones, which emit nothing and stay invisible to any receiver. The project is by Agam Rossen and it only detects and alerts, with no jamming, interception or countermeasures.

The heart is an ESP32-S3. Four ICS-43434 MEMS microphones are arranged in a cross, 79 mm from corner to corner, and synchronised from a single source. The four streams are summed: at these frequencies 79 mm is a tenth of a wavelength, so sound from any direction adds up in phase while each capsule’s own noise does not. The gain is about 6 dB of signal-to-noise ratio for free, with a nearly omnidirectional response.

The spectrum is computed with a 2048-point FFT every 512 samples at 16 kHz, that is one window every 32 ms. The detector keeps a running picture of what the site normally sounds like at every frequency and subtracts it. Quiet things are learned in about 6 s, loud broadband bursts in less than a second.

The harmonic comb of the propellers and the six consecutive windows

A propeller with B blades at R revolutions per second generates a harmonic comb with evenly spaced peaks and silent valleys. For every candidate speed from 70 to 2000 Hz the firmware sums the energy on the teeth of the comb, subtracts that in the valleys, normalises and keeps the best score. On top of that the same speed has to win six consecutive windows within 2%: a single high-scoring frame means nothing, noise does not behave like that, a rotor does.

Four independent detectors work on the same spectrum and the alarm is whichever one is sure first. The measured latencies vary a lot from one to another:

• detector 1, fast comb: 0.23 s latency
• detector 2, slow comb: 1.4 to 4 s latency
• detector 3, envelope wash: 1 to 3 s latency
• detector 4, no-floor comb: 5 to 15 s latency

On 6 September 2026 tier 4 triggered the alarm at 104.2 m, 114 yards on the map. On 21 August, on open ground with variable wind, detection was at 14 m with loaded propellers and no false alarm all day. On 28 August, with the first production board, the detector output was identical to the reference on the golden vectors, with a worst frame of 29.4 ms against the 32 ms budget over 1,938 frames and no overrun.

Power, e-paper screen and an 18-byte LoRa packet

Power starts from a 1S 2500 mAh LiPo, charged by the BQ24074 and regulated to 3.3 V by the TPS63020 buck-boost. The claimed runtime is 18-22 h on a single charge. The case measures 91 mm and also houses an e-paper screen. The radio is a Ra-01H module: the LoRa alarm packet is 18 bytes, so a few dozen bytes are enough to let a second VolAnti know there is something in the air.

The frequency has to be set for the country of use: 868 MHz in the UK and the EU, 915 MHz in the United States. In still air and at a quiet site you can expect 100 to 200 m. At a windy or noisy site you can expect 15 to 50 m, and above 8 m/s detection is assumed to be degraded. So far there have been no false alarms in the field sessions.

Rebuilding it takes very little. The breadboard build costs about £35-45 and an evening, while the complete unit costs about £50-80 and a weekend. In Agam Rossen’s repository you will find the firmware, the live simulator, the breadboard guide, the complete unit guide, the flashing instructions, running multiple units, the audio test and the website source.

Assembly needs no exotic tools: soldering, a bit of patience and the willingness to tune the detector to your own site. A school or a farm can build and repair it themselves, without depending on a supplier. For anyone who wants to start from a development board, the ESP32-C6-Zero IoT kit is an option for practising with Wi-Fi 6 and built-in peripherals, while a round ESP32-S3 HMI display with a touch knob and LVGL graphics can serve as the control interface in a more ambitious version.

Source: https://github.com/agamrossen/VolAnti

The post VolAnti: the open source acoustic drone detector that listens to propellers appeared first on Open Electronics.

A seemingly newer device reverts to a legacy processor architecture, in comparison to one certified earlier that same year. Does it matter? And was it due to Matter?

This is the end
Beautiful friend
This is the end
My only friend, the end
The Doors, “The End”

In quoting those epic song lyric opening lines, I’m not referencing an ex-girlfriend, as Jim Morrison had reportedly done, but instead applying them to my long-running TP-Link smart plug teardown series. This entry, for the Tapo P110M, marks the end of the publication line (at least as currently planned) for this specific content cadence.

I hope you enjoyed reading about the various devices as much as I enjoyed taking them apart, photographing them, and writing about them.

The series, up to this point, began in February and encompasses the following analyses:

• TP-Link’s Kasa HS103: A smart plug with solid network connectivity
• TP-Link’s Kasa EP10: If at first it doesn’t connect, buy, buy again
• TP-Link’s Kasa EP25: Energy monitoring for a hoped-for utility bill nose-dive
• TP-Link’s Tapo P105: A Kasa EP10 clone, or evolutionarily derived?
• TP-Link’s Tapo P125: A smart plug with an Apple HomeKit vibe
• TP-Link’s Tapo P115: Smart plug subtracts Apple, adds energy tracking

Reiterating a frequently uttered mission-statement mantra I most recently chanted last month, my basic aspiration with this project was to ascertain to what degree (if any) differences in the company’s various smart plug products’ feature sets, broadly between the Kasa and Tapo product lines as well as between products within a given line, were due to hardware variability versus (or in addition to) software-implemented inconsistency. Last month’s unveil was a particular hardware-evolution doozy, as it involved the processor architecture (and associated codebase) conversion from Arm (Realtek) to RISC-V (Espressif Systems).

So, what will we find inside the Tapo P110M? Usually, but not always, TP-Link’s Matter smart home and IoT protocol-supportive product variants include “M” as a product name suffix. More generally, in addition to Matter cognizance (or lack thereof), we’ve seen over this past year that the manufacturer also differentiates devices based on parameters such as energy monitoring and reporting capabilities and Apple HomeKit protocol support, not to mention cosmetic variances such as size, shape and weight, switch and activity LED locations, and the like.

The P110M, for example, implements all three aforementioned software enhancements, only one of which (energy tracking) last month’s Tapo P115 shares. Let’s see how, if at all, this differentiation is also reflected in hardware.

Feature set confusion

Here’s a stock shot of the Tapo P110M, to start.

 

Looks curiously familiar, albeit not identical, doesn’t it?

My two-pack came from Amazon’s Resale (formerly Warehouse) website area and set me back $13.77 after a 30%-off promotion active at the time, plus tax. While just now revisiting the product page both there and at TP-Link’s website, I was initially confused, since the latter (not to mention TP-Link’s broader product line overview page) explicitly notates support for Apple’s Home protocol, Siri interface and the like, all absent from the former. However, I suspect Matter cognizance is at the nexus of the discrepancy, as noted at Amazon product page verbiage:

Matter-certified devices, regardless of brand, can work together and are compatible with most major smart home platforms like Amazon Alexa, Apple HomeKit, Google Home, and Samsung SmartThings. Enjoy more flexible and unified control.

Translation, I’m guessing: the Tapo P110M doesn’t support Apple services directly, but instead implicitly via their common Matter-support intermediary.

Here’s the usual suite of outer box shots, complete with the also-as-usual 0.75″ (19.1 mm) diameter U.S. penny accompaniment for size comparison purposes. Top.

Front.

Left side.

Back.

Right side.

And bottom.

Peeling away an edge of the information-obscuring Amazon Resale sticker exposes to view the hardware revision number—v1.6—of what’s inside. And in a rarity, at least from my experience, that’s the only hardware version option listed on the product support page (albeit with multiple to-date associated firmware updates). A refreshing change of pace.

Sticker shock

Let’s now see what’s inside.

One of the two Matter stickers was previously removed from its original backing sheet. Wonder where it went? Hold that thought.

I’ve usually found with returned-and-resold stuff like this that only one of the multiple items in the package has been removed prior to re-placement and shipment back to Amazon. I generally select that same one as my teardown “victim”. But which one was it in this case?

Again, it’s usually the one on the left (or upper left corner for a quad-device box). But the prior owner of this set might have been left-handed; the indent for the device on the right is frayed.

Here’s more visual evidence of the prior owner’s out-of-box device selection preference. There’s the missing sticker, now adhering to the top of the smart plug on the right.

Putting the left-side device aside, let’s continue with the usual overview photo set. Top, again.

Front.

Left side.

Back.

Right….what?

Turns out the Matter QR code and info are already stamped on the device’s right side. Turns out, too, the owner stuck the supplemental sticker on the wrong device, because the info sets don’t match. Truthfully, I don’t think the sticker’s intended for the device at all (unnecessary info redundancy, don’cha know) but if so, it instead should have been stuck on top of the other device.

Maybe owner confusion is at the core of why it ended up getting returned? Dunno. Onward.

As usual, the bottom of the device contains useful info, including the FCC ID (2BCGWBP1100).

Quick aside: the FCC ID for last month’s Tapo P115 is quite similar (2BCGWP110). That’s not confusing at all, right? Its listed “final action date” is February 29, 2024 (a leap year, obviously) while this one’s “final action date” is July 24 of that same year. Therefore the “seemingly newer device…in comparison to one certified earlier that same year” prose in the subhead.

A screw loose

One more stock shot to go: the latest in a line of company-supplied interior concept images:

And that’s enough for the setup; let’s get inside. Practice makes perfect, apparently; this time not only was no bloodshed, but no tabs were snapped, either.

Voila.

Here’s the half you’re undoubtedly all most interested in.

Device-to-device design consistency takes the form, among other things, of the relay, yet another Churod A16-V-105DA2F.

The bottom and right-side views are equally reminiscent and unmemorable.

And, last but not least, what of the digital PCB on the left? I’ll start with the boring side (unless you’re into traces, test points, and/or embedded antenna silhouettes, that is).

That’s what’s known as a tease. Here’s the side you care about, especially after last month’s reveal that it had captured and encompassed a processor architecture shift.

No such momentous luck this time, alas. We’re back to Realtek’s RTL8720CM at left, previously found on several previous TP-Link smart plug designs, along with a Shanghai Belling BL0937 single-phase energy monitoring IC and Eon Silicon’s EN25Q32B 32 Mbit serial flash memory on the right (not found with the Tapo P115, since the NVM was single-package integrated alongside the processor in that case).

And of course there’s also yet another mysterious five-lead packaged device in the lower left corner (I’m still sticking with my guess that these are dual transistor devices), this time marked “TACeE1” . Plus, we can’t forget the switch alongside an LED smack dab in the center.

Why the seeming chronological advancement but architectural back-step from RISC-V is back to Arm this time? My guess is that it has everything to do with Matter. The specification is in the rapid-evolution early phase of its technology lifecycle, and with no offense intended to Espressif Systems, Realtek is the more mature of the two suppliers in this particular product category.

Sound off with your theories and/or broader thoughts in the comments!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• Tapo or Kasa: Which TP-Link ecosystem best suits ya?
• TP-Link’s Tapo P115: Smart plug subtracts Apple, adds energy tracking
• TP-Link’s Kasa EP10: If at first it doesn’t connect, buy, buy again
• TP-Link’s Kasa EP25: Energy monitoring for a hoped-for utility bill nose-dive

The post TP-Link’s Tapo P110M smart plug: Matter and energy monitoring matter appeared first on EDN.

​Semiconductor manufacturer onsemi, specialised in silicon carbide (SiC) power solutions, power management, and CMOS image sensors has introduced its Embedded Power Platform—a wafer-level power architecture that integrates multiple dies (silicon, SiC, and GaN) directly into a single system. This new semiconductor packaging architecture enables higher power density, lower electrical losses and easier power-system integration. This platform has the potential to develop traction inverters and power systems that are smaller and more efficient for EVs.

​onsemi announced the Embedded Power Platform on September 16, 2026, at its headquarters in Scottsdale, Arizona, USA. The architecture delivers up to 3-5 times higher power density compared to current traction inverters. It supports the integration of silicon, silicon carbide, and gallium nitride (GaN) technologies, along with field-effect transistors, drivers, and controllers.

Advance Packaging Reduces EV Power Losses

Traction Inverter design developed by onsemi replaces high-in​ductive wire bonds with precision wafer-level redistribution layers (RDLs). This method reduces inductance and performs higher switching frequency operations. It reduces heat loss and increases EV traction inverter efficiency by addressing both thermal and performance challenges simultaneously.

Subaru Corporation, a Japanese multinational automotive manufacturing company has shown interest in implementing this embedded power platform developed by onsemi in its future electrified vehicles. This technology engagements between industries allows them to explore methods that can enhance energy efficiency, reduce development complexity, and facilitate next-generation vehicle architecture.

The post onsemi Unveils Advanced Semiconductor Packaging to Improve EV Inverter Efficiency appeared first on ELE Times.

​Multilayer electrodes combine different electrode layers with varying material compositions. This approach provides multilayer coating, enabling better ion transport and mechanical stability compared to a conventional single coating layer in battery technology. While traditional electric vehicle (EV) batteries rely on single-layer coatings, implementing a multilayer coating architecture could enhance battery performance and extend its service life.

Enhancing EV Battery Performance

Researchers will examine different coating materials, layer thicknesses, and electrode structures for both anodes and cathodes. The team will then investigate coating formulation and drying conditions to understand their impact on electrode quality and manufacturing process consistency.

The project will evaluate two coating techniques, which include two-layer slot-die coating and multilayer curtain coating under Aachen’s Chair of Production Engineering of E-Mobility (PEM) to develop scalable production process. On the basis of outcome results of these two methods, the projects aim to broader the thickness of electrode from 200 micrometres to 800 micrometres. This increase in thickness of electrode coating will allow battery manufacturers to reduce the number of inactive layers (separators and current collectors) within a cell and lower the cost of production while expanding energy density.

The post RWTH Aachen Develops Scalable Manufacturing Technology for Multilayer EV Battery Electrodes appeared first on ELE Times.

​Battery technology plays a central role in describing the performance and driving range of an electric vehicle. The more advanced and efficient the battery technology, the better the overall performance of an electric vehicle will be. With advancements in battery technology over time, ProLogium, a battery developer company has officially announced its Dynamic Coordination Network (DCN) electrolyte architecture for its next-generation GEN4 battery technology.

The purpose of introducing this technology is to showcase advancement in electrolyte design, combining characteristics of liquid and solid electrolytes. The company aims to address key challenges in next-generation batteries such as charging speed, ion transport and battery performance.

DCN Electrolyte Architecture Targets Faster Charging

Electrolyte is a key factor in battery technology, determining how well the battery works, how long it lasts, and how safe it is. It enables lithium ions to move between positive and negative electrodes during charging and discharging. The physical and chemical properties of an electrolyte such ionic conductivity, viscosity, transference number, electrochemical stability window, and solid electrolyte Interphase (SEI) influence charging performance, energy efficiency and battery operation.

The role of ProLogium’s DCN architecture is the coordination of lithium ions within the electrolyte, aiming to improve ion transport and support fast charging in its next-generation battery technology.

As per the company’s report on internal test results, the battery can charge from 5% to 80% in 6.4 minutes. This figure is from the company’s end and should not be considered as independent verification of performance on a production electric vehicle. Charging time in real-life scenarios depends on many factors like battery temperature, charger capacity, cell design, and the battery management system.

The post ProLogium Unveils New Electrolyte Architecture to Advance Ultra-Fast-Charging EV Batteries appeared first on ELE Times.

Keysight Technologies, today announced the Keysight Velocity 4900 Series, a new line of cost-effective Layer 1 automation switches designed to transform and automate physical connectivity in network test labs. Through software controlled testbed configuration and management, the Velocity 4900 Series enables engineering teams to dynamically connect and automate the management of test equipment using an intuitive graphical user interface. Teams can securely access and share valuable lab resources remotely, improve equipment utilization, and create repeatable testing workflows for increasingly complex networks and infrastructure supporting AI, 6G, and next-generation services.

As the speed, scale, and complexity of next-generation network technologies and services increase, test teams face growing pressure to perform accurate, comprehensive validation and keep pace with rising traffic volumes. Yet many test labs still depend on manual cabling and time-consuming physical topology changes, leaving valuable equipment underutilized or inaccessible to remote teams. These limitations slow testing, increase the risk of configuration errors, and prevent physical lab infrastructure from integrating fully with automated development and continuous integration and continuous delivery (CI/CD) workflows. Without greater lab automation, organizations risk delays to product releases and critical network updates, with significant business and operational consequences.

The Velocity 4900 Series addresses this physical-connectivity bottleneck by replacing manual testbed configuration with software-controlled Layer 1 connectivity. Engineering teams can quickly create, save, modify, and reproduce physical test topologies while securely sharing expensive or scarce test resources with authorized users across teams and locations.

The first two models in the series are:

• Keysight Velocity 4902: A high-speed Layer 1 automation switch supporting Ethernet speeds from 100 megabits per second (Mbps) to 100 gigabits per second (Gbps). The platform includes 72 QSFP28 ports, supporting 288 connections with breakouts, providing flexible connectivity for high-speed functional and performance testing.
• Keysight Velocity 4906: A high-density Layer 1 automation switch with 288 SFP28 ports supporting Ethernet speeds from 100 Mbps to 25 Gbps. Its density enables organizations to connect and automate a larger portion of their lab inventory within a compact, fixed-form-factor platform.

Both switches provide true Layer 1 forwarding through an ultra-low-latency, fully transparent switching matrix. They support single-mode fiber, multimode fiber, direct-attach copper, and active optical cables, with port speed and media configuration available on a per-port basis. Additional capabilities include port tapping for diagnostics and monitoring and one-to-many traffic replication for broader test coverage and more efficient reuse of traffic-generation resources.

Key benefits of the Velocity 4900 Series include:

• Faster, repeatable testing: Automates physical testbed setup and teardown, allowing teams to provision complex topologies in seconds or minutes rather than relying on hours or days of manual cabling.
• Secure global resource sharing: Helps authorized engineers remotely access and share test equipment across labs, teams, and geographic locations without requiring physical cable changes or equipment movement.
• Improved equipment utilization: Enables organizations to pool scarce or expensive instruments, make underused assets available to more teams, and reduce unnecessary equipment duplication.
• Greater test coverage: Allows engineers using Velocity software to configure physical test environments, run tests, and automatically tear down and recreate those configurations in seconds, expanding test coverage while reducing manual effort and connection errors.
• Incremental lab modernization: Provides a cost-effective starting point for Layer 1 automation that can expand into resource reservation, lab orchestration, test automation, and CI/CD integration through the broader Keysight Velocity portfolio.

Anil Kollipara, Vice President and General Manager of Automated Testing and Assurance, Keysight, said: “Engineering teams developing services and infrastructure to support AI, 6G, and next-generation systems need test environments that can change as rapidly as the technologies they are creating. The Velocity 4900 Series gives organizations a practical and cost-effective way to begin modernizing the physical lab. By automating Layer 1 connectivity and making test resources easier to share, Keysight is helping customers improve utilization, expand test coverage, and accelerate development without requiring a complete lab transformation.”

The Velocity 4900 Series integrates with the broader Keysight Velocity portfolio, enabling organizations to extend software controlled physical connectivity into resource reservation, lab orchestration, test automation, and CI/CD workflows. The Keysight Velocity 4902 and 4906 are available for order immediately with initial deliveries planned for January 2027.

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L&T Technology Services, a global leader in Engineering Intelligence Solutions and ER&D services, today inaugurated its AI-Native EI Lab for Digital Manufacturing in Bengaluru, an experiential environment where customers and partners can see AI-Native Manufacturing, digital twins and autonomous systems working together across the lifecycles.

The EI Lab extends LTTS’ Engineering Intelligence charter to the manufacturing shop floor. Through live and scenario-based demonstrations, customers can move from digital engineering and virtual validation to connected production, intelligent operations and autonomous systems, and see the integration pathways that make each of them scale.

The EI Lab is built around multiple capability pillars:

• Manufacturing Intelligence: An AI-powered Manufacturing Command Center with executive decision-intelligence, manufacturing data fabric with knowledge graph, a supply chain control tower supported by AI-powered production planning and multi-plant benchmarking.
• AI-Native Factory: Here intelligent machines, robots, cobots, drones, autonomous systems and digital twins work together to execute manufacturing tasks with minimal human intervention. This layer has an NVIDIA accelerated environment combining NVIDIA Omniverse, NVIDIA Isaac Sim, NVIDIA PhysX, NVIDIA Cosmos, NVIDIA NeMo, and OpenUSD-based digital twin workflows, accelerated visualization and technologies for Industrial and Physical AI.
• Operations AI & Autonomy: A verification gate that checks actions generated by agent swarms before they reach a machine, enabling verifiable, governed execution and safer autonomous decision-making across connected manufacturing environments. This layer encompasses factory design & simulation, production, process & quality control, maintenance and sustainability.
• LTTS’ Unified Intelligence platform: EIOne, the Engineering Intelligence platform that brings together 100+ EI solutions and agentic AI swarms into a single, governed ecosystem. It empowers enterprises to rapidly build, deploy, orchestrate, and scale AI-driven engineering & manufacturing workflows while ensuring enterprise-grade security, governance, and operational control.

“AI is transforming how manufacturers design, simulate and operate the factories of the future,” said Vishal Dhupar, Managing Director for South Asia at NVIDIA. “The EI Lab, combining NVIDIA technologies spanning hardware, software and world models and LTTS’ deep engineering expertise, gives manufacturers a practical environment to explore and validate AI-driven, simulation-first approaches to industrial transformation.”

“The EI Lab for Manufacturing brings Engineering Intelligence into a tangible manufacturing environment, allowing customers to experience how engineering, AI and physical systems can come together to address real-world challenges. Our collaboration with NVIDIA adds advanced simulation and accelerated computing capabilities to this environment, while our engineering & manufacturing expertise helps translate these technologies into practical manufacturing solutions for our global customers,” said Amit Chadha, CEO & Managing Director, L&T Technology Services and NASSCOM Executive Council Member.

The post L&T Technology Services Launches EI Lab for Manufacturing and Operations, Featuring NVIDIA Accelerated AI-Native Factory appeared first on ELE Times.

The TP4056 module with protection charges single-cell 3.7 V Li-Ion and Li-Po batteries. It also protects them from overcharge, deep discharge and short circuit. Compared with the basic version it adds the DW01A chip. AdriRobot describes its schematic, the charge current and a few practical hookups. We already covered the version without protection in Li-Ion Charger with the TP4056 Module.

The variants of the TP4056 module

Several boards are built around the TP4056. The one without protection only handles charging. The one with protection, the subject of this article, adds the DW01A and a dual MOSFET. That way it shields the battery from overcharge, deep discharge and short circuit.

There are also versions with an MT3608 step-up converter, adjustable from 4 to 28 V. The J5019, also called HW-357, does not protect the battery. The LX-LCBST, on the other hand, also has an HM5434 protection chip.

How the TP4056 module works

The TP4056 uses the CC/CV algorithm, constant current and constant voltage, typical of lithium-ion batteries. Charging happens in three phases. First, below about 2.9 V, it applies a reduced current (trickle charge) to bring the cell back to a safe level. Then it delivers the set current, up to 1 A. Finally, when the battery reaches 4.2 V, it holds the voltage and gradually reduces the current until charging ends.

The module is preset to 1 A, suitable for cells such as the 18650. The input is Micro USB or USB Type-C at 5 V. Two LEDs also show the status: red while charging, blue or green when charging is complete. The dimensions are 22 × 17.3 × 3.6 mm, the weight 2 g.

• input: 4.5 to 5.5 V;
• charge current: up to 1000 mA, adjustable with a resistor;
• end-of-charge voltage: 4.2 V with 1% tolerance;
• protections: overcharge at 4.3 V, deep discharge at 2.4 V, overcurrent at 150 mV;
• operating temperature: -10 to +85 °C.

The schematic: TP4056, DW01A and FS8205A

In the charging section the TP4056 is a linear controller for single cells. Resistor R3, 1.2 kΩ, sets the current to about 1 A. Two LEDs with 1 kΩ resistors indicate charging and end of charge, while a 0.1 µF capacitor stabilises the chip. Protection is handled by the DW01A, which monitors the cell, and by the FS8205A, a dual MOSFET. When the DW01A detects an abnormal condition, it turns off the MOSFETs and isolates the load.

There are three pairs of terminals. IN+ and IN- take the 5 V, as an alternative to the USB connector. B+ and B- go to the battery, while OUT+ and OUT- power the load. The load must be connected to OUT+ and OUT-, not directly to the battery, otherwise the protections will not kick in.

Setting the charge current

The current depends on resistor Rprog on pin 2 of the TP4056. The formula is I = Vprog / Rprog × 1200, with Vprog equal to 1 V. With 1.2 kΩ you get 1 A. With 2 kΩ it drops to about 580 mA and with 3 kΩ to 400 mA. With 5 kΩ the current is 250 mA, with 10 kΩ it is 130 mA. For a small battery, for example 500 mAh, it is better to raise Rprog: the current falls and damage is avoided.

Practical hookups and use with Arduino

In the basic hookup the battery goes to B+ and B-. The 5 V comes in from USB or from IN+ and IN-. Also, only one battery is charged at a time, respecting the polarity. At 1 A the chip can get hot, so it is better to mount it in a ventilated spot. While charging, powering a heavy load such as a motor is not recommended. The module might in fact fail to recognise the end of charge.

For a 3.3 V microcontroller, the OUT+ and OUT- output feeds a CN3903 DC/DC converter. The converter keeps the voltage stable even when the battery drops from 4.2 to 3.0 V. The solution is intended for ESP8266, ESP32-C3, STM32 and IoT sensors. For higher voltages the same output can feed an MT3608 step-up, adjustable from 5 to 28 V. It is useful for custom power banks and portable instruments. In both cases the converter must be connected to OUT+ and OUT-.

With an Arduino UNO you read the charge status from pins CHRG (7) and STDBY (6) of the TP4056. A low level indicates charging in progress and charge complete respectively. With a network-connected board such as an ESP32-C3, the battery can also be monitored remotely.

If the battery does not charge, check the voltage on VCC, which must be between 4.0 and 8.0 V. Then check the value of Rprog and the connection to the BAT and GND pins. If instead the chip gets hot, reduce the current or improve the heat dissipation.

There is the 1A charging module for lithium battery, Type-C, with protection and USB Type-C input.

Source: the original article by AdriRobot also includes the sketch for Arduino UNO and the TP4056 pin table.

Related products

• 1A charging module for lithium battery – TYPE-C

The post TP4056 Module with Protection: Li-Ion and Li-Po Charger appeared first on Open Electronics.

Five-year agreement aims to expand production and sustainment of a key naval and land-based ballistic missile defence system.

Raytheon, an RTX business, has been awarded a contract valued at up to $6.3 billion to manufacture and sustain Standard Missile-3 Block IB (SM-3 IB) interceptors. RTX and the US Department of War announced on 8 th October that the contract is for five years with two option years. The SM-3 IB missile is a short- to intermediate-range ballistic missile defence interceptors, and is deployed as part of the US and allied missile-defence network to shoot down mid-course flight phase targets during the mid-course phase of flight. The SM-3 IB missile can be launched from Ships or land-based missile launchers to provide integrated air and missile defence coverage.

The deal is designed to accelerate the replenishment of interceptor inventories and offer more stability for production and sustainment. Raytheon said most work will be performed at its sites in Tucson, Arizona, and Huntsville, Alabama. The company also announced investments to increase production capacity of the broader Standard Missile family. The contract comes amid a rising demand for missile defence systems among governments reassessing stockpiles, manufacturing capacity and protection for armed forces and strategic sites. A maximum contract ceiling of $6.3 billion, however, does not equate to an expectation that the entire sum will be spent straight away.

Increased production of complex interceptors demands specialised components, testing facilities and supply chain. To increase throughput, it will not be enough simply to speed up assembly; suppliers and manufacturers will need to meet demanding quality and performance standards. The deal underscores the need to maintain military industrial capacity as missile technology developers, as armed forces search for credible ballistic missile defences.

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Nexus software connects radar, sensors and drone systems to improve detection, information sharing and defensive decision-making.

Dutch software firm Intelic is working on AI-enabled coordination technology to help Ukraine counter an increasing onslaught of hostile drones and missiles, a Reuters report published on 8 th October said. According to the news agency’s report, the company’s Nexus software was tested at a converted airbase and was being rolled out in Ukraine’s battle environment. The software is meant to correlate information from different radar systems, sensors, and image-recognition systems so operators can piece together a clearer picture of aerial threats. The efforts target one of the key problems in modern air defences- where equipment made by different manufacturers does not always easily work with other systems.

Reuters reports, Intelic has been awarded a 30m contract by the Dutch Defence Ministry. It is designed to integrate new systems as the nature of battlespace needs shift- a key feature in a war where drone specifications, countermeasures and combat strategies are constantly changing. More generally, it illustrates how these sorts of technologies are replacing traditional hardware as the fundamental component of defence systems, with the thrust increasingly on integrating different sensors, information and weaponry, and sharing it so the system can act as a cohesive whole.

AI can identify information processing and threat detection; however, the reliability and accuracy of this is based on the sensors and the quality of training data, communications, and operational testing. Human supervision and engagement rules still need to exist, especially for automated systems that support key and high-consequence decisions. Ukraine’s application is being observed by the world as militaries research more flexible, software-based approaches to counter-drone efforts.

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Indigenous underwater robotics strengthens inspection, reconnaissance and maritime infrastructure-support capabilities.

EyeROV, a deep-tech company in Kochi, has delivered two indigenous military-grade ROVs, remotely operated underwater vehicles, to the Indian Navy. As per a report published in 9th October, the delivery is a milestone for Indian underwater robotics ecosystem and maritime support capabilities. The underwater ROVs are remotely operated vehicles (ROVs) that have a camera, lights, thrusters, and optional mission-specific sensors and systems. These are remotely operated underwater vehicles that enable inspection of underwater structures, remote investigation of locations that are otherwise dangerous to human divers, and a variety of search missions depending on their configurations.

EyeROV’s platform TROUT is for underwater observation and inspection. The technology is relevant to the navy as underwater inspection can pinpoint damage, conduct underwater inspections and maintenance, and support the work of divers without them having to perform all of the work directly. Military-grade equipment can offer higher environmental protection, reliable control links and integrated special-purpose services when compared to commercial inspection equipment. The Indo-Russian equipment aligns with India’s larger efforts to develop a wide range of defence technology across the air, land, sea, and underwater domains. Indigenously developed systems increase access to technical support, although long-term performance depends on reliability, operator training, maintenance, and integration with existing naval practices.

The project also highlights the entry of Indian deep-tech firms into niche defence markets traditionally dominated by international incumbent vendors. As demands for maritime security are likely to increase, underwater robotics could prove to be a situational awareness. The operational use and set-up of the delivered systems have not been explicitly discussed.

The post Indian Navy Receives Military-Grade Underwater ROV Systems from EyeROV appeared first on ELE Times.

New unmanned aircraft expand the Indian company’s portfolio across long-range surveillance and tactical unmanned combat aerial vehicle applications.

Indian autonomous aerial vehicle supplier ideaForge Technology has announced the launch of two variants of its ZOLT tactical UAV system- the ZOLT Hybrid and the ZOLT Unmanned Combat Air Vehicle (UCAV). The move signals ideaForge’s entry into the long-range (ISR) as well as tactical unmanned aerial system (UAS) markets. The existing ZOLT system is already thought to be in service with the Indian Army. The ZOLT Hybrid is reported to have endurance of more than six hours and a range of 100km, and is intended for long-range surveillance and persistent overwatch missions. Its hybrid architecture, combining vertical take off and landing and longer endurance, offers an advantage in areas lacking suitable runways or where they are too hard to create.

The ZOLT UCAV is intended for tactical operations and precision effects. Endurance is said to be 2-3 hours, with a launch and recovery range of 30- 50 km. Its use case is clearly different from a surveillance aircraft, with more focus on the tactical deployment and mission-specific payloads. While this introduction mirrors a broader transition in defence aviation to unmanned aircraft that operate in difficult terrain and minimise personnel exposure to immediate risk, operational performance of these aircraft will always be defined by payload mix, comms resilience, environmental conditions and mission parameters.

The development also underscores the increasing involvement of indigenous manufacturers providing specialised UAV system services to India’s armed forces. Procurement, as well as the operational evaluation and fielding of the systems, will influence the pace at which the new variants are further integrated.

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Indigenous lighter-than-air system reaches 21 km altitude during flight trial, supporting India’s high altitude surveillance ambitions.

India’s Defence Research and Development Organisation (DRDO) has successfully flight-tested a domestically developed High Altitude Platform (HAP) for long endurance surveillance and Communication capabilities. The Press Information Bureau of the Ministry of Defence issued a statement: “The experimental platform attained an altitude of 21 kilometres above mean sea level (AMSL) and hovered at 20 kilometres (AMSL) for over 30 minutes before being commanded to descend. The flight test system was recovered successfully, and post-flight performance data was analysed. The lighter-than-air platform, a potential long endurance, stratosp​heric surveillance system, is being developed by DRDO’s Aerial Delivery Research and Development Establishment (ADRDE), Agra.”

High-altitude platforms would enhance the satellite, aircraft, and existing UAS fleet by enabling constant monitoring of an area. These platforms might be used for border monitoring, intelligence collection, weather monitoring, communications support, etc. Its utility will be based on how long the platforms can stay in flight, how much equipment they can carry, and how well they can withstand the weather and stay where they are supposed to, which is called station-keeping. The trials required coordination with the Indian Air Force, CEMILAC, the Directorate General of Civil Aviation, the Airport Authority of India, and other organizations. “During the flight test, engineers at the ground control station collected real-time video as well as vehicle parameter data. The high-altitude platform payload carried an inertial measurement unit, a Global Positioning System (GPS) receiver, onboard cameras, an altitude-control system, and other electronic subsystems,” the statement mentioned.

The post DRDO Tests High-Altitude Platform, Advancing India’s Persistent Surveillance Capabilities appeared first on ELE Times.

A language model with 14.3 million parameters runs entirely on an ESP32-S3 microcontroller and makes every high-level decision for a small virtual fish tank: eat, hide, explore, socialize, rest or flee. The brain is a 7.56 MB file read directly from flash, with no network and no cloud. The tank lives on a 1.8-inch AMOLED display you can hold in one hand. So no server, no API, no connection is needed: the fish decides on its own, on the spot.

The project shows that a model distilled from roughly 26 billion parameters can run on a microcontroller costing a few dollars. The student, called pocket-tank, has 14.3 million parameters with dimension 384, 8 layers and 8 heads: about 1,818 times fewer than the teacher. In fp32 it would weigh 57 MB, in 4 bit it drops to 7.56 MB. The vocabulary is 58 tokens, a closed-schema lexicon, against the teacher’s roughly 262K tokens.

Three separate layers compiled identically in the simulator and the firmware

The architecture is split into three layers, all in common/ and compiled identically in the simulator and the firmware: the reflexes layer (tank.c), the LLM advisor (llm/) and the progression layer (progression.c). The reflexes layer handles physics, steering, schooling, touch gestures, feeding, hunger economy, vegetation and algae. It runs every frame and never blocks on the model. The LLM advisor, on the other hand, is a 4-bit inference engine in the style of llama2.c, with a word-level tokenizer and a shared state encoder.

The model runs on the ESP32-S3’s second core with SIMD dot products and quantized activations in internal SRAM. The weights are memory-mapped from flash and are never copied. That’s why the tank stays smooth while the model thinks. The fish are re-queried only when their situation changes significantly: that way four or six fish share a single brain.

Each fish periodically describes its situation to the model as a single line of text: hunger, energy, fear, who is nearby, its personality.

The model completes the line with a goal and an urgency.

The reflexes layer turns that goal into movement.

The model samples its own distribution instead of always taking the most likely answer.

When the top two choices are close, the fish visibly hesitates before committing.

There are no fallback heuristics that override the decision.

The training numbers tell the story of the distillation quality. The model was trained on 51,162 situations labeled by the teacher. The student picks the teacher’s goal in 75% of cases, while the teacher agrees with itself in 82% of cases. A hungry fish chooses seek_food at least 89% of the time, across all personalities. So the 4-bit quantization did not destroy the learned behavior.

The board, the 8 MB partition and the bench-measured timings

The firmware is based on ESP-IDF v5.4 with 8 MB octal PSRAM. The target board has 16 MB of flash and 8 MB of PSRAM. The model lives in a dedicated 8 MB raw partition and only needs to be flashed once, at address 0x290000. On the hardware side the project uses a Waveshare ESP32-S3-Touch-AMOLED-1.8 with ESP32-S3R8, a 368×448 AMOLED display, capacitive touch, IMU, PMIC and RTC. The board with screen and battery actually costs around 35 dollars.

On the real board a decision takes about 3.6 s at 12 tokens per second, with the tank rendered at 25-30 fps in parallel on the other core. In QEMU emulation a decision takes about 2.3 s. The tank starts with two fry and grows to as many as five fish. A meal lasts five or six minutes while awake. Holding a finger on the glass for three seconds calls over the fish that trust you. The reset prompt disappears after twenty seconds of silence.

For anyone who wants to rebuild the project, the software chain includes ESP-IDF v5.4, a 4-bit inference engine in the style of llama2.c, LVGL v9.2.2, SDL2, ESP Web Tools, the scripts tools/make_installer.py and tools/director.py, and QEMU in the Espressif version. Those who want to look at a board with an integrated display and LVGL graphics can also consider other options, but the heart of the project remains the 8 MB raw partition and the 4-bit engine. The code, the scripts and the flash procedure are collected in the project code repository.

Anyone who wants to replicate the sensor part can consider a 9-axis IMU module to add orientation and movement to the tank, or an RTC circuit on an I2C bus to keep track of time even when the board is off. Both components already appear on the target board, so the wiring stays consistent with the existing firmware.

Source: https://github.com/mediacutlet/pocket-tank

Related products

• Blebrick 9-Axis IMU
• I2C Real-Time Clock IC

The post A 14.3-million-parameter language model runs on an ESP32-S3 and decides the life of a virtual aquarium appeared first on Open Electronics.

I just made my first handmade PCB. I drilled and cut the lanes my self. There is no short circuit and its little bit messy. If you have any advice maybe how i can do it beter i will take it. I know i could make it with those fluid chemicals or whatever but my parents don't trust me with it. Its a 5-35 to 5 V Circuit with a LED that indicates when the 5V pin gives out power. submitted by /u/Current-Drama846 [link] [comments]

A 16-bit relay computer that computes Pi, the square root of 60000 and Dijkstra’s shortest paths. It sounds like an exercise in style, but it is a real machine, built by Peter with self-holding relays running at 24 V. The CPU is an accumulator-register design: all math and load/store operations go through the ACCU. The instruction set has 19 commands, enough to run algorithms that are usually seen on far more generous machines.

Computing Pi to 2 decimal places took a little over 8 minutes and 1,377 instructions. The integer square root of 60000 came out as 244, that is 1111 0100 in binary. These numbers say two things: the machine is slow, but the project is complete. In fact it is not a partial prototype, but a system with memory, peripherals and a 32×32 LED matrix display connected at the output.

The program counter, the SR register and the three addressing modes

The program counter has a dedicated adder capable only of adding 1. That is why the PC_HR register exists: the modified PC cannot be written directly to its own value plus 1, so an intermediate holding place is needed. The SR register stores the return address, so the CALL and RET instructions handle a single level of subroutine. No stack is implemented, and with 19 instructions none is needed.

The CPU supports 3 addressing modes. With LD X the ACCU is loaded with the word at the address following the opcode. With LD M(X) it loads the data at the address contained after the opcode. With LD M(M(X)) we reach indirect addressing. So the programmer already has the tools for pointers and tables, not just for adding two numbers.

• 16-bit CPU with ACCU accumulator register
• 19 instructions in the CPU instruction set
• 3 addressing modes, including indirect M(M(X))
• SR register for one subroutine level with CALL and RET
• Clock made up of 4 different pulses
• Relay supply at 24 V
• Memory implemented with an Arduino
• Clock generator built with a second Arduino
• 32×32 LED matrix display
• 16-bit I/O register and 16-port input register

Why the clock has 4 pulses and the memory sits on an Arduino

All registers made with self-holding relays must be cleared before any write. Consequently the clock is not a normal digital clock, but is made up of 4 different pulses. This is the part that surprises anyone trying to rebuild the project: the clear and load sequence has to be designed together with the logic, not afterwards.

The memory is not relay-based. It is implemented by an Arduino, and a second, separate Arduino builds the clock generator. That way the project stays within reach of anyone with a normal workbench: the relays do the electromechanical part, the microcontrollers do the rest. In addition, the I/O register is 16 bits and the input register is 16 ports.

Before soldering, Peter verified everything in simulation. The full-system simulation runs in the game Logic World, used to design the system and try out variants. Then there are the Excel macros, which execute the code step by step and show registers and memory up to a breakpoint. Finally the VHDL code on FPGA emulates the relay system design. Anyone who wants to follow the same path will find the full story on the page for Peter’s project on Hackaday.io, with the tests carried out and the variants discarded.

For anyone who wants to replicate the display part, the catalogue offers an FPGA-based development board to drive LED matrices, while a small 2.42-inch OLED module with 128×64 resolution and SPI or I²C interface can serve as a status panel for registers and memory. They are two different routes: the first stays consistent with programmable logic, the second costs little and connects with just a few wires.

Rebuilding this relay computer means starting from the 4-pulse clock and the accumulator register. Without those two pieces nothing runs, not even with 19 perfect instructions. The rest is patience: 24 V on the relays, flyback diodes, one Arduino for the memory and one for the clock.

Source: https://hackaday.io/project/189725-homebrew-16-bit-relay-computer

The post A 16-bit Relay Computer Runs Pi and Dijkstra appeared first on Open Electronics.

ROHM Semiconductor announces that its innovative power semiconductor solutions are being used in the electric vehicle platforms of a world-leading premium car manufacturer. In BMW’s “Neue Klasse”, ROHM’s SiC (silicon carbide) chips support the efficiency, performance and reliability of the vehicle’s electric powertrain systems.

Integrating SiC MOSFETs into the BMW Neue Klasse

The high-performance components integrated into the vehicle support key power electronics functions and are designed to ensure high energy efficiency as well as reliable operation under the harsh operating conditions typical of the automotive sector. In this way, they make an important contribution to optimising the range, charging performance and overall energy efficiency of electric vehicles.

Statements

“The integration of our SiC MOSFETs into the Gen 6 of BMW’s electric vehicles, better known as the Neue Klasse, highlights ROHM’s technological expertise in power electronics for the automotive sector,” says Wolfram Harnack, President of ROHM Semiconductor GmbH: “We are proud to contribute, together with the BMW Group, to the progress of next-generation electric mobility through our SiC technology. This underlines our long-term commitment to supporting our automotive customers with high-quality products and extremely high reliability.”

ROHM’s experience in the automotive sector

ROHM has extensive experience in the development and production of power semiconductors for automotive applications and works closely with vehicle manufacturers and Tier 1 suppliers worldwide to actively promote the electrification and decarbonisation of mobility.

For further information, visit the ROHM website.

The post ROHM Supplies SiC Chips for BMW Neue Klasse Electric Powertrain appeared first on Open Electronics.

Capturing 3D images without lenses is possible. The okooptics project proves it, starting from a Raspberry Pi camera whose lens is removed and over whose sensor a thin layer of Scotch tape is applied. The tape is not a market-stall trick: it acts as an optical diffuser and imprints a known point spread function on the scene. With the right deconvolution maths, the initially blurry capture turns into a real image.

The interesting part comes next. By weighting the point spread function across three dimensions and adding a directional bias to the process, an effect similar to a shift in the camera position is created. That way the scene reveals itself from a slightly different angle, without physically moving anything.

How deconvolution works on a blurry capture

The workflow starts with a Raspberry Pi camera without the normal lens element installed. In place of the lens, a thin layer of Scotch tape is applied over the sensor. That veil diffuses light in a controlled way, so every point in the scene leaves a blob of predictable shape on the sensor. The shape of the blob is the point spread function.

• You start with a Raspberry Pi camera without the lens element.
• In place of the lens, a thin layer of Scotch tape is applied over the sensor.
• The tape acts as a diffuser and defines the point spread function.
• Deconvolution recovers a real image from the initially blurry capture.
• Weighting the point spread function across three dimensions extends the concept to 3D.
• Adding a directional bias simulates a shift of the camera.

Once the point spread function is known, deconvolution is a linear algebra problem. The software takes the blurry capture and the diffuser function, then estimates the original image. It is the same idea behind many computational imaging techniques, but here the diffuser is a piece of tape costing a few cents instead of an optical element designed in a lab.

The directional bias that shifts the viewpoint

The jump to 3D lies in the weight given to the point spread function. Instead of treating it as a flat function, the project weights it across three dimensions. What is more, by adding a directional bias to the process, an effect similar to a shift in the camera position is created. The result is a view from a slightly different angle of the objects in front of the camera.

Rebuilding the project takes only a few parts. The Raspberry Pi camera is the heart of the system, and the Scotch tape is the diffuser. Anyone who wants to start from scratch can use a 5 megapixel camera module for Raspberry Pi, which carries the Omnivision 5647 sensor. The rest is software: the deconvolution runs on a computer or on the board itself, depending on how much power you have available.

If you want to do the computation directly on board, you need a board with a bit of muscle. The Raspberry Pi 5 with 2 GB of memory carries the Broadcom BCM2712 quad-core processor at 2.4 GHz with Arm Cortex-A76 architecture, so it handles deconvolution operations on reasonably sized images well.

The author does not hide the practical limits of the technique. The quality of the reconstruction depends on how precisely the point spread function is estimated, and tape is not an optical component that stays stable over time. What is more, deconvolution amplifies noise, so it is best to work with well-lit scenes and sensors with low background noise. Anyone who wants to dig deeper into the science behind the project will find the full story and references to the research papers on the project page on Hackaday.io.

Source: https://hackaday.io/contests

Related products

• 5 Mpx Camera for Raspberry Pi
• Raspberry Pi 5 – 2 GB memory version

The post Lensless 3D imaging with Scotch tape and deconvolution appeared first on Open Electronics.

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