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​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.

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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.

The post Keysight Introduces Velocity 4900 Series to Accelerate Test Lab Modernization for AI and 6G appeared first on ELE Times.

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.

The post Raytheon Secures Up to $6.3 Billion Contract for SM-3 Block IB Interceptors appeared first on ELE Times.

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.

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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.

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.

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📰 Газета "Київський політехнік" № 33-34 за 2026 (.pdf) kpi сб, 10/10/2026 - 21:26

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GlitchCaster is an evaluation device for electromagnetic fault injection, or EMFI. It generates an electromagnetic pulse of about 250 V through coil probes and mounts directly on the head of an old 3D printer or a CNC machine. That makes security testing repeatable, because the probe moves along the X, Y and Z axes with the precision the machine’s mechanics already guarantee. The project comes from Diffused Tactics.

The coil is brought close to the target device and the pulses fire with precise timing. The disturbance is temporary and aims to make the target fail at the exact moment it performs a check. The mount also follows the same topology as 40 mm fans, so the bracket fits many heads already found in a workshop. The module has no built-in controller: it exposes 2.54 mm headers and screw terminals, and you choose the GPIO and PWM controller.

The 250 V flyback and the 630 V 470 nF capacitor

The high-voltage section uses a DC/DC flyback circuit built around the LDA111 and the ATB3225. The storage capacitor is rated 630 V 470 nF, so its working voltage sits comfortably above the 250 V of the pulse. The included probe coil is 4.7 uH, with a 4 x 15 mm ferrite core and a 17.5-turn winding. An adapter board lets you swap between coils with a through-hole lead and coils with an SMA connector.

The measurements of the three PCBs help you judge the footprint. The main board measures 71 mm x 32 mm x 1.65 mm. The SMA adapter board measures 11.6 mm x 10.1 mm x 1.65 mm, which becomes 29.3 mm once the connectors are included. Finally, the controller board measures 71 mm x 30 mm x 1.65 mm. The supplied jumper wires are 24 inches long, enough to carry the signals from the machine head to the controller without straining the cables.

PicoEMP firmware and interchangeable SMA connectors

The firmware stays compatible with that of PicoEMP-based projects, so anyone who has already worked on that design will find the same logic. On top of that, the firmware can be customised by advanced users, who can adapt the pulse timing to their own test bench. Mounting on the printer or CNC rail does the rest: the Raspberry Pi Pico kit with sensors and actuators covers the prototyping side around the controller, if you need something to connect to the GPIOs.

Anyone who wants to rebuild the test bench will find a description of the device and its parts in the Diffused Tactics campaign on Crowd Supply. For the rest, the list of parts that matter is short and worth reading through again in order.

• LDA111 and ATB3225 for the DC/DC flyback of the high-voltage section
• 630 V 470 nF storage capacitor
• 4.7 uH probe coil with a 4 x 15 mm ferrite core and 17.5 turns
• 2.54 mm connectors, 2.54 mm screw terminals and SMA connectors
• controller board based on the Raspberry Pi Pico
• 24-inch jumper wires and adapter PCB for the probes

The strong point is the recycled mechanics. A 3D printer sitting idle in a corner becomes a three-axis positioner for the probe, and every pulse starts from the same point with the same delay. As a result, tests can be repeated and compared, instead of depending on the hand holding the coil. The GlitchCaster makes EMFI security testing accessible and repeatable by exploiting a machine that is already in the workshop.

Source: https://www.crowdsupply.com/diffused-tactics/glitchcaster

Related products

• Raspberry Pi Pico Primer Kit – Plug&Play Sensors and Actuators

The post GlitchCaster: EM Fault Injection Rides on a 3D Printer Head appeared first on Open Electronics.

Melexis has introduced the MLX91224 and MLX91225, expanding its portfolio of isolated current sensors for high-voltage applications in the automotive and industrial sectors. Offering plug-and-play measurements up to 100 A RMS, ASIL B compliance and a fast analog response in three package options, these new devices simplify the design of electric vehicle powertrains, DC-DC converters, chargers and solar systems.

The challenge of current measurement in electrification

As electrification continues to advance across many applications (renewable energy, industrial, automotive, energy storage systems…), current measurement becomes increasingly complex. Designers need accurate, isolated measurements for system control and safety. For measuring small currents, a common solution is the shunt-based approach. However, these solutions generally involve increased ohmic losses, the need for isolated amplifiers, greater assembly complexity and a significant commitment to end-of-line calibration.

The MLX91224 and MLX91225 overcome these challenges by integrating the current conductor, the sensing element, signal conditioning and isolation into a compact SOIC package. Current flows directly through the package’s low-impedance leadframe, while two sets of Hall plates differentially detect the magnetic flux generated by the current. This architecture minimizes interference from external magnetic fields and delivers a linear, high-speed analog output proportional to the measured current, with isolation values that depend on the package and support working voltages up to 1640 V RMS.

Built-in safety and fast current measurement

For automotive applications, Melexis brings to market an integrated current sensor platform with ASIL B certification unprecedented in the industry for high-voltage current measurement, in the form of the MLX91224 and MLX91225. Developed as Safety Elements out of Context (SEooC) according to ISO 26262, the devices give designers a more solid starting point for measuring safety-related current in high-voltage automotive systems. This platform ensures a fast response for current measurement in motor control, electric drives, DC-DC converters, AC and DC chargers, solar installations, power supplies and many other applications.

The devices are available in DE, DG and DH package options to meet different current, isolation and impulse overvoltage requirements. The compact DE package offers a continuous operating current capability up to 50 A (RMS value) and overcurrent detection (OCD) in a cost-effective SOIC6FL format. In the SOIC16W format, the DG variant supports a similar continuous operating current range and working voltage, guaranteed by greater isolation and creepage distance. For higher-current designs, the DH package extends the platform to a continuous operating current capability of 100 A RMS in the SOIC12W FL package, with excellent impulse overvoltage values up to ±29 kA.

A versatile solution for automotive and industrial power

All devices support AC and DC current measurement, bipolar or unipolar detection and ratiometric or fixed output configurations. The MLX91224 is designed for 5 V systems, while the MLX91225 supports 3.3 V systems, allowing designers to select the device best suited to the available supply voltage and microcontroller architecture. The OCD function is provided through a dedicated pin, with thresholds defined internally or externally via the OCD-TH pin, to support protection and monitoring functions. With an operating temperature range from -40 °C to 150 °C, the platform is designed for use in particularly demanding industrial and automotive applications.

«Many customers design solutions across a wide range of power levels, from low-power motors to higher-current infrastructure», said Bruno Boury, Product Line Director at Melexis. «The MLX91224 and MLX91225 offer a current sensor platform that can grow with their power requirements, while helping to simplify isolation, overcurrent detection and functional safety integration.»

The MLX91224 and MLX91225 extend Melexis’ integrated current sensor portfolio toward higher-current applications, supporting the company’s strategy focused on electric vehicle powertrains, electrification and sustainable energy infrastructure. The devices are designed for use in the automotive sector and beyond, covering applications such as AC and DC chargers, electric drives, DC-DC converters, solar systems, data center power distribution units, power supplies and load and demand control.

The MLX91224 and MLX91225 are available in three package options. For more information, visit www.melexis.com/MLX91224-MLX91225.

The post Melexis MLX91224 and MLX91225: Integrated Current Sensors up to 100 ARMS appeared first on Open Electronics.

The TinyGo Starter Kit with Seeed Studio XIAO brings the Go language to embedded hardware. At its centre is the pre-soldered Seeed Studio XIAO ESP32-C3 board, with 2.4GHz WiFi and BLE 5.0, which plugs into the Grove Base for XIAO. The kit includes 11 plug-and-play Grove modules and a set of official tutorials for learning by building real projects. The price is 53.54 dollars.

Go was released to the public in 2009 and today is a widely used language on the server side. TinyGo brings a version designed for microcontrollers, with support for more than 150 development boards and WebAssembly as a target. Anyone who already programs in Go therefore finds a direct path into the embedded world, without changing language.

XIAO ESP32-C3, Grove Base and 11 modules with no soldering

Assembly requires no soldering. The XIAO ESP32-C3 board slots onto the Grove Base for XIAO, which acts as an expansion board for the modules. From there each module connects with the Grove connector, so you go straight to the code. For anyone who wants to start with a cheap and versatile board, an ESP12 board with mikroBus and Grove slots remains a different but equally easy-to-program option.

• Inputs: button, touch, rotary, light, sound, temperature, vibration and acceleration
• Outputs: RGB LED, OLED display and buzzer
• Sensors included: Grove Light Sensor v1.2 with LS06-S phototransistor, Piezo Vibration Sensor, Temperature Sensor, Rotary Angle Sensor
• Other modules: Piezo Buzzer/Active Buzzer and Sound Sensor based on the LM358 amplifier
• 0.66-inch Grove OLED display (SSD1306), IIC, 3.3V/5V
• Grove RGB LED Stick with 10 WS2813 Mini 3535 LEDs

Every hardware module is backed by programming tutorials written by Patricio Whittingslow of TinyGo. The path therefore does not stop at the board: you move from the light sensor to the buzzer, from the OLED display to the RGB LED, building projects that really work. For anyone looking for a compact board for embedded and IoT prototyping, a mini development board based on the RP2040 is an interesting alternative, but here the point is something else: the Go language.

TinyGo v0.41 and native wireless support on XIAO

TinyGo v0.41 brought native wireless support to the XIAO ESP32-C3 and XIAO ESP32-S3. As a result, you write Wi-Fi applications in Go and flash them directly onto the boards, without going through other languages. The WebAssembly target also remains available, so the same environment covers both the microcontroller and the browser.

The kit sells for 53.54 dollars and includes 11 plug-and-play Grove modules. The XIAO ESP32-C3 board has 2.4GHz WiFi and BLE 5.0, so it covers both network connectivity and connectivity to nearby devices. For anyone who wants to understand how to write a Wi-Fi application in Go on this board, the maker’s site with the TinyGo tutorials is the place to start.

The kit is designed for those who already know Go and want to bring it to hardware, but also for those who want to learn a language different from the usual ones. The combination of a pre-soldered board, plug-and-play modules and official tutorials reduces the barrier to entry to a minimum. The code does the rest.

Source: https://www.seeedstudio.com/tinygo-xiao-starter-kit.html

Related products

• GANIMEDE.E12 ESP12 Board with USB, Wi-Fi, mikroBus and Grove Sockets
• RP2040 Mini Development Board

The post TinyGo Starter Kit with XIAO ESP32-C3: Go Comes to Hardware appeared first on Open Electronics.