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A portable square wave generator covering from 2 Hz to about 33.5 MHz, with adjustment steps of 2 Hz. The heart of the project is an AVR16EB28 microcontroller, which handles both signal generation and the user interface. Power is supplied by a LiPo battery, while an OLED display, rotary encoder, and push-button keypad provide full control. The project is by David Johnson-Davies, known for his experiments with AVR microcontrollers.

The frequency is set with precision, and the reading appears on the OLED display. The rotary encoder allows rapid variations, while the keypad is used to enter exact values. The whole thing fits in a compact enclosure, suitable for the workbench or the field. The 2 Hz resolution across the entire range is remarkable, and makes the device useful for testing audio circuits, filters, and timing.

Circuit and control with AVR16EB28

The schematic is simple: the AVR16EB28 microcontroller generates the square wave directly from a pin, with the frequency calculated in software. Control is via an OLED display, rotary encoder, and push-button keypad. The LiPo battery powers the whole system, with a regulator for a stable voltage. The project is designed to be replicated with easily available components.

The firmware handles the 2 Hz steps and updates the display in real time. In addition, the rotary encoder allows scrolling through frequencies smoothly, while the keypad allows direct entry of a value. The code is available on the maker’s website, and includes libraries for the display and encoder. The result is a stable and repeatable device.

Construction, power, and practical use

Construction requires a PCB, which can be made with a milling machine or through an external service. Assembly is within reach of those with SMD soldering experience, since the microcontroller is in a surface-mount package. The LiPo battery connects on the back, and the front panel hosts the display, encoder, and keypad. The whole thing is compact and easily portable.

For power, a 3.7 V LiPo battery is sufficient, with a voltage regulator for the 3.3 V of the microcontroller. Consumption is low, thanks to the OLED display and efficient sleep management. Practical use is immediate: turn it on, select the frequency, and connect the output to the circuit under test. The precision of the 2 Hz steps makes it suitable even for fine adjustments.

David Johnson-Davies’s website hosts the source code and construction details. Those who want to go deeper can consult the complete documentation, including schematics and assembly photos. The project demonstrates how a modern AVR microcontroller can generate high-frequency signals with precision, without complex external components. An elegant solution for those seeking a reliable square wave generator.

In summary, this square wave generator offers a wide range and fine resolution, all in a portable format. The choice of an AVR16EB28 ensures programming simplicity and low cost. The OLED display and manual controls make it intuitive to use, even for those unfamiliar with professional instruments. A project worth replicating.

Source: http://www.technoblogy.com/show?5QE2

The post Square Wave Generator from 2 Hz to 33.5 MHz with AVR16EB28 appeared first on Open Electronics.

As AI data centers scale to support high-power GPU clusters, the industry is shifting toward 800V DC rack power architectures to improve distribution efficiency, increase power density and support next-generation server designs. To help accelerate this transition, Microchip Technology and Navitas Semiconductor (Nasdaq: NVTS) have collaborated on an 800V DC-to-6V DC reference design for AI data center rack power applications.

The platform combines Microchip’s digital power control and security technologies with Navitas’ GaNFast gallium nitride (GaN) power devices to give developers a practical path to implement high-efficiency power conversion aligned with the Open Compute Project (OCP) 800V DC standard. Complete with reference hardware, software and design documentation, the solution helps reduce design risk, shorten development cycles and accelerate deployment of next-generation AI infrastructure.

“AI infrastructure optimization is driving one of the most significant power architecture transitions the data center industry has experienced in decades,” said Joe Thomsen, corporate vice president of Microchip’s digital signal controller business unit. “As the ecosystem moves toward higher-voltage rack power systems, developers need proven control and security to help reduce implementation risk. Our collaboration with Navitas combines digital control, hardware-based security and advanced GaN power conversion to help customers bring 800V rack power systems to market more quickly.”

At the core of the reference platform are Microchip’s dsPIC33AK Digital Signal Controllers (DSCs) and TA100 CryptoAuthentication security IC, paired with Navitas’ GaNFast FETs. The dsPIC33AK provides deterministic digital power control for high-frequency, high-efficiency DC/DC conversion, while the TA100 helps establish a hardware root of trust for authentication, secure boot and protected firmware updates. Together, these technologies make up the precision control and security foundations required for connected OCP power supply designs. The dsPIC33AK256MPS306 family is powered by a 200 MHz 32-bit core with a double-precision floating-point unit (FPU), 78 ps high-resolution Pulse Width Modulators (PWMs) and multiple 12-bit Analog-to-Digital Converters (ADCs) operating at up to 40 MSPS. The devices include library support for Commercial National Security Algorithm (CNSA) Suite 2.0 recommended post-quantum cryptographic algorithms and hardware-accelerated cryptographic functions for connected real-time control designs.

This PDB is powered by 16 × NV6034, 650 V, 17 mΩ GaNFast FETs in a stacked half-bridge topology on the primary side. The DFN8×8 dual-side-cooled package extends the performance advantages of GaN by reducing thermal resistance, allowing higher continuous power operation while maintaining exceptional efficiency. The PDB targets delivering up to 96% peak efficiency at full load with 1 MHz switching frequency, enabling a power density of 2,100 W/in³.

Approximately 20% thinner than a mobile phone, its ultra-low profile enables extremely close integration with the GPU board, maximizing transient performance and improving power distribution efficiency. Navitas’ system-level approach helps translate advances in GaN technology into measurable improvements in efficiency, power density, transient performance and total cost of ownership. Direct conversion from 800V DC to 6V DC combines both 800V DC to 50V DC and 50V DC to 6V DC conversion stages into one converter, delivering higher end-to-end efficiency.

“As AI infrastructure scales to support increasingly demanding computing platforms, Navitas’ GaNFast technology is a critical enabler of higher power density, greater efficiency and improved system performance,” said Vipin Bothra, vice president of Global Solution Marketing at Navitas Semiconductor. “By combining Navitas’ leadership in power semiconductors with Microchip’s digital control expertise, this collaboration accelerates the delivery of advanced power solutions tailored to the evolving requirements of next-generation AI data centers.”

Hardware-based security is integrated through Microchip’s TA100 CryptoAuthentication IC, enabling developers to establish a trusted foundation for system authentication and protection without implementing these capabilities from scratch. The TA100 device provides support for code authentication, including secure boot, Message Authentication Code (MAC) generation, trusted firmware updates, multiple key management protocols including Transport Layer Security (TLS), and other root-of-trust-based operations.

The Microchip and Navitas reference design provides a platform for developing high-voltage, high-power, compact rack power systems for AI data centers. The design is supported with a reference board, software and documentation, giving developers access to the resources needed to evaluate and accelerate deployment of 800V DC power conversion systems for AI data center applications.

The post The Data Center Is Moving to 800V: Microchip and Navitas Are Enabling the Transition appeared first on ELE Times.

​AI is leaving the question-answering and content-generation stage. The frontier of the next generation of AI is agentic AI. That’s an AI that perceives an objective, reasons about the problem by planning, compares multi-step plans, accesses external tools, and acts independently without a human operator. Moving from a stand-alone question-answering bot that responds to a prompt to an agent to execute a task means asking enterprise data stories, working with platforms, running workflows, testing and accessing outputs, and changing the data. By 2026, enterprises will shift from publishing generative AI experiments and exploration to launching agentic workflows-connecting foundation models to enterprise data, programs, and operations.

What Is Agentic AI?

Agentic AI is the type of AI that is trying to obtain a goal and perform a multi-step task on its own. Rather than requiring someone to make every decision for it, an agent can decompose a goal, develop a plan to attack it, determine what tools are going to be needed, do the work, and evaluate the results. A traditional AI assistant, for instance, might answer a question about whether a customer’s order is ready. An agentic AI would be able to retrieve the order details, identify that the order is held up, tap into the source system, compose an updated message to the customer, and create a case for escalation to a human customer support representative when necessary.

How Does Agentic AI Work?

So, in essence, it’s just a perception, reasoning, planning, and acting loop: A goal or task is communicated or perceived by the agent; the agent gathers data from the environment (databases, files, APIs, enterprise applications, API or other connected systems); the agent reasons with its AI model and takes actions. The agent further decomposes the goal into intermediate, achievable steps (if needed) through a set of tools that it gathers. If the step wasn’t completed successfully or if the conditions change, the agent updates the plan and repeats. Put simply, the working process of an agentic automation is something like goals, perception, reasoning, planning, tool use, action, evaluation, next action. This continuous agentic loop is what sets agentic AI apart from automation that relies on us anticipating all potential options and expressing every possible path in advance.

Agentic AI Architecture and Its Components

The architecture of a production-grade agentic AI system contains multiple layers. The first layer, that of the agents themselves, can include an AI model, such as a large language model or other foundation model that can comprehend instructions, assess context, apply reasoning, and figure out what actions can be taken. Businesses can choose various models, based on a task’s complexity, latency, cost, privacy needs, or safety. But the model is just the start of a good agentic system.

An orchestrator controls how the agent will go about completing a task. An orchestrator can decide the order of operations, control context, route information, coordinate tool calls, and manage multiple specialised agents working together. This is especially critical when an enterprise task involves multiple steps and multiple agents or multiple AI agents working together. An orchestrator offers the structure around model logic and allows organisations to manage agent-to-business system interactions.

Tools and APIs supply the AGI agent with the ability to act. A tool might be a database, enterprise search engine, API, CRM, and ERP systems, software development environment, code execution platform, communication channel, or almost any enterprise system. Without the ability to access the tools, the basic AI model is capable of modifying or creating data. With the ability to do so within a closed environment, the agent can access data and perform the appropriate activities.

Knowledge and grounding are another aspect of architecture to consider. It’s not wise to depend solely on the information within a foundation model; that’s when your enterprise agents need factually accurate and pertinent knowledge. Retrieval augmented generation, enterprise knowledge bases, semantic search, structured data, and application data can all supply this context, grounding an agent in enterprise information and decreasing the potential for generating contradictory outputs.

Memory and context enable an agent to remember information during a task or conversation, depending on your implementation. Short-term memory can give the agent a sense of how a current conversation or task is proceeding, and long term memory might contain information that will be useful for some future task. However, be cautious when considering memories in the enterprise; agents will have access to private data regarding customers, financial data, operations, and employees.

Security and governance are another critical architecture level. An autonomous system that has business systems and skills to act must be granted the right. An agent’s authority and role-based access control, the principle of least privilege, the ability to be audited, observability, and monitoring, policies, human-in-the-loop, and safety guardrails reduce the likelihood of an agent taking an unauthorised action. As enterprise AI becomes ever more autonomous, it is becoming an architectural requirement.

What Are Agentic Workflows?

Agentic workflows are flexible sequences in which agents think, plan, carry out multiple steps, assess results, and adapt appropriately. For example, at an IT-support desk, an agent could not only give instructions on how to fix an employee’s malfunctioning app but could also diagnose the issue, survey the computer’s activity logs, review recent changes to its settings, identify likely causes, offer a fix, and- with permission- carry out the fix. The agent could check whether the app’s restored to normal.

In a higher-level workflow, there may be a team of agents; one particular expert agent may go through the technical solution, another cybersecurity agent may look into potential security issues, and another could look at the solution details prior to the ultimate actions being approved. This is the multi-agent system and allows an organisation to distribute many complex workflow jobs over a number of specialist AI agents while maintaining overall control.

Enterprise Use Cases of Agentic AI

The scope of enterprise agentic AI use cases is growing fast. In customer service, for instance, agentic AI can help employees by classifying support requests, loading customer information, troubleshooting frequent issues, generating responses, recording updates, and escalating sophisticated cases to human agents. This doesn’t mean, however, that they will replace human support teams. Instead, they can automate mundane workflows and free employees to focus on more nuanced, human interactions.

An additional significant domain is software development. In agentic coding systems, AI could possibly investigate and alter web pages, generate or suggest code, run and review code, test and examine bugs, and suggest or make fixes. This is a shift from AI as a coding assistant that shows code snippets to AI programs that can perform many stages of the software development process.

Agentic Automation in security: Security is a good fit for agentic work because you wouldn’t want a security team to look at lots of alerts and aggregation points. The agents can track activity, investigate anomalies, link information, recognize attacks, specify a response, and in certain cases take predefined remediation actions. But for high-consequence actions, they need to have limited authority, be approved, audited, and reviewed by humans.

Enterprise AI agents can also perform document analysis, fraud investigations, compliance processing, customer service, research, and any other enterprise activity involving a large set of business data. For example, supply-chain agents can work out whether a supply disruption is imminent using knowledge of suppliers’ status, inventory position, demand, and logistics, and then suggest remedial measures for procurement, inventory, and logistics, and so on.

Healthcare is yet another option, mainly for administrative work such as data entry, making appointments, finding the right data, and road-mapping the workflow. AI’s use for clinical purposes has to go through a stronger validation process, as the wrong decision on the part of the AI could directly endanger patients’ lives.

Why Enterprise Agentic AI Needs Strong Governance

There are also risks associated with using an autonomous agentic AI. Entering an instruction wrong, not using the correct tool, escaping, leaking, or bending a malicious prompt are all ways to the dark side. Failure can cascade across agents in multi-agent systems and to the wider systems. Companies should regard AI agents as operational software rather than a new chat service.

Good governance might include implementing identity and access management, least-privilege permissions, requiring approvals for high-risk actions, tracking and logging actions with audit trails, providing data-protection controls, immediate defence against injection attacks, model assessment, and validation of tools and controls on resources. Also, enterprises will need accountability and ownership of what AI agents are doing. So, the new model will not be one of total freedom but controlled freedom.

The Future of Agentic AI

The advent of agentic AI will probably see the development of more specialised agents within shared enterprise IT systems. Rather than a generalist to do all types of jobs, firms will deploy specialist agentic models in software engineering, customer service, cybersecurity, finance, supply chain and more. They will operate alongside each other on shared levels of orchestration, identity, observability and governance.

This shift is also transforming how organisations conceptualize their enterprise software landscape. More and more, AI agents are being considered an operational layer that communicates with the applications already in place- rather than an expansion that necessitates replacements for every single back-end application. As agentic workflows develop further, tools such as agent registries, observability tools, policy engines, security measures, and standards for interoperability may come into play.

So, the big change, therefore, is not so much between chatbots and autonomous AI. It also falls somewhere in the middle of AI as a set of features in the software process beneath. Enterprises that can master the art of good model construction, having access to high-quality data, appropriate tooling, targeted orchestration, and appropriate governance, will be the ones to benefit from agentic AI.

Conclusion

Agentic AI is the future of enterprise AI. By integrating data, orchestration, and governance with reasoning models, enterprise AI agents become everlasting multi-step workers and not static question-answering tools. Use cases for agentic AI are emerging in customer service. Software engineering, security, finance, healthcare, and supply chain.

And that smart model isn’t enough to make it enterprise-ready. Security, permission, observability, reliability, interoperability, and human oversight will all play a role as organisations consider whether to gate agents’ transition from pilots to production. As companies start to reorganise their workflows around autonomous and semi-autonomous systems, technology leaders should have a firm grasp of agentic AI architecture, its underlying components, how it operates, and its enterprise use cases.

The post What Is Agentic AI ? Architecture, Components, Workflows, and Enterprise Use Cases appeared first on ELE Times.

Add cellular connectivity on LTE bands, both for phone calls and for broadband Internet access.

Partly because cellular networks supporting the latest data communication standards are so widespread and readily available, and partly because of the difficulty and poor economic convenience of bringing in wired telephone lines, more and more users are turning to radio-mobile telephone connections, especially when they need to work in areas that high-speed lines have not reached yet; in such cases various solutions are used, depending on the goal to be achieved. If the connection is needed to run a more or less automatic control system, you need a cellular module, whereas if you have to interface a computer, microprocessor or microcontroller to the Internet, it is essential to adopt a cellular module with a suitable data access technology.

In the latter case, given that the current focus is very much on 4G and 5G and that UMTS/HSDPA (better known as 3G) is being gradually abandoned, you need a module/modem that is at least LTE, while for phone calls (typical of remote control systems, which work with simple phone calls or SMS) 2G (GSM) or GPRS (2.5G) is still available. The project described in these pages is precisely a device that implements cellular connectivity with LTE data support, based on a recent GSM module from SIMCom, capable of supporting both ordinary phone calls and the SMS (Short Message Service) messaging service and data communication protocols from 2G up to the latest 4G.

Circuit diagram

To make it clear what we are dealing with, let us take a look at the diagram of the device, published in these pages, which shows that everything is based on the SIMCom A7682E module, which is in practice the only active element on the board; around it are some passive components, six NPN bipolar transistors, plus a TVS (Transient Voltage Suppressor) overvoltage protection array and a socket for a microSIM SIM card.

So let us start with the description of the circuit, for which we have provided only a Quadriband cellular module, namely the SIMCom A7682E, which is able to cover up to 4G and is therefore up to date with the new wireless communication technologies on radio-mobile telephone networks, at least with those currently most used, if we consider that 5G does not yet have a significant spread. Its printed circuit board has two miniature connectors, one for the connections to the outside needed for use and integration into other equipment (we can consider it a header…) and the other for firmware updating (labelled UPG). The main connector, a 20-pin one in two rows with 2×2 mm pitch, also carries the positive and negative supply, as well as the power-on control line (PWR), all the signals and communication lines to and from the SIMCom module, but also the grounds of the analog and digital sections of the module (contacts 18 and 20).

Power-on and reset control

So let us describe how the circuit works, starting from the power supply control section, which operates by acting from the outside on the ON/OFF line (pin 1); this line is used to switch the GSM/LTE module on and off while keeping it constantly powered from the Vcc and GND contacts of the pin-strip; in fact our GSM1 module is always under voltage, supplied by the Vcc line (pins 17 and 19 of the 20-pole connector) to pins 34 and 35 (labelled Vbat, because the module was designed for use in battery-powered devices) and is switched on or off by the logic level applied to pin 39 (PWR), which internally is connected to a pull-up resistor and is active at logic zero, so to switch on the GSM1 module you have to bring the ON/OFF line (contact 1 of the pin-strip) to a high logic level and drive transistor T2 into saturation, which pulls the PWR line of GSM1 low.

Reset control works in a similar way: the SIMCom module provides a reset input (RST, located at pin 83, active at logic zero and fitted with an internal pull-up resistor); the reset is obtained by bringing pin 16 (RST) of the 20-pole connector to logic 1, whereupon transistor T3 goes into saturation and pulls the RST line of GSM1 low; at the same time, VDD_EXT of the same module is brought to logic 1.

The UART lines and level shifting

Let us go on with the UART control lines, namely RTS, CTS, DTR, DCD, which go to the outside through contacts 2, 4, 10 and 6 of the connector respectively; the same applies to VRTC (contact 5) and ADC0 (7). Note that by means of jumpers JP1, JP2, JP3 and JP4 it is possible to connect or disconnect the CTS, RTS, DTR and DCD control signals on board; normally these jumpers are open and if they need to be closed, this is meant to be done by soldering the pads of the ones you want.

About the UART, note the particular configuration of the TXD and RXD lines, each of which is interfaced through an NPN transistor configured in common base, so as not to sit directly on the corresponding contacts of the pin-strip; in particular, RXD (which is an input), fitted with a pull-up resistor, is connected to the collector of T5, whose emitter is connected to the RXD pole (contact 14) of the pin-strip, and therefore when the latter is in the open state or is at a high level (voltage equal to VDDEXT of the GSM1 module) T5 is off and the module’s RXD is at a high level. TXD instead (which is an output of GSM1) drives the emitter of T4, whose collector is fitted with pull-up resistor R13, in parallel with capacitor C11 which filters out noise) and is therefore at a high level (the same potential as VDDEXT) when the SIMCom module pad is at logic 1 and at zero when it is at a low level. The external connection of TXD is located at pin 12 of the 20-pole connector. Transistors T4 and T5 ultimately serve as repeaters of the logic states and as level adapters between the module’s VDDEXT voltage (which is 1.8V, like the logic levels the TXD and RXD signals work with) and the TTL standard, whose levels are 0/5V.

The RI signal and audio

The RI signal (ring indicator for an incoming phone call) comes out of contact 8 of the connector, which leads to the collector of transistor T6, an NPN used as a static switch to repeat the module’s RI to the outside; therefore when contact 7 of GSM1 goes to a high level, pin 12 of the strip takes on the low logic level and vice versa (the high level is obtained only if pin 8 is brought to the supply positive through a resistor of suitable value). The open-collector output makes it possible to provide the incoming call signal to devices and systems with a different supply from that of our circuit, perhaps 12V, or to drive actuators.

The audio, which uses two contacts for the microphone (it is a differential input) and as many for the loudspeaker, passes through contacts 15, 13, 11, 9, which correspond respectively to MIC1P and MIC1N (microphone positive and negative) and SPK1N and SPK1P (loudspeaker negative and positive respectively).

Antenna and field LED

The antenna needed for the GSM1 module to work is connected through a gold connector on the cellular board, leading to contact 32 (RF ANT); the connector is an MMCX type. Let us go on with transistor T1, which here is used to drive the cellular module’s “field” LED locally: its base is biased by the logic level present on pin 41 (NETLIGHT) of GSM1. From the collector of the transistor runs the line that leads to contact 3 (LED) of the 20-pole connector, through which the host microcontroller (or in general the system using our module) learns about the conditions of the cellular network (presence, signal strength, availability) as well as the connection state of the module (no network signal, network present, etc.).

SIM management

Let us conclude the analysis of the circuit diagram by dealing with the SIM management lines, which interface to the contacts of the dedicated bus on the GSM1 module through the SIM_CLK (clock), SIM_RST (reset) and SIM_DATA (data channel) lines; the SIM_VDD line is used to switch the SIM on and off (power it and remove power from it) and is managed by the GSM1 module.

TVS protection and power supply

Note the presence of the array of four Zeners (D1) made up of TVS elements, that is, special diodes used to protect the SIM card from any voltage spikes caused by interference, which can travel from the power supply line all the way to the lines of the communication bus between the SIM and the cellular module. In fact, in the schematic we included it for future developments, even though it was not fitted on the prototype because the documentation provided by SIMCom does not consider it necessary for the A7682E module used here. We provided for it anyway, at the printed circuit board level, because in theory the board can support other pin-to-pin compatible SIMCom modules that might require it, or in any case if you should run into interference problems in your application.

Further protection of the communication between the SIM and the cellular module is provided by capacitors C5-C9, C6, C7, C8 connected respectively to the SIM_VDD, SIM_RST, SIM_CLK and SIM_DATA lines toward ground, whose purpose is to filter those lines from impulsive interference that could affect communication between the card (chip-card) and the GSM1 module.

The Vcc supply of the circuit is expected to be around 4 V, because the module is designed to be powered by a single-cell lithium battery (which at full charge sits at around 4.2 V…) and therefore with a DC voltage between 3.6 and 4.2 volts; it is nevertheless possible to power it from a “fixed” source, that is, from a mains power supply or a line coming from another device, as long as you stay within the range given above.

We finish the description of the schematic with the connector labelled UPG (CN1), which is a miniature 6-pole male single-in-line type with a very tight 1 mm pitch: besides carrying the 5 volt supply and ground, it conveys the DP, DM and Vbus lines of the integrated USB 2.0 connection and the Boot line to be used for programming the SIMCom A7682E cellular module. The connector is visible at the top right in the photograph of the prototype shown in Fig. 1.

Practical construction

Well, now that the schematic has been explained in detail we have to move on to the practical side: the board requires a double-sided printed circuit board, whose copper-side traces can be downloaded from the Download Sources and Gerber Files section on the presentation page for this issue. For making the PCBs you can use the inexpensive PCBPRODUCTION service. Once you receive the PCB you can fit the necessary components onto it, following, for the polarised ones, the orientation shown by the assembly drawing you see in these pages.

SMD preparation and soldering

The build requires some care, since the circuit is surface-mount and also requires a minimum of equipment consisting of at least a very fine-tipped soldering iron, solder wire (or solder paste) with a maximum diameter of 0.5 mm, medium-density flux paste, a magnifying lens and tweezers for placing the components. Soldering the SIMCom module requires the use of a hot-air station and, preferably, a heating plate able to bring the underside of the printed circuit board to a temperature of at least 100 °C. Alternatively, you can use an oven specifically for soldering or reworking SMD components. In this case, the procedure calls for first applying a low-density flux to the pads of the SIMCom module, followed by a uniform layer of solder paste. The module must then be positioned precisely in the centre of the pads, strictly respecting the orientation indicated in the prototype images and in the assembly drawing. Without moving the module, the printed circuit board goes into the oven drawer.

Once the machine has started, you follow the appropriate soldering cycle, determined by the type of paste used: lead-free (compliant with RoHS regulations) or leaded (containing lead). It is important to note that these ovens generally come with factory-preset soldering profiles, but in some cases custom profiles can be configured according to specific needs. Once the A7682E module is in place, which is the first component to solder so as to avoid having to heat the others in the “little oven” and therefore subject them to thermal stress, you can proceed with the remaining components. If you wish, it would be possible to solder all the components in one go (except for the connectors, to be soldered at the end, as well as the microSIM socket, to be soldered by hand) and in that case take the printed circuit board, spread solder paste on the pads intended for the SMD components and then put everything in the little oven, making sure that no element has moved.

Manual components and antenna

If you choose to solder the discrete components by hand, get yourself a pencil soldering iron (or a soldering station) with a fine tip and a power rating of no more than 30 watts. Apply some flux to the pads and start tinning the resistors and capacitors. Then move on to the LED and the transistors, making sure you respect the correct orientation of the terminals for the transistors, since their arrangement is unambiguous. As for the tantalum electrolytic capacitors and the LED, follow the orientation indicated in the assembly drawing shown in these pages. Pay particular attention while soldering the LED: try to minimise the exposure time to heat so that the small transparent resin window through which the light passes does not deform. Finally, insert into their respective holes the 20-pole connectors for interconnecting the board and the one for implementing firmware update and programming (UPG or CN1, as you prefer to call it).

For connecting the GSM antenna there is a special gold-plated MMCX connector, in THT format, to be soldered by hand into the dedicated pads.

The cellular antenna to be used with the circuit proposed here must be compatible with all the bands supported by the SIMCom A7682E module, therefore 850/900/1800/1900 MHz; a good example is the product 8170-ANTGSMSTL-MMCX, which is a cellular whip antenna with a magnetic base and 3 metres of cable. RG174 cable, female MMCX connector. 12 cm long, this antenna is Quadriband, compatible with GSM networks with mobile radio network frequencies at 850/900/1800/1900 MHz (824~894 MHz / 1710~1990 MHz – 880~960 MHz / 1710~1990 MHz – 1920~2170 MHz).

Staying on the build, note that jumpers JP1, JP2, JP3 and JP4 on the printed circuit board are not ordinary 2.54 mm pitch pin-strip jumpers, but are made using pads on the underside of the printed circuit board, to be joined with a drop of solder when you want one or more of them closed (ON state); in normal conditions, therefore, they are open (OFF) and if you need to connect the CTS, RTS, DTR and DCD control signals you must join the respective pads by melting solder over them until those of each jumper are united.

For use, remember that the circuit works with a power supply capable of delivering 4 volts and a current of at least 800 mA, which is the peak draw at maximum transmission power.

Let’s do a quick test

To test the operation of the cellular module we can connect it to a Personal Computer via USB, inserting a TTL/USB converter for the purpose, then issuing basic AT commands from a terminal emulator, for example those for dialling a phone number and managing a phone call (ATDT followed by the number to call and a final ;). The task becomes simpler using the base for GSM modules presented in issue no. 236 of Elettronica In (Fig. 3) and available already assembled with the code FT1427.

The FT1427 base

This base board features a 20-pin female connector compatible with the 4G module, and it packs several useful functions: power-on control, module reset and a CH340 TTL/USB converter. The latter is followed by a MOSFET logic-level translator, which adapts the TTL UART interface to the voltage levels (0/3.3V) required by the cellular module. The FT1427 board also brings the audio and microphone signals out to two jack sockets, so phone calls can be made. Finally, it integrates a switching DC/DC converter (based on the LC3406 IC) running at a high frequency (1.5 MHz, which keeps the size of the required reactive components down) that delivers 3.6V to power the SIMCom module.

Drivers and PC connection

This board is easily recognised and managed by a Personal Computer running a recent operating system (for example Windows 8, 10 or 11), but there are no problems with older Windows versions either: just download the drivers for the CH340 IC from the Internet. It is one of the most popular TTL/USB converters, and it is also used by some Arduino boards, so much so that the drivers can be downloaded, among other places, from the chip manufacturer’s website ( https://wch-ic.com/products/CH340.html ).

To manage the modem built into the GSM/4G module from Windows you can use any terminal emulator (for example Hyperterminal, MobaxTerm, Telnet…) by setting the virtual COM port assigned by the operating system once the drivers are correctly installed, and then issuing the appropriate AT commands; remember that to see the virtual COM port you have to go into Windows Device Manager and open the COM/LPT ports. The drivers also let Windows access the Internet by going into network connections and creating a connection based on a USB modem, which in this case will correspond to the adapter board.

Well, with this we think we have explained everything you will need to use the cellular module; all that is left is to wish you happy working!

Related products

• GSM 4G/LTE Cellular Module – SIMCom A7682E
• GSM Whip Antenna with MMCX Connector

The post A Miniature 4G Module: Compact LTE Cellular Connectivity appeared first on Open Electronics.

WIN Semiconductors Corp of Taoyuan City, Taiwan — which provides pure-play gallium arsenide (GaAs) and gallium nitride (GaN) wafer foundry services for the wireless, infrastructure and networking markets — has added quick-turn semiconductor backend service provider SawStreet LLC of Orlando, FL, USA to its WIN Alliance Partner Program. The partnership complements WIN’s in-house backend processes, giving customers another trusted partner for wafer grinding and thinning, dicing, pick-and-place and die inspection...

Multi-national industrial and energy company METLEN Energy & Metals S.A. of Athens, Greece — which operates the only vertically integrated bauxite, alumina and primary aluminium production unit in the European Union (EU) — has signed a long-term commercial agreement with a major Japanese chemical company for the future supply of gallium, representing up to 16% of METLEN’s total annual gallium production from its production facility in Greece, which is currently under construction...

FREE-WILi 2 is a portable open-hardware multitool that packs an entire embedded hacking lab into a single device. Inside, you’ll find two RP2350B MCUs, an ESP32-C5, a Lattice iCE40UP5K FPGA, and a Raspberry Pi CM0 running Linux. Designed for hardware hackers, penetration testers, and embedded systems developers, it lets you perform wireless testing, hardware debugging, real-time control, and electronic development all in one tool.

The board integrates a range of radios and interfaces that would normally require a desk full of modules. The ESP32-C5 provides dual-band Wi-Fi 6, Bluetooth LE, and 802.15.4. The STM32WLE5JC module handles the SX126x LoRa radio, while the CC1101 covers sub-GHz communication. The ST25R3916B takes care of NFC and RFID, so you can read, clone, or emulate tags. Additionally, the iCE40UP5K FPGA has all its pins exposed and 8 MB of dedicated SRAM for custom logic experiments.

Two RP2350s and a Raspberry Pi CM0 for computing power

At the heart of the system are two Raspberry Pi RP2350B chips. One handles the main controls, while the other is dedicated to managing the 3.5-inch touchscreen display with 480×320 resolution. For heavier tasks, there’s the Raspberry Pi CM0 module with a Broadcom BCM2710A1 SoC, a quad-core Cortex-A53 CPU at 1.0 GHz, 512 MB of LPDDR2 RAM, and 8 or 16 GB of eMMC storage. This module runs a full 64-bit Linux, so you can launch even substantial Python scripts directly on the device.

Connectivity is complete: there are 3 USB host ports, 2 of which run at 12 Mbps connected to the display RP2350 and one at 480 Mbps connected to the CM0. For the lab, you’ll find 13 GPIOs with SPI, I2C, and UART, plus software-selectable I/O voltage. There are also 4 analog inputs from 0 to 5 V with op-amp buffering, PGA, and window comparator, and 2 analog outputs from 0 to approximately 4.84 V at 25 kHz. CAN FD supports up to 8 Mbit/s with a CAN SIC transceiver.

Programmable power supply and voltage glitching

One of the most interesting features for hardware hackers is the programmable 5.5 V / 1.5 A power supply with MOSFET crowbar. This enables voltage glitching, a technique used to test device robustness. The battery is 3000 mAh with USB-aware charging, and sleep current is just 60 µA. The whole thing fits in a 152.4 x 78.9 x 22.3 mm enclosure weighing 290 grams, with 14 gamer buttons for interaction.

The stock firmware exposes a USB CLI, an on-device GUI, and the OneWili API for control from Python, Rust, or C/C++. The device also supports executing code generated by LLMs directly on it, a feature that makes it particularly suited for experimenting with AI applied to hardware. For those who want to dive into code examples and libraries, the project’s code repository is the right starting point.

The FREE-WILi 2 GUI is not just a control panel: it shows the registers of I2C devices and offers graphical controls to interact with connected components. You can also configure the connection to the Claude API to use AI models directly from the device. This makes it a versatile tool for both debugging and rapid prototyping of ideas.

For those working in the embedded world, having all this in a portable device changes the workflow. No more carrying a full test bench: with FREE-WILi 2 you can test radios, read NFC tags, debug over I2C, and even attempt voltage glitching, all from a single instrument. The presence of two separate RP2350s ensures the interface never slows down the main processes.

The board is open-hardware, so you can study the designs and adapt them to your needs. The pre-order price for the Founder Edition is $400, with shipping expected in Q4 2026. If you work with embedded systems, this is an investment that pays off in flexibility and compactness.

Source: https://github.com/freewili/onewili

The post FREE-WILi 2: An Open-Hardware Multitool for Embedded Hacking appeared first on Open Electronics.

Continuity is the first and most basic test performed on a device (chip). It checks the electrical connections between the device under test (DUT) and the automatic test equipment (ATE). Its purpose is to confirm that:

• No device pin is shorted to another pin.
• No device pin is accidentally shorted to power or ground.
• No open circuit exists inside of the device.
• Each device pin has a complete electrical path to the correct test resource inside the ATE.
• The connection between the device and the tester is neither open nor incorrectly connected.

This test is usually performed at the beginning of the test flow. Detecting opens or shorts early confirms that the device is properly connected to the tester and that the test hardware is functioning correctly. A short can also damage the device, especially if it connects a pin to an incorrect voltage, power, or ground. If an open or short is not detected, it can cause incorrect measurements, false test failures, or unreliable results in later tests.

Figure 1 The block diagram shows the required electrical connectivity from the DUT to the corresponding ATE resource through the socket, DIB, and connectors. Source: Author

Before going deeper into continuity testing, it’s important to understand the ESD protection circuit connected to the device pins.

ESD, or electrostatic discharge, is a sudden flow of electrical charge that can damage the internal circuits of a device. To protect the device, many pins include ESD protection diodes. These diodes help direct excessive current away from the sensitive internal circuitry.

A typical input/output pin may have two ESD protection diodes:

• One diode connects the I/O pin to the positive supply voltage, VDD.
• The other diode connects the I/O pin to ground, VSS or GND.

Depending on the design, some pins may have only one protection diode connected to either VDD or GND. Other types of pins, such as power, ground, or special-purpose pins, may use a different protection structure. These ESD diodes are important during continuity testing because they provide a known electrical path between the device pin and the supply or ground pins.

The ATE applies a small voltage or current to the pin and measures the response. The measured response is then compared with the expected behavior of the protection circuit.

This allows the tester to determine whether:

• The pin is properly connected to the tester.
• The device is correctly installed in the socket.
• An open connection exists in the path.
• A pin is shorted to another pin, VDD, or GND.
• The device may be damaged or incorrectly connected.

Therefore, continuity testing does not only check a direct wire connection. It also uses the expected electrical behavior of the device’s ESD protection circuit to verify that the pin and its connection path are functioning correctly.

Figure 2 Here is an illustration of the ESD diode connections for a typical I/O pin. Source: Author

Let us now understand the continuity test using a device pin with two protection diodes, as shown in Figure 2. This test first checks whether the protection diode connected to VDD is functioning correctly (neither open nor shorted), as shown in Figure 3.

Figure 3 The test shows the electrical path of the current between the “resource inside of the ATE” to the VDD diode. Source: Author

The following steps are used to perform the test:

1. Ground all pins except the one under test.
2. Using a parametric measurement unit (PMU), force a small positive current into the pin, typically +100 µA to +500 µA.
3. Apply a +3-V voltage clamp at the same time. This acts as a safety limit, preventing the voltage from climbing too high if the diode isn’t conducting properly (for instance, in an open-circuit condition).
4. While forcing this current, use the PMU to measure the resulting voltage at the pin.

Based on the measured voltage, the test result will fall into one of the following categories.

This method is generally used to test signal pins, such as input and output pins. It’s not normally used for power pins, such as VDD or VSS.

Next, we check whether the protection diode connected to GND is functioning correctly, as shown in Figure 4.

Figure 4 The image shows the electrical path of the current between the “resource inside of the ATE” to the GND diode. Source: Author

We can check the protection diode by following the steps below:

• With pins still grounded, force a small negative current into the pin, typically between -100 µA to -500 µA. This forward-biases the GND diode.
• Apply a – 3 V voltage clamp to prevent the voltage from dropping too far.
• While forcing this current, use the PMU to measure the resulting voltage at the pin.

Based on the measured voltage, the test result will fall into one of the following categories.

The same procedure is repeated sequentially for each applicable device pin until the continuity check is complete. It’s important to note that power pins, such as VDD and GND, are not typically tested using this method. These pins require a different testing approach, which will be discussed separately in a future article.

Keep in mind that each device has its own characteristics, so you may need to experiment to find the test method that works best for your device.

In the upcoming articles, we will build on the fundamentals covered here and take a more practical look at continuity testing. Topics will include:

1. Writing a continuity test method in C++: A step-by-step look at how a continuity test can be implemented in an ATE test program.
2. Reviewing a real continuity test data log: Understanding how to read the test results and interpret pass and fail conditions.
3. Debugging continuity test failures: A practical approach to identifying the possible causes of continuity failures and the steps used to troubleshoot them.

Usman Khan is an analog/mixed-signal test engineer.

Related Content

• Anatomy of ATE PCB Assembly
• Guidelines facilitate load-board design
• Key Design Steps for DUT Areas on ATE PCBs
• 3D TSV Test: ATE challenges and potential solutions
• Modular programmable power supply platform supports ATE

The post Continuity testing in semiconductor ATE: Fundamentals and test methods appeared first on EDN.

Nuvoton Technology will begin providing samples of the KA85010UA, a 26-cell battery monitoring IC equipped with integrated electrochemical impedance spectroscopy (EIS) functionality, in January 2027. The IC uses Nuvoton’s proprietary inductor-based EIS method instead of the conventional resistor method. By using an inductor to store and circulate energy as AC measurement current, the technology significantly reduces energy loss and heat generation during EIS measurements. According to Nuvoton’s evaluation, the method reduces both power loss and PCB temperature rise by approximately 93% compared with the conventional resistor method. This low-power and low-heat operation facilitates the integration of EIS into automotive batteries and energy storage systems. The EIS functionality can support battery degradation diagnosis, state-of-health (SOH) estimation, lifetime prediction, and early detection of cell abnormalities. Nuvoton also provides EIS analysis algorithms and end-to-end implementation support to help customers integrate battery diagnostic functions into their applications.

As vehicle electrification and the adoption of energy storage systems (ESS) continue to expand, batteries have become essential components supporting modern infrastructure, making battery safety more important than ever. In particular, as battery systems increase in capacity and output, there is growing demand for technologies that detect early signs of internal cell short circuits, abnormal heating, and other issues to prevent serious failures and accidents.

Conventional battery systems estimate battery condition by measuring voltage, current, and temperature, but it is difficult to directly capture changes occurring inside the battery. EIS measurements, by contrast, can quantitatively capture changes in internal battery conditions as impedance characteristics and are therefore expected to be used for estimating state of health (SOH) and internal temperature, as well as for early detection of signs of cell abnormalities. Until now, however, EIS assessments have mainly been conducted in laboratory environments using dedicated measurement equipment. Implementing EIS in applications has presented various challenges, including managing heat and noise, controlling measurement timing according to operating conditions, and developing EIS analysis algorithms for the acquired data.

To address these challenges, the Company has developed the KA85010UA, a Gen 6 battery monitoring IC equipped with EIS functionality. The inductor method suppresses heat generation and enables EIS measurements across a wide range of operating states, including operation, charging, and rest. In addition, by providing EIS analysis algorithms that leverage expertise gained through joint verification with automakers and research institutions, we help customers accelerate the realization of battery diagnostic solutions.

Features of New Product:

1. EIS is integrated into the BM-IC, enabling quantification of internal battery conditions Enables degradation diagnosis, lifetime prediction, and early detection of signs of cell abnormalities, contributing to improved safety in vehicles and energy storage systems.

In addition to battery monitoring functions, this product integrates EIS-based impedance measurement into a single chip. EIS is a technique that superimposes an AC current on a battery and calculates impedance from the resulting voltage and current responses. This makes it possible to perform EIS measurements within the system, without the dedicated measurement equipment previously required, enabling continuous monitoring and diagnosis of internal battery conditions.

The impedance characteristics obtained through EIS contain different information about the battery interior depending on the frequency range. The high-frequency range reveals changes in terminals, wiring, and electrolyte; the mid-frequency range reveals changes in the negative electrode; and the low-frequency range reveals changes in the positive electrode and internal reactions. By quantitatively assessing internal battery conditions that are difficult to determine from conventional voltage, current, and temperature measurements alone, EIS can estimate SOH and internal temperature, predict battery lifetime, and provide early detection of signs of cell abnormalities, contributing to improved battery safety in vehicles and energy storage systems.

2. Proprietary inductor method enables low-power-consumption impedance measurement Reduces PCB temperature rise during EIS measurements by approximately 93% compared with conventional methods, facilitating implementation of EIS functionality in automotive and energy storage systems.

EIS measurements require a circuit that applies an AC current to the battery. With conventional resistor methods, several amperes of current flow through a resistor, causing much of the energy to be dissipated as heat. As a result, excessive heat generation and increased power consumption have been key challenges.

This product uses the Company’s proprietary inductor method instead of the conventional resistor method. Two switches (FETs) control the current direction, and energy stored in a coil (inductor) is circulated as AC current to generate the measurement current, significantly reducing energy loss. In the Company’s evaluation, it was confirmed that both power loss and PCB temperature rise could be reduced by approximately 93% compared with the conventional resistor method.

These low-power and low-heat characteristics facilitate implementation of EIS functionality in automotive batteries and energy storage systems. They also enable stable acquisition of EIS measurement data across a wide range of operating states, including operation, charging, and rest, contributing to advanced battery condition monitoring and diagnostics.

3. Provides EIS analysis algorithms and end-to-end support, from setting up the EIS measurement environment to product implementation Shortens the development cycle by supporting rapid implementation of battery diagnostic solutions.

Battery diagnostics using EIS require more than simply acquiring impedance data. To make use of the data, analysis techniques are needed to estimate SOH and internal temperature and to detect cell abnormalities. Commercial implementation also requires an environment for accurate impedance measurements, EIS analysis algorithms tailored to the battery type and application, and software for system implementation.

Drawing on EIS data-analysis expertise gained through verification with automakers and research institutions, the Company provides end-to-end support, from setting up the EIS measurement environment and acquiring and organizing battery data to implementing estimation and abnormality-detection functions and integrating them into applications.

This support allows even customers who are just beginning to use EIS functionality to build battery diagnostic systems more easily, start up their systems sooner, and shorten development cycles.

Applications:

Automotive (EVs), large-scale energy storage systems (ESS), reused-battery diagnostic systems, etc.

Product name:

EIS-equipped Gen 6 battery monitoring IC “KA85010UA”.

Specifications:

Item	Description
Product number	KA85010UA
Maximum number of connected cells	26 cells
Main functions	Cell voltage, current, and temperature measurement; EIS measurement
EIS method	Inductor excitation method; V/I complex calculation using quadrature detection
Voltage measurement accuracy	±2.0 mV (EOL)
Functional safety and quality	ISO 26262 compliant (ASIL-D); AEC-Q100 compliant

The post Nuvoton Introduces 26-Cell EIS Battery Monitoring IC for EV Battery Diagnostics appeared first on ELE Times.

It smells exactly how you’d imagine submitted by /u/jellzey [link] [comments]

As AI data centers scale to support high-power GPU clusters, the industry is shifting toward 800V DC rack power architectures to improve distribution efficiency, increase power density and support next-generation server designs. To help accelerate this transition, power semiconductor firm Navitas Semiconductor Corp of Torrance, CA, USA and Microchip Technology Inc of Chandler, AZ, USA have collaborated on an 800V DC-to-6V DC reference design for AI data-center rack power applications...

Layer <0,1,0,1,0,0,0> segment(01,01). 10kx10k image, zoom in. submitted by /u/protofield [link] [comments]

A retro-arcade clock on a 128×64 RGB LED matrix driven by an ESP32-S3 brings 1980s video game icons to the workbench. Keralots’ project combines fourteen animated clock styles, weather without an API key, and PC performance monitoring in a single desktop device. Configuration happens from the browser, without recompiling the firmware.

The hardware foundation is two Waveshare P2.5 64×64 HUB75E panels, scan 1/32, chained into a single 128×64 pixel canvas. The FM6126A driver requires initialization that the firmware handles automatically at startup. The control board is an ESP32-S3-WROOM-1 (N16R8) devkit, with 8MB of PSRAM and 16MB of flash: enough space for the firmware, GIFs, and custom animations.

Styles, Cycle All, and custom animations

The clock styles cover arcade classics: Mario, Space Invaders, Pac-Man, Snake, Tetris, Asteroids, Dino Runner, and Matrix Rain, plus Weather variants and others. Cycle All mode alternates styles in sequence, with a configurable duration from 5 to 3600 seconds per style. Ambient screensavers and user-uploaded GIFs can also replace the clock, with hourly scheduling.

Storage for animations on the board is 128KiB, within the 4MB of available flash. An empty tested device holds about 23 animation frames. The input limit for GIFs is 8MiB, while the converted .pca file has a maximum of 1.5MiB and up to 360 frames. The command-line tool tools/gif2pca.py, which requires pillow, converts GIFs into the proprietary format.

Power supply and measured consumption

Power comes from a dedicated 10A 5V supply, with a 2200µF, 25V capacitor on the bus. Estimated consumption in use is about 10W, with measurements under 30W in normal conditions. The power connection goes through a USB-C power breakout and an XT60 panel feed, keeping wiring tidy and safe.

PC monitor mode receives metrics over local UDP on port 4210 from a companion app for Windows or Linux. The app sends up to 20 CPU, GPU, RAM, and network metrics, with a default update interval of 3 seconds. When the PC is offline, the clock automatically returns to displaying the time.

The optional weather feature uses Open-Meteo, which requires no account or API key, with updates every 10 minutes. The built-in web interface configures clock style, per-element colors, brightness with scheduled night dimming, timezone with automatic DST, and OTA updates. All code, firmware, and conversion tools are collected in Keralots’ repository.

For those who want to rebuild the project, the documentation includes the wiring diagram for the HUB75E panels and instructions for flashing the firmware. Keralots’ repository is the starting point for downloading the code and utilities. Keralots’ repository contains the full firmware and the companion app.

The component list for the build is as follows:

• ESP32-S3-WROOM-1 (N16R8) devkit
• Waveshare ESP32-S3-Zero
• 2x Waveshare P2.5 64×64 HUB75E panels
• FM6126A driver
• 2200µF, 25V capacitor
• USB-C power breakout
• XT60 panel feed

Choosing an ESP32-S3 with generous PSRAM and flash keeps the project smooth even with complex animations. The combination of P2.5 panels with a 2.5mm pitch offers good pixel density for a desk clock. The result is a device that changes appearance with a click, from minimalist clock to arcade system monitor.

Source: https://github.com/Keralots/AnimatedPixelClock

The post Retro-Arcade Clock on RGB LED Matrix with ESP32-S3 appeared first on Open Electronics.

On a safety module.l took apart to take a look, i have no intention to put it back in production. submitted by /u/PatrikuSan [link] [comments]

Is there actually any AI in the “AI Box”? Highly doubtful. That said, it’s still reasonably durable, along with being platform-nimble. And the price-attractiveness is indisputable.

Sorry, readers. To quote baseball terminology, when it comes to teardowns of Apple CarPlay and Google Android Auto wireless adapters, I’m “0-for-3”. Not when it comes to fundamentally doing them, mind you; here are the two that EDN has published so far:

• Adapter makes Apple CarPlay connectivity wireless
• Adapter makes Google’s wireless Android Auto a reality

And the “combo” conclusion is appearing today. But none of them match the ones I told you I’d dissect at the end of my initial conceptual coverage in January.

Why? Well, as I mentioned at the time, the original CarPlay one I ordered never ended up getting delivered, so I’ll deflect any personal responsibility for that particular change in plans. And as for the Android Auto one, I realized upon further reflection that the device I was planning on disassembling was already obsolete, so I instead went with something more up-to-date and otherwise interesting; that NFC subsystem, for example.

In this case, as I re-read the initial January coverage, I realized that I’m being a bit overly harsh on myself. I’d actually never published a promise to subsequently tear down one of the combo devices (with both Apple CarPlay and Google Android Auto support) I’d gotten working in my wife’s Land Rover, although that was always my “inner voice” intent. Specifically, I’d been targeting the one from VCARLINKPLAY as my patient.

But it’s admittedly difficult for me to destructively disassemble perfectly good (and already setup and ready-to-go) hardware, as anyone who’s seen my editorial celebrations after successful re-assemblies and subsequent donations already realizes. That said, from past experiences with the CarPlay- and Android Auto-only devices, I doubt I’d have similar back-together success this time. So, when I came across a cool-looking alternative on eBay for only $9.98 plus tax, I couldn’t resist.

AI? Really?

This first photo of the outer packaging for the actual product I was shipped will, I think, explain the AI references in the title and subhead, not to mention in this particular section header.

As I recently wrote, regarding a different device I was in the process of dissecting:

Is there something explicitly artificial intelligence-related to this product (and/or the manufacturer, more generally), or is it just one of those cases nowadays where “anything sounds more important if you tack “AI” onto it”?

I think we all already know the answer to that question, right? Anyway, onward with the remaining few meaningful sides of the outer packaging.

As usual with products like this, there’s an FCC logo suggestive of certification printed on the packaging, but no actual certification ID to be found anywhere. Again, sigh.

And now let’s open ‘er up.

A sliver of literature, and a nicely included USB-A to USB-C adapter.

And now, last but definitely not least, our patient, initially complete with protective plastic on both the glass (presumed) top and USB-A plug.

A vented metal (aluminum, again presumed) chassis.

Once again, no FCC certification ID. But at least we now know the product code: Q1AS. Not that it helps us much. Google it and you’ll find devices in multiple shapes, sizes and colors, and in both CarPlay-only, Android Auto-only and combo flavors, the latter suggestive of common hardware differentiated solely via software.

I’m guessing this is for attachment to a keychain? Or a leash, mebbe?

And, wrapping up the overview, let’s ditch the plastic bits.

Diving inside

I’d previously mentioned that I thought the topside was glass, therefore the “reasonably” qualifier alongside “durable” (referencing the otherwise-metal body) in the subhead. Let’s test that hypothesis.

Where have I seen something like this before? Ah yes. Yep, glass.

And we’re in.

That glue you see on both ends of the PCB doesn’t seemingly do much; popping out the circuit board was easy.

Let’s look more closely at the topside first.

A nifty marking pattern atop the largest IC, presumably the application processor, although the absence of any meaningful accompanying info isn’t helpful. Arm-based, presumably. Albeit not definitively.

The other two ICs, both above it, include XTX Technology’s XT25F128F serial NOR flash memory, presumably storing system firmware along with various data bits, and to its right a smaller chip marked as follows, whose identity escapes me (readers?).

B11
3959
2512

At bottom right is a 24-MHz crystal, presumably feeding oscillation to the application processor. And on the right edge is the multi-function LED, whose illumination heads out the top via an integrated light pipe.

Now for the PCB’s other side.

The largest silicon sliver this time is the AIC8800M40, a dual-band Wi-Fi-plus-Bluetooth controller from Shenzhen TrolinkTek Technology. The PCB-embedded antenna is to its left, although reiterating what I’ve said before, I only see one of them and I therefore don’t know how it’s capable of handling both 2.4 GHz and 5 GHz duties.

Also located on this particular piece of PCB real estate are four odd-lead-count packaged devices, three of them (all five-lead) marked “t50KfP1” and the other three-lead one stamped with “ADAA” on top. I’m guessing they’re transistors (duals in the five-lead cases) but again welcome reader suggestions. And once again we find a user-inaccessible SPST switch, in the diagonally opposite corner from the aforementioned antenna. Freakish.

Thus concludes another editorial series. I hope you’ve found it to be as educational and otherwise enjoyable as I have. Closing reader thoughts are as-always welcome in the comments!

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

Related Content

• Adapter makes Google’s wireless Android Auto a reality
• Adapter makes Apple CarPlay connectivity wireless
• Apple CarPlay and Google Android Auto: Usage impressions and manufacturer tensions
• Spotify’s Car Thing: “Why does it exist” is the crux of the questioning
• Amazon’s Echo Auto Assistant: Legacy vehicle retrofit-relevant

The post An Apple-plus-Google adapter that’s AI in name only (sigh) appeared first on EDN.

Murata Manufacturing, a Japanese global leader in electronic components and communication modules has begun mass production of the LLD series, the world’s smallest line of three-terminal, low equivalent series inductance (ESL) multilayer ceramic capacitors (MLCC). Measuring just 0201-inch size (0.6 × 0.3 mm), the new products reduce mounting area by approximately 64% compared with Murata’s previous smallest 0402-inch size (1.0 × 0.5 mm), freeing up valuable PCB space in compact devices such as smartphones and wearables.

Today, as the use of powerful ICs is embedded into smartphones and wearables, providing a stable power source is difficult. During high speed operation, the current fluctuates very fast, resulting in fluctuations in voltage to the IC. Reducing this instability to the lowest possible level is critical. Three-terminal capacitors with their four current paths are shorter than two-terminal devices and exhibit a lower ESL than traditional two-terminal components.

At the same time, thinner and smaller electronic devices require efficient use of limited PCB space. However, miniaturizing three-terminal capacitors has been difficult because their internal and external electrode structures are more complex than those of conventional two-terminal types.

Murata overcame this challenge by optimizing the electrode design and advancing its manufacturing processes. The resulting 0201-inch products enable high-density mounting while stabilizing the power supply voltage near ICs, even at high frequencies. This gives designers greater flexibility in component placement and circuit design around ICs, supporting higher performance in space-constrained electronic devices.

Two models are available. LLD033R60G105ME01 has a capacitance of 1 µF, a rated voltage of 4 Vdc, and an operating temperature range of -55 to +85°C. LLD033D80E105ME01 has a capacitance of 1 µF, a rated voltage of 2.5 Vdc, and an operating temperature range of -55 to +105°C.

Murata will continue developing compact, high-performance MLCCs for mobile and wearable devices, contributing to further miniaturization and enhanced performance of electronic equipment.

The post Murata Begins Mass Production of World’s Smallest 0201-Inch Three-Terminal Low-ESL MLCCs appeared first on ELE Times.

​According to General Motors’ official newsroom, General Motors (GM) and LG Energy Solution are collaborating to produce lithium manganese-rich (LMR) battery technology that could reduce the cost of electric vehicles while delivering high energy density. This joint venture is upgrading its Ultium Cells plant in Spring Hill, Tennessee, to produce LMR battery cells for future EV applications.

The manufacturing plant is expected to be completed by 2028 to produce LMR cells that could deliver 33% higher energy density than Lithium Iron Phosphate (LFP) battery cells at a comparable cost. The major components of LMR battery technology contain 35% nickel, 65% manganese, and virtually no cobalt, targeting future GM electric full-size trucks and SUVs. LFP batteries are widely used in electric vehicles because of their low cost and thermal stability, but they require large and heavy battery packs to cover a longer range.

LMR addresses this challenge with higher energy density, allowing manufacturers to build smaller and lighter battery packs while delivering the same amount of power. The chances of adopting this concept are high, as major manufacturers look for alternatives that can balance cost, energy density and performance. The move also highlights the growing focus on developing new technologies at scale using existing manufacturing facilities.

If this concept is successfully commercialized, the rate of adopting this technology for future electric vehicles will depend on several factors such as cycle life, charging performance, thermal management, manufacturing cost, and the amount of production.

The post GM and LG Develop Lithium Manganese-Rich Batteries to Lower EV Costs, Increase Energy Density appeared first on ELE Times.

​Chinese automobile manufacturer Geely launched Geely Smart Charging, an ultra-fast AI-powered charging system on September 23 in Ningbo, China, featuring real-world demonstrations. The new technology introduced by Geely significantly reduces charging time, charging an electric vehicle from 10% to 70% in 4.5 minutes and to 97% in 8 minutes and 40 seconds.

The core components used in building this efficient charging technology include Xingrui PowerMind AI to adjust power dynamically, Fifth-Gen Charging Station (C12) that delivers power up to 2,250 kW (2.25 MW) per connector, Next-Gen Ultra Short Blade Battery that reduces charging heat by 10% and supports ultra-fast charging speed, and Lithium-Ion Pulse Restoration Tech, which uses micro-pulse currents and a 5-point liquid-cooling system to increase battery cycle life by 20%.

The technology combines 2.25 MW of charging power with a high-rate battery designed to store a large amount of electrical power in a very short time interval. The company says this technology is introduced to address a major problem in EV adoption: charging vehicles for long hours compared with traditional refueling.

The company is also incorporating artificial intelligence in this charging solution to monitor battery conditions and dynamically manage parameters such as temperature and charging performance during a charging state. Using artificial intelligence in high power charging solution is aimed at making fast charging more practical while maintaining battery safety.​

The post Geely Launches 4.5-Minute EV Charging Technology With 2.2 MW Power appeared first on ELE Times.

Davisan1001’s project solves a classic problem for anyone working with LED matrices: getting an even, uniform glowing surface, without halos and without seeing the individual pixels. The solution is a 3D printed enclosure for a 64×64 RGB LED matrix, with a mounting point for a Raspberry Pi. The front uses simple materials: a 3D printed grid, baking paper and a sheet of clear plastic.

The grid is the heart of the system. Each cell of the grid lines up with one LED of the matrix and blocks the light that would otherwise spill onto the neighbouring LEDs. This way every light point stays separate from the others and the diffusion happens in a controlled way. The baking paper, placed above the grid, acts as the diffuser: it is thin, easy to handle, flat and even in colour.

3D printing the grid

The grid does not require complex 3D modelling. Davisan1001 exploits the slicer settings: a flat square is printed with a grid infill and zero solid top and bottom layers. The slicer generates the cell structure by itself. The result is a precise grid, with thickness and cell size controlled by the print parameters.

The choice of diffusion materials is not random. Plain white paper blocks too much light, while waxed paper gives poor output and a poor off-axis view. Baking paper, on the other hand, is opaque enough to diffuse the light without blocking it. A sheet of clear plastic completes the assembly, protecting the paper and giving rigidity to the surface.

Materials and assembly

Rebuilding the project takes only a few components. Besides the 64×64 RGB LED matrix and the Raspberry Pi, you need the 3D printed grid, baking paper and a sheet of clear plastic. The printed enclosure includes the mounting point for the Raspberry Pi, so the electronics stay integrated and tidy.

• 64×64 RGB LED matrix
• Raspberry Pi
• 3D printed grid
• Baking paper
• Sheet of clear plastic

The assembly is within anyone’s reach: place the grid over the matrix, then the baking paper and finally the plastic sheet. Everything closes up inside the enclosure. For those who want to dig deeper, the page of Davisan1001’s project collects the files to print and the useful instructions to replicate the diffuser.

The result is an LED panel with a smooth surface and well-defined square pixels. The solution is inexpensive and uses materials that almost everyone has at home. What is more, the technique of the slicer-generated grid can be reused for other LED matrix sizes or for similar projects.

Source: https://www.thingiverse.com/thing:6687509

The post Cheap LED diffuser with a 3D printed grid appeared first on Open Electronics.

India’s Ministry of Defence announced on September 23 that it has entered into a contract with the government-owned enterprise, Bharat Dynamics Limited (BDL), to procure 160 Satellite Smart Anti-Airfield Weapons for the Indian Air Force, worth roughly 811 crores. The contract is a step towards augmenting the Air Force’s precision-strike capabilities and promoting a robust domestic defence production ecosystem. BDL is a state-owned defence company that manufactures guided weapons and other equipment.

BDL Contract Supports Indigenous Air-Launched Weapon Procurement

A satellite-guided weapon uses its positioning information to estimate the location and guide the weapon to programmed points. There are two possibilities depending on the weapon type: some cartridges use inertial navigation combined with satellite information, which means the weapons estimate a displacement between two waypoints based on an internal inertial navigation device. The fusion of the two-navigation mode allows better guidance continuity in a degraded environment. An anti-airfield weapon is designed for an airfield-related target.

Such cartridges need to be suited to the aircraft as well as specific mission planning and release procedures. They must meet preconditions such as accuracy, navigation capabilities, robustness, and compatibility with the launch platform. The statement also mentions that Indian defence manufacturers will be providing the supply of equipment to the armed forces. India has to develop industry and supply chains, along with maintenance skills, with respect to indigenous manufacturing. The timings of manufacturing, milestones, and operationalisation are subject to contractual obligations and the procurement process.

The post India Signs Rs 811 Crore Contract for 160 Satellite Smart Anti-Airfield Weapons appeared first on ELE Times.