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КПІшники — перші серед 787 команд на міжнародних змаганнях з кіберзбезпеки! KPI4U-2 ср, 11/12/2025 - 16:35

Blockchain Forensic Forum kpi ср, 11/12/2025 - 16:06

Infineon Technologies AG of Munich, Germany says that it is providing its gallium nitride (GaN) technology for the next generation of solar microinverters from global energy technology company Enphase Energy Inc, which is said to be the world’s leading supplier of microinverter-based solar and battery systems. Infineon’s CoolGaN bi-directional switch (BDS) technology enables significant enhancements in power output, energy efficiency and system reliability for Enphase’s IQ9 Series Microinverters. For the new IQ9N-3P Commercial Microinverter, this helps to simplify design complexity and lower installation and balance of system costs...

In the era of Industry 4.0, manufacturing is no longer defined solely by mechanical precision; it’s now driven by data, connectivity, and intelligence. Yet downtime remains one of the most persistent threats to productivity. When a machine unexpectedly fails, the impact ripples across the entire digital supply chain: Production lines stop, delivery schedules are missed, and teams scramble to diagnose the issue. For connected factories running lean operations, even a short interruption can disrupt synchronized workflows and compromise overall efficiency.

For decades, scheduled maintenance has been the industry’s primary safeguard against unplanned downtime. Maintenance was rarely data-driven but rather scheduled at rigid intervals based on estimates (in essence, educated guesses). Now that manufacturing is data-driven, maintenance should be data-driven as well.

Time-based, or ISO-guided, maintenance can’t fully account for the complexity of today’s connected equipment because machine behaviors vary by environment, workload, and process context. The timing is almost never precisely correct. This approach risks failing to detect problems that flare up before scheduled maintenance, often leading to unexpected downtime.

In addition, scheduled maintenance can never account for faulty replacement parts or unexpected environmental impacts. Performing maintenance before it is necessary is inefficient as well, leading to unnecessary downtime, expenses, and resource allocations. Maintenance should be performed only when the data says maintenance is necessary and not before; predictive maintenance ensures that it will.

To realize the promise of smart manufacturing, maintenance must evolve from a reactive (or static) task into an intelligent, autonomous capability, which is where Industry 4.0 becomes extremely important.

From scheduled service to smart systems

Industry 4.0 is defined by convergence: the merging of physical assets with digital intelligence. Predictive maintenance represents this convergence in action. Moving beyond condition-based monitoring, AI-enabled predictive maintenance systems use active AI models and continuous machine learning (ML) to recognize and alert stakeholders as early indicators of equipment failure before they trigger costly downtime.

The most advanced implementations deploy edge AI directly to the individual asset on the factory floor. Rather than sending massive data streams to the cloud for processing, these AI models analyze sensor data locally, where it’s generated. This not only reduces latency and bandwidth use but also ensures real-time insight and operational resilience, even in low-connectivity environments. In an Industry 4.0 context, edge intelligence is critical for achieving the speed, autonomy, and adaptability that smart factories demand.

AI-enabled predictive maintenance systems use AI models and continuous ML to detect early indicators of equipment failure before they trigger costly downtime. (Source: Adobe AI Generated) Edge intelligence in Industry 4.0

Traditional monitoring solutions often struggle to keep pace with the volume and velocity of modern industrial data. Edge AI addresses this by embedding trained ML models directly into sensors and devices. These models continuously analyze vibration, temperature, and motion signals, identifying patterns that precede failure, all without relying on cloud connectivity.

Because the AI operates locally, insights are delivered instantly, enabling a near-zero-latency response. Over time, the models adapt and improve, distinguishing between harmless deviations and genuine fault signatures. This self-learning capability not only reduces false alarms but also provides precise fault localization, guiding maintenance teams directly to the source of a potential issue. The result is a smarter, more autonomous maintenance ecosystem aligned with Industry 4.0 principles of self-optimization and continuous learning.

Building a future-ready predictive maintenance framework

To be truly future-ready for Industry 4.0, a predictive maintenance platform must seamlessly integrate advanced intelligence with intuitive usability. It should offer effortless deployment, compatibility with existing infrastructure, and scalability across diverse equipment and facilities. Features such as plug-and-play setup and automated model deployment minimize the load on IT and operations teams. Customizable sensitivity settings and severity-based analytics empower tailored alerting aligned with the criticality of each asset.

Scalability is equally vital. As manufacturers add or reconfigure production assets, predictive maintenance systems must seamlessly adapt, transferring models across machines, lines, or even entire facilities. Hardware-agnostic solutions offer the flexibility required for evolving, multivendor industrial environments. The goal is not just predictive accuracy but a networked intelligence layer that connects all assets under a unified maintenance framework.

Real-world impact across smart industries

Predictive maintenance is a cornerstone of digital transformation across manufacturing, energy, and infrastructure. In smart factories, predictive maintenance monitors robotic arms, elevators, lift motors, conveyors, CNC machines, and more, targeting the most critical assets in connected production lines. In energy and utilities, it safeguards turbines, transformers, and storage systems, preventing performance degradation and ensuring safety. In smart buildings, predictive maintenance monitors HVAC systems and elevators for advanced notice of needed maintenance or replacement of assets that are often hard to monitor and cause great discomfort and loss of productivity during unexpected downtime.

The diversity of these applications underscores an Industry 4.0 truth: Interoperability and adaptability are as important as intelligence. Predictive maintenance must be able to integrate into any operational environment, providing actionable insights regardless of equipment age, vendor, or data format.

Intelligence at the industrial edge

The edgeRX platform from TDK SensEI, for example, embodies the next generation of Industry 4.0 machine-health solutions. Combining industrial-grade sensors, gateways, dashboards, and cloud interfaces into a unified system, edgeRX delivers immediate visibility into machine-health conditions. Deployed in minutes, it immediately begins collecting data to build ML models for deployment from the cloud back to the sensor device for real-time inference on the sensor at the edge.

By processing data directly on-device, edgeRX eliminates the latency and energy costs of cloud-based analytics. Its ruggedized, IP67-rated hardware and long-life batteries make it ideal for demanding industrial environments. Most importantly, edgeRX learns continuously from each machine’s unique operational profile, providing precise, actionable insights that support smarter, faster decision-making.

TDK SensEI’s edgeRX advanced machine-health-monitoring platform (Source: TDK SensEI) The road to autonomous maintenance

As Industry 4.0 continues to redefine manufacturing, predictive maintenance is emerging as a key enabler of self-healing, data-driven operations. EdgeRX transforms maintenance from a scheduled obligation into a strategic function—one that is integrated, adaptive, and intelligent.

Manufacturers evaluating their digital strategies should ask:

• Am I able to remotely and simultaneously monitor and alert on all my assets?
• Are our automated systems capturing early, subtle indicators of failure?
• Can our current solutions scale with our operations?
• Are insights available in real time, where decisions are made?

If the answer is no, it’s time to rethink what maintenance means in the context of Industry 4.0. Predictive, edge-enabled AI solutions don’t just prevent downtime; they drive the autonomy, efficiency, and continuous improvement that define the next industrial revolution.

The post Predictive maintenance at the heart of Industry 4.0 appeared first on EDN.

Just finished installing the new desk for electronics. I used to both study and do my electronics on the white desk but it would take too long to move all of my electronics every time that I wanted to study and reversed. So I just got this cheap ikea desk for electronics and the white desk is for studying strictly. I still have many ideas to improve it, starting with a rotating chair for convenience and a whiteboard on the wall which is already waiting for me to mount it. If you have any tips or criticisms feel free to share submitted by /u/DaddyPattyBatman [link] [comments]

This project made me feel a kind of kinship with Diogenes, although I was searching for the item described in the title rather than for an honest man.

Figure 1 “Diogenes Looking for an Honest Man,” a painting attributed to Johann Heinrich Wilhelm Tischbein (1751-1829). The author of this DI has a more modest goal.

Wow the engineering world with your unique design: Design Ideas Submission Guide

I wanted a design that did not require the evaluation and selection of one out of a group of components. I’d tolerate (though not welcome) the use of frequency compensation and even an automatic gain control (AGC) to achieve predictable performance characteristics. Let’s call my desired design “reliably repeatable.”

Standard MLS digital circuit

Initially, I thought none of the listed accommodations would be necessary, and that a simple well-known digital circuit—a maximal length sequence (MLS) Generator [1]—would fit the bill. This circuit produces a pseudorandom sequence whose spectral characteristics are white. A general example of such is shown in Figure 2.

Figure 2 The general form of an MLS generator. A reference lists a table of 2 to 5 specific taps for register lengths from N = 2 to 32 to produce repeating sequences of length 2N-1. Register initialization must include at least one non-zero value. The author first listened to a version using only one exclusive or gate with N = 31 registers, in which the outputs of only the 28th and 31st registers were sampled.

It was simple to code up the one described in the Figure 2 caption with an ATtiny13A microprocessor and obtain a 1.35 µs clock period. Of course, validation is in the listening. And indeed, the predominant sound is the “shush” of white noise.

But there are also audible pops, clicks, and other unwanted artifacts in the background. I had a friend with hearing better than mine listen to confirm my audition’s disappointing conclusion. And so, I picked up my lantern and moved on to the next candidate.

Reverse-biased NPN

I was intrigued by reverse-biasing a transistor’s base-emitter junction with the collector floating (see Figure 3).

Figure 3 Jig for testing the noise characteristics of NPN transistors with reverse-biased base-emitter junctions.

I tested ten 2N3906 transistors with values of R equal to 103, 104, 105, and 106 ohms. Both DC voltages and frequency sweeps (of voltage per square-root spectral densities in units of dBVrms / Hz1/2) were collected.

It was evident that as R decreased, average noise decreased and DC voltages rose slightly, remaining in the range between 7.2 and 8.3 volts. This gave me hope that a simple AGC scheme in which the transistor bias current was varied might satisfy my requirements.

Alas, it was not to be. Figure 4a, Figure 4b, Figure 4c, and Figure 4d show spectral noise in the lower frequency range. (Additional filtering of the 18-V supply had no effect on the 60 Hz power line fundamental or harmonics—these were being picked up from my test environment. The 60-Hz fundamental’s level was about 10 µV rms.)

Figure 4a Note the power line harmonics “hum” problem that the “quiet orange” transistor in particular introduces.

Figure 4b Biasing the “orange” transistor at a lower current raised the noise and hid the power line harmonics, but not the fundamental.

Figure 4c As the bias current is reduced, some but not all transistors’ noises mask the 60 Hz fundamental.

Figure 4d Regardless of whether the power line noise can be masked or eliminated, it’s clear for all resistor R values that there is no consistent shape to the frequency response.

I’ve tried other transistors with similar results. Being unable to depend on a specific frequency response shape, the reverse-biased base-emitter transistor is not a suitable signal source for a reliably predictable design. It’s time to pick up the lantern again and continue the search.

A shunt regulator

Within several datasheets of components in the ‘431 family and in the TLVH431B’s in particular, there is a figure showing the devices’ equivalent input noise. See Figure 5.

Figure 5 The equivalent input noise and test circuit for the TLVH431B (Figure 5-9 in the part’s datasheet). Source: Texas Instruments

The almost 3 dB of rise in noise from 300 Hz down to 10 Hz could be compensated for if it were repeatable from device to device. I looked at the cathode of ten devices using the test jig of Figure 6. The spectral responses are presented in Figure 7.

Figure 6 The test jig for TLVH431B spectral noise. There was no significant difference in the results shown in Figure 7 when values of 1kΩ and 10 kΩ were used for R. 100kΩ and  1MΩresistances supplied insufficient currents for the devices’ operation.

Figure 7 The TVH431B spectral noise, 10 samples with the same date code.

Although the TLVH431B is a better choice than the 2N3904, there are still variations in its noise levels, necessitating some sort of AGC. And the power line signals were still present, with no mitigation available from different values of R. The tested parts all have the same date code, and there are no numerical specs available for limits on noise amplitudes or frequency responses.

Who knows how other date codes would behave? I certainly can’t claim from the data that this component could be part of a “reliably repeatable” design as I defined the term. But you know what? Carrying this lantern around is getting to be pretty annoying.

Xorshift32

I kept thinking that there had to be a digital solution to this problem, even if it couldn’t be the one that produces an MLS. I did some research, and the option of what is called “xorshift” came up, specifically xorshift32 [2].

Xorshift32 starts by initializing a 32-bit variable to a non-zero value. A copy of this variable is created, and 13 zeros are left-shifted into the copy, eliminating the 13 left-most original register values.

The original and the shifted copy are bit-for-bit exclusive-OR’d and stored in the original variable. A copy of this result is made. 17 zeros are then right-shifted into the copy, eliminating the 17 right-most copy’s values. The shifted copy is again exclusive-OR’d bit-by-bit with the updated original register and stored in that register.

Again, a copy of the original’s latest update is made. 5 zeroes are left-shifted into the newest copy, which is then exclusive-OR’d with the latest original update and stored in that original. As this three-step process is repeated, a random sequence of length 232-1 consisting of unique 32-bit integers is generated.

This algorithm was coded into an ATtiny13A microprocessor running at a clock speed of 9.6 MHz, yielding a bit shift period of 5.8 µs. (Assembly source code and hex file are available upon request.) The least significant register bit was routed to bit 0 of the device’s portb (pin 5 of the eight-pin PDIP package.)

This pin was AC-coupled to a power amplifier driving a Polk Audio bookshelf speaker. My friend and I agreed that all we heard was white noise; the pops and clicks of the MLS sequence were absent.

Figure 8 and Figure 9 display frequency sweeps of the voltage per square-root spectral densities of the MLS and the xorshift sequences.

Figure 8 Noise spectral densities from 4 to 1550 Hz of the two auditioned digital sequences produced with 5V-powered ATtiny13A microprocessors.

Figure 9 Noise spectral densities from 63 to 25000 Hz of the two auditioned digital sequences produced with 5V-powered ATtiny13A microprocessors.

There are a few takeaways from Figures 8 and 9.

The white noises of the sequences are at high enough levels to mask my testing environment’s power line fundamental and harmonics that are apparent when evaluating the 2N3904 and the TLVH431B.

The difference in levels of the two digital sequences is due to the higher clock rate of the MLS, which spreads the same total energy as the xorshift over a wider bandwidth and results in a lower energy density within any given band of frequencies in the audible range.

Finally, the xorshift32 has a dip of perhaps 0.1 dBVrms per root Hz at 25 kHz. If the ATtiny13A were clocked from an external 20-MHz source, even this small response dip would disappear.

Audibly pure white noise source

 An audibly pure white noise source for the band from sub-sonic frequencies to 20 kHz can be had by implementing the xorshift32 algorithm on an inexpensive microprocessor.

The result is reliably repeatable, precluding the need to select an optimal component from a group. The voltage over the audio range is:

10 (-39dBVrms/20 ) / √Hz · (200000.5 √Hz),

which evaluates to a 1.6-Vrms signal. This method has none of the disadvantages of the analog noise sources investigated. There is no need to deal with low values and uncertainties of signal level, necessitating the application of a large amount of gain and an AGC, frequency-shaping below 300 Hz or elsewhere, and environmental power line noise at levels comparable to the intentional noise.

I can finally put that darn lantern down. I wonder how Diogenes made out.

Related Content

• Earplugs ready? Let’s make some noise!
• A Portable White Noise Generator Circuit
  
• Pocket-Size White Noise Generator for Quickly Testing Circuit Signal Response
• Simple White Noise Generator
• White noise source flat from 1Hz to 100kHz

References

1. https://liquidsdr.org/doc/msequence/. In the table, the exponents of the polynomials in x are the outputs of the shift registers numbered so that the first (input) register is assigned the number 1.
2. https://en.wikipedia.org/wiki/Xorshift

The post A non-finicky, mass-producible audio frequency white noise generator appeared first on EDN.

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The world is not just connected; it is smart, fast, and relentlessly wireless. From the milliseconds it takes for a smart doorbell to notify your phone, to the instantaneous navigation updates in a self-driving car, modern life operates on a foundation of seamless, high-reliability data transfer. This relentless demand for stability, speed, and ubiquity, largely driven by consumer and industrial “smart” technologies, has radically transformed the invisible backbone of our digital existence: Radio Frequency (RF) and Microwave engineering.

Once considered a niche domain dominated by military and aerospace contractors, RF and microwave technology has sprinted into the mainstream, changing its development trajectory entirely. This shift is not just about moving to higher frequencies; it is about a fundamental change in material science, component integration, and system architecture to guarantee flawless connectivity.

The Original Spectrum: From Radar to GaAs

The initial development track of RF and microwave technology was defined by the defense. The invention of radar during the World Wars solidified the strategic importance of high-frequency electromagnetic waves. For decades, the primary goal was high power, long range, and robustness in harsh environments.

Semiconductor development in this era focused heavily on specialized materials. While early commercialization saw the use of Germanium and then Silicon Bipolar Junction Transistors (BJTs) for lower-frequency consumer applications (TVs, early analog cellular), high-frequency, high-power needs necessitated the use of compound semiconductors. Gallium Arsenide (GaAs) became the workhorse. With its higher electron mobility compared to Silicon, GaAs enabled the creation of high-performance Low-Noise Amplifiers (LNAs) and Power Amplifiers (PAs) necessary for satellite communication and early digital cellular systems.

However, the components remained largely discrete or housed in specialized Monolithic Microwave Integrated Circuits (MMICs), making them expensive and power-hungry—adequate for a small, specialized market, but fundamentally unsuitable for the coming wave of mass-market, battery-powered smart devices.

The Reliability Catalyst: Smart Devices and the Data Deluge

The true turning point arrived with the proliferation of the smartphone and the emergence of the Internet of Things (IoT). Suddenly, RF and microwave systems were no longer serving a few specialized users; they were serving billions, demanding not just speed, but absolute, unwavering reliability.

This reliability challenge manifests in several ways:

1. Capacity and Latency: The shift to 5G and beyond required exponentially more data capacity and ultra-low latency. This pushed engineers into the extremely high-frequency world of millimeter-wave (mmWave) (30 GHz to 300 GHz). At these frequencies, signals travel shorter distances and are more susceptible to attenuation, demanding sophisticated beamforming and massive Multiple-Input, Multiple-Output (Massive MIMO) antenna systems—systems that require hundreds of highly integrated, reliable RF components.
2. Energy Efficiency: Billions of IoT sensors and smartphones demand low power consumption to maximize battery life. This forced a pivot away from power-intensive legacy architectures.
3. Integration and Size (SWaP-C): Smart technology requires components that adhere to stringent Size, Weight, Power, and Cost (SWaP-C) constraints. RF chips needed to shrink and integrate baseband and analog functionality seamlessly.

The Semiconductor Pivot: GaN and the Silicon Comeback

This new reality forced the development track of RF semiconductors to split and evolve dramatically, prioritizing materials that could handle high power density while also promoting system-level integration.

1. The GaN Power Leap (High Reliability/High Power)

The most significant change in material science has been the adoption of Gallium Nitride (GaN). GaN, a wide-bandgap (WBG) semiconductor, is a game-changer because it offers superior power density and thermal conductivity compared to both Si and GaAs.

• Impact: GaN is now revolutionizing the base station infrastructure and defense systems. Its ability to produce five times more power than conventional GaAs amplifiers makes it the material of choice for the high-power, high-efficiency needs of 5G Massive MIMO radios, Active Electronically Scanned Array (AESA) radar, and electronic warfare systems, where reliable, sustained performance under stress is non-negotiable.

2. The SiGe/CMOS Integration Push (High Volume/High Integration)

For high-volume, low-cost consumer devices and integrated modules, the trend shifted toward maximizing the performance of existing Silicon processes. Silicon Germanium (SiGe) BiCMOS and advanced RF CMOS have seen a resurgence.

• Impact: By leveraging the huge, low-cost fabrication capability of the silicon industry and combining it with heterojunction structures (SiGe HBTs) or clever process engineering (RF CMOS), engineers can now integrate complex RF front-ends, digital baseband processing, and control logic onto a single, reliable chip. This capability is vital for mmWave modules in consumer electronics (like 60 GHz WiGig or short-range 5G), ensuring a reliable, low-cost solution where integration outweighs the need for maximum power.

The Next Frontier: Cognitive RF and Terahertz

Looking ahead, the evolution of RF and microwave technology continues to be driven by the quest for unparalleled reliability and spectral efficiency.

The upcoming 6G standard is already pushing semiconductor research towards Terahertz (THz) frequencies (above 300 GHz), promising truly massive bandwidth. Furthermore, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is redefining system reliability through Cognitive Radio. AI algorithms are optimizing network performance in real-time, dynamically adjusting beamforming vectors, predicting component maintenance needs, and ensuring signal quality far beyond what fixed human-designed systems can achieve.

In the span of two decades, RF and microwave engineering has transitioned from a specialized, discrete component field to the vibrant heart of the semiconductor industry. Its current development track is focused entirely on materials like GaN and integrated platforms like SiGe BiCMOS—all working to meet the insatiable, non-negotiable demand for high-speed, always-on, and utterly reliable connectivity that defines the smart world. The invisible hand of wireless demand is now shaping the visible future of electronics.

The post The Invisible Hand: How Smart Technology Reshaped the RF and Microwave Development Track appeared first on ELE Times.

Led by Rensselaer Polytechnic Institute (RPI) of Troy, NY, USA, a team that also includes researchers at the National High Magnetic Field Laboratory, Florida State University and SUNY Buffalo has deepened the understanding of remote epitaxy, a manufacturing technique that entails growing high-quality semiconducting films on one substrate and then transferring them to a different one...

Photonic application-specific integrated circuit (PASIC) chip designer and manufacturer OpenLight of Santa Barbara, CA, USA (which launched as an independent company in June 2022, introducing the first open silicon photonics platform with heterogeneously integrated III-V lasers, modulators, amplifiers and detectors) says that, at the 15th International Symposium on Photonics and Electronics Convergence (ISPEC 2025) at Institute of Science Tokyo (17-18 November), its principal engineer and photonic integrated circuit (PIC) design expert Dr Molly Piels is giving a presentation on the latest advances in the heterogeneous integration of silicon and III-V materials and highlighting how they can be used to enable the fabrication of photonic components at scale. This includes a case study of the OpenLight Photonics Development Kit (PDK) that is helping to underpin growth in photonic IC manufacturing...

Як у КПІ, Україні та світі відзначали Всесвітній тиждень космосу 2025 Інформація КП ср, 11/12/2025 - 09:43

КПІ ім. Ігоря Сікорського продовжує цикл зустрічей з представниками «Азову» KPI4U-1 ср, 11/12/2025 - 08:49

Nuvoton Technology announced the launch of NuMicro M5531 series microcontrollers, powerful MCUs designed to deliver advanced digital signal processing performance. Based on the Arm Cortex-M55 processor, the M5531 series runs at speeds up to 220 MHz, delivering up to 371 DMIPS of computing performance. It also features excellent noise immunity, passing 3 kV ESD HBM and 4.4 kV EFT tests, providing users with stable and high-speed system performance.

Comprehensive Security Features Designed to Meet PSA Level 2 Requirements

Recognizing the growing demand for enhanced product security, the M5531 series integrates multiple hardware-based security mechanisms to strengthen system integrity and protection. These include Arm TrustZone, Secure Boot, cryptographic engines (AES-256, ECC-571, RSA-4096, SHA-512, HMAC-512), Key Store, Key Derivation Function, True Random Number Generator (TRNG), eXecute-Only-Memory, One-Time Programmable Memory (OTP), and tamper detection pins.

In terms of power efficiency, the M5531 series delivers impressive performance with a dynamic power consumption as low as 94.5 µA/MHz. It also offers multiple low-power peripherals such as LPSRAM, LPPDMA, LPTimer, and 12-bit LPADC, allowing the system to maintain essential functions in low-power modes.

Application Fields

The M5531 series is suitable for a wide range of industrial, consumer, and connected products, including:
 Industrial IoT (e.g., industrial gateways, communication modules)
 Industrial automation (e.g., PLC protocol converters)
 Smart building systems (e.g., fire alarm systems, LED advertising display)
 HMI applications (e.g., smart thermostats)
 Sensor fusion (e.g., environmental data collectors)

The post Nuvoton Technology Launches NuMicro M5531 Series Microcontrollers appeared first on ELE Times.

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

Hey everyone! Hope the UK timezone rule for the WBW still holds true haha. Almost exactly three years back I posted my beginner hobbyist bench on the sub and got a ton of kind and helpful feedback from y'all. This new album is a present-day update three years later, after many changes and acquisitions during and for my projects. Overall I learned a ton about what I actually use the most and tried to make it all zero nuisance to get to. If it takes too many steps to get out or get ready it's no good. Some of your predictions back then also came true! I did lob a 3D printer onto that side table and got into CAD and printing in a major way, which I now use in a lot of projects for case parts, mechanical bits, etc. I wound up building a few robots out of a mix of alu extrusion beams and 3D-printed parts. This is why you see a lot more tools now dedicated to mechanics - measuring, fastening, glueing, cutting, deburring, etc. - to complement the electronics toolset. I ended up getting a low-end benchtop meter after I got tired of getting out and throwing around my $20 handheld. Much happier with a permanent fixture. Dremel (well, Proxxon) on a steel cable retractor, permanently plugged in. Actually best idea ever! My circumstances don't really allow for a ton of dust and dirt creation, but for a quick cut or a grind/polish this is so convenient to grab down, and managable. I got my hands on two airline galley trolleys with pull-out tables. In my limited space this is perfect for additional on-demand benchtop space, and it's where I much of my growing electronics stock and some simple hardware. Shoutout to that Omnifixo. The hype for once is true. I've never been happier soldering and use it daily. It's of course hardly ever this presentable. I just had some time off work and did a big tidying pass that reminded me of the older beauty shots. submitted by /u/NewbieSone [link] [comments]

TDK Corp. adds a new single-phase series of DIN-rail-mount power supplies to the TDK-Lambda range of products for industrial and automation applications. The cost-effective  D1SE entry-range series provides an AC and DC input and is rated for continuous operation at 120 W, 240 W or 480 W with a 24-V output. These power supplies deliver an efficiency of up to 95.1%, reducing energy consumption and internal losses, which lower the internal component temperatures and improve long-term product reliability.

(Source: TDK Corp.)

Thanks to the push-in wire terminations, the D1SE series can be quickly mounted, reducing installation time in a variety of control cabinets, machinery, and industrial production systems. In addition to a conventional 100 to 240-VAC nominal input, the D1SE is safety certified for operation from a 93 to 300-VDC supply. Designed to meet growing customer demand, the DC input addresses applications where the energy supply is coming from a common DC bus voltage or a battery.

The 120-W rated model can deliver a boost power of 156 W for 80 seconds; the 240-W rated model offers a boost of 312 W for 10 seconds; and the 480-W rated model provides a boost of 552 W for an extended 200 seconds. The 24-V output can be adjusted from 22.5 V to 29 V to allow compensation for cable drops, redundancy modules, or setting to non-standard output voltages.

All three power supplies are available with or without a DC-OK contact. For applications in challenging environments, a printed-circuit-board coating option is available, and all models feature a high-quality electrolytic capacitor which extends lifetime, according to TDK.

The DIN-rail-mount power supplies are housed in a rugged metal enclosure with a width of 38 mm for the 120-W models, 44 mm for the 240 W, and 60 mm for the 480 W. The narrow design saves space on the DIN rail for other components, the company said.

Other key specs include input-to-output isolation of 5,000 VDC, input-to-ground at 3,100 VDC, and output-to-ground at 750 VDC. The D1SE models are convection-cooled and rated for operation in the -25°C to 70°C ambient temperature range, with derating above 55°C.

Series certifications include IEC/EN/UL/CSA 61010-1, 61010-2-201, 62368-1 (Ed.3), and IS 13252-1 standards. The power supplies also are CE and UKCA marked to the Low Voltage, EMC, and RoHS Directives, and meet EN 55011-B and CISPR11-B radiated and conducted emissions.

The series also complies with EN 61000-3-2 (Class A) harmonic currents and IEC/EN 61000-6-2 immunity standards. The power supplies come with a three-year warranty.

The post Compact DIN-rail power supplies deliver high efficiency appeared first on EDN.

ABLIC Inc. upgrades its CLEAN-Boost energy-harvesting technology for the U.S. and EU markets. The battery-less drip-level water leak sensor now offers a communication range that is approximately 2× that of its predecessor and an expanded operating temperature range of up to 85°C from 60°C. 

ABLIC said it first launched the CLEAN-Boost energy harvesting technology in 2019 to generate, store, boost, and transform microwatt-level energy into electricity for wireless data transmission. Since that launch, the Japan-market model earned positive evaluations from over 80 customers, and given increased inquiries from U.S. and European customers, the company  obtained the necessary certifications from the U.S. Federal Communications Commission and the EU’s Conformité Européenne, confirming compliance with key standards.

CLEAN-Boost can be used in any facility where a water leak poses a potential risk. It uses microwatt energy sources to generate electricity from leaking water and transmits water signals wirelessly. The latest enhancements enable the sensor’s use in a wider range of applications and high-temperature environments, the company said.

Applications where addressing water leaks is critical include automotive parts factories with stamping processes, chemical and pharmaceutical plants, and food processing facilities as well as in aging buildings where pipes may have weakened or in high-temperature operations such as data centers and server rooms.

(Source: ABLIC Inc.)

ABLIC claims the water leak sensor is the industry’s first sensor capable of detecting minute drops of water. It can detect as little as three drops of water (150 μl minimum). In addition, operating without an external power source eliminates the need for major installation work or battery replacement, making it suited for retrofitting into existing infrastructures.

The water leak sensor also helps reduce environmental impact by eliminating the need to replace or dispose of a battery. For example, the sensor has been certified as a MinebeaMitsumi Group “Green Product” for outstanding contribution to the environment.

ABLIC’s CLEAN-Boost technology works by capturing and amplifying microwatt-level environmental energy previously considered too minimal to use. It combines energy storage and boosting components, designed for ultra-low power consumption. The boost circuit operates at 0.35 V for the efficient use of 1 μW of input power. It incorporates a low-power data transmission method that optimizes the timing between power generation and signal transmission, ensuring maximum efficiency and stable operation even under extremely limited power.

(Source: ABLIC Inc.)

The sensor features simple add-on installation for easy integration and sends wireless alerts to safeguard against catastrophic water damage.

The sensor technology is available as a wireless tag (134 × 10 × 18 mm with the main body measuring 65 × 10 × 18 mm), or sensor ribbons (sensor ribbon 0.5 m, sensor ribbon 2.0 m, and sensor ribbon 5.0 m), measuring 700 ×13 × 8 mm, 2200 × 13 × 8 mm, and 5200 × 13 × 8 mm, respectively. They can be connected up to 15 m.

The post ABLIC upgrades battery-less water leak detection sensor appeared first on EDN.

My first time designing a microcontroller board. I wanted to look into getting it assembled by overseas manufacturers but they wanted to charge me over $100 and take over a month to assemble and I said nah I’ll do it myself. I got a convection toaster oven off of facebook marketplace for like $10 and drilled a small hole in the back for a thermocouple which is connected to an ESP32 dev board. I didn’t create a controller which is something I might do eventually but for the time being I had to manually adjust the oven temperature to try and match the reflow curve as best as I could. You can see in the third picture the red line is the expected reflow curve from the solder paste datasheet and the blue line was the real time temperature readings. I was using that graph in real time to make my adjustments. Placing all the components took me about an hour and I had practiced following the reflow curve twice lol but the end result was a really nice looking PCB! Not only that, but my PC was able to detect the board as a USB DFU device when I pressed the boot switch while plugging the cable into the board! All in all very happy with how this turned out and I think I did pretty well for my first time doing something like this! TL;DR Reflowed a board for the first time using a convection toaster oven that I manually controlled and everything worked out :) submitted by /u/jrabr [link] [comments]

First 2 pictures are corner to corner and last is just bent in half. Found on ali. submitted by /u/Whyjustwhydothat [link] [comments]