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Виставка викладача ВПІ Сергія Гулєвича в художньому музеї Інформація КП пт, 05/30/2025 - 14:15

Courtesy: u-blox

LEAP brings pinpoint accuracy and ultra-low power consumption to smartwatches, fitness trackers, and sports wearables.

The wearables conundrum
Smartwatches, fitness trackers, and GPS-enabled sports wearables have become essential tools for millions of users tracking their daily activities and athletic performance. But with these compact devices come big challenges – especially when it comes to delivering highly accurate GNSS positioning without draining the battery.

For device designers and users alike, the demands are increasing. Accurate tracking is expected even in dense cities, deep forests, or open water. And nobody wants to charge their wearable every day. The push to squeeze more performance into ever-smaller packages has created a tension between precision and power.

To meet this challenge head-on, u-blox has introduced LEAP (Low Energy Accurate Positioning), a powerful new GNSS technology built into the u-blox M10 platform. It enables wearables to deliver consistently accurate positioning while extending battery life –
solving one of the most persistent problems in wearable design.

Why low power and accuracy usually don’t mix
Accurate GNSS positioning is hard work, especially in the kinds of environments wearables are typically used in. In open-sky conditions things aren’t so bad. But in a dense forest, an urban canyon, or on the side of a mountain, GNSS signal quality starts to drop and accuracy starts to suffer. Add in dynamic movements like arm swings or vibration, and things get even more complicated. Plus, the GNSS antennas that wearables use aren’t very big to begin with.

The consequence is that the device uses lots of power trying to receive weak signals, and filtering out noise, reducing battery life. This is why traditional low power GNSS solutions tend to compromise on accuracy in order to conserve battery – and why high-accuracy solutions often drain battery quickly. LEAP was designed to avoid this compromise.

The solution: How LEAP works

LEAP is a smart GNSS mode developed by u-blox to deliver optimal performance for wearables while also extending battery life.

• Smart signal selection is at the core of LEAP. Rather than using all available GNSS signals, LEAP selectively uses only those that offer the strongest signal or the most accurate data. It dynamically filters out low-elevation or noisy signals that could introduce errors, and applies multipath mitigation techniques to reduce the impact of reflected signals common in cities or wooded areas.

• External low-noise amplifier (LNA) switching also helps minimise battery usage. LEAP can automatically switch the device’s LNA on or off based on real-time signal conditions. If signal quality is already high, the LNA can be disabled to save power. When signals are weak or noisy, the LNA reactivates to maintain positioning performance.

Together, these innovations ensure that a device using LEAP doesn’t waste power trying to receive poor quality data – and also improve accuracy, giving your device a powerful edge in the wearables space.

To further improve accuracy, LEAP includes activity-aware dynamics: tailored motion models for activities such as running, cycling, and hiking. These models account for specific movement patterns, like arm swings or stride variations, allowing the GNSS system to make smarter assumptions and corrections based on user behaviour. LEAP has even been validated for various sports like running, hiking and cycling – and further enhancements are in development.

What LEAP delivers

In side-by-side tests with standard u-blox M10 GNSS mode, LEAP reduced power consumption by up to 50%, while delivering similar or better positioning accuracy. In forest environments, LEAP delivered a circular error probable (CEP95, which essentially means that the probability of the data being at the stated accuracy is 95%) of 8 metres, compared to 14 metres from competitor products. In opensky tests, the improvement was from 4 metres to 2 metres. These kinds of gains matter. Whether users are running under tree
cover, hiking through gorges, or biking through the city, they can trust that their wearable device is delivering accurate, energy-efficient tracking.

Why it matters for designers

For wearable device designers, LEAP opens up a new range of possibilities. By delivering high-accuracy GNSS positioning at low power, it enables smaller batteries and slimmer form factors without compromising user experience. The chip package is impressively compact at just 2.39 x 2.39 mm, making it ideal for modern wearables, including those with severe
space and weight constraints. Whether you’re designing a rugged GPS sports watch or a lightweight everyday fitness tracker, LEAP will fit.

It’s also a future-proof solution. Firmware upgrades, which can be delivered via external flash or a connected MCU, mean LEAP can continue to evolve after deployment. Future firmware updates could introduce new models for additional activities, further optimise
power savings, or enhance positioning accuracy based on the latest innovations. This ensures that devices built with LEAP can stay competitive and adapt to emerging user needs. It supports Android systems and is fully compatible with u-blox AssistNow, enabling faster positioning and lower startup power draw. Built-in support for protocols like SUPL means LEAP can integrate seamlessly into today’s connected wearable ecosystems.

A LEAP forward for wearable GNSS

With LEAP, u-blox has redefined what’s possible for wearable GNSS. By combining low power consumption with high accuracy, it solves one of the biggest challenges in GNSS for wearables. And by making smartwatches and sports watches more capable, it gives users the freedom to explore further, train harder, and go longer between charges.

The post Maximize positioning accuracy and battery life with LEAP : Low Energy Accurate Positioning for wearables appeared first on ELE Times.

Skyworks Solutions Inc of Irvine, CA, USA (which manufactures analog and mixed-signal semiconductors) says that, due to an unforeseen medical condition, Mark Dentinger will not be joining it as chief financial officer. It had been announced on 7 May that on 2 June he would be succeeding Kris Sennesael, who has stepped down as CFO to pursue another opportunity...

Micro-LED technology firm VueReal Inc of Waterloo, ON, Canada has announced a strategic partnership for its reference design kits (RDKs) and micro-LED evaluation platforms to be exclusively distributed across Canada by ACA TMetrix Inc (a Canadian distributor of high-tech test & measurement solutions), empowering engineering teams to accelerate the adoption of next-generation display technologies in key markets such as automotive, defense, and consumer electronics...

Бюлетень для експертів Quarterly Newsletter kpi пт, 05/30/2025 - 11:21

На Міжнародній конференції з історії науки, техніки та освіти "Пріоритети української науки" Інформація КП пт, 05/30/2025 - 10:10

A high-performance DAQ system that meets the latest evaluation and test needs in the automotive, mechatronics, and power electronics fields 

Yokogawa Test & Measurement Corporation announces the release of the SL200 High-Speed Data Acquisition Unit, a ScopeCorder series product with a wide range of data logging functionalities for evaluation and test applications, including high-speed sampling and analysis. The SL2000 is a modular platform that combines the functions of a mixed signal oscilloscope and a data acquisition recorder, and is designed to capture fast signal transients and long-term trends. It is suitable for applications such as R&D, validation, and troubleshooting.

The SL2000 can be used separately or in combination with the DL950 ScopeCorder, depending on the application. No other product family on the market offers this level of flexibility in handling multi-channel measurements. With the ScopeCorder product family, Yokogawa provides a multifaceted, total solution for the high-precision mechatronics and electric power markets that is contributing to the advancement and development of new technologies and applications. 

Development Background
In the four years since the DL950 ScopeCorder was first brought to the market by Yokogawa Test & Measurement, there have been many technical advances in the electric vehicle (EV), renewable energy, and other industrial fields. Today, there is an ever-greater requirement for the capability to simultaneously measure multiple parameters and for the systemization of mechatronic measurements in product development. For example, in the development of motors for industrial and EV systems, one essential test for checking and improving a product is the durability test. This test takes a long time to complete and requires a highly reliable measuring instrument and high sampling rates.

Main Features
1. Enabling both high-speed sampling and multi-channel measurement
The SL2000 performs long-duration multi-channel measurements while precisely analyzing even the most detailed aspects of waveforms. With its dual capture function, the SL2000 can perform durability tests over long periods of time at speeds of up to 200 MS/s.

By using the IS8000 integrated software platform, it is easier to perform the long-term measurements required for durability testing, helping to improve the efficiency of product design and evaluation work. In addition, isolation measurement technology ensures the noise resistance required for durability testing in harsh environments.

2. Supporting simultaneous measurement of a wide variety of devices
The SL2000 has eight available slots (with up to 32 channels), for which over 20 types of input modules are available to enable measurements of electrical signals, mechanical performance parameters indicated by sensors, and decoded vehicle serial bus signals. To increase the number of measurement channels, up to five SL2000 and DL950 units can be synchronized.

Major Target Markets
•    Transportation (automotive, rail, aviation, etc.)
•    Power and energy (renewable energy, smart cities/homes, data centers, etc.)
•    Mechatronics, including industrial robots and motors

Applications
•    Durability and reliability testing of components and vehicles that requires high sampling rates and multi-channel simultaneous measurement of analog signals and in-vehicle bus signals such as CAN and CAN FD
•    Simultaneous measurement and evaluation of temperature, vibration, and other mechanical signals that change relatively slowly as well as mechatronic and other such high-speed control signals
•    Electrical analysis and control signal evaluation

The post Yokogawa Test & Measurement Releases SL2000 High-Speed Data Acquisition Unit appeared first on ELE Times.

Industry-first inertial measurement unit (IMU) with dual MEMS accelerometer
and embedded AI measures accurately up to 320g full-scale range

STMicroelectronics, a global semiconductor leader serving customers across the spectrum of electronics applications, has revealed an inertial measurement unit that combines sensors tuned for activity tracking and high-g impact measurement in a single, space-saving package. Devices equipped with this module can allow applications to fully reconstruct any event with high accuracy and so provide more features and superior user experiences. Now that it’s here, markets can expect powerful new capabilities to emerge in mobiles, wearables, and consumer medical products, as well as equipment for smart homes, smart industry, and smart driving.

The new LSM6DSV320X sensor is an industry first in a regular-sized module (3mm x 2.5mm) with embedded AI processing and continuous registration of movements and impacts. Leveraging ST’s sustained investment in micro-electromechanical systems (MEMS) design, the innovative dual-accelerometer device ensures high accuracy for activity tracking up to 16g and impact detection up to 320g.

“We continue to unleash more and more of the potential in our cutting-edge AI MEMS sensors to enhance the performance and energy efficiency of today’s leading smart applications,” said Simone Ferri, APMS Group VP, MEMS Sub-Group General Manager at STMicroelectronics. “Our new inertial module with unique dual-sensing capability enables smarter interactions and brings greater flexibility and precision to devices and applications such as smartphones, wearables, smart tags, asset monitors, event data recorders, and larger infrastructure.” 

The LSM6DSV320X extends the family of sensors that contain ST’s machine-learning core (MLC), the embedded AI processor that handles inference directly in the sensor to lower system power consumption and enhance application performance. It features two accelerometers, designed for coexistence and optimal performance using advanced techniques unique to ST. One of these accelerometers is optimized for best resolution in activity tracking, with maximum range of ±16g, while the other can measure up to ±320g to quantify severe shocks such as collisions or high-impact events.

By covering an extremely wide sensing range with uncompromised accuracy throughout, all in one tiny device, ST’s new AI MEMS sensor will let consumer and IoT devices provide even more features while retaining a stylish or wearable form factor. An activity tracker can provide performance monitoring within normal ranges, as well as measuring high impacts for safety in contact sports, adding value for consumers and professional/semi-pro athletes. Other consumer-market opportunities include gaming controllers, enhancing the user’s experience by detecting rapid movements and impacts, as well as smart tags for attaching to items and recording movement, vibrations, and shocks to ensure their safety, security, and integrity.

With its wide acceleration measurement range, ST’s sensor will also enable new generations of smart devices for sectors such as consumer healthcare and industrial safety. Potential applications include personal protection devices for workers in hazardous environments, assessing the severity of falls or impacts. Other uses include equipment for accurately assessing the health of structures such as buildings and bridges.

The sensor’s high integration simplifies product design and manufacture, enabling advanced monitors to enter their target markets at competitive prices. Designers can create slim, lightweight form factors that are easy to wear or attach to equipment.

The post STMicroelectronics combines activity tracking and high-impact sensing in miniature AI-enabled sensor for personal electronics and IoT appeared first on ELE Times.

Courtesy: Avnet

Myth #1: Demo code is production-ready
AI demos always look impressive but getting that demo into production is an entirely different challenge. Productionizing AI requires effort to ensure it’s secure, optimized for your hardware, and
tailored to meet your specific customer needs.
The gap between a working demonstration and real-world deployment often includes considerations like performance, scalability
and maintainability. One of the biggest hurdles is maintaining AI
models over time, particularly if you need to retrain the application
and update the inference engine across thousands of deployed devices. Ensuring long-term support, handling versioning and managing updates without disrupting service add layers of complexity
that go far beyond an initial demo.
Additionally, the real-world environment for AI applications is dynamic. Data shifts, changing user behavior, and evolving business
needs all require frequent updates and fine-tuning.
Organizations must implement robust pipelines for monitoring
model drift, collecting new data and retraining models in a controlled and scalable way. Without these mechanisms in place, AI
performance can degrade over time, leading to inaccurate or unreliable outputs.
Emerging techniques like federated learning allow decentralized
model updates without sending raw data back to a central server,
helping improve model robustness while maintaining data privacy.

Myth #2: All you need is Python
Python is an excellent tool for rapid prototyping, but its limitations
in embedded systems become apparent when scaling to production.
In resource-constrained environments, languages like C++ or C
often take the lead for their speed, memory efficiency and hardware-level control. While Python has its place in training and experimentation, it rarely powers production systems in embedded
AI applications.
In addition, deploying AI software requires more than just writing
Python scripts. Developers must navigate dependencies, version
mismatches and performance optimizations tailored to the target
hardware.
While Python libraries make development easier, achieving real-time inference or low-latency performance often necessitates
re-implementing critical components in optimized languages like
C++ or even assembly for certain accelerators. ONNX Runtime and
TensorRT provide performance improvements for Python-based AI
models, bridging some of the efficiency gaps without requiring full
rewrites.

Myth #3: Any hardware can run AI
The myth that “any hardware can run AI” is far from reality. The
choice of hardware is deeply intertwined with the software requirements of AI.
High-performance AI algorithms demand specific hardware accelerators, compatibility with toolchains and memory capacity. Choosing mismatched hardware can result in performance bottlenecks or even an inability to deploy your AI model.
For example, deploying deep learning models on edge devices requires selecting chipsets with AI accelerators like GPUs, TPUs or
NPUs. Even with the right hardware, software compatibility issues
can arise, requiring specialized drivers and optimization techniques.
Understanding the balance between processing power, energy consumption, and cost is crucial to building a sustainable AI-powered
solution. While AI is now being optimized for TinyML applications
that run on microcontrollers, these models are significantly scaled
down, requiring frameworks like TensorFlow Lite for Microcontrollers for deployment.

Myth #4: AI is quick to implement
AI frameworks like TensorFlow or PyTorch are powerful, but they
don’t eliminate the steep learning curve or the complexity of real-world applications. If it’s your first AI project, expect delays.
Beyond the framework itself, one of the biggest challenges is creating a toolchain that integrates one of these frameworks with the
IDE for your chosen hardware platform. Ensuring compatibility, optimizing models for edge devices, integrating with legacy systems,
and meeting market-specific requirements all add to the complexity.
For applications outside the smartphone or consumer tech domain,
the lack of pre-existing solutions further increases development
effort.

Myth #5: Any OS can run AI
Operating system choice matters more than you think. Certain AI
platforms work best with specific distributions and can face compatibility issues with others.
The myth that “any OS will do” ignores the complexity of kernel
configurations, driver support and runtime environments. To avoid
costly rework or hardware underutilization, ensure your OS aligns
with both your hardware and AI software stack.
Additionally, real-time AI applications, such as those in automotive
or industrial automation, often require an OS with real-time capabilities. This means selecting an OS that supports deterministic execution, low-latency processing, and security hardening.
Developers must carefully evaluate the trade-offs between flexibility, support, and performance when choosing an OS for AI deployment. Some AI accelerators require specific OS support.

What’s Next for AI at the edge?
We’re already seeing large language models (LLMs) give way to
small language models (SLMs) in constrained devices, putting the
power of generative AI into smaller products. If this is the direction
you’re going, talk to the experts at Witekio.

The post 5 myths about AI from a software standpoint appeared first on ELE Times.

Learning about LLC resonant power supplies and micropython for Pico W. submitted by /u/coderlogic [link] [comments]

🎥 Студенти КПІ відвідали Броварський завод котельного устаткування ARDENZ kpi чт, 05/29/2025 - 22:20

The ESP32 C3 is connected to a DHT11 and a 4x 8x8 MAX7219 LED matrix. The cable management wasn't remotely as relaxing as I imagined it in my fantasy. submitted by /u/satina_nix [link] [comments]

Aeluma Inc of Goleta, CA, USA — which develops compound semiconductor materials on large-diameter substrates — has announced an advance in silicon photonics that could accelerate the adoption of quantum computing and communication at commercial scale, it is reckoned...

Built on a 22-nm process, Silicon Labs’ SiXG301 and SiXG302 wireless SoCs deliver improved compute performance and energy efficiency. As the first members of the Series 3 portfolio, they target both line- and battery-powered IoT devices.

Designed for line-powered applications such as LED smart lighting, the SiXG301 integrates an LED pre-driver and a 32-bit Arm Cortex-M33 processor running at up to 150 MHz. It supports concurrent multiprotocol operation with Bluetooth, Zigbee, and Matter over Thread, and includes 4 MB of flash and 512 kB of RAM. Currently in production with select customers, the SiXG301 is expected to be generally available in Q3 2025.

Extending the Series 3 platform to battery-powered applications, the SiXG302 features a power-efficient architecture that consumes just 15 µA/MHz when active—up to 30% lower than comparable devices. It is well-suited for battery-powered wireless sensors and actuators using Matter or Bluetooth. Sampling is expected to begin in 2026.

The SiXG301 and SiXG302 families will initially include two types of devices: ‘M’ variants (SiMG301 and SiMG302) for multiprotocol support, and ‘B’ variants (SiBG301 and SiBG302) optimized for Bluetooth LE.

Series 3 product page 

Silicon Labs 

The post Wireless SoCs drive IoT efficiency appeared first on EDN.

ST offers three antenna-matching companion chips for STM32WL33 wireless MCUs to help streamline the development of IoT, smart metering, and remote monitoring systems. The MLPF-WL-01D3, MLPF-WL-02D3, and MLPF-WL-04D3 integrate impedance matching and harmonic filtering on a single glass substrate to boost RF performance.

By integrating antenna protection, matching, and filtering, the devices simplify RF routing, improve reliability, and reduce BOM cost by replacing multiple discrete components. The three Series 3 chips will be joined by four new variants, supporting radio optimization across high-band (826–958 MHz) and low-band (413–479 MHz) ranges, high-power (16/20 dBm) and low-power (10 dBm) modes, and 2-layer or 4-layer PCB designs.

The MLPF-WL-01D3, MLPF-WL-02D3, and MLPF-WL-04D3 antenna-matching ICs are available now in 5-bump chip-scale packages, priced from $0.15 each in 1000-unit quantities. Release dates for the additional variants were not available at the time of this announcement.

MLPF-WL-0xD3 product page

STMicroelectronics

The post Antenna-matching ICs cut RF design complexity appeared first on EDN.

The NTAG X DNA from NXP is an ISO/IEC 14443-4 Type 4 NFC tag that enables secure authentication of NFC-enabled mobile devices. It features 16 kB of memory, high-speed data transfer, and Secure Unique NFC (SUN) authentication to protect devices across healthcare, smart home, consumer electronics, and industrial markets.

Supporting device-only, device-to-device, and device-to-cloud authentication, the NTAG X DNA secures data transfer via NFC or I²C interfaces at speeds up to 848 kbps and 1 MHz, respectively. A direct MCU connection enables device diagnostics, while the tag’s memory allows access to stored authentication data—even without power. Sensitive information can also be erased in power-off conditions to protect user privacy.

Designed to combat counterfeits and support Digital Product Passport (DPP) compliance, the NTAG X DNA offers strong security with Common Criteria EAL 6+ certification and PKI-based asymmetric cryptography. It is backed by NXP’s EdgeLock 2GO service for UID and certificate delivery, as well as on-demand certificate generation.

NTAG X DNA product page

NXP Semiconductors 

The post IC safeguards NFC communication appeared first on EDN.

Qorvo’s QPA3311 and QPA3316 hybrid power doubler amplifiers are optimized for DOCSIS 4.0 downstream operations up to 1.8 GHz. They support the transition to Unified DOCSIS and smart amplifier architectures that enhance visibility, efficiency, and adaptability in hybrid fiber-coax (HFC) systems.

Based on a GaAs/GaN die, the devices operate from 45 MHz to 1794 MHz and provide 23 dB of gain. They are well-suited for DOCSIS 4.0 CATV nodes and amplifiers. High total composite power and improved signal integrity reduce cascade requirements and enhance end-of-line performance, helping lower infrastructure costs by eliminating the need for booster amps.

The QPA3311 and QPA3316 power doublers operate from 24-V and 34-V supplies, respectively, with power consumption of 12.5 W and 18 W. At 51 dB CNN, total composite power reaches 74 dBmV for the QPA3311 and over 75 dBmV for the QPA3316.

Both the QPA3311 and QPA3316 power doubler amplifiers are housed in SOT-115J packages and are now in production.

QPA3311 product page

QPA3316 product page 

Qorvo

The post Power doublers enable smooth DOCSIS 4.0 upgrades appeared first on EDN.

Asahi Kasei has developed the Sunfort TA series of dry film photoresist for next-generation semiconductor packages requiring circuit patterns with line/space widths of 2/2 µm or less. The film offers high resolution with conventional stepper and laser direct imaging (LDI) systems—used to transfer circuit patterns onto substrates—enhancing precision in back-end processes.

The TA series supports fine wiring formation in panel-level packages and related applications. It enables patterning with a 1.0-µm resist width using LDI exposure in a 4-µm pitch design, as required for redistribution layer (RDL) formation (Figures a and b). The resulting fine resist pattern can be plated by a semi-additive process, then stripped to yield a 3-µm wide plating pattern within the same 4-µm pitch (Figure c).

Asahi Kasei states that Sunfort dry film photoresist will remain integral to advancing panel-level packaging technology as panel sizes increase. With its ability to achieve finer wiring and improve production efficiency, the TA series addresses the rising demand for advanced semiconductor package substrates and interposers in AI, automotive, communications, and IoT markets.

TA series product page

Asahi Kasei 

The post Dry film photoresist enables fine circuit formation appeared first on EDN.

Макулатура зі змістом: КПІ передає списану літературу на переробку на благо природи та розвитку Університету kpi чт, 05/29/2025 - 18:21

Україна – Європа: жіноче обличчя Інформація КП чт, 05/29/2025 - 17:44