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Intro to reed switches, magnets, magnetic fields

This brief introductory video on the DigiKey site offers tips for engineers designing with reed switches. Dr. Stephen Day, Ph.D. from Coto Technology gives a solid overview on reed switches -- complete with real-world application examples -- and a detailed explanation of how they react to magnetic fields.
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Bi-color LEDs to light up your designs

Created with engineers and OEMs in mind, SpectraBright Series SMD RGB and Bi-Color LEDs from Visual Communi-cations Company (VCC) deliver efficiency, design flexibility, and control for devices in a range of industries, including mil-aero, automated guided vehicles, EV charging stations, industrial, telecom, IoT/smart home, and medical. These 50,000-hr bi-color and RGB options save money and space on the HMI, communicating two or three operating modes in a single component.
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All about slip rings: How they work and their uses

Rotary Systems has put together a really nice basic primer on slip rings -- electrical collectors that carry a current from a stationary wire into a rotating device. Common uses are for power, proximity switches, strain gauges, video, and Ethernet signal transmission. This introduction also covers how to specify, assembly types, and interface requirements. Rotary Systems also manufactures rotary unions for fluid applications.
Read the overview.

Seifert thermoelectric coolers from AutomationDirect

Automation-Direct has added new high-quality and efficient stainless steel Seifert 340 BTU/H thermoelectric coolers with 120-V and 230-V power options. Thermoelectric coolers from Seifert use the Peltier Effect to create a temperature difference between the internal and ambient heat sinks, making internal air cooler while dissipating heat into the external environment. Fans assist the convective heat transfer from the heat sinks, which are optimized for maximum flow.
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EMI shielding honeycomb air vent panel design

Learn from the engineering experts at Parker how honeycomb air vent panels are used to help cool electronics with airflow while maintaining electromagnetic interference (EMI) shielding. Topics include: design features, cell size and thickness, platings and coatings, and a stacked design called OMNI CELL construction. These vents can be incorporated into enclosures where EMI radiation and susceptibility is a concern or where heat dissipation is necessary. Lots of good info.
Read the Parker blog.

What is 3D-MID? Molded parts with integrated electronics from HARTING

3D-MID (three-dimensional mechatronic integrated devices) technology combines electronic and mechanical functionalities into a single, 3D component. It replaces the traditional printed circuit board and opens up many new opportunities. It takes injection-molded parts and uses laser-direct structuring to etch areas of conductor structures, which are filled with a copper plating process to create very precise electronic circuits. HARTING, the technology's developer, says it's "Like a PCB, but 3D." Tons of possibilities.
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Loss-free conversion of 3D/CAD data

CT CoreTech-nologie has further developed its state-of-the-art CAD converter 3D_Evolution and is now introducing native interfaces for reading Solidedge and writing Nx and Solidworks files. It supports a wide range of formats such as Catia, Nx, Creo, Solidworks, Solidedge, Inventor, Step, and Jt, facilitating smooth interoperability between different systems and collaboration for engineers and designers in development environments with different CAD systems.
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Top 5 reasons for solder joint failure

Solder joint reliability is often a pain point in the design of an electronic system. According to Tyler Ferris at ANSYS, a wide variety of factors affect joint reliability, and any one of them can drastically reduce joint lifetime. Properly identifying and mitigating potential causes during the design and manufacturing process can prevent costly and difficult-to-solve problems later in a product lifecycle.
Read this informative ANSYS blog.

Advanced overtemp detection for EV battery packs

Littelfuse has introduced TTape, a ground-breaking over-temperature detection platform designed to transform the management of Li-ion battery systems. TTape helps vehicle systems monitor and manage premature cell aging effectively while reducing the risks associated with thermal runaway incidents. This solution is ideally suited for a wide range of applications, including automotive EV/HEVs, commercial vehicles, and energy storage systems.
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Benchtop ionizer for hands-free static elimination

EXAIR's Varistat Benchtop Ionizer is the latest solution for neutralizing static on charged surfaces in industrial settings. Using ionizing technology, the Varistat provides a hands-free solution that requires no compressed air. Easily mounted on benchtops or machines, it is manually adjustable and perfect for processes needing comprehensive coverage such as part assembly, web cleaning, printing, and more.
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LED light bars from AutomationDirect

Automation-Direct adds CCEA TRACK-ALPHA-PRO series LED light bars to expand their offering of industrial LED fixtures. Their rugged industrial-grade anodized aluminum construction makes TRACKALPHA-PRO ideal for use with medium to large-size industrial machine tools and for use in wet environments. These 120 VAC-rated, high-power LED lights provide intense, uniform lighting, with up to a 4,600-lumen output (100 lumens per watt). They come with a standard bracket mount that allows for angle adjustments. Optional TACLIP mounts (sold separately) provide for extra sturdy, vibration-resistant installations.
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World's first metalens fisheye camera

2Pi Optics has begun commercial-ization of the first fisheye camera based on the company's proprietary metalens technology -- a breakthrough for electronics design engineers and product managers striving to miniaturize the tiny digital cameras used in advanced driver-assistance systems (ADAS), AR/VR, UAVs, robotics, and other industrial applications. This camera can operate at different wavelengths -- from visible, to near IR, to longer IR -- and is claimed to "outperform conventional refractive, wide-FOV optics in all areas: size, weight, performance, and cost."
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Orbex offers two fiber optic rotary joint solutions

Orbex Group announces its 700 Series of fiber optic rotary joint (FORJ) assemblies, supporting either single or multi-mode operation ideal for high-speed digital transmission over long distances. Wavelengths available are 1,310 or 1,550 nm. Applications include marine cable reels, wind turbines, robotics, and high-def video transmission. Both options feature an outer diameter of 7 mm for installation in tight spaces. Construction includes a stainless steel housing.
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Mini tunnel magneto-resistance effect sensors

Littelfuse has released its highly anticipated 54100 and 54140 mini Tunnel Magneto-Resistance (TMR) effect sensors, offering unmatched sensitivity and power efficiency. The key differentiator is their remarkable sensitivity and 100x improvement in power efficiency compared to Hall Effect sensors. They are well suited for applications in position and limit sensing, RPM measurement, brushless DC motor commutation, and more in various markets including appliances, home and building automation, and the industrial sectors.
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Panasonic solar and EV components available from Newark

Newark has added Panasonic Industry's solar inverters and EV charging system components to their power portfolio. These best-in-class products help designers meet the growing global demand for sustainable and renewable energy mobility systems. Offerings include film capacitors, power inductors, anti-surge thick film chip resistors, graphite thermal interface materials, power relays, capacitors, and wireless modules.
Learn more.

Caltech says traditional computers can solve some quantum problems

There has been a lot of buzz about quantum computers and for good reason. The futuristic computers are designed to mimic what happens in nature at microscopic scales -- yielding the power to better understand the quantum realm and speed up the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals, and more. However, experts say viable quantum computers are still a decade away or more. What are researchers to do in the meantime?

A new California Institute of Technology (Caltech) study in the journal Science describes how machine learning tools, run on classical computers, can be used to make predictions about quantum systems and thus help researchers solve some of the trickiest physics and chemistry problems. While this notion has been shown experimentally before, the new report is the first to mathematically prove that the method works.

"Quantum computers are ideal for many types of physics and materials science problems," says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, the Richard P. Feynman Professor of Theoretical Physics and the Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Institute for Quantum Science and Technology (IQIM). "But we aren't quite there yet and have been surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this paper is about showing what humans can learn about the physical world."

At microscopic levels, the physical world becomes an incredibly complex place ruled by the laws of quantum physics. In this realm, particles can exist in a superposition of states, or in two states at once. Superposition of states can lead to entanglement, a phenomenon in which particles are linked, or correlated, without even being in contact with each other. These strange states and connections, which are widespread within natural and human-made materials, are very hard to describe mathematically.

"Predicting the low-energy state of a material is very hard," says Huang. "There are huge numbers of atoms, and they are superimposed and entangled. You can't write down an equation to describe it all."

The new study is the first mathematical demonstration that classical machine learning can be used to bridge the gap between us and the quantum world. Machine learning is a type of computer application that mimics the human brain to learn from data.

"We are classical beings living in a quantum world," says Preskill. "Our brains and our computers are classical, and this limits our ability to interact with and understand the quantum reality."

While previous studies have shown that machine learning applications have the ability to solve some quantum problems, these methods typically operate in ways that make it difficult for researchers to learn how the machines arrived at their solutions. "Normally, when it comes to machine learning, you don't know how the machine solved the problem. It's a black box," says Huang. "But now we've essentially figured out what's happening in the box through our numerical simulations. "Huang and his colleagues did extensive numerical simulations in collaboration with the AWS Center for Quantum Computing at Caltech, which corroborated their theoretical results.

The new study will help scientists better understand and classify complex and exotic phases of quantum matter.

"The worry was that people creating new quantum states in the lab might not be able to understand them," Preskill explains. "But now we can obtain reasonable classical data to explain what's going on. The classical machines don't just give us an answer like an oracle but guide us toward a deeper understanding."

Co-author Victor V. Albert, a NIST (National Institute of Standards and Technology) physicist and former DuBridge Prize Postdoctoral Scholar at Caltech, agrees. "The part that excites me most about this work is that we are now closer to a tool that helps you understand the underlying phase of a quantum state without requiring you to know very much about that state in advance."

Ultimately, of course, future quantum-based machine learning tools will outperform classical methods, the scientists say. In a related study appearing June 10, 2022, in Science, Huang, Preskill, and their collaborators report using Google's Sycamore processor, a rudimentary quantum computer, to demonstrate that quantum machine learning is superior to classical approaches. "We are still at the very beginning of this field," says Huang. "But we do know that quantum machine learning will eventually be the most efficient."

The Science study, titled "Provably efficient machine learning for quantum many-body problems," was funded by the J. Yang & Family Foundation, the Department of Energy, and the National Science Foundation (NSF).

Source: California Institute of Technology (Caltech)

Published October 2022

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