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The EDA industry is known for the trio—Cadence, Siemens EDA and Synopsys—that dominates it and how these companies turned into giants by acquiring smaller EDA outfits. Now, another EDA player is on the horizon, taking a similar path of serial acquisitions to attain design automation software glory.

Altair, a supplier of simulation and data analytics solutions, is cutting deals to expand its EDA footprint in several design automation areas. It has just announced that it will acquire Metrics Design Automation, a Canadian company built on a simulation-as-a-service (SaaS) business model for semiconductor simulation and design verification.

Figure 1 Merging simulation with workload and workflow optimization technology could bolster design verification tools. Source: Altair

The cloud-based business model has the potential to make high-caliber EDA tools much more affordable and accessible at a time when IC design verification has high licensing costs and may require hundreds and sometimes thousands of seats to run a single-chip simulation. Moreover, these EDA tools run on desktop machines and are not typically cloud-native or cloud-enabled.

Altair plans to combine its silicon debug tools with Metrics’ digital simulator, DSim, to offer simulation and debug capabilities as a desktop app, on company servers, or in the cloud. This will allow design engineers to pay only for what they use. DSim will be available through Altair One, Altair’s cloud gateway, where it will also be available for desktop download.

The combined solution will support Verilog and VHDL RTL for digital circuits in ASICs and FPGAs. Metrics is led by Joe Costello, an EDA industry veteran credited with turning Cadence Design Systems into a billion-dollar firm.

A plethora of EDA deals

Earlier this year, Altair named EDA Expert a channel partner for distributing its HyperWorks design and simulation platform within France. EDA Expert, founded in 2012 and headquartered in Arcueil, France, provides technical expertise and training to help manufacturers define suitable solutions for designing and manufacturing electronic systems and analyzing electronic boards.

Then, in June 2022, Altair announced acquisition of Concept Engineering, a supplier of automatic schematic generation tools, electronic circuit and wire harness visualization platforms that provide on-the-fly visual rendering, and electronic design debug solutions. Concept Engineering’s software would be integrated into Altair’s Electronic System Design suite and available via Altair Units.

Concept Engineering’s reactive visualization technology would help organizations accelerate their designs that have specific design architecture requirements as well as rigorous service needs. Next, its design debug solutions covered register transfer level (RTL), gate, and transistor design abstractions for both analog and digital disciplines.

Figure 2 Concept Engineering’s automatic schematic generation and visualization software components help developers create high-performance debugging cockpits, shorten software tool development cycles, lower software development and maintenance costs, and increase the product quality of EDA tools. Source: Altair

Finally, in September 2017, Altair announced that it would buy Runtime Design Automation, a Santa Clara, California-based company specializing in scalable solutions for high-performance computing (HPC). Runtime primarily served design engineers leveraging EDA tools to design CPUs, GPUs, and system-on-chips (SoCs).

Carving an EDA niche?

Altair calls itself a computational intelligence specialist, but its technology roadmap is increasingly converging and colliding with EDA tool offerings. It’s steadily accumulating EDA solutions in its technology arsenal to claim a stake in the EDA industry, which is now being transformed by artificial intelligence (AI) and cloud computing technologies.

Moreover, HPC, which Altair calls its forte, is taking center-stage in the semiconductor realm. So, the Troy, Michigan-based company might be aiming to carve out an EDA niche in this burgeoning market.

Still, Altair is nowhere near the EDA’s big three: Cadence, Siemens EDA and Synopsys. So, will Altair continue the acquisition spree and eventually challenge the dominance of the EDA trio? Or will it become an acquisition target over time due to its strengths in HPC, cloud, and AI? We at EDN will closely watch the developments in the acquisition sphere of the EDA industry.

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I purchased my Epson Artisan 730 color inkjet all-in-one (printer, copier, and scanner):

in September 2012, coincident with a move to Colorado (my even older Artisan 800 is still in occasional use by my wife). Speaking of “occasional use”, I’ve also used mine only sporadically, given that the monochrome outputs of the Brother laser multifunction printers in both of our offices work fine for most purposes and are significantly less expensive to operate on a per-page basis. Truth be told, mine probably still had its original ink cartridges installed when I recently tried to use it to print out a “batteries inside” notice, which needed to be bright red in color, to be taped to the outside of a package I was preparing to ship. And unsurprisingly, therefore, the result wasn’t as desired; the printer spat out a completely blank sheet of paper.

The inkjet cartridges, it turns out, were dried up inside (and/or empty; the software driver’s built-in diagnostics routine can’t differentiate between the two possible states). But when I replaced the cartridges with fresh ones:

the printer still spat out blank sheets of paper. That’s because, I eventually realized, the flexible multi-tube-harness that transports the ink from the cartridges to the print heads was also clogged by desiccated ink remnants (full disclosure: the following photo was snapped after the completion of the procedure described in the next couple of paragraphs):

Replacement harnesses weren’t available, my research indicated, and it also suggested that attempts to disassemble the printer were highly likely to lead to its demise. Determined to do everything possible to prevent this otherwise perfectly good device from ending up at the landfill, I kept plugging away with Google searches and eventually came across this video:

I went with this cleaning solution, and it took several fluid applications, each time followed by a few hours’ wait and then head clean and nozzle check operation attempts, but the Artisan 730 is thankfully back in business. I was left with the aforementioned “dead” inkjet cartridges:

which piqued my curiosity; how did they work, actually? And how did Epson and its competitors, such as long-disdained HP, both determine a particular cartridge’s remaining-ink level and attempt to prevent printer owners from using less expensive third-party alternatives? I decided to take one apart, randomly grabbing the light magenta one as my chosen victim:

Conceptually, here’s a how-it-works video I found that Wired Magazine did about a decade ago:

It’s not directly relevant here because, as I earlier noted, the print heads aren’t built into the cartridges; instead, they’re on the other end of the now-unclogged flexible tubing. But I still found the video interesting. And here’s a how-they-work (both in an absolute sense and vs thermal alternatives) Epson tech brief that I came across, which may also be of interest to you.

Also, in the earlier rubber-banded-stack photos, you might have noticed that the black ink cartridge has a “98” moniker while the others are “99”. Epson sells two versions of each cartridge color variant; “98s” have higher ink capacity than the less expensive “99” ones. A typical six-color bundle sold at retail combines a high-capacity black “98” (since monochrome printing is more common than full color, per my earlier mentioned Brother laser case study example) with standard capacity “99” variants of the others (more expensive all-“98” bundles are also available, obviously, as my earlier photos of the replacement cartridges indicate).

With that background info out of the way, let’s dive in. The cartridge enclosure construction is pretty beefy, understandably so due to the obvious desire to prevent leaks, and is further bolstered by a nearly impenetrable (for reasons that will soon be visibly obvious) sticker on one side:

That said, the seam around the install-orientation hole, whose purpose will be obvious once you see what the bay looks like absent cartridges (note the mounting pins toward the bottom):

and is on the opposite end from the same-side ink nozzle, looks promising:

And we’re inside. Behind that tough black plastic cover is, I suspect, the ink reservoir:

But for now, this electrical engineer’s top priority is checking out that multi-contact mini-PCB:

This side we’ve already seen in its installed state:

but the underside is now first-time exposed to view, too:

I’m guessing that under that opaque epoxy blob is the authentication chip (more likely, die). But if you look closely at the earlier mini-PCB-less shot, you’ll note that there’s still more “guts” to go below. Let’s get the broader plastic end assembly off next:

Whatever this is, I assume it modulates (and measures?) the amount of ink in the “tank” and flowing through the nozzle. Specific ideas, readers? A piezo something-or-other, mebbe?

And here are some views of what’s driving it (along with the earlier-seen mini-PCB, of course):

With no further meaningful progress seemingly possible here, I returned my disassembly attention to the sticker side, aided by a box cutter and focusing on the circular-pattern section you might have noticed in previous photos:

Hmmm. It appears that I’ve found the “port” used to fill the cartridge with ink on the assembly line. And it also appears that the cartridge still has at least some viable ink inside:

The light magenta dribble eventually petered out:

And after tediously piece-by-piece ripping off the recalcitrant black plastic sheet you saw earlier covering the other side, here’s what I found inside:

The ink-input port is the circular section in the upper left. Why there are so many chambers inside… And the output nozzle extends downward in the lower left area. I’ll conclude with a few more shots of both it and the “flow-control faucet” for it from both sides:

And wrap up with this no more revealing, but far more messy, alternative teardown clip I found:

Those preparatory gloves, paper towel and newspaper sure were a wise move! Let me know your thoughts on what I uncovered, along with what’s still to be identified, in the comments.

—Brian Dipert is the Editor-in-Chief of the Edge AI and Vision Alliance, and a Senior Analyst at BDTI and Editor-in-Chief of InsideDSP, the company’s online newsletter.

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The post Resurrecting an inkjet printer, and dissecting a deceased cartridge appeared first on EDN.

https://preview.redd.it/15xvt1a3w2ad1.png?width=1890&format=png&auto=webp&s=5be3ac2a5aeeca3e2937e6232abb22a154f82ab1 My current design on a MPPT solar charge controller i am designing for fun. A standard buck MPPT has issues with current feedback with no solar power, so i thought why not add a boost stage, now i can charge batteries at super low light levels, and no current from the battery through the panel at night. decided on a 555 timer charge pump to get around the duty cycle limit of high side nmos bootstrap gate drivers. This will eventually have a 12 or 15v supply for gate drivers and 555, and be able to accept battery and solar panel voltages up to around 60v submitted by /u/Acanthaceae_Strange [link] [comments]

GlobalFoundries (GF) of Malta, NY, USA has acquired the proprietary and production-proven power gallium nitride (GaN) IP portfolio of Chicago-based fabless firm Tagore Technology Inc, which was founded in 2011 and has design centers in Arlington Heights, IL, USA and Kolkata, India developing gallium nitride-on-silicon (GaN-on-Si) and gallium nitride-on-silicon carbide (GaN-on-SiC) technology for RF and power management applications...

The manufacturing industry has always been at the forefront of technological innovations. From the advent of the steam engine in the 1700s to the invention of the assembly line by Henry Ford, manufacturing has been one of society’s greatest drivers of change.

Today, the manufacturing industry is once again experiencing a series of unprecedented changes.

The proliferation of robotics, advanced sensors, device connectivity, and advanced analytics has led to modern Industry 4.0 manufacturing. Now, the field is poised for yet another change, with a transition into the world of Industry 5.0.

This blog discusses the differences between Industry 4.0 and Industry 5.0, the challenges facing the evolution from Industry 4.0 to Industry 5.0, and some ways to make the transition as seamless as possible.

Industry 4.0 Versus Industry 5.0

At present, most modern factories are classified as “Industry 4.0,” and are commonly referred to as “Smart Factories.”

Industry 4.0 is marked by a significant shift toward a more interconnected and intelligent manufacturing environment, leveraging advancements such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and edge computing. This era introduces the capability to gather, analyze, and use vast amounts of data in real time, which enhances decision-making processes, predictive maintenance, and overall operational efficiency. Ultimately, the result is a comprehensive advancement over Industry 3.0 to create more agile, efficient, and responsive manufacturing systems. However, Industry 4.0 remains focused mainly on the manufacturing side of things, answering questions about how to make more products faster, more consistently, of better quality, and at lower cost.

Industry 5.0 expands the technological advancements of Industry 4.0 and augments them by considering human factors. It seeks to redefine roles within manufacturing and beyond—encompassing supply chains and entire operational landscapes—to create a more cohesive, adaptive, human-centric, and sustainable industrial environment.

This paradigm shift moves beyond viewing machines as mere tools for productivity and advocates for a collaborative synergy in which technology enhances human capabilities, creativity, and decision-making processes. The fundamental aim is to achieve a balanced symbiosis between advanced technological systems and the unique insights and values humans bring to the table, ensuring that industrial progress supports both efficiency and the well-being of society at-large. Within this industrial revolution, a significant focus lies on fostering seamless communication and interactions between humans and machines.

Drivers of the Evolution

While the differences between Industry 4.0 and Industry 5.0 are clear and defined, understanding the larger societal and geopolitical factors driving this evolution is essential.

One major driver of Industry 5.0 is resilience in the face of global challenges.

Starting in 2020, the COVID-19 pandemic and subsequent disruptions exposed vulnerabilities within global manufacturing networks. In the face of these challenges, Industry 5.0 emphasizes resilience and flexibility, aiming to build systems that can adapt and recover from unforeseen events more efficiently.

In a similar vein, the COVID-19 pandemic heightened consciousness surrounding worker health and safety. Industry 5.0 seeks to leverage the technological advancements of Industry 4.0—namely, sensors and vision systems—to mitigate risks, create safer work environments, and minimize accidents.

Naturally, another significant driver of this evolution is the demand for more innovative, sustainable, and reliable products that can reach the market faster. But how will Industry 5.0 enable these changes?

Industry 5.0 will leverage the integration of digital twins with generative AI, which simplifies the prototyping of new concepts. This integration accelerates the development process and enhances the quality of the final product by allowing for the evaluation of multiple design options in terms of cost, performance, quality, and durability before a product reaches the market. Furthermore, Industry 5.0 has a major focus on sustainability, with products designed to be more recyclable and less polluting. Thus, driven by technological advancements and changing consumer demands, Industry 5.0 holds the promise of delivering innovative, high-quality, and sustainable products more efficiently.

Hurdles for the Evolution

Despite the clear benefits and reasons for moving toward Industry 5.0, the evolution is not without its challenges. Paradoxically, the still ongoing adoption of Industry 4.0 is a major hurdle to adopting Industry 5.0.

Between the advent of Industry 3.0 and the emergence of Industry 4.0, nearly four decades elapsed. Remarkably, in just one decade, we transitioned from Industry 4.0 to the dawn of Industry 5.0, a testament to the rapid pace of technological advancement and its transformative impact on manufacturing and production.

Consequently, now we are witnessing an overlap in which many companies are still in proof-of-concept evaluations of Industry 4.0 while others are leveraging Industry 5.0 already. Currently, while large corporations have made substantial investments in Industry 4.0, the adoption rate among smaller manufacturing sites and processes through full-scale digitalization projects remains relatively low.

Major barriers to adopting of Industry 4.0 technologies include the high capital expenditures needed to purchase new equipment, as well as new skill sets and expertise, along with a lack of trust in modern paradigms such as cloud computing and reliance on data security. Until the industry at large fully embraces Industry 4.0, Industry 5.0 will take some time to reach widespread adoption.

Another challenge of adopting Industry 5.0 is that it represents an inherently different mindset and approach to manufacturing.

Unlike previous evolutions (e.g., Industry 3.0 to Industry 4.0), the Industry 4.0 to Industry 5.0 evolution is not so much a challenge of technological advancement but more so a challenge of mindset advancement. Integrating new digitalization projects requires strong alignment all the way from the chief executive officer to the operator to the manufacturing floor. The organization needs to have a clear vision of what its version of Industry 5.0 will look like and then deploy this idea at all levels. This calls for new processes and ways of operating, including more delegation from the operators to those on the floor.

Conclusion

Like the many industrial revolutions in human history, modern manufacturing is currently on the precipice of another remarkable evolution. The transition from Industry 4.0 to Industry 5.0 marks a significant change in the manufacturing world.

Achieving this transition requires an industry-wide mindset shift in how our factories operate, how workers interact with machinery, and how the whole ecosystem can converge. Once this happens, we can enter the era of Industry 5.0, enabling greater sustainability and resilience, increased worker safety, and a more robust supply chain.

The post The Evolution from Industry 4.0 to Industry 5.0 appeared first on ELE Times.

The new funding will expand the company’s product offerings and align it with the generative AI movement.

The new microcontroller adds robust wireless connectivity and offloading to bolster the host processor.

While the talk about artificial intelligence (AI) at the edge is all the rage, there are fewer design examples of how it’s actually done. In other words, how AI applications are implemented at the edge. Below is a design example of how Panasonic implemented an AI function in its e-assisted bike.

Panasonic recently launched electric assist bicycle for school commuting, TiMO A. This e-assisted bike bypasses the need for additional hardware such as a sensor for tire air pressure. Instead, it incorporates a microcontroller (MCU) alongside an edge AI development tool to create a tire pressure monitoring system (TPMS) that leverages an AI function.

Figure 1 The e-bike powertrain comprises basic units, including a power unit (with an on-board charger, junction box, inverter, and DC-to-DC converter) and a motor unit. Source: STMicroelectronics

The bike runs an AI application on the MCU to infer the tire air pressures without using pressure sensors. If necessary, the system generates a warning to inflate the tires based on information from the motor and the bicycle speed sensor. As a result, this new function simplifies tire pressure monitoring system (TPMS) design while enhancing rider safety and prolonging the life of tires.

Panasonic combined the STM32F3 microcontroller from STMicroelectronics with its edge AI development tool, STM32Cube.AI, which converts neural network (NN) models learned by general AI frameworks into code for the STM32 MCU and optimizes these models.

STM32F3 is based on the Arm Cortex-M4, which has a maximum operating frequency of 72 MHz. It features a 128-KB flash along with analog and digital peripherals optimal for motor control. In addition to the new inflation warning function, the MCU determines the electric assistance level and controls the motor.

STM32Cube.AI enabled Panasonic to implement this edge AI function while fitting into STM32F3 embedded memory space. Panasonic leveraged STM32Cube.AI to reduce the size of the NN model and optimize memory allocation throughout the development of this AI function. STM32Cube.AI optimized the NN model developed by Panasonic Cycle Technology for the STM32F3 MCU quickly and implemented it in the flash memory, which has limited capacity.

Figure 2 STM32Cube.AI, which makes artificial neural network mapping easier, converts neural networks from popular deep learning libraries to run optimized inferences on STM32 microcontrollers. Source: STMicroelectronics

This design example shows how edge AI works in both hardware and software, which can facilitate a wide range of designs in industrial and consumer domains.

“By combining the STM32F3 MCU with STM32Cube.AI, we were able to implement the innovative AI function without the need to change hardware,” acknowledged Hiroyuki Kamo, manager of the software development section at the Development Department of Panasonic Cycle Technology.

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The post Implementing AI at the edge: How it works appeared first on EDN.

A scheme of simple band-pass RC- and LR- filters on operational amplifiers containing only one capacitor or inductor and 3 resistors is proposed. A comparison is made of the amplitude-frequency characteristics of the proposed filters, as well as the RC filter of Robert Allen Pease and its modified LR- variant.

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

From the whole set of simple low-frequency filters, one can highlight the Sallen-Key filters [1, 2]. Despite their attractive external simplicity, these filters are far from easy to set up and require the use of coordinated parts.

The RC filter, proposed in 1971 by an engineer of George A. Philbrick Research—Robert Pease—Figure 1 [3, 4], has several unique properties. It is extremely simple, and its resonant frequency is controlled by only one potentiometer R2, and the transmission coefficient of the filter almost does not depend on the resistance value of this potentiometer. The amplitude-frequency characteristics of this filter when adjusting the potentiometer R2 are shown in Figure 1 [5].

Figure 1 Electrical diagram of the Pease RC-filter and its amplitude-frequency characteristics when R2: 1) 10.0 kΩ; 2) 3.0 kΩ; 3) 1.0 kΩ; 4) 0.3 kΩ; 5) 0.1 kΩ; 6) 0.03 kΩ.

By slightly modifying Pease’s circuit, namely, by replacing capacitors with inductors, we get a modified filter circuit. The amplitude-frequency characteristics of the modified LR-filter during the adjustment of the R2 potentiometer are shown in Figure 2 [5].

Figure 2 Electrical diagram of the modified LR-filter and its amplitude-frequency characteristics when R2: 1) 0.03 kΩ; 2) 0.1 kΩ; 3) 0.3 kΩ; 4) 1.0 kΩ; 5) 3.0 kΩ; 6) 10.0 kΩ. L1=L2=20 mH.

In addition to the op-amp, the filters discussed above contain 5 components each. However, it is possible to offer even simpler filters that contain only 4 where the elements R3 + R4 can be replaced with one potentiometer.

The “resonant” frequency of the RC filter, Figure 3, is determined from the expression:

where f0 is in Hz, R is in Ω, C is in F, a is a constant depending on the model of the op-amp.

So, for example, for LM324 a ≈ 426. The equivalent Q-factor of the filter Q is proportional to the expression:

where b is a constant (b ≈ 110).

In the calculations: C = C1; R = R3 + R4. Thus, the “resonant” frequency of the filter depends only on the nominal values of the elements R = R3 + R4 and C = C1. The ratio R2/R1 does not affect the frequency of the “resonance”, but affects only the value of the equivalent quality factor of the filter and the transmission coefficient of the filter at the frequency of the “resonance”.

Figure 3 Electrical diagram of the RC-filter with the adjustment of the “resonance” position by the potentiometer R4.

The amplitude-frequency characteristics of the RC-filter are shown in Figure 4.

Figure 4 Amplitude-frequency characteristics of the RC-filter with the adjustment of the “resonance” position when the resistance value R = R3 + R4 varies.

Replacing the capacitor C1 with the inductor L1 and swapping the frequency-determining components R and L, we get the LR-version of the filter, Figure 5. Its amplitude-frequency characteristics with varying values of R are shown in Figure 6.

The “resonant” frequency of the LR-filter, Figure 3, is determined from the expression:

where f0 is in Hz, R is in Ω, L is in H, and a is a constant. The ratio R2/R1 affects the same parameters as before.

Figure 5 Electrical diagram of the LR-filter with the adjustment of the “resonance” position by the potentiometer R4.

Figure 6 Amplitude-frequency characteristics of the LR-filter with the adjustment of the “resonance” position when the resistance value R = R3 + R4 varies.

Michael A. Shustov is a doctor of technical sciences, candidate of chemical sciences and the author of over 800 printed works in the field of electronics, chemistry, physics, geology, medicine, and history.

Related Content

• Component tolerance sensitivities of single op-amp filter sections
• Parsing PWM (DAC) performance: Part 3—PWM Analog Filters
• Single op-amp Sallen-Key filters, filter fanatics
• Painless reduction of analog filter noise
• Toward better behaved Sallen-Key low pass filters
• Optimizing a simple analog filter for any PWM

References

1. Sallen R.P., Key E.L. “A Practical Method of Designing RC Active Filters”. IRE Transactions on Circuit Theory, 1955, Vol. 2, № 1 (March), pp. 74–85.
2. Tietze U., Schenk Ch. “Halbleiter-Schaltungstechnik”, 12. Auflage, Berlin-Heidelberg, Springer Verlag, 2002, 1606 S.
3. Pease R. “An easily tunable notch-pass filter”. Electronic Engineering, December 1971, p. 50.
4. Hickman I. “Notches, Top”. Electronics World Incorporating Wireless World, 2000, V. 106, No. 2 (1766), pp. 120–125.
5. Shustov M.A. “Circuit Engineering. 500 devices on analog chips”. St. Petersburg: Science and Technology, 2013, 352 p.

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NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and develops and manufactures high-power industrial blue lasers — has revised the date for implementation of its 1-for-40 reverse stock split, from 1 July to 10 July, in order to “better coordinate with our overall strategic efforts to pursue resuming trading on NYSE and attract larger investors who may not otherwise invest in low-price stock,” says CEO Brian Knaley...

STMicroelectronics, a global semiconductor leader serving customers across the spectrum of electronics applications, today announced the availability of ST Edge AI Suite, bringing together tools, software and knowledge to simplify and accelerate building edge-AI applications.

The ST Edge AI Suite is an integrated collection of software tools, designed to facilitate the development and deployment of embedded AI applications. This comprehensive suite supports both optimization and deployment of machine-learning algorithms, starting from data collection to final deployment on hardware, streamlining the workflow for different types of users.

The tools from the suite are covering a broad range of ST products, from smart sensors to microcontrollers and microprocessors including upcoming STM32N6 neural-processing microcontrollers.

“ST Edge AI Suite is the new starting point on st.com for embedded AI development. Here, today’s innovators, driven by imagination, can find help to realize tomorrow’s smart things that sense, infer, and respond intelligently, autonomously, and efficiently to the world around,” said Alessandro Cremonesi, Executive Vice President, Chief Innovation Officer and General Manager of STMicroelectronics’ System Research and Applications Group. “This environment brings developers simplicity through an easy choice of models and data sources, finding the right tools quickly and easily, optimizing and benchmarking, then automatically generating code and libraries – all within one unified framework.”

While working across multiple hardware platforms, ST Edge AI Suite meets the needs of different types of users such as data scientists, embedded software developers, and hardware system engineers.

There is seamless access to online tools such as the ST Edge AI Model Zoo and Developer Cloud, as well as desktop tools for data tuning and model optimization on the chosen hardware platform. These include NanoEdge AI Studio for generating machine learning libraries automatically, and STM32Cube.AI, MEMS Studio, and Stellar Studio, for model optimization on STM32 devices, MEMS inertial sensors, and Stellar processors. All are ready to use free of charge.

The release of this suite is also an opportunity to introduce world-first innovations for AI applications in the MEMS Studio tool: the “ISPU NN model optimizer” and “automatic selection of MLC features and filters.”

Lead customers’ experiences:

Honeywell, Soxai, and HPE Group explain how tools such as MEMS Studio and Stellar Studio in the ST Edge AI Suite help simplify and accelerate edge-AI development.

Israel Herrera, Firmware Architect & Embedded Systems Engineer, Honeywell Fire, commented, “We recognize the innovative strides ST has made in the realm of edge computing with their sensors. MEMS Studio, as featured in the ST Edge AI Suite, has proved to be a great software development tool to help us perform quick tests using multiple MEMS sensor modules from ST. It enables us to create proof of concepts easily through its code generation feature. MEMS Studio is also very useful in testing different and independent Machine Learning and Data Analysis algorithms.”

Tatsuhiko Watanabe, CEO & Founder of SOXAI, which is using ST’s edge-AI sensors, said, “While we were developing SOXAI RING 1, ST introduced the world’s first sensor with edge AI, and we quickly adopted it to boost our product’s performance. The machine-learning core software tool featured in the ST Edge AI Suite helped us quickly integrate this new technology and allowed our developers to harness the full potential of the sensor with minimal effort. Compared to the first generation, our new SOXAI RING 1 now boasts an extended battery life of at least 10 hours, a game-changer in the world of wearable technology.”

Andrea Bozzoli, CEO of HPE Group, added, “HPE is at the forefront of transforming the automotive sector through our collaboration with STMicroelectronics and their Stellar MCU and Stellar Studio software, which is part of the ST Edge AI Suite. Our Prometeo Joint Innovation Lab efforts are set to deliver a proof-of-concept for next-generation vehicle powertrains, leveraging AI to enhance predictive maintenance and control systems. This synergy will not only elevate electric vehicle performance but also enrich the digital cockpit experience, setting a new standard for smart, sustainable mobility.“

ST Edge AI Suite is now live at https://www.st.com/content/st_com/en/st-edge-ai-suite.html

The post ST Edge AI Suite goes live, accelerating AI-enabled product development with STMicroelectronics’ technologies appeared first on ELE Times.

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

Im very new to electronics. Im trying to learn as I go at my job, as it requires working with alot of obsolete parts. I know nothing about these type displays. But I hope you all enjoy them. Any information would be welcome! submitted by /u/NorthRhubarb2335 [link] [comments]

The hysteresis effect is one of the main sources of loss in ferromagnetic materials. In this article, we learn to calculate the hysteresis loss of a magnetic core and work through some example problems.

Need some advice on how to stop this thing from whining so much. It is a very high pitch whine coming from the buck converter module while under load. At Idle, the input voltage is 13.8v and output is 3.45v with no whine. When the Apple Tv is powered on the output is 3.39v. OEM PSU is 3.4v. Is there anything I can do or will I need a different module? submitted by /u/Jvinsnes [link] [comments]

I bought these thermocouple wires off the top google search results. The gauge was unlisted, brown is 20 gauge for reference. Has anyone successfully used such thin thermocouples? These are for extreme cold temperatures submitted by /u/Siliybob [link] [comments]

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Just got a job in a lab for PCB design and this is the soldering station. submitted by /u/Kreton31 [link] [comments]