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It's been a busy summer of partnerships, acquisitions, and fab expansions for GlobalFoundries. What does this say about the company's silicon roadmap?

❤️ Запрошуємо переглянути експозицію мистецького проєкту «Об'єднані перемогами» kpi вт, 09/02/2025 - 20:15

Vannevar Bush shaped how the United States builds, funds, and thinks about technology. As both inventor and institutional architect, his work laid the foundations for digital logic, wartime research coordination, and the entire modern R&D landscape.

I recently published a simple design for a platinum resistance detector (PRTD) 4 to 20mA transmitter circuit, illustrated in Figure 1.

Figure 1 The PRTD 4 to 20 mA loop transmitter with constant current PRTD excitation that relies on 2nd order software nonlinearity correction math, ToC= (-u + (u2 – 4wx)1/2)/(2w).

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The simplicity of Figure 1’s circuitry is somewhat compromised, however, by its need for PRTD nonlinearity correction in software:

u and w constant and x = RPRTD@0oC – RPRTD@0oT
ToC= (-u + (u2 – 4wx)1/2)/(2w)

Unfortunately, implementing such quadratic floating-point arithmetic in a small system might be inconveniently costly in code complexity, program memory requirements, and processing time.

But fortunately, there’s a cool, clever, comparably accurate, code-ware-lite, and still (reasonably) uncomplicated alternative (analog) solution. It’s explained in this article “Design Note 45: Signal Conditioning for Platinum Temperature Transducers,” by (whom else?) famed designer Jim Williams.

Figure 2, shamelessly copied from William’s article, showcases his analog solution to PRTD nonlinearity.

Figure 2 A platinum RTD bridge where feedback to the bridge from A3 linearizes the circuit. Source: Jim Williams

Williams explains: The nonlinearity could cause several degrees of error over the circuit’s 0°C to 400°C operating range. The bridge’s output is fed to instrumentation amplifier A3, which provides differential gain while simultaneously supplying nonlinearity correction. The correction is implemented by feeding a portion of A3’s output back to A1’s input via the 10k to 250k divider. This causes the current supplied to Rp to slightly shift with its operating point, compensating sensor nonlinearity to within ±0.05°C.

Figure 3 shows William’s basic idea melded onto Figure 1’s current transmitter concept.

Figure 3 A PRTD transmitter based on the classic LM10 op-amp plus a 200 mV precision reference combo.

R5 provides PRTD-linearizing positive feedback to sensor excitation over the temperature range of -130 °C to +380 °C.

Here, linearity correction is routed through R5 to the LM10 internal voltage reference, where it is inverted to become positive feedback. The resulting “slight shift in operating point” (about 4% over the full temperature range) duplicates William’s basic idea to achieve the measurement linearity plotted in Figure 4.

Figure 4 Positive feedback reduces linearity error to < ±0.05 oC over -127 oC to +380 oC. The x-axis = Io (mA),  left y-axis = PRTD temperature, right y-axis = linearity error. T oC = 31.7(Io – 8mA).

Of course, to consistently achieve this ppm level of accuracy and linearity probably needs an iterative calibration process like the one William’s describes. Figure 5 shows the modified circuit from Figure 3, which includes three additional trims to enable post-assembly tweaking using his procedure.

Figure 5 Linearized temperature transmitter modified for a post-assembly tweaking using his procedure.

Substituting selected precision resistors for the PRTD at chosen calibration points is vital to making the round-robin process feasible. Using actual variable temperatures would take impossibly long! Unfortunately, super precise decade boxes like the one William’s describes are also super scarce commodities. So, three suitable standard value resistors, along with the corresponding simulated temperatures and 4-20 mA loop currents, are suggested in Figure 5.  They are:

51.7 Ω = -121 oC = 4.183 mA
100 Ω = 0 oC = 8.000 mA
237 Ω = 371 oC = 19.70 mA

Happy tweaking!

Oh yeah, to avoid overheating in Q1, it should ideally be in a TO-220 or similar package if Vloop > 15 V.

Related Content

• Simple but accurate 4 to 20 mA two-wire transmitter for PRTDs
• The power of practical positive feedback to perfect PRTDs
• Improved PRTD circuit is product of EDN DI teamwork
• Platinum-RTD-based circuit provides high performance with few components
• DIY RTD for a DMM

The post Positive analog feedback linearizes 4 to 20 mA PRTD transmitter appeared first on EDN.

It finished and it work im proud of it and ım 15 submitted by /u/Careful-Rich9823 [link] [comments]

OEMs must ensure their avionics are electromagnetically clean and do not pollute other sub-systems with unwelcome radiative, conducted, or coupled emissions. Similarly, integrators must ensure their space electronics are not susceptible to RFI from external sources, as this could impact performance or even damage hardware.

As a product provider, how do you ensure that your subsystem can be integrated seamlessly and is ready for launch? As an operator, how does EMI affect your mission application and the quality of service you deliver to your customers?

EMI is unwanted electrical noise that interferes with the normal operation of spacecraft and satellite avionics, generated when fast switching signals with rapid changes in voltage and current interact with unintended capacitances and inductances, producing high-frequency noise that can radiate, conduct, or couple unintended energy into nearby circuits or systems. No conduction exists without some radiation and vice versa!  

Fast switching signals with rapidly changing currents and voltages energise parasitic inductances and capacitances,

causing these to continuously store and release energy at high frequencies. These unintended interactions become stronger as the rate of change increases, generating transients, ringing, overshoot and undershoot, crosstalk, as well as power and signal-integrity problems that impact satellite applications.

Sources of EMI

Modern avionics use switching power supplies, e.g., isolated DC-DCs or point-of-load (POL) regulators, CPUs, FPGAs, clock oscillators, and speedy digital interfaces, all of which switch at high frequencies with increasingly faster edge rates that contain RF harmonics. These functions have become more tightly coupled as OEMs integrate more of these into physically smaller satellites, exacerbating the potential to form and spread EMI.

Furthermore, they typically share power or ground return rails, and a signal or noise in one circuit affects the others through common-impedance coupling via the shared impedance, contributing to power-integrity issues such as ground bounce.

Similarly, satellites use motors, relays, and mechanical switches to deploy and orient solar arrays, point antennae, control reaction wheels and gyroscopes, for robotics and to enable/disable redundant sub-systems. Rapid changes in current and voltage during their operation generate conductive and radiative EMI that impacts nearby circuits, caused by arcing, brush noise within motors, inductance kickback from coils, and contact bounce from mechanical switches.

EMI can also enter spacecraft from the external space environment, i.e., high-energy radiation from solar flares and cosmic rays can induce noise resulting in discharges and transient spikes. Over time, charged particles from the Earth’s magnetosphere, solar wind, or from geomagnetic storms, such as electrons and ions, accumulate on satellite surfaces, forming large potential differences. When the amassed electric-field strength exceeds the breakdown voltage of materials, ESD-induced EMI generates a fast, high-energy transient pulse that can couple into signal lines, disrupting or damaging space electronics. Conductive coatings and grounding networks are used to equalise surface potentials, as well as plasma contactors to remove built-up charge.

EM impact of a high dI/dt and dV/dt

EMI can be generated, coupled, and then conducted through physical wires, traces, connectors, and cables. Conductors separated by a dielectric form a capacitor, even unintentionally, and a fast signal on one trace switching at nanosecond speeds, i.e., a high dV/dt, energizes a changing electric field that can capacitively couple noise onto an adjacent track, e.g., a sensitive analogue signal.

Similarly, any loop of wire or a PCB trace intrinsically contains inductance and a high dI/dt and energizes a changing magnetic field that can inductively couple (induce) noise onto an adjacent trace or circuit.

In both cases, inherent parasitic capacitance or inductance provides a lower impedance to current than the intended path. Since current must flow in a loop to its source, loop impedance is the key!

The faster the rate of change, the stronger the electromagnetic coupling, and a changing electric field generates a corresponding magnetic field, which will radiate as an antenna if its loop area is large, contains high-frequency harmonics, or if there is not tight coupling between the forward and return paths. The radiated EM wave couples into nearby conductive structures such as cables, traces, metal enclosures, and sensors, receiving the unwanted RFI.

Any conductor with a time-varying current creates an EM field, and the signal wire and its return path form a loop which can become an antenna when carrying fast-switching currents. Similarly, a PCB trace can start radiating, even if the fundamental signal frequency is low, but contains fast edges, if its forward path is not referenced to an adjacent solid ground plane or if the track length approaches 1/10th or more of the signal wavelength, when the EM fields no longer cancel, forming standing waves that radiate from the track. As a simple example, a 10-cm trace resonates around 350 MHz, depending on the PCB dielectric, and an edge rate of 1 ns contains harmonics up to this frequency that will radiate.

EMI issues in modern modulation techniques

For telecommunications applications, EMI can raise the noise floor masking low-power uplink carriers (Figure 1), impacting receiver sensitivity and dynamic range, lowering SNR, and reducing channel capacity (). Unintended, in-band spurs can distort modulation constellations, leading to bit/symbol errors, degrading error vector magnitude (EVM). Energy from unwanted spurs can completely mask narrowband carriers or leak into adjacent channels, impacting performance and regulated RFI emissions levels.

Figure 1 Q-PSK and 16-PSK constellations before (left) and after (right) EMI. 

Telecommunication satellites provide a continuous service with tight regulatory limits, and even small EMI emissions can be problematic. Payloads typically process many channels and frequency bands, receiving low-level uplinks, so any unwanted noise impacts the overall link budget and operational integrity.

RFI coupling into the low noise amplifiers (LNAs), frequency converters, and filters can generate harmonic distortion, intermodulation products, and crosstalk between channels.

EMI issues in space applications

Earth-observation applications rely on high-precision optical, LiDAR, radar, or hyperspectral sensors, and unwanted EMI can introduce noise or distortion into the receive electronics, degrading resolution, accuracy, and calibration, misinterpreting the collected data (Figure 2).

Figure 2  Earth-observation imagery before (left) and after (right) EMI. Source: Spacechips

Signals intelligence (SIGINT) satellites rely on the accurate detection, reception, and analysis of weak, distant, and often low-power carriers, and unwanted EMI can severely degrade receiver performance, limit intelligence value, or even render it ineffective (Figure 3). RFI can reduce sensitivity and dynamic range, or overload (jam) RF front-ends, causing non-linear distortion. Internally generated noise can mimic the characteristics of actual intercepted signals, resulting in false-positive classifications or geolocation, misleading analysts or automated processing systems.

EMI from the on-board electronics or switching power supplies can raise the receiver’s noise floor, making it harder or impossible to detect weak signals of interest.

Figure 3 SIGINT spectra before (left) and after (right) EMI. Source: Spacechips

For in-space servicing, assembly, and manufacturing (ISAM) applications, unwanted EMI from motors, actuators, and robotics can impact LiDAR, radar, cameras, and proximity sensors, resulting in loss of situational awareness, errors in docking and alignment, and reduced control accuracy.

For space exploration, EMI can affect sensitive instruments, corrupting measurements, resulting in the misinterpretation of scientific data. For example, magnetometers are used to detect weak, planetary magnetic fields and their variation, and artificial emissions from the avionics or spacecraft motors can mask or distort real science. As shown in Figure 4, magnetometers are often mounted on long booms away from the satellite to reduce the impact of EMI from the on-board electronics.

Figure 4 NASA’s MESSENGER Spacecraft with Magnetometer Boom. Source: NASA

For all applications, unintended and uncontrolled EMI on power, ground, and signal cables/traces affects on-board circuits and overall system performance. If not managed, RFI can pose a greater threat to avionics than the radioactive environment of space, damaging sub-systems, impacting mission reliability, and satellite lifetime.

Regulatory agencies

For decades, many OEMs have built avionics with little regard to EMI, only to discover emissions are too high or their sub-systems are susceptible to external RFI. Considerable time is then spent identifying the source of the interference, retrofitting fixes to patch the problem, and pass the mission’s EMC requirements. Often, the root cause is never found or fully understood, and this ‘sticking-plaster’ approach increases product cost, both non-recurrent and recurring, as well as delaying time-to-market.

What should you do if you discover EMI with your latest hardware? For all applications, unwanted noise could result in RFI emissions that violate spectral regulations and interfere with other satellites or terrestrial systems. The UN’s ITU defines how the radio spectrum is allocated between different services and sets maximum allowable levels for out-of-band emissions, spurs, effective radiated power (EIRP), and the received power flux density on Earth.

National regulators, such as the FCC (US), Ofcom (UK), CEPT (Europe), and ETSI (global), enforce these limits before granting operating licenses. Agencies provide EMC standards to guide OEMs developing avionics hardware, e.g., MIL-STD-461, AIAA S-121A, and ECSS-E-ST-20C.

Characterizing EMI

The first step in determining the origin of unwanted EMI is to understand whether this is being radiated, conducted, coupled, or a combination of these. EM hardware is often tested as a proof-of-concept PCB in a lab. without a case using unshielded cables and connectors, making system validation more susceptible to external pick-up and common-mode noise.

This interference needs to be initially characterized (probe ground to understand the measurement noise floor) and managed using ferrite-bead clamps, for example, to avoid false positives. Figure 5 and Figure 6 show EM testing with significant common-mode noise picked up by the setup that appears on all the power rails and the ground plane. Both the supply and return cables are around eighteen inches in length, mostly untwisted and unprotected from EMI: 

Figure 5 Typical EM testing in a lab using exposed hardware. Source: Spacechips

Figure 6 Common and differential-mode scope measurements of 1V8 power rail. Source: Spacechips

Testing in an anechoic chamber isolates the device under test (DUT) from external interference as well as internal reflections, simulating open-space conditions, allowing you to measure the actual emissions from your avionics to understand their origin and mitigate their impact.

Engineering qualification model (EQM) and flight model (FM) hardware are typically verified in a sealed metal box with gaskets, shielded cables, and connectors, providing a protective Faraday cage for the DUT. This makes the system less susceptible to external EMI and minimizes RFI emissions from the avionics.

Reducing EMI

To reduce EMI in existing avionics, filters, chokes, and ferrite beads (lossy as opposed to energy-storing inductors) are added to lower conducted noise on power, signal, and data cables. The most obvious way to decrease EM coupling is to increase the physical separation between conductors, but this may not always be possible. The use of twisted pairs equalizes field coupling between two wires, resulting in common-mode interference that can subsequently be removed. Similarly, differential signalling cancels EM fields.

Clamp-on ferrites choke high-frequency common-mode noise on conductors, allowing low-speed signals to pass while dissipating RF interference as heat. If the same EMI could have generated radiated emissions from long cables, then the ferrites would indirectly reduce this antenna effect. Chip-bead ferrites can suppress both differential and common-mode noise, depending on their placement.

Shielding reduces radiated EMI by creating a physical barrier that reflects or absorbs EM fields before they can escape, as well as preventing external noise from entering avionics. Gaskets maintain an electrically conductive seal, preventing external EMI radiation from entering through openings or internal RFI from escaping through gaps or seams in a metal enclosure. Gaskets ensure a continuous Faraday cage, maintaining a low-impedance electrical path to ground, reducing potential differences that could allow common-mode currents and radiation. The gasket redirects EM fields along the enclosure or to ground, instead of allowing them to radiate into or out of the avionics.

I’ve seen absorbing foam added to many avionics products to soak up unwanted radiated emissions, both internal reflections to prevent these bouncing around within enclosures, coupling and inducing further EMI, as well as reducing the strength of RF energy before it escapes through gaps or seams or conducts onto cables and traces. The foam contains carbon or ferrite particles that create resistive losses when RF fields interact with them. An electronic case can act as a cavity that resonates at a certain frequency, and the use of foam can reduce such standing waves.

Tips for proper EMC design

While the addition of EMI filters, RF absorbing foam, and ferrites is very helpful, they should be the last line of defense, not the first solution. If you design it right, you won’t need to fix it later! Sometimes there will be exceptions to the rule, and I have used a high-speed semiconductor in a large ceramic package whose intrinsic parasitic inductance generated an EMI spur. Initially, this was an issue for both the OEM and the telecommunications operator, who cleverly positioned the problematic channel over a low-traffic region of the Indian Ocean.

Likewise, when observing and measuring signals, you must ensure your test equipment does not pick up unwanted interference, confuse decision-making, and delay time-to-market by incorrectly diagnosing a working sub-system as a faulty, noisy one. A scope probe and its ground lead form a loop creating a closed-circuit path that can pick up signals or interference due to electromagnetic induction. Faraday’s Law states, “a changing magnetic field through a closed loop induces an EMF in the loop.” The larger the loop area or the faster the rate of change in the magnetic field, the greater the induced voltage.

Proper EMC design and mitigation are essential to ensure data integrity, mission reliability, and satellite longevity. As avionics sub-systems become faster and more integrated, a more proactive approach is required to deliver right-first-time, EMC-compliant hardware and satellite applications:

1. EMC compliance must be a key part of early product design.
2. Understand the sources of emissions and how to control them – 90% of all EMI originates from unintentional signal flow, e.g., crosstalk or return currents flowing where they were never intended to be, such as to close to the edge of a PCB. All unwanted EMI originates from intentional signals!
3. Simulate before building hardware: current radiates, not voltage, check its spectrum before building hardware. The radiated electric field, in V/m, from a current loop in free space can be simplified as, where I is the current amplitude, A the loop area, and k a constant for a given frequency and observation point. The corresponding magnetic near field in A/m can be approximated as: , where S is the loop separation and D the measurement distance.
4. The most common cause of EMI from products is unintentional common-mode currents on external cables and shields as a result of voltage differences relative to the chassis.
5. Manage the layout of your return currents by providing dedicated ground planes, their spread (path of least impedance dominated by inductance) on these reference planes to avoid them coupling, minimize loop area, and provide adjacent ground layers for signals. The following Hyperlynx simulation in Figure 7 predicts current-flow density from a SIGINT SDR:

Figure 7 Siemens’ Hyperlynx Post-Layout Prediction of Return-Current Flow. Source: Spacechips

6. Minimize loop area by keeping PCB trace lengths and cables < λ/10 of the highest harmonic frequency within a signal, and not just the fundamental component.
7. When probing signals using an oscilloscope, use the smallest ground lead possible to minimize loop area to reduce the amount of induced magnetic flux and hence EMI. A shorter ground connection also has less inductance, which means less distortion and a more accurate representation of the signal under test. Probing in differential mode cancels common-mode noise at the measurement point, and the use of a ferrite-bead clamp around the cable reduces the amount of external noise picked up (induced) by the lead entering the scope. Null probing of ground baselines, the noise floor, and future measurements!
8. When testing EM hardware in the lab, exposed circuit boards and/or unshielded power and ground cables pick up EMI interference. These can pollute measurements and obfuscate decisions, validating the system design.
9. Test in an anechoic chamber to isolate the avionics from external interference as well as internal reflections to measure the actual emissions from your hardware to understand their origin and mitigate their impact.
10. Design your PCB stack, floorplan, and layout to prevent the generation of EMI: assign routing layers between neighbouring ground planes to contain the spread of return currents and maintain good Z0. Never route across a power or ground-plane split!

There’s so much more to say and if you would like to learn more, Spacechips teaches courses on Right-First-Time PCB Design for Spacecraft Avionics as well as EMI Fundamentals for Spacecraft Avionics and Satellite Applications.

Spacechips’ Avionics-Testing Services help OEMs and satellite integrators solve EMI issues that are preventing them from meeting regulatory targets and delivering hardware on time.

Dr. Rajan Bedi is the CEO and founder of Spacechips, which designs and builds a range of advanced, AI-enabled, re-configurable, L to K-band, ultra high-throughput transponders, SDRs, Edge-based on-board processors and Mass-Memory Units for telecommunication, Earth-Observation, ISAM, SIGINT, navigation, 5G, internet and M2M/IoT satellites. The company also offers Space-Electronics Design-Consultancy, Avionics Testing, Technical-Marketing, Business-Intelligence and Training Services. (www.spacechips.co.uk).

Related Content

• Satellite avionics grounding and design for EMC, part 1
• Power electronics in space: A technical peek into the future
• Time-to-digital conversion for space applications
• The EMC space

The post EMI fundamentals for spacecraft avionics & satellite applications appeared first on EDN.

Analog/mixed-signal and specialty foundry X-FAB Silicon Foundries SE of Tessenderlo, Belgium is building on its expertise in gallium nitride (GaN) processing technology for high-power applications by introducing GaN-on-silicon foundry services for dMode devices as part of its XG035 technology platform. The move further leverages X-FAB’s capabilities as a pure-play foundry that now offers a set of processing technologies for GaN and other wide-bandgap (WBG) materials – including silicon carbide (SiC) – to help fabless semiconductor companies...

Decision tree algorithms continue to be one of the most reliable methods for converting unprocessed data into useful insights as artificial intelligence transforms various industries. With its quickly expanding tech sector, India is home to a number of businesses that are highly skilled at developing and implementing decision tree-based solutions in a variety of sectors, including banking, healthcare, retail, telecommunications, and agriculture. In order to provide highly accurate, scalable AI solutions, these companies use decision trees not only for classification and regression tasks but also incorporate them into sophisticated ensemble techniques like Random Forests and Gradient Boosted Trees. This article will examine the top 10 companies that are at the forefront of machine learning innovation powered by decision trees.

1. TCS

TCS uses decision tree models in Ignio in IT automation, including anomaly detection and predictive analytics. Its solutions span banking, manufacturing, and retail, assisting organizations in making reliable scalable advanced data-backed decisions.

2. Infosys

With its proprietary Nia platform, Infosys is able to use decision tree algorithms for customer analytics, supply chain optimization, and fraud detection. This company is also known for combining decision trees with deep learning to improve both the interpretability and accuracy of the system.

3. Entropik Tech

Entropik uses decision tree algorithms in emotion AI to classify user responses and predict behaviour. Their platforms combine decision trees with computer vision and EEG data to help brands decode consumer sentiment and improve engagement strategies.

4. Wipro

With the help of Wipro’s HOLMES AI and Automation platform, decision tree models can be used for cognitive automation, IT service management, and predictive maintenance. Wipro also combines decision trees with reinforcement learning and NLP to provide smart solutions in the healthcare, energy, and telecommunications industries.

5. Artivatic.ai

Artivatic.ai uses decision trees for its underwriting, fraud detection, and claims automation in insurance technology. Using them along with neural networks, Artivatic.ai’s platform provides explainable AI in health and life insurance, where decision trees are commonly used.

6. Fractal Analytics

Fractal uses algorithms based on decision trees in its Qure.ai and Cuddle.ai platforms, which specialize in healthcare diagnostics and business intelligence. By integrating decision trees with deep learning, they strive to elevate the interpretability and accuracy of their solutions in critical settings.

7. HCLTech

HCLTech’s DRYiCE suite uses decision tree algorithms to improve business functions, pinpoint anomalies, and improve workflows. Their models are applied and further developed with other methods in financial services, the automotive industry, and life sciences to improve functionality and scalability.

8. Zensar Technologies

Zensar uses decision tree algorithms in the reshaping of customer experiences, predictive analytics, and in the digital supply chain. Their AI-powered platforms deliver retail and logistics business intelligence and leverage decision trees to provide real-time analytics for better business decision-making.

9. Mu Sigma

Mu Sigma exploits decision tree techniques in their decision sciences, facilitating risk, churn, and operational optimization analytics for Fortune 500 firms. The company’s unique frameworks integrate decision trees with Bayesian methods, yielding more reliable analyses.

10. Tredence

Tredence creates AI-driven models for retailers by integrating decision trees with demand prediction, inventory management, and customer segmentation. The models function on analytics platforms and can scale on the cloud.

Conclusion:

The use of machine learning models based on decision trees has become pivotal in India’s evolving AI landscape. Numerous organizations, ranging from major IT corporations like TCS, Infosys, and Wipro to niche analytical businesses like Fractal Analytics, Mu Sigma are showcasing the capabilities of decision trees particularly in conjunction with ensemble methods like Random Forests and Gradient Boosted Trees in offering actionable, explainable, and scalable industry solutions.

The post Top 10 Decision Tree Learning Companies in India appeared first on ELE Times.

Union Electronics and IT Minister Ashwini Vaishnaw has said that India is making rapid strides in the semiconductor sector and is on track to be among the world’s top five chip‑making nations by 2032.

In a recent interaction, Vaishnaw mentioned that SEMICON India 2025 would be an important event to gain global partnerships, attract investments, and showcase India’s growing semiconductor ecosystem. He added, “With policy support and industry collaboration, our aim is to turn India into the semiconductor hub of the world.”

Recalling the chip making vision of the government, Vaishnaw talked about achievements in chip design, advanced packaging, and talent development. Regarding this, he mentioned that the first commercially available semiconductor chip made in India would be released soon.

The India Semiconductor Mission has allocated $10 billion for its first phase. This funding incorporates a plethora of initiatives such as manufacturing incentives, display fabrication units, compound semiconductors, design linked schemes, and research driven collaborations.

An end to end semiconductor ecosystem approach is being adopted encompassing chip design, equipment, materials and manufacturing so that India is fully plugged into the global semiconductor value chain. This approach is expected to lay a strong foundation for sustained industry growth.

Regarding the talent, 270 universities and 70 startups have been provided with advanced semiconductor design tools. Students have already designed 20 chipsets, several of which have been sent for fabrication, showcasing the country’s growing design capabilities.

With six semiconductor production facilities currently authorized or in development nationwide, manufacturing momentum is increasing in the meantime. In order to increase domestic output and lessen dependency on imports, these facilities are expected to be essential.

He emphasised India’s competitive advantage as policy support, engineering talent, and industry collaboration. The country’s electronics exports have already crossed $40 billion, which is an eightfold increase over the last 11 years.

Vaishnaw stressed the Indian Electronics System Design and Manufacturing (ESDM) industry’s strengths because of the semiconductor companies’ policy support and the robust engineering talent base. The ecosystem is getting more robust with several global semiconductor companies starting large R&D and design centres in India.

As India gears up to host SEMICON India 2025, it is expected that the semiconductor industry officials and the policy makers will come up with a roadmap that will expedite the journey of India to become semiconductors self-reliant and also strengthen the role in global supply chain.

By 2032, if present trends continue, India may rank among the world’s top five semiconductor powers, revolutionizing the country’s electronics manufacturing sector and making a substantial contribution to economic growth.

The post India Set to Be Among World’s Top 5 Semiconductor Nations by 2032: Vaishnaw appeared first on ELE Times.

 

• The upcoming facility in Gwalior, Madhya Pradesh, will have an annual capacity of producing 105MW of electrolysers, with a roadmap to scale up to 500 MW by 2030, contributing direction to the National Green Hydrogen Mission.
• Supported by ₹157.5 crore Production Linked Incentive (PLI) subsidy, the project stems from a landmark MoU between GH2 Solar and AHES Ltd. It will be backed by GH2 Solar’s UK based partner Rhizome Energy.
• ₹400 crore of total investment, with ₹100 crore allocated in the first phase to set up up a 3 GWh BESS assembly line, and the remaining ₹300 crore to be invested in phases by 2030 to expand the facility.
• The facility is expected to create 300+ direct jobs in the clean energy sector.
• Supported by Invest India and Skill council for Green Jobs to build renewable energy capabilities and train future workforce in line with the Government of India’s Atmanirbhar Bharat Mission.

GH2 Solar Limited, a next generation renewable energy company and one of only five companies in India Government’s PLI scheme for both green hydrogen production and electrolyser manufacturing, announced a major milestone under India’s Green Hydrogen Mission its upcoming state-of-the-art Green Hydrogen Electrolyzer Manufacturing Facility in Gwalior, Madhya Pradesh, in joint venture with South Korea-based Advanced Hydrogen Energy Solutions (AHES) Ltd.

The facility located in Pipersewa, Morena district (Madhya Pradesh), will begin with an annual manufacturing capacity of 105 MW awarded under SECI’s SIGHT program, supported by ₹157.5 crore Production Linked Incentive (PLI) subsidy. The total investment in the project is approximately ₹400 crore, with ₹100 crore allocated in the first phase to set up a 3 GWh BESS assembly line, and the remaining ₹300 crore to be invested in phases by 2030 to expand the facility. GH2 Solar has also outlined plans to expand the electrolyser capacity to 500 MW by 2030, directly contributing to the National Green Hydrogen Mission’s target of producing 5 million tonnes of green hydrogen annually by 2030. The announcement was marked by a Bhoomi Pujan ceremony in Gwalior, graced by Shri Dr. Mohan Yadav Ji, Hon’ble Chief Minister of Madhya Pradesh. The project was formally announced by Mr. Anuraj Jain, CEO and Founder of GH2 solar, alongside Prof. Joong-Hee Lee, CEO of AHES Ltd and Mr. Raj Sharma, Director of Rhizome Energy, UK, both key international partners of GH2 Solar’s green hydrogen journey.

Through the JV with AHES Ltd, GH2 Solar will bring advanced alkaline electrolyzer technology to India, with future expansion into PEM and other generation systems. In addition, GH2 Solar’s partnership with Rhizome Energy (UK) will embed sustainable design principles and advanced engineering practices,  to ensure the facility is competitive, efficient and manufactures tailored solutions as per Indian conditions, strengthening the vision of making India a global hub for green hydrogen.

Speaking on the occasion, Mr. Anurag Jain, Founder and CEO, GH2 Solar, “As India advances towards energy independence and transitions from fossil fuels to green hydrogen, our Electrolyser Manufacturing Facility will play a critical role in this journey. Through  global partnerships, we are bringing cutting-edge decarbonization technologies, while government support enables us to effectively leverage local resources. We are also committed to collaborating with academic institutions and skill development centers to train engineers and technicians, ensuring India has a robust workforce to drive green hydrogen technologies forward. Ultimately, our goal is to build a complete clean energy ecosystem that positions India as a leading producer and exporter of green hydrogen, with the workforce and technology to truly realize the vision of Atmanirbhar Bharat”

Adding to his perspective, Prof. Joong-Hee Lee, CEO of AHES, said, “The future is green, and no nation can achieve it alone. The world must unite in its commitment to sustainable energy. Our joint venture with GH2 Solar, brings this vision closer by producing electrolysers in India for the world. India already has skilled manpower, strong public institutions, and crucial government policy and funding support. We are happy to contribute to this ecosystem and believe our Gwalior facility will be an important step in shaping the world’s green future.”

On the public institution side, the project is supported by Invest India and Skill Council for Green Jobs. The facility’s operations are expected to create over 300 direct jobs in manufacturing, operations and research, along with hundreds of secondary jobs across supply chain, logistics, and renewable energy services. By building renewable energy capabilities and training future workforce, the facility also makes a significant contribution to the Government of India’s Atmanirbhar Bharat Mission.

The project supports the Government of India’s National Green Hydrogen Mission, which targets 5 million metric tonnes of annual green hydrogen production by 2030 and underpins India’s ambition to achieve net zero by 2070. The Gwalior facility is expected to play a crucial role in decarbonizing high-emission sectors such as steel, fertilizers, and refineries, while also creating opportunities for export to Europe and East Asia.

The post GH2 Solar announces ₹400 Crore Green Hydrogen Electrolyzer Manufacturing Facility in joint venture with Korea-based AHES Ltd appeared first on ELE Times.

🇯🇵 Набір на курси японської мови в Українсько-Японському центрі КПІ ім. Ігоря Сікорського! kpi вт, 09/02/2025 - 03:22

Because the old one was corroded to the point in which it was basically impossible to take out I've used this knitting needle to burn the plastic that was holding it in place and then I've "welded" the new one in place with the same plastic so it's fixed in place . Now it remains to weld the red wire with soldering iron and it's a job done . This radio is a family heirloom from my grandpa . He used to take it with him when he went fishing . RIP grandpa . submitted by /u/MudMurky5087 [link] [comments]

In this roundup, we cover three sensors pushing the boundaries of durability.

As regular readers may recall, I’m fond of acquiring gear from the “Warehouse” (now renamed as “Resale”) area of Amazon’s website, particularly when it’s temporary-promotion marked down even lower than the normal discounted-vs-new prices. The acquisitions don’t always pan out, but the success rate is sufficient (as are the discounts) to keep me coming back for more.

Today’s product showcase was a mixed-results outcome, which I’ve decided to tear down to maximize my ROI (assuaging my curiosity in the process). Last October, I picked up EBL’s 8-bay charger with eight included NiMH batteries (four AA and four AAA), $24.99 new, for $17.22 (post-20%-off promo discount) in claimed “mint” condition:

The price tag was the primary temptation; that said, the added inclusion of two USB-A power ports was a nice feature set bonus that I hadn’t encountered with other multi-bay chargers. And Amazon also claimed that this Warehouse-sourced device was the second-generation EBL model that supported per-bay charging flexibility.

Not exactly (or even remotely) as-advertised

When it arrived, however, while the device itself was in solid cosmetic condition, its packaging, as-usual accompanied by a 0.75″ (19.1 mm) diameter U.S. penny in the following photos for size comparison purposes, definitely wasn’t “mint”:

and the contents (including the quick start guide, which I’ve scanned for your educational convenience) were also quite jumbled:

(I belatedly realized, by the way, that I’d forgotten one piece of paper, the also-scanned user manual, in the previous box-contents overview photo)

Not to mention the fact that the charger ended up being the first-generation model, not the second-gen successor, thereby requiring that both bays of each two-bay pair be populated (also with the same battery technology—Ni-MH or Ni-Cd—and size/capacity) to successfully kick off the charging process. When I grumbled, Amazon offered $4.49 in partial-refund compensation, which I begrudgingly accepted, rationalizing that the eight included batteries were still fine and the charger seemed to function fine for what it truly was. Only later did I realize that the charger was actually extremely finicky, rejecting batteries that other chargers accepted complaint-free:

Turning lemons into lemonade

And like I said before, I’d always been curious to look inside one of these things. So, I decided to pull it out of active service and sacrifice it to the teardown knife instead. Here’s our patient:

Note how both sides’ contact arrangements support both AA and AAA battery sizes:

Onward. Top:

Bottom:

Left and right sides:

And back, also including a label closeup:

Before continuing, here are both ends of the AC cord that powers the charger:

When at first you don’t succeed, muscle your way in

And now it’s time to dive inside. No visible (or even initially invisible) screws to speak of:

So, I resorted to “elbow grease”. The device didn’t give up its internal secrets easily (an understandable reality, given that its target customers are largely-tech-unsavvy consumers, and it has high-voltage AC running around inside it), but it eventually succumbed to my colorful language-augmented efforts:

Mission (finally) accomplished:

Some side (left, then right, at least when the device is upright…remember that right now it’s upside-down) shots of newly exposed circuit glimpses before proceeding:

Close only counts in horseshoes and…

And now let’s get that PCB outta there. At first glance, I saw only three screws holding it in place:

Uhhhh…nope, not yet:

Oh wait, there’s another one, albeit when removed, still delivering no dissection luck:

A bit more blue-streak phrasing…one more peek at the PCB, this time with readers…and…

That’s five minutes of my life I’m never gonna get back:

Upside: the PCB topside’s now exposed to view, too. Note, first off, the four multicolor LEDs (one per pair of charging bays) running along the left edge:

Binary deficiency

I was admittedly surprised, albeit not so much in retrospect, at just how “analog” everything was. I’d expect a higher percentage of “digital” circuitry were I to take apart my much more expensive La Crosse Technology BC-9009 AlphaPower charger (I’m not going to, to be clear):

 

Specifically, among other things, I was initially expecting to see a dedicated USB controller IC, which I regularly find in other USB-inclusive devices…until I realized that these USB-A ports had no data-related functions, only power-associated ones, and not even PD-enhanced. Duh on me:

Flipping the PCB back over once again revealed the unsurprising presence of a hefty ground plane and other thick traces. The upper right quadrant (upper left when not upside-down):

handles AC to DC conversion (along with the transformer and other stuff already seen on the other side); the two dominant ICs there are labeled (left to right):

CRE6536
2126KD
(seemingly an AC-DC power management IC from China-based CRE Semiconductor)

and:

ABS210
(which appears to be a single-phase bridge rectifier diode)

 while the upper left area, routing the generated DC to the USB ports on the PCB’s other side (among other things), is landscape-dominated by an even larger SS54 diode.

Further down is more circuitry, including a long, skinny IC PCB-marked as U2 but whose topside markings are illegible (if they even ever existed in the first place):

I’ll close out with some side-view shots. Top:

Right:

Bottom:

And left:

And I’ll wrap up with a teaser photo of another, smaller, but no less finicky battery charger that I’ve also taken apart, but, due to this piece as-is ending up longer-than-expected (what else is new?), I have decided to instead save for another dedicated teardown writeup for another day:

With that, I’ll turn it over to you, dear readers, for your thoughts 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.

Related Content

• Resurrecting a 6-amp battery charger
• Tricky 12V Battery Charger Circuit
• Simplifying multichemistry-battery chargers
• 12V Battery Charger Circuit using SCR

The post Tearing apart a multi-battery charger appeared first on EDN.

WiFi-connected LED matrix clock and weather station based on ESP8266/ESP32 and MAX7219. It displays the current time, day of the week, and local weather (temp/humidity/weather description) fetched from OpenWeatherMap. Setup and configuration are fully managed via a built-in web interface. Glucose monitoring and countdown mode are also available ;) Code is available here: https://github.com/mfactory-osaka/ESPTimeCast submitted by /u/mfactory_osaka [link] [comments]

Співпраця для відбудови енергетичної системи України kpi пн, 09/01/2025 - 14:35

Decision Tree learning is a widely used method in machine learning and data analysis for making decisions and predictions. It employs a tree-like model of decisions, where each internal node represents a test on a feature, each branch corresponds to an outcome of the test, and each leaf node signifies a final decision or classification. The process begins at the root node, which encompasses the entire dataset, and progressively splits into branches based on feature values, ultimately leading to distinct outcomes. This hierarchical structure allows for intuitive visualization and interpretation of decision-making processes. Decision Trees are incredibly versatile and find applications across a wide range of fields. Highlighted below are the top 10 decision tree learning real-world applications and use cases.

1. Fraud Detection

Identifying and preventing fraudulent transactions is one of the primary use cases of Decision Trees, and they are especially beneficial in banking as well as e-commerce centers. For instance, Decision Trees can flag suspicious transactions such as sudden exorbitant spending or transactions from new locations, which helps enterprises to minimize financial risks and combat security threats.

2. Customer Segmentation

Decision Trees are particularly useful in marketing, where customers can be classified into groups based on age, income, and even purchase and browsing history. This form of segmentation is especially useful for marketing as it helps personalize communication and enhances engagement by ensuring the right message is delivered to the appropriate audience.

3. Medical Diagnosis

Decision trees in the healthcare sector are essential for assisting clinicians in making predictions about the likelihood of a disease for a patient. This is derived from the patient’s symptoms, tests, and previous medical records. The trees’ logic is clear, which gives the doctors a chance to follow each step of reasoning, and this makes the tools invaluable in clinical decision support systems.

4. Recommendation systems

Decision trees are used in recommendation systems, such as on Netflix and Amazon, to suggest items, movies, or services by analyzing user preferences, browsing history, and ratings. These models help personalize the user experience and increase engagement by suggesting items that align with individual tastes.

5. Predictive Maintenance

In the sectors of manufacturing and transportation, decision trees based on sensor data, usage patterns, and equipment operating conditions are used to forecast equipment failure. This provides timely maintenance and improves the chance to provide uninterrupted service.

6. Autonomous Driving Decision Systems

Decision trees are important to the development of autonomous vehicles because they incorporate decision making models in driving systems. With their complex environments, these vehicles have to make safe and efficient decisions while learning the rules of the road, functionality of other vehicles, and traffic control. The vehicles accelerate, brake, and even change lanes based on the output of decision trees.

7. Cybersecurity Threat Detection

The use of decision trees in threat detection provides a more in-depth look into network traffic, different login schemes and their failures, as well as different system behaviors. Their use aids in the prevention of attacks and protection of crucial information.

8. Filtering of Email Spam

In order to classify messages, email providers analyze the words used, the sender’s reputation, and the structure of the message. They classify the messages using decision trees as either spam or legitimate email. Making email spam free and increasing security for the users.

9. Space Agencies and Aerospace Companies

Space and aerospace companies use decision trees in monitoring spacecraft systems and in predicting component failure and assist in mission planning. They help ensure safety and reliability in high-stakes environments.

10. Navigation and GPS Functionality

Decision trees are used by mapping and navigation software to provide the best possible route possibilities while accounting for user preferences, roadwork, and traffic conditions. Decision trees also consider the user’s objectives, whether to minimize travel time, fuel consumption, or increase safety. 

Conclusion:

Decision trees learning have a wide array of uses in data driven decision making, and thus can be considered a very strong and useful methodology. Their unique and flexible structure, ease of understanding and use, and transparency make decision trees very useful from the healthcare sector and the finance sector all the way to public administration and environmental care sectors. Decision trees can be used and are very crucial in the healthcare sector to help make very important and life saving decisions, and businesses also stand to benefit through the use of decision trees in optimizing their strategies. The impact of decision trees is very important and will grow even further as technology advances.

The post Top 10 Decision Tree Learning Applications and Use Cases appeared first on ELE Times.

🎓 Адаптаційні курси КПІ для першокурсників kpi пн, 09/01/2025 - 14:02

Gallium oxide (Ga2O3) can make electronic devices much more energy efficient than existing silicon-based technology. For electronic diodes, researchers have been able to reliably produce n-type gallium oxide layers but struggled to create stable p-type layers because gallium oxide's crystal structure naturally resists the atoms needed for these layers. This limitation resulted in gallium oxide semiconductors with poor performance and reliability issues...

Prime Minister Narendra Modi and his Japanese counterpart Shigeru Ishiba visited the Tokyo Electron Factory (TEL Miyagi) in Sendai. This visit was significant because it marked a focus of India and Japan’s cooperation in advanced technologies, especially semiconductors. The two leaders also emphasised the importance of this industry by taking the bullet train from Tokyo to Sendai, which is more than 300 km.

During the visit, Modi engaged with TEL executives regarding their position in the global semiconductor ecosystem and future partnerships with India. He emphasized how India’s growing manufacturing ecosystem and Japan’s cutting-edge semiconductor machinery and technology work in tandem.

In his remarks at the India–Japan Economic Forum, Modi highlighted semiconductors, batteries, and robotics as focus areas for Make in India collaborations. Prime Minister Ishiba laid out three goals: building stronger people-to-people ties, fusing technology with green initiatives, and boosting cooperation in high-tech fields, especially semiconductors.

The visit to Sendai came as a follow-up of the bilateral agreements made under the India-Japan Industrial Competitiveness Partnership and the Economic Security Dialogue. Both these agreements cover fields like critical minerals, ICT, pharmaceuticals, and more. An understanding was made to speed up the projects in these fields alongside semiconductors.

Involvement from the private sector is increasing steadily. Japanese firms have entered into around 150 MOUs over the last two years in sectors such as aerospace, automotive, semiconductors, energy, and human resources, as per the Ministry of External Affairs of India. Modi also remarked that the Digital Partnership 2.0, AI collaboration, and work on rare earth minerals will continue to be the focus of partnership.

Modi and Ishiba reiterated their vision of developing strong and trusted supply chains and India and Japan’s roles as critical partners in the framework of global technology security by keeping semiconductors as the focus of this visit.

The post PM Modi, Japan’s Ishiba Visit Sendai Plant to Boost Semiconductor Ties appeared first on ELE Times.