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The University of Wisconsin-Madison’s Engineering Centers Building on 5 August hosted the grand opening of the Ultra-Wide Bandgap Semiconductor Metal-Organic Chemical Vapor Deposition (MOCVD) Laboratory, attended by university and national R&D leaders including UW-Madison’s vice chancellor for research Dorota Grejner-Brzezinska; College of Engineering Dean Devesh Ranjan; and Vivek Prasad, VP for design engineering ecosystem enablement at NatCast (a nonprofit that operates the National Semiconductor Technology Center). They joined Susan Hagness, chair of the Department of Electrical and Computer Engineering and ECE assistant professor Shubhra Pasayat, who oversees the new facility as the lab’s principal investigator...

made this diy relay modules with relays I had lying around and made it smart using the esp32 submitted by /u/Rare-Town5273 [link] [comments]

It's been a while since my previous prototype. I always test new projects on proto boards, since parts on Spice can't explode :). This is a NE555 PWM MOSFET driver with adjustable off and on state pulse width. On state is about 100ms and off state is about three seconds. This is a part of a flyback driver for electrical fence. Since everything works fine it's time to fire up KiCad and map everything. submitted by /u/orefat [link] [comments]

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Learn how a varactor diode's variable capacitance, together with an LC tank circuit, can drive a voltage-controlled oscillator (VCO) to create FM waveforms.

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Iv never actually messed with the electronics, iv only ever handled the programming. submitted by /u/Bobert_DaZukin [link] [comments]

The photorelay aims at 800 V battery management systems in electric vehicles and energy storage applications.

Needed to test a circuit on a breadboard that needs a RRIO Op Amp. Didn't have any DIP ones on hand, so "dead bugged" a surface mount MCP6001 to an 8-pin IC socket. submitted by /u/PleasantCandidate785 [link] [comments]

Amp Output.. If I succeed in making it, I'll upload it to Reddit and YouTube. submitted by /u/Time_Double_1213 [link] [comments]

For coverage of all the key business and technology developments in compound semiconductors and advanced silicon materials and devices over the last month, subscribe to Semiconductor Today magazine...

1500pcs in box submitted by /u/Time_Double_1213 [link] [comments]

Retro Register is a new All About Circuits column that explores technologies of the past and the lessons they hold for the future. Come along for our first deep dive on the MOS 6502.

Back on December 3, 2024, a Design Idea (DI) was published, “Single-supply single-ended inputs to pseudo class A/B differential output amp,” which created some discussion about using the circuit as a full wave rectifier.

DI editor Aalyia has kindly allowed a follow-up discussion about a circuit which could be utilized for this, but is better suited for square-law functions.

The circuit shown in Figure 1 is an LTspice implementation built around a bipolar differential amplifier with Q1 and Q3 serving as the + and – active differential input devices, respectively.

Figure 1 An LTspice implementation built around a bipolar differential amplifier with Q1 and Q3 serving as the + and – active differential input devices, respectively, allowing the circuit to be better suited for square-law functions.

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

Additional devices Q2 and Q4 are added at the “center point” between Q1 and Q3, and act such that the collector currents of all devices are equal when no differential voltage is present.

This occurs because resistors R7 and R8 create a virtual differential zero-volt “center point” between the + and – differential inputs, and all device Vbe’s are the same, neglecting the small voltage drop across R7 and R8 due to Q2 and Q4 base bias currents.

R7 and R8 set the differential input impedance for the circuit configuration, where R1 and R3 set the signal source differential impedances for the simulations.

The device emitter currents are controlled by the “tail current source” I1 at 4 mA; thus, each device has an emitter current of ~1 mA with zero differential input. Note the -Diff Input signal is created by using a voltage-controlled voltage source with an effective gain of -1 due to the inverted sensing of the +Diff Input voltage (VIN+). This arrangement allows the input signal to be fully differential when LTspice controls the VI+ voltage source during signal sweeps.

This is not part of the circuit but used for comparisons: Voltage-controlled current source, B1, is configured to produce an ideal square-law characteristic by squaring the differential voltage (Vin+ –Vin-) and scaling by factor “K”.

Figure 2 shows the simulation results of sweeping the differential input voltage sources from -200 mV to +200 mV while monitoring the various device currents. Note the differential output current, which is:

[Ic(Q1)+Ic(Q3)] – [Ic(Q2)+Ic(Q4)]

closely approximates the ideal square-law with a scale factor of 0.3 (amps/volt) for differential input voltages of ±60 mV.

Figure 2 Simulation results of sweeping the differential input voltage sources from -200 mV to +200 mV while monitoring the various device currents.

Please note this circuit is a transconductor type where the output is a differential current controlled by a differential input voltage.

Anyway, thanks to Aalyia for allowing us to follow up with this DI, and hopefully some folks will find this and the previous circuits interesting.

Michael A Wyatt is a life member with IEEE and has continued to enjoy electronics ever since his childhood. Mike has a long career spanning Honeywell, Northrop Grumman, Insyte/ITT/Exelis/Harris, ViaSat and retiring (semi) with Wyatt Labs. During his career he accumulated 32 US Patents and in the past published a few EDN Articles including Best Idea of the Year in 1989.

 Related Content

• Single-supply single-ended input to pseudo class A/B differential output amp
• Simple 5-component oscillator works below 0.8V
• Applying fully differential amplifier output-noise analysis to drive high-performance ADCs
• Understanding output filters for Class-D amplifiers

The post Simple diff-amp extension creates a square-law characteristic appeared first on EDN.

Reinforcement learning (RL), a subfield of machine learning in which agents learn by interacting with their surroundings, is gaining significant popularity in India’s quickly developing AI ecosystem. RL is being used in a variety of areas, including financial modeling, smart energy grids, and autonomous systems. Indian businesses are using RL to innovate and create scalable solutions that are on par with international standards, rather than merely adopting it. The top 10 reinforcement learning companies in India will be explored in this article:

1. Tata Consultancy Services (TCS)

As the global IT leader, TCS focuses on integrating RL into supply chain optimization, autonomous systems, and intelligent automation. It is AI laboratories work on adaptive algorithms that learn from changing environments in logistics, manufacturing, and operations for better decision making. The company also uses its platform TCS iON to apply RL to the fields of education and skill development, employing gamified and tailored learning to increase motivation and achieve better educational results.

2. Infosys

As led by the Infosys Topaz platform, the AI-first initiative of the company shows faster advances in Reinforcement Learning (RL). The platform’s robotics, enterprise automation, and conversational AI are improved by RL and RLHF (Reinforcement Learning with Human Feedback). The completion and integration of these technologies enable the creation of adaptive, scalable, and self-learning enterprise solutions, such as automated fraud detection systems, predictive analytics, and enhanced customer care.

3. Wipro

Wipro is currently engaging with Reinforcement Learning (RL) to upgrade automation, simulation, and intelligent systems across multiple sectors. The company utilizes RL in industrial automation and flight simulation, employing adaptive learning models to improve control mechanisms and decision-making procedures. Wipro’s investigations also extend to scalable RL methodologies for manufacturing and financial services, which facilitate more intelligent resource allocation and operational forecasting.

4. HCL Technologies

HCL Technologies is continuously refining the applications of Reinforcement Learning (RL) across various focus areas, including cybersecurity, workforce analytics, and education. In workforce analytics, HCLTech uses RL for the customization of learning pathways and the prediction of talent development, enabling companies to match employee evolution with their strategic objectives. Their partnership with Pearson brings even greater value in the education sector, where RL-driven adaptive learning systems customize services to the learners and enhance the mastery of skills.

5. ValueCoders

ValueCoders is an Indian software company specializing in adaptive smart system software development for healthcare, finance, and education sectors. They use computer vision, reinforcement learning, and MLOps to ease decision automation, enhance personalization, and boost system performance over time for their clients.

6. Locus

Locus is a top-class supply chain and logistics company that focuses on streamlining and automating supply chain operations with the use of reinforcement learning (RL). With Locus, businesses can now enhance the planning of delivery routes, scheduling of deliveries, and even the allocation of resources. This allows companies to better control and reduce costs, increase the efficiency of their operations, and better respond to fluctuating demand and traffic conditions.

7. Mad Street Den

Mad Street Den is the only company to blend reinforcement learning and computer vision through its Vue.ai platform to enhance personalized retail experiences. Their adaptive systems are designed to optimize merchandising, styling, and customer engagement on behalf of global fashion and e-commerce brands.

8. Arya.ai

With a deep focus on reinforcement learning and deep neural networks, Arya.ai addresses autonomous decision systems. Their SaaS products with real-time adaptation enabled for finance, insurance, and robotics industries address fraud detection, claims automation, and smart underwriting.

9. Infilect

Infilect uses visual intelligence platforms to implement RL in retail. Their technologies optimize pricing, merchandising, and shelf availability using RL-driven analytics, which helps brands lower stockouts and increase in-store compliance.

10. Flutura Decision Sciences

The major industries of oil and gas, chemicals, and heavy machinery benefit from Flutura Decision Sciences’ artificial intelligence and reinforcement learning approaches to machine learning, which are used to develop their industrial internet of things platform, Cerebra. With Flutura, these industries can improve asset performance, anticipate failures, and minimize downtime. To offer complex system digital twins, Cerebra delivers diagnostics and prognostics, which are supported by physics models, heuristics, and machine learning.

Conclusion:

With smart healthcare, smart agriculture, and smart city systems, autonomous systems powered by reinforcement learning are ready to take off, marking the beginning of the AI revolution. With the development of edge AI and quantum computing, real-time decision-making will be dominated by RL. Due to the culture of innovation, availability of skilled resources, and the country’s bold vision, India has the potential to lead the world in adaptive intelligent systems in the upcoming years.

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

КПІ долучається до національної акції "Стіл пам'яті" kpi пт, 08/29/2025 - 12:33

Fusion360 does not have the best libraries available, so I decided to start building an electronics library for all the boards/components that came with my arduino starter kit (plus a pico). Once I finish this , I plan on adding many other components that aren't available in Fusion. submitted by /u/teslah3 [link] [comments]

High Security Integration, Low Power, and Small Package, Providing Cost-Effective RoT

Nuvoton Technology released the upgraded NuMicro M2354, tailored for applications such as server RoT, smart city, IoT, and smart metering.

NuMicro M2354 is an Arm TrustZone microcontroller based on the Armv8-M architecture and powered by the Arm Cortex-M23 CPU, designed to enhance IoT security. It is suitable for long-term confidentiality requirements and highly sensitive data protection scenarios.

The M2354 operates at frequencies up to 96 MHz, offers a wide operating voltage range of 1.7V to 3.6V, and a broad operating temperature range of -40°C to +105°C. The power consumption is 89.3 μA/MHz in LDO mode and 39.6 μA/MHz in DC-DC mode. The Standby Power-down mode consumes less than 2 µA, and the Deep Power-down mode without VBAT consumes less than 0.1 µA, effectively extending the device’s battery life and meeting the needs of long-term IoT operation.

For Secure FOTA, the M2354 has built-in dual-bank Flash Memory of up to 1024 KB and 256 KB of SRAM. In addition to supporting eXecute-Only-Memory (XOM) to prevent code theft, it also integrates a cryptographic hardware accelerator that supports FIPS PUB 197/180/180-2/180-4 and NIST SP 800-38A, as well as a hardware key store to protect against side-channel and fault injection attacks. In terms of secure boot mechanism, the upgraded M2354 supports the Root of Trust architecture based on DICE, implemented in Mask ROM, and supports ECDSA P-521. This feature automatically generates a unique device identity and establishes a chain of trust during boot, effectively verifying firmware version and preventing firmware rollback and tampering attacks. Furthermore, M2354 is compliant with PSA Level 3 and SESIP Level 3 security certifications, which meet the demands of the EU’s Cyber Resilience Act (CRA).

M2354 supports a wide range of peripherals, including CAN, USB 2.0 full-speed OTG, PWM, UART, SPI/I2S, Quad-SPI, I²C, and RTC.

M2354 also integrates several analog components, including analog comparators, ADC, and DAC.

The package options include LQFP-48, LQFP-64, and LQFP-128. The upgraded M2354 also offers a compact WLCSP49 package. With support of the SPDM (Security Protocol and Data Model) secure communication protocol, the upgraded M2354 is well-suited for Root of Trust applications in server motherboards and daughterboards.

The post Nuvoton Technology Unveils Upgraded NuMicro M2354 MCU: Enhanced Security and Compact Footprint for Server, IoT, and Edge appeared first on ELE Times.