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An MCU facilitating real-time control in motor control and power conversion applications incorporates post-quantum cryptography (PQC) requirements for firmware protection outlined in the Commercial National Security Algorithm (CNSA) Suite 2.0. These MCUs also support Platform Security Architecture (PSA) Level 3 compliance.

PSA Certified Level 3 is an Internet of Things (IoT) security standard that focuses on robust protection against software and hardware attacks on a chip’s root of trust. It provides an independently evaluated and validated environment that can securely house and execute the PQC algorithms.

Figure 1 PQC encompasses the replacement of Elliptic Curve Cryptography (ECC)-based asymmetric cryptography as well as increasing the size of Advanced Encryption Standard (AES) keys and Secure Hash Algorithm (SHA) sizes. Source: Infineon

“By adopting both PSA Certified Level 3 and PQC compliance with other regulations, companies can proactively address current and future cyber threats,” said Erik Wood, senior director of cryptography and product security at Infineon Technologies. He is responsible for defining the security requirements of Infineon MCUs.

Quantum computers, exponentially faster than classical computers, are still under development. However, cybercriminals can collect encrypted data now and decrypt it later using quantum computers. That calls for futureproofing of current systems to ensure that companies remain secure as quantum computing technologies advance.

Enter PQC, a collection of cryptographic algorithms designed to be secure against attacks from powerful quantum computers. In MCUs, which mainly use cryptography during boot-time and run-time operations, it commands significant changes in security architecture amid evolving regulations.

For instance, MCU’s memory size is a key design consideration. “More memory size is required because encryption keys are longer,” Wood said. “The certificate size is different because the signatures of these certificates are much bigger.”

Figure 2 PSOC Control C3 MCU’s embedded security provides stringent protection against quantum-based attacks on critical systems. Source: Infineon

Next comes the throughput shortfall. “While certificates are currently transferred through an I2C bus, the throughput falls short with QPC use,” he added. “Now you need to have three I3C buses.” Wood said that the industry is even procrastinating about whether every MCU will have a USB port in four years.

In other words, integrating QPC into MCUs will entail a primary upgrade of cryptographic algorithms. Next come memory upgrades, and finally, interface upgrades will follow.

Wood claimed that Infineon is the first MCU supplier to have integrated and ported PQC algorithms. “We offer an integrated library already hooked up to the accelerators for peak optimization and performance in a PSA-3 level device.”

Related Content

• Inside the PQC Overhaul, a Year Later
• Post-Quantum Cryptography: Moving Forward
• An Introduction to Post-Quantum Cryptography Algorithms
• Release of Post-Quantum Cryptographic Standards Is Imminent
• The need for post-quantum cryptography in the quantum decade

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Reinforcement Learning (RL), unlike other machine learning (ML) paradigms, notably supervised learning, has an agent learning to act optimally within a given environment, one step at a time. At each step, it is given feedback in the form of a reward or a penalty. The goal is to learn a policy a strategy for selecting actions that maximize the total reward over a certain time horizon. There are no inputs or outputs to fit to (as in traditional supervised learning), so RL agents must balance exploring unknown actions to discover their worth and exploiting known good actions to maximize rewards.

Reinforcement Learning History:

Reinforcement learning began with behavioural psychology’s theory of behaviourism in the early 1900s. Behaviourism postulated learning as a trial and error process propelled by rewards and punishments. This concept was later adapted and formalised into computer science mathematical models that paved the way for the development of optimisation and machine learning algorithms. Reinforcement learning is akin to optimising methods where the desired function is not explicitly given but is instead hinted at through trial and error.

How does reinforcement learning work:

To enhance decision-making, reinforcement learning works by training an agent to interact with an environment. The agent gets to perform actions. After each action, the agent gets feedback in terms of rewards or penalties associated with the specific action.

Types of Reinforcement Learning:

1. Value-Based Reinforcement Learning

This method requires an agent to learn a value function that predicts the reward for performing an action in a particular state and Q-learning is the most well-known. An agent updates its Q-values in Q-learning according to the received reward and acts to maximize these Q-values.

2. Policy-Based Reinforcement Learning

Policy-based methods focus on learning the policy itself, which is the set of rules mapping states to actions, instead of estimating value functions. This is crucial in cases with complex or continuous action spaces. Methods like REINFORCE and Proximal Policy Optimization (PPO) are good examples of algorithms that follow this paradigm.

3. Model-Based Reinforcement Learning

This refers to methods which try to construct a model of the environment that can predict the following state and reward given the current state and action. Using this model, the agent can plan and make decisions ahead of time. While this method is efficient in terms of samples, its implementation can be complicated to do correctly.

4. Actor-Critic Methods 

These hybrid methods combine the strengths of value-based and policy-based approaches. The actor updates the policy based on feedback from the critic, which evaluates the action taken. This results in more stable and efficient learning, especially in complex environments.

Applications of Reinforcement Learning:

1. Self-Driving Cars

Self-driving cars use reinforcement learning to understand their surroundings. They identify the best routes, change lanes, avoid obstacles, and optimize their overall driving.

2. Automated Machines

Automated machines use reinforcement learning to master new skills like walking, picking up objects, and putting them together. As they deal with new items and different tasks, they improve how they do things in due course.

3. Medicine

Personalized treatment is now possible because of reinforcement, which allows crafting adaptive treatment plans for patients. It is also useful in optimizing clinical trials and in the management of chronic illness.

4. Investment

In portfolio management and trading, reinforcement learning technologies attempt to make investment choices by evaluating prevailing market patterns and modifying tactics geared towards greater returns.

5. Recommendation Systems

Reinforcement learning is used to improve the recommendation systems. As users interact with the content, the system learns users preferences and dynamically suggests content making the platform personalized and more engaging.

Reinforcement Learning Examples:

Reinforcement learning is integrated into numerous fields enabling the technology to thrive. In game playing, RL has enabled breakthroughs like AlphaGo which mastered complex games such as Go and chess through self-play. In autonomous driving, self-driving cars use RL to make decisions like lane changes and obstacle avoidance by learning from real and simulated environments. In robotics, RL helps machines learn tasks like walking, grasping, and assembling by adapting to physical feedback. In finance, RL algorithms optimize trading strategies and portfolio management by analyzing market data. Lastly, in recommendation systems, platforms like Netflix and Amazon use RL to suggest content or products based on user behavior, enhancing engagement and satisfaction.

Reinforcement Learning Advantages:

Reinforcement learning is adaptive and its methods are goal driven. As an example, it can be very effective in environments that are constantly changing and that require very little supervision. It is a type of learning that is guided by rewards or feedback, in which an agent learns to improve its behavior over time based on interaction with the environment.

Conclusion:

As the rest of intelligent systems, reinforcement learning is, for now, an incredible advancement and is bound to become even more so. The level of innovation that RL will bring about will be unimaginable given the availability of more processing power and much more sophisticated algorithms. Preemptive systems, self-learning autonomous agents, and machines that collaborate with humans are only the beginning. Personalized medicine, self-developing robots, and adaptive learning systems will all lean on RL technologies. These technologies will not just adapt to the world, but will actively ‘mold’ it, in essence, making the word ‘transformative’ obsolete in describing the level of change these technologies will bring.

The post Reinforcement Learning Definition, Types, Examples and Applications appeared first on ELE Times.

Infineon Technologies AG announced that its microcontrollers (MCUs) in the new PSOC Control C3 Performance Line family are compliant with Post-Quantum Cryptography (PQC) requirements for firmware protection outlined in the Commercial National Security Algorithm (CNSA) Suite 2.0. The MCUs also support PSA (Platform Security Architecture) Level 3 compliance. By complying with both standards, Infineon’s PSOC Control C3 Performance Line meets the security needs of a wide range of industrial applications and eases their transition to increased security in the PQC era.

“With the PSOC Control C3 family, we are setting a new standard for security in industrial microcontrollers, building on decades of proven experience in MCUs and secured electronic systems,” said Steve Tateosian, SVP and General Manager, IoT, Consumer and Industrial MCUs, Infineon Technologies. “Infineon is committed to meeting and evolving industry requirements for MCU embedded security that provides stringent protection against quantum-based attacks on critical systems.”

Changes in security architecture for the PQC era include the replacement of Elliptic Curve Cryptography (ECC) based asymmetric cryptography as well as increasing the size of Advanced Encryption Standard (AES) keys and Secure Hash Algorithm (SHA) hash sizes. The algorithms and implementation guidelines provided by CSNA 2.0 help to facilitate a smoother transition to Post-Quantum Cryptography.

About PSOC Control C3 family

The PSOC Control C3 family of MCUs provide real-time control for motor control and power conversion applications. New MCUs of the PSOC Control C3 Performance Line enable system performance at high switching frequencies and increase control loop bandwidth. That is achieved with proprietary autonomous hardware accelerators as well as high resolution and high performing analog peripheral support. The family supports systems designed with wide-bandgap switches while achieving best-in-class control loop frequencies, accuracy and efficiency for applications such as data centers, telecom, solar and electric vehicle (EV) charging systems.

Specific security features include support for Leighton-Micali Hash-Based Signatures (LMS), which is an efficient post-quantum cryptography FW verification algorithm integrated with SHA-2 hardware acceleration for peak performance. To maximize ease of use, Infineon’s Edge Protect Tools and ModusToolbox will support everything a customer needs to provision LMS keys as well as options for hybrid post-quantum cryptography where customers may use both LMS and ECC to sign firmware updates which can be verified by Infineon chips.

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Decision Tree Learning is a type of supervised machine learning used in classification as well as regression problems. It tries to mimic real-world decision making by representing decisions and their possible outcomes in the form of a tree. Each internal node in the tree denotes a test on a feature, each branch denotes an outcome of the test, and the leaf node gives the final decision. It is easy to understand, requires no complex data preprocessing, and is visually very informative.

Decision tree learning history:

The concept of decision trees has roots in decision analysis and logic, but their formal application in machine learning began in the 1980s. The ID3 algorithm, developed by Ross Quinlan in 1986, was one of the first major breakthroughs in decision tree learning. It introduced the use of information gain as a criterion for splitting nodes. This was followed by C4.5, an improved version of ID3, and CART (Classification and Regression Trees), developed by Breiman et al, which used the Gini index and supported both classification and regression tasks. These algorithms laid the foundation for modern decision tree models used today.

How does decision tree learning work:

Decision tree learning is a type of algorithm in machine learning where data gets split into smaller subsets and gets organized in the form of a tree. The splitting is based on the value of the data features. At the beginning, with the root node, a feature of the data gets selected. This selection feature tends to be the one that gets deemed most informative by the Gini impurity or entropy criteria. As mentioned earlier, internal nodes get to represent a certain decision rule. This process continues until the data is sufficiently partitioned or a stopping condition is met, resulting in leaf nodes that represent final predictions or classifications. The tree structure makes it easy to interpret and visualize how decisions are made step by step.

Types of Decision Trees:

1. Classification Trees

These are utilized when the dependent variable is categorical. Such trees assist in categorizing the dataset into specific categories (e.g., spam and non-spam). Each split aims to enhance class separation based on certain features.

2. Regression Trees

These trees are used when the dependent variable is continuous. Unlike categorization, these trees aim to provide numerical predictions (e.g., house prices). The split in these trees is done for minimizing prediction error.

Examples of Decision Tree Learning:

• Email Filtering: Marking emails as spam or not using keywords and sender details.
• Loan Approval: Deciding loan approval using income, credit score, and employment status.
• Medical Diagnosis: Identifying a disease with the help of symptoms and test results.
• Weather Prediction: Predicting rain using humidity, temperature, and wind speed.

Applications of Decision Tree Learning:

1. Finance

Decision trees analyze customer data and transaction behavior for credit scoring, fraud detection, and risk management.

2. Healthcare

With the use of medical records and test outcomes, they aid in disease diagnosis, treatment suggestions, and patient outcome predictions.

3. Marketing

Segmenting customers, predicting buying behavior, and optimizing campaign strategies based on demographic and behavioral data.

4. Retail

Forecasting sales, managing inventory, and personalizing product recommendations.

5. Education

Predicting student performance, dropout risk, and tailoring learning paths based on academic data.

Decision Tree Learning Advantages:

Decision Tree learning has numerous benefits, all of which contribute to its widespread use in machine learning. It is simple to grasp and analyze because the structure of the tree is akin to human decision-making and can be easily visualised. It can process both numerical and categorical data without the need for advanced data preprocessing or feature scaling. Decision trees are not affected by outliers or missing data, and they can model non-linear patterns in data. It requires very little in the way of data preparation and is immensely powerful and user-friendly because it inherently takes into account feature combinations through its hierarchical splits.

Conclusion:

Decision Tree Learning is going to mature into a dynamic, real-time intelligence system processing complex data, providing direction to autonomous systems, and enabling accountable decision-making in all sectors. These trees will, in time, become self-optimizing systems that reason, tell stories, and co-exist with human cognition, and they will serve as the ethical and intellectual foundation of future AI.

The post Decision Tree Learning Definition, Types, Examples and Applications appeared first on ELE Times.

New solution empowers engineers to meet HDMI Forum compliance standards while optimizing signal integrity and performance across high-bandwidth video applications

Keysight Technologies, Inc. announced the release of its enhanced physical layer compliance test solution for high-definition multimedia interface (HDMI), delivering robust compliance and performance validation capabilities for transmitter [source] and cable devices. The Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI addresses the growing complexity, and bandwidth demands of modern HDMI applications, including ultra-high definition (UHD) video, high dynamic range (HDR) content, and immersive audio experiences.

With the rising demand for 8K/12K video, HDR content, and high-speed connectivity, engineers face growing challenges in maintaining signal integrity across HDMI interfaces. The recent release of the HDMI 2.2 test specification by the HDMI Forum introduces more stringent compliance requirements for transmitters and cables, highlighting gaps in traditional test coverage. Without robust validation tools, manufacturers risk costly redesigns and certification delays. As HDMI technology advances, the need for comprehensive, automated test solutions is critical to ensure performance, reliability, and faster time-to-market.

As the HDMI ecosystem evolves to support higher resolutions, faster refresh rates, and increased bandwidth demands, the Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI offers a fully automated and scalable platform for professionals in design, engineering, and compliance testing to validate device performance with confidence and precision. The new test solution provides a unified platform for automated electrical testing as specified in the HDMI 2.2 test specification, ensuring that device manufacturers can confidently validate product performance at the transmission and cable, while reception testing is introduced at a later stage.

Keysight’s physical layer compliance test solution for HDMI meets the latest technical and procedural demands of the HDMI Forum. Designed for precision and efficiency, the solution integrates high-bandwidth measurement hardware with automated compliance workflows to manage complex test scenarios across transmitters and cables. The modular architecture of the solution supports flexible test configurations, while built-in diagnostics provide deep insight into the root causes of signal degradation. This enables design and validation teams to not only verify compliance but also optimize performance early in the development cycle.

Han Sing Lim, Vice President and General Manager of Keysight’s General Electronic Measurement Division, said: “With the introduction of the Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI, our customers can accelerate time-to-market for next-gen consumer electronics while ensuring robust integrity and regulatory compliance. By incorporating the latest version of HDMI technology in our solution, we are enabling leading consumer electronics designers and manufacturers to continue to push the boundaries of digital display and multimedia performance.”

Backed by Keysight’s global expertise in compliance testing and proven in high-volume production environments, the new solution delivers a trusted path to certification readiness and superior end-product performance.

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Ultra-low-power RL78/L23 MCUs with segment LCD displays & capacitive touch for HMI applications

Renesas Electronics Corporation, a premier supplier of advanced semiconductor solutions, introduced the new 16-bit RL78/L23 microcontroller (MCU) group, expanding its low-power RL78 family. Running at 32MHz, the RL78/L23 MCUs combine industry-leading low-power performance with essential features such as dual-bank flash memory, segment LCD control, and capacitive touch functionality to support smart home appliances, consumer electronics, IoT and metering systems. These compact, cost-effective devices address the performance and power requirements of modern display-based human-machine interface (HMI) applications.

Ultra-Low Power Operation with Optimized LCD Performance

The RL78/L23 is optimized for ultra-low power consumption and ideal for battery-powered applications that spend the majority of time in standby. They offer an active current of just 109μA/MHz and a standby current as low as 0.365μA, along with a fast 1μs wake-up time to help minimize CPU activity. The LCD controller’s new reference mode, VL4, reduces LCD operating current by approximately 30 percent when compared to the existing RL78/L1X group. The MCUs come with SMS (SNOOZE Mode Sequencer), which enables dynamic LCD segment display without CPU intervention. By offloading tasks to the SMS, the devices minimize CPU wake-ups and contribute to system-level power savings. These innovations significantly extend battery life, simplify design and reduce replacement costs, while minimizing environmental impact.

The RL78/L23 offers a wide operating voltage range of 1.6V to 5.5V, which supports direct operation from 5V power supplies commonly used in home appliances and industrial systems. This capability reduces the need for external voltage regulators. The MCUs also integrate key components such as capacitive touch sensing, a temperature sensor, and internal oscillator, reducing BOM cost and PCB size.

Feature-Rich Peripherals for HMI Systems

Designed to meet the dynamic requirements of the HMI market, RL78/L23 integrates a suite of advanced features in a compact, cost-effective package. Its built-in segment LCD controller and capacitive touch realize sleek, responsive user interfaces for products such as induction cooktops and HVAC systems. The IH timer (Timer KB40) enables precise multi-channel heat control, which is essential in smart kitchen appliances such as rice cookers and IH cooktops. The devices include dual-bank flash memory for seamless firmware updates via FOTA (Firmware Over-the-Air), allowing continuous system operation in applications like metering, where downtime must be minimized. The dual-bank architecture allows one memory bank to run the user program, while the other receives updates. This approach keeps the system functional throughout the process for improved reliability.

“The Renesas RL78 Family of 16-bit microcontrollers has been one of the most successful products since its launch more than 10 years ago, particularly in home appliances,” said Daryl Khoo, Vice President of Embedded Processing at Renesas. “I’m pleased to announce the RL78/L23, a new generation of RL78 microcontrollers with rich features, ideally suited for smart home appliances and cost-sensitive IoT solutions. With these devices, we aim to provide a better user experience with our intuitive development environment so that customers can get to production faster with confidence, based on market-proven Renesas technologies.”

Key Features of the RL78/L23

16-bit RL78 microcontroller running at 32MHzBuilt-in segment LCD controller and capacitive touchUp to 512KB of dual-bank flash memory for seamless FOTAUp to 32KB of SRAM and 8KB of data flashSMS for ultra-low power operationIH Timer (KB40) supporting up to 3-channel induction heating controlWide operating voltage range from 1.6V to 5.5VOperating temperature range of -40°C to +105°CMultiple serial interfaces including UART, I2C, CSIIEC60730-compliant self-test library44-100-pin LFQFP, LQFP and HWQFN packages.

Intuitive Development Environment for Faster Time-to-Market

The RL78/L23 comes with an easy-to-use development environment. Developers can leverage robust support tools such as Smart Configurator and QE for Capacitive Touch to streamline system design.

The post Renesas Introduces Ultra-Low-Power RL78/L23 MCUs for Next-Generation Smart Home Appliances appeared first on ELE Times.

I bought inexpensive LCR meter(?) from Aliexpress $10 I don't believe testing results. These results are not 100% accurate, so please use them for reference only. It's so funny. Display is good. If you're curious, you can see a video on YouTube. https://youtu.be/lvv2YHXiezY submitted by /u/Time_Double_1213 [link] [comments]

The design of RF systems requires engineers to face complex challenges. Learn how automated tools augmented with AI are becoming available to help with this.

South Korean LED maker Seoul Semiconductor Co Ltd says that the Supreme Court of Korea has recently upheld a lower court ruling convicting Taiwan-based LED maker Everlight Electronics Co Ltd of violating the Act on Prevention and Protection of Industrial Technology for misappropriating Seoul Semiconductor’s patented LED technology....

The ongoing electrification of cars brings new trends and requirements with every new design cycle. One trend for battery electric vehicles is reducing the size of the low-voltage batteries, which power either 12-V or 48-V systems. Some auto manufacturers are even investigating whether it’s possible to eliminate low-voltage batteries completely. Regardless, you’ll need isolated high- to low-voltage DC/DC converters as a backup or buffer for the low-voltage battery rail. In all of these cases, the high-voltage battery powers the DC/DC converters. Many high-voltage battery systems in cars currently in production or in development use a 400-V or 800-V architecture.

Given the disadvantages of discharging the high-voltage battery more than necessary, high- to low-voltage DC/DC converters need to support operation with the highest possible efficiency. Different activity states in the car require different power levels in the subsystems—for example, 60 W when the driver opens the car, 300 W when the car is in standby but not moving, and 3 kW or more when the car is in drive and fully operational. It is not possible to optimize a single DC/DC converter to cover all three potential output power levels with high-efficiency operation over the whole load range; in the examples given here, you would need two or three independent power converters.

Converter topology selection

In this power tip, I will focus on the 300-W output power range, also known as a micro DC/DC converter. Suitable DC/DC topologies for this output power range include half- and full-bridge converters. Resonant topologies such as half-bridge inductor-inductor-capacitor (LLC) converters offer higher efficiency conversion than their hard-switched counterparts through zero-voltage switching (ZVS) on the primary side and zero-current switching (ZCS) on the secondary side. Another potential topology is the phase-shifted full-bridge (PSFB) topology, which also employs soft switching by leveraging ZVS but is less cost-effective for the 300-W target output power level, since it requires four switches on the primary side.

Figure 1 shows the converter efficiency for various input voltages and load values for the Texas Instruments (TI) Automotive 300 W Micro DC/DC Converter Reference Design Using Half-Bridge LLC. Optimized for 400-V battery inputs and a 48-V output, this design reflects a good compromise between efficiency and cost of the four different topologies.

Figure 1 Efficiency plot of the automotive 300-W micro DC/DC converter reference design. Source: Texas Instruments

In an electric vehicle with a 400-V architecture, the battery voltage can vary from 200 V to 450 V. In general, LLC converters are not known to work well with very wide input voltage ranges because, with peak current-mode control, such a wide input voltage range could lead to the converter prematurely entering light-load efficiency mode (also known as burst mode) under full load conditions, or reaching overload conditions too early under low input-voltage conditions. The reason for both effects is that the feedback voltage is scaled in the controller with the input voltage, making it switching frequency-dependent.

So why should you even consider an LLC for this type of application? The UCC256612-Q1 LLC controller from TI uses input-power proportional control (IPPC), which overcomes these limitations. The feedback voltage only scales with the input power, and stays quasi-constant over the whole input voltage range for a constant load current. Figure 2 shows the differences between IPPC feedback voltage behavior (Figure 2a) and traditional peak current-mode control feedback voltage behavior (Figure 2b).

Figure 2 Feedback over input voltage using (a) IPPC and (b) traditional LLC control. Source: Texas Instruments

Accurate output voltage regulation with isolation

The proper regulation of isolated power supplies in electric vehicles is a tricky topic. Optocouplers, typically used for secondary-side regulation (SSR) in nonautomotive applications, are considered unreliable in automotive applications because of aging effects on the internal glass passivation over their lifetime. An alternative way to provide output feedback to a controller on the primary side is primary-side regulation (PSR) through an auxiliary winding. PSR is not very accurate for high output currents because the voltage drop across the rectifier(s) and droop across traces to the load will be current-dependent but not visible on the auxiliary winding. A second option is to use isolated amplifiers.

For SSR, the reference design uses the TI ISOM8110-Q1 automotive-qualified pin-to-pin replacement for traditional optocoupler devices. Superior aging performance and smaller current transfer ratio (CTR) variations of the ISOM8110-Q1 enable more accurate and reliable designs, which are crucial for automotive systems with expected lifetimes of at least 10 years. In addition, the ISOM8110-Q1 has a slightly different transfer function than traditional optocouplers, enabling higher control loop bandwidths that can ultimately save costs because lower output capacitance values will be able to meet similar load transient requirements.

Figure 3 shows a load transient from 3 A to 6.25 A and back to 3 A for the reference design with a 48-V output. The output voltage deviation with four 82-µF output capacitors is only 400 mV.

Figure 3 Load transient behavior, 400 VIN, 3 A to 6.25 A, and back to 3 A. Source: Texas Instruments

Apart from dynamic output accuracy, load regulation under static load conditions is important too. Figure 4 shows the load regulation across different input voltages for the reference design.

Figure 4 Load regulation over various input voltage levels, illustrating good load regulation under static load conditions. Source: Texas Instruments

For full functionality, the ISOM8110-Q1 requires a bias current of at least 700 µA on the diode side of the device and 700 µA multiplied by the worst-case CTR on the transistor side, which is 155% with a 5 mA bias current and 180% with a 2 mA bias current. Because some control ICs are optimized for minimum standby power, the feedback pin of such a controller might not be capable of sourcing sufficient current to supply the ISOM8110-Q1 on its own. A simple workaround for such a scenario is to provide the bias current with a pull-up resistor from a regulated voltage rail to the feedback pin. The UCC256612-Q1 generates a 5-V rail with an internal low-dropout regulator, which is externally accessible and can therefore provide the bias current for the opto-emulator IC. The block diagram in Figure 5 demonstrates the implementation of this workaround.

Figure 5 Secondary-side feedback implementation using the ISOM8110-Q1, with external bias from a control IC on the primary side. Source: Texas Instruments

Alternative for micro DC/DC converters

The reference design demonstrates that the half-bridge LLC topology can be a viable alternative for automotive micro DC/DC converters in the 300 W power range, demonstrating good efficiency as well as excellent static and dynamic output voltage regulation.

The ISOM8110-Q1 is a cost-effective, accurate and reliable option to close the loop of isolated power converters in automotive applications. It works well with controllers optimized for low standby power when there is the possibility of an external bias voltage.

Markus Zehendner is a systems engineer and Member Group Technical Staff in TI’s EMEA Power Supply Design Services group. He holds a bachelor’s degree in electrical engineering and a master’s degree in electrical and microsystems engineering from the Technical University of Applied Sciences in Regensburg, Germany. His main focus lies on automotive low-voltage designs for advanced driver assistance systems and infotainment, as well as high-voltage designs for hybrid and electric vehicle applications.

 Related Content

• Power Tips #75: USB Power Delivery for automotive systems
• Power Tips #118: Using interleaved ground planes to improve noise filtering from isolated power supplies
• Power Tips #101: Use a thermal camera to assess temperatures in automotive environments
• Power Tips #104: Design considerations for boosting power density in infotainment
• Power Tips #129: Driving high voltage silicon FETs in 1000-V flybacks

The post Power Tips #144: Designing an efficient, cost-effective micro DC/DC converter with high output accuracy for automotive applications appeared first on EDN.

Старт набору студентів на онлайн-курси проєкту TANDEM-UA-DE в зимовому семестрі 2025/2026 (реєстрація до 09.09.2025) kpi ср, 08/27/2025 - 16:49

💢 Сесія професорсько-викладацького складу 2025 kpi ср, 08/27/2025 - 15:33

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

Anand Kumar is the Managing Director of STMicroelectronics (ST), India, and has held this position since June 2025. He heads the Global IP and Library Design team, located across France and India, within ST’s Global Technology R&D organization. Kumar’s team takes IPs from concept to production maturity and brings variants over time, across the full portfolio of ST’s proprietary, differentiated technologies and technology platforms.

Anand Kumar began his career in 1999 as an Analog Designer in ST’s Central R&D organization. With over 25 years of experience as a designer, design team lead, and global lead of IP design, he has deep expertise in designing Analog and Mixed-Signal IPs and leading high-performing IP and Library Design teams delivering a wide range of IPs. These include memories, IOs, standard cells, clock-generation IPs, data converters, sensors, power-management IPs, fuses, and other analog and digital IPs across all ST technology nodes.

Kumar holds more than 20 patents and has presented at multiple top-tier international scientific and technical semiconductor conferences.

Anand Kumar was born in Narwana (Haryana state), India, in 1976, and holds a degree in Electronics and Communication Engineering from NSUT (Netaji Subhas University of Technology, formerly Delhi Institute of Technology), Delhi.

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Specialty analog foundry Tower Semiconductor Ltd of Migdal Haemek, Israel and Xscape Photonics Inc of Fort Lee, NJ and Santa Clara, CA, USA (which is funded by industry leaders such as NVIDIA and Cisco, and develops silicon photonics interconnects for AI data centers) have announced the successful prototyping and validation kit availability of what is claimed to be the industry’s first on-chip, optically pumped, multi-wavelength laser source. Built on Tower’s mature, high-volume PH18 silicon photonics platform, the solution supports CWDM and DWDM wavelength grids and is tailored for AI data-center fabrics, where bandwidth density, power efficiency and scalability are essential...

The bottom 3 leds are not working There was water in the casing submitted by /u/Nice_Specific [link] [comments]

Nowadays, individuals own an increasing number of devices—such as fitness trackers and smartphones that continuously generate valuable data. At the same time, organizations like banks, hospitals, and enterprises produce vast amounts of sensitive information. However, due to strict privacy regulations, this data cannot be openly shared for centralized processing. In such scenarios, federated learning offers a transformative solution: it enables machine learning models to be trained directly on-device or within institutional boundaries, without transferring raw data. This approach preserves privacy while unlocking powerful, collaborative AI capabilities. As a result, data from diverse sources both personal and institutional can be securely leveraged to extract insights and drive smarter decisions. Below are 10 compelling real-world applications where federated learning is making a significant impact.

1. Telecommunications

The federated model enables telecommunication firms to study patterns of their clients, enhance network performance, and make accurate tele-service projections for their distributed systems. This fosters efficient network systems while safeguarding customer information. In the same context, mobile operators stand to enhance calling services from user data sourced from spatially dispersed systems.

2. Autonomous Vehicles

Self-driving cars and connected vehicles utilize federated learning to collaborate vehicles enhance navigation, obstacle identification, and safety measures. This eliminates the need to consolidate personal driving information. Drivers of self-driven automobiles and fleet operators utilize federated learning to enhance safety, navigation, and object detection with the aid of local sensor data consisting of cameras, LIDAR, and object detection.

3. Finance

Banks and fintech companies use federated learning for detecting fraud, credit scoring, and modeling credit risk. One example is the training of a multi-bank fraud detection model to recognise suspicious transactions while safeguarding user information.

4. Smart Devices & IoT

Smartphones, as well as other wearable devices, use federated learning to enhance voice recognition, keyboard prediction, and health tracking functions. An instance is the Gboard keyboard from Google, which leverages federated learning to upgrade its autocorrect as well as next-word prediction features grounded on users’ typing patterns.

5. Cybersecurity

Federated learning is employed in factories for process optimization, predictive maintenance, and even defect detection. Federated learning enables multiple organizations to collaboratively train intrusion detection models using local network logs. This approach enhances threat detection accuracy while preserving sensitive data and complying with privacy regulations.

6. Manufacturing

Factories use federated learning for predictive maintenance, defect detection, and process optimization. For instance, multiple production lines can train a model to predict equipment failure using local sensor data, reducing downtime.

7. Energy & Utilities

Energy companies and power grids use advanced techniques to forecast demand and anticipate failures in the system by learning from distributed sensor data across substations and smart meters. Use Case includes a national utility company uses federated learning to predict peak electricity usage across cities, helping balance load distribution without accessing individual household data.

8. Retail & E-commerce

Retailers customize product recommendations cross-sell and up-sell suggestions and basket-level cross-product purchase analytics across different store locations without sharing any stepwise item-level purchase data of shoppers. A classic use case is a global fashion retailer who wants to suggest outfit combinations based on current trends of different geographies. The retailer can now use the federated approach, enabling training of the model across all the stores in the regions while protecting shopper and purchase data.

9. Content Platforms

With less risk to user privacy, platforms can better personalize user feeds and automatically moderate content by learning locally from user interactions. Use Case: A video streaming app enhances its recommendation system by locally training on user watch histories stored on devices, ensuring tailored recommendations while refraining from uploading any viewing data to the cloud.

10. Aviation

Carriers and aircraft manufacturers develop models from flight execution and servicing records over different fleets in an attempt to improve safety and cut downtime, with the added benefit of keeping proprietary data private. A use case is offered by the federated model training from different airlines that enables the prediction of an engine’s wear and tear based on flight conditions, which aids in the scheduling of proactive maintenance without the need to share sensitive operational data.

Conclusion:

Federated learning protects privacy while facilitating cooperative model training across dispersed data sources. It lowers the risks associated with data transfers, conforms with data protection laws, and enables businesses to leverage insights without jeopardizing user privacy.

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Announced today, the new MCUs future-proof industrial and data center systems in anticipation for the post-quantum world.

Photonic application-specific integrated circuit (PASIC) chip designer and manufacturer OpenLight of Santa Barbara, CA, USA (which launched as an independent company in June 2022, introducing the first open silicon photonics platform with heterogeneously integrated III-V lasers, modulators, amplifiers and detectors) has closed its oversubscribed $34m Series A fundraising round, which was co-led by Xora Innovation and Capricorn Investment Group. Other participants include Mayfield; Juniper Networks (now part of HPE); Lam Capital (the corporate venture arm of Lam Research Corp); New Legacy Ventures; and K2 Access...

Federated learning is transforming AI’s potential in India by allowing models to be trained without infringing on the privacy of decentralized data. Federated learning is of critical importance in healthcare, finance, and consumer technology due to the rising needs of industries for AI that is secure, compliant with regulations, and privacy-preserving. Due to India having a flourishing technology ecosystem as well as a strong pool of AI talents, India is emerging as a leader in this technology. This article will discuss the leading 10 companies in India that focus on federated learning.

1. TCS Research

TCS Research as the innovation wing of Tata Consultancy Services, TCS Research collaborates with federated learning for enterprise AI. Their initiatives cover healthcare, banking, and smart city projects, centering on the safe training of models over distributed data silos.

2. Wipro HOLMES

Wipro’s AI platform, uses federated learning to provide intelligent automation and edge AI. Its application in telecommunications, manufacturing, and IT services aids in the development of AI models without eroding data privacy.

3. Infosys Nia

Infosys Nia An all-in-one AI platform, Infosys Nia also offers federated learning for decentralized data modeling, which is especially beneficial in retail, and finance, where data sensitivity is high and compliance is critical.

4. SigTuple

With its headquarters in Bengaluru, SigTuple is a health tech company which employs federated learning to streamline the analysis of medical images and diagnostics, while still maintaining patient data privacy. Their AI solutions not only save time but also improve the decision-making processes of pathologists and radiologists.

5. Qure.ai

With over a decade of specialization in AI-driven radiology, Qure.ai is a clear leader. They are notable examples of the application of federated learning in radiology, not only for advancing diagnostic precision but also for safeguarding critical medical information.

6. Vaidik AI

Vaidik AI marks a new chapter in the federated learning narrative of India. It launched an extensive selection of AI services, including the fine-tuning of LLMs and multilingual AI. Its multidisciplinary expertise in data annotation and the privacy-first approach to AI model development is well known. It provides healthcare, finance, and education sectors with economical and scalable solutions.

7. ActionLabs AI

ActionLabs AI is located in Bengaluru and works with federated learning, edge AI, and generative model creation. Though healthcare and fintech startups appearing to be ActionLabs’ primary areas of focus, the company’s small size allows it to efficiently cater to a wider range of companies.

8. Accenture India

Accenture adapts federated learning to its Responsible AI framework, assisting clients spanning the energy sector to public services in securely training models on decentralized data.

9. Fractal Analytics

Fractal Analytics Fractal applies federated learning to generate consumer insights for retail and CPG. Their solutions enable brands to analyze consumer behavior without pooling sensitive data.

10. Intel India

Intel India, with its offices in Bengaluru and Hyderabad, is pivotal in advancing federated learning as it refines secure hardware platforms such as Trusted Execution Environments (TEEs) and furthers AI research through Intel Labs. It also champions privacy-preserving AI in healthcare, smart cities, and edge computing.

Conclusion:

The federated learning ecosystem in India is evolving rapidly with the presence of global technology leaders such as Intel and the innovative local startups such as ActionLabs AI, Vaidik AI, and SigTuple. These firms not only expand the frontiers of privacy-preserving AI but also position the federated learning ecosystem to thrive on data collaboration devoid of security risks. With growing demand across healthcare, finance, and edge computing, federated learning is becoming a cornerstone of ethical AI development in India.

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