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A starter kit from Prophesee enables low-power, high-speed event-based vision on the Raspberry Pi 5 single-board computer. Based on the GenX320 Metavision event-based vision sensor, the kit accelerates development of real-time neuromorphic vision applications for drones, robotics, industrial automation, security, and surveillance. The camera module connects directly to the Raspberry Pi 5 via a MIPI CSI-2 (D-PHY) interface.

Consuming less than 50 mW, the 1.5-in. GenX320 sensor provides 320×320-pixel resolution with an event rate equivalent to ~10,000 fps. It offers >140-dB dynamic range and sub-millisecond latency (<150 µs at 1,000 lux).

Software resources include OpenEB, the open-source core of Prophesee’s Metavision SDK, with Python and C++ API support. Drivers, data recording, replay, and visualization tools can be found on GitHub.

The GenX320 starter kit is available for pre-order through Prophesee and authorized distributors. The Raspberry Pi 5 board is sold separately.

GenX320 starter kit product page

Prophesee

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Renesas’ RL78/L23 16-bit MCUs provide segment LCD control and capacitive touch sensing for responsive HMIs in smart home appliances, consumer electronics, and metering systems. Running at 32 MHz, these low-power MCUs include 512 KB of dual-bank flash memory, enabling seamless over-the-air firmware updates.

The MCUs offer an active current of 109 µA/MHz and a standby current as low as 0.365 µA, with a fast 1‑µs wakeup time. With a wide voltage range of 1.6 V to 5.5 V, they can operate directly from 5‑V power supplies commonly used in home appliances and industrial systems.

The reference mode of the integrated LCD controller reduces display power by approximately 30% compared to the RL78/L1X series. A snooze mode sequencer (SMS) enables dynamic segment updates without CPU intervention, further enhancing energy efficiency.

Development tools for the RL78/L23 include the Smart Configurator and QE for Capacitive Touch, which simplify system design and firmware setup. Renesas also provides the RL78/L23 Fast Prototyping Board, compatible with the Arduino IDE, and a capacitive touch evaluation system for hardware testing and validation.

RL78/L23 MCUs are available now from the Renesas website or distributors.

RL78/L23 product page 

Renesas Electronics 

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The Apollo510B wireless SoC from Ambiq combines a 48-MHz dedicated network coprocessor with a Bluetooth LE 5.4 radio for power-efficient edge AI. Its Arm Cortex-M55 CPU, enhanced with Helium vector processing and Ambiq’s turboSPOT dynamic scaling, delivers up to 30× greater AI efficiency and 16× faster performance than Cortex-M4 devices. 

With 64 KB each of instruction and data cache, 3.75 MB of RAM, and 4 MB of embedded nonvolatile memory, the Apollo510B provides fast, real-time processing. Its 2D/2.5D GPU handles vector graphics, while SPI, I²C, UART, and high-speed USB 2.0 support flexible sensor and device connections. High-fidelity audio is enabled via a low-power ADC and stereo digital microphone PDM interfaces.

Apollo510B also integrates secureSPOT 3.0 and Arm TrustZone, enabling secure boot, firmware updates, and protection of data exchange across connected devices. These features make the device well-suited for always-on, intelligent applications such as wearables, smart glasses, remote patient monitoring, asset tracking, and industrial automation.

The Apollo510B SoC will be available in fall 2025.

Apollo510B product page 

Ambiq Micro

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Smart Bench Essentials Plus is an enhanced set of Keysight test instruments offering improved precision and reliability. The core instruments—a power supply, waveform generator, digital multimeter, and oscilloscope—meet industry and safety standards such as ISO/IEC 17025, IEC 61010, and CSA. All instruments are managed from a single PC via PathWave BenchVue software, simplifying test automation and workflows.

According to Keysight, Smart Bench Essentials Plus delivers 10× higher DMM resolution, 5× greater waveform generator bandwidth, 4× more power supply capacity, and 64× higher oscilloscope vertical resolution over the previous series. Development engineers can test, troubleshoot, and qualify electronic designs while leveraging these benefits:

• Reduce measurement errors with Truevolt technology in a 6.5-digit dual-display digital multimeter.
• Generate accurate waveforms with Trueform technology in a 100-MHz waveform/function generator.
• Deliver reliable, responsive power with a 400-W, four-channel DC power supply.
• Capture even the smallest signals with a portable four-channel oscilloscope featuring a custom ASIC and 14-bit ADC.

Instruments have intuitive, color-coded interfaces and standardized menus to improve productivity. Built-in graphical charting tools make it easy to visualize and analyze test results.

To learn more about the Smart Bench Essentials Plus portfolio and request a bundled quote, click here.

Keysight Technologies 

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Diodes’ AL5958Q matrix LED driver integrates a 48-channel constant-current source and 16 N-channel MOSFET switches for automotive dynamic lighting. Two cascade-connected drivers support up to 32 scans, well-suited for narrow-pixel mini- and micro-LED displays that use multiple RGB LEDs to deliver animated lighting effects and information.

The AEC-Q100 qualified driver employs multiplex pulse density modulation (M-PDM) control to raise the refresh rate of dynamic scanning systems without increasing the grayscale clock frequency or introducing EMI. Built-in matrix display command functions reduce processing overhead on the local MCU. These functions include automatic black-frame insertion, ghost elimination, and suppression of shorted-pixel caterpillars.

Operating from a 3-V to 5-V input, the AL5958Q’s 48 constant-current outputs supply up to 20 mA per LED channel string. Current accuracy between channels and matching across devices is typically ±1.5%.

The AL5958Q LED driver costs $1.60 each in lots of 2500 units.

AL5958Q product page

Diodes

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Three recent research efforts highlight how the quantum field is moving from laboratory experiments to scalable, commercial-ready technologies.

It goes without saying that battery-powered devices are sensitive to power draw, especially during periods of inactivity. One such use case is in sensor nodes or portable sensors—these devices passively monitor a specific condition. When the threshold is exceeded, they trigger an alarm or log the event for further analysis. Since most devices incorporate some form of microcontroller (MCU), selecting an MCU with intelligent analog peripherals can reduce the Bill of Materials (BOM) by performing the same functions of a discrete device while potentially saving power by disabling the analog functionality when not needed.

To demonstrate these features, we built two demos on the PIC16F17576 microcontroller family. One demo aims to use as little power as possible while detecting temperature changes, while the other utilizes the embedded op-amps to dynamically adjust the gain based on the input signal.

Power consumption

Let’s start at the top—power consumption. No matter how you slice it, all roads will lead to the same basic tenets:

• Keep VDD as low as possible
• Minimize oscillator frequency
• Turn off all unused peripherals and external circuits, when possible, and as much as possible
• Avoid floating nodes on digital I/O

Beyond this advice, it becomes a lot more application-specific. For instance, most op-amps and ADCs don’t have an OFF switch. This is where intelligent analog peripherals fit into designs.

The “intelligent” part of their name is derived from the fact that they can be controlled in software. While most analog peripherals would not be considered power hungry, when optimizing battery life, every little bit of current matters, and generally, there is a higher quiescent current draw that the discrete device would have due to process limitations.

However, there are special low-power peripherals that allow for ultra-low power operation, even when enabled all the time. For instance, the Low Power Voltage Reference (VREFLP) and Low Power Analog Comparator (CMPLP) in the PIC16F17576 family of MCUs draw minimal power but can trigger interrupts to wake the CPU if action is needed.

For devices without these lower power peripherals, another peripheral available in PIC MCUs is the Analog Peripheral Manager (APM). The APM is a specialized counter that can toggle power ON/OFF to the analog peripherals while allowing the CPU to remain continuously in sleep.

If an event occurs, requiring intervention from the CPU, the peripherals can generate an interrupt to wake the device. This avoids having to perform the following sequence: wake the CPU, power on the peripherals, check the results, perform an action, shut down the peripherals, and return to deep sleep.

Low-power demo

The objective of the low-power demo is to demonstrate the new CMPLP and VREFLP as a temperature alarm. This application could be used for cold asset tracking to log when an event over the expected temperature occurs. For the demo implementation, we designed a circuit to detect when a person touches the thermistor(s), causing a rise in temperature.

Figure 1 A finished low power demo prototype that will detects the temperature rise that occurs when a person touches the thermistor(s).

Theory of operation

This circuit is composed of two PIC16F17576 MCUs; one device acts like the device under test (DUT) while the other handles power measurement and display.

Power measurement and display

To measure the minuscule amount of current pulled by the MCU DUT, it was important to design a circuit that could perform high-side current sensing while also being capable of maintaining the power supply at 1.8 V, which is the lowest recommended operating voltage for this device family. For reference, the minimum operating voltage is 1.62 V, which provides a 10% margin on the power supply before the device is out of specified operating conditions.

To measure the quiescent current of the MCU and low-power analog peripherals, a precision 1:1 current mirror IC was used to supply current to the DUT (Figure 2). This IC has a settable compliance output limit, but the tolerancing and ranging on the internal reference was not acceptable for our purposes, so we overdrive the integrated circuit with an external 1.8-V reference (MCP1501-18E) to avoid having to calibrate each unit individually.

Figure 2 The high-side current circuit to measure the minuscule amount of current pulled by the MCU DUT, and 1.8-V DUT power supply.

This ensures the power rail for the DUT is as close as possible to 1.8 V. Guard rings and planes are placed on the PCB to minimize the leakage current of this rail as much as possible. The 1:1 current output goes through a sense resistor, and then a differential measurement of the voltage at the resistor is performed with a 24-bit delta-sigma ADC (MCP3564R) with an external 2.048-V voltage reference (MCP1501-20E). This is shown in Figure 3. The resulting measurement is then displayed on the OLED screen attached to the board.

Figure 3 The ADC implementation where the differential measurement of the voltage at the resistor is performed with a 24-bit delta-sigma ADC with an external 2.048-V voltage reference.

A (good) problem we discovered late in the process was that the current measurement in this configuration is so stable, it looks hard-coded on the display. Thankfully, this can be easily disproved by gently touching the DUT’s decoupling capacitors with a finger or other slightly conductive object and observing the change in measured current.

DUT

The DUT device performs a simple but crucial role in detecting temperature changes with as little power consumption as possible. For this, CMPLP and VREFLP are used together with the Peripheral Pin Select (PPS) system to output the state of the CMPLP without waking the CPU.

In an actual application, CMPLP’s output edge (LOW HIGH) would be used to wake the CPU to perform some action like logging a temperature event or sounding an alarm.

Using the high-side current measurement circuit designed, we found the current of the microcontroller in this state is ~2.2 to 2.4 μA, but there is room for a tiny bit of extra power savings.

VREFLP is comprised of two separate subsystems: a low-power 1-V reference and a low-power DAC. This application uses the slightly more power-hungry low-power DAC instead of the fixed 1-V reference because the temperature change from physical contact is very small, and the system must recalibrate the threshold on startup to account for environmental variance. In an application where a few degrees of tolerance are acceptable, using the 1-V reference would save a few fractions of a microamp.

Notably, this demo does not use the APM because the APM requires an oscillator to remain active, consuming a little bit more power (~2.8 μA) than simply leaving these ultra-low power modules on. In a situation where multiple analog peripherals are being used, such as the integrated op-amps, ADC, etc., the APM would provide significant savings in power.

Dynamic gain

Another feature of intelligent analog peripherals is the ability to adjust on the fly. In some cases, a signal may have a large dynamic range that is tricky to measure without clipping.

Clipping a signal is usually considered undesirable, as waveform information about the signal is lost. A simple example of this is a microphone: whispering requires a high gain while shouting requires a low gain. With a fixed gain, designers pick the worst (reasonable) conditions to avoid signal clipping, but this, in turn, reduces the signal resolution.

A way around this problem is to use embedded op-amps. These op-amps aren’t going to outmatch the high-end op-amps, but they are often comparable to general-purpose ones.

And, in many cases, the integrated op-amps contain built-in resistor networks that allow the op-amp(s) to adjust the circuit gain as needed. This requires no extra components or specialized circuitry as it’s already integrated into the die.

Dynamic gain demo

One of the main use cases for the integrated op-amps inside MCUs is to dynamically switch gains depending on how strong the signal is. This is often performed to avoid clipping the signal when the signal strength is high.

This application creates a simple demonstration of this use case by amplifying the output of a pressure sensor and displaying it visually on an LED bar graph.

Figure 4 A dynamic gain demo that amplifies the output of a pressure sensor and displays it visually on an LED bar graph.

Theory of operation Pressure sensor

The pressure sensor in this application changes resistance depending on the amount of pressure applied. This resistor is used as part of a resistor divider network to generate an output signal from 0 to 2 V. Since both the discrete op-amp and the integrated op-amp have high-input impedances, the two circuits can share the same signal without loading down the network.

Dynamic gain circuit

The PIC16F17576 MCU has four op-amps, with two of them containing integrated resistor ladders. These ladders have eight steps, plus an additional option for unity gain (1x), for a total of nine options. Alternatively, resistors or other components can be connected to the I/O pins to assign an arbitrary gain or function, if desired.

In this demo, the MCU’s op-amp is switched between a gain of 2x (LOW) and 4x (HIGH) at runtime depending on the measured signal.

In most applications, when the signal strength is low, the gain would be HIGH. However, it is worth noting that in this demo, the inverse is true. This is purely for visual reasons; otherwise, the clipping condition would have more lights ON and thus appear “better” than the dynamic gain version at a glance. As the gain of the embedded op-amps is set up in software, it was easily reconfigured to match the desired behavior.

Measurement and display

The PIC16F17576 MCU also performs the measurement of both op-amp outputs to display on the LED bar graph. The internal Fixed Voltage Reference (FVR) is used to generate a stable 4.096 V from the +5-V (USB) supply for conversions. MCP23017 I2C I/O expanders are used to drive the LEDs of the display. 

Putting it all together

Adjusting the circuit gain without any external circuitry greatly simplifies designs where there are large signal ranges. These peripherals, of course, will not replace high-performance op-amps, ADCs, DACs, or voltage references, but embedded analog peripherals are a good way to handle signals that require some conditioning but aren’t particularly sensitive. This, coupled with low power functionality, makes them a useful tool to reduce circuit complexity, time to market, and ultimately the BOM in your design.

Robert Perkel is an application engineer for Microchip Technology. In this role, he develops technical content such as App Notes, contributed articles, and videos. He is also responsible for analyzing use cases of peripherals and the development of code examples and demonstrations. Perkel is a graduate of Virginia Tech, where he earned a Bachelor of Science degree in Computer Engineering.

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• Developing window security nodes with level shifting I/O – Part 2
• Fundamentals of I3C interface communication
• Slideshow: The most-popular MCUs ever

The post Mixed signals, on a power budget: Intelligent low-power analog in MCUs appeared first on EDN.

Toshiba Electronic Devices & Storage Corp of Kawasaki, Japan has launched three 650V silicon carbide (SiC) MOSFETs equipped with its latest third-generation SiC MOSFET chips and housed in general-purpose surface-mount TOLL packages...

SweGaN AB of Linköping, Sweden — a manufacturer of custom gallium nitride on silicon carbide (GaN-on-SiC) epitaxial wafers, based on proprietary growth technology — is coordinator of the project ‘GaN6G+: Unlocking Performance and Efficiency in Future 6G Power Amplifiers’, partnered by communications network provider Ericsson, defense and security company Saab and Chalmers University of Technology in Gothenburg, Sweden. Funded by Swedish innovation agency Vinnova and lasting from September 2025 to August 2027, the project aims to revolutionize GaN-based power amplifier (PA) technology for next-generation 6G networks...

📰 Газета "Київський політехнік" № 29-30 за 2025 (.pdf) Інформація КП чт, 08/28/2025 - 15:57

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.

The post Infineon drives industry transition to Post-Quantum Cryptography on PSOC Control microcontrollers appeared first on ELE Times.

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.

The post Keysight Unveils Physical Layer Compliance Test Solution for HDMI to Meet Rising Demands for Ultra-High Resolution and High Dynamic Range appeared first on ELE Times.

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]

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