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Miniaturization and power efficiency have long defined sensor designs. Enter artificial intelligence (AI) and software algorithms to dramatically improve sensing performance and enable a new breed of features and capabilities. This trend has been apparent at this year’s CES in Las Vegas, Nevada.

See full story at EDN’s sister publication, Planet Analog.

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Artificial General Intelligence (AGI) represents the ultimate frontier in the world of artificial intelligence—a vision of machines that think, learn, and understand as flexibly and broadly as humans do. Unlike today’s narrow AI systems that excel in specific tasks, such as translating languages or diagnosing diseases, AGI aims to bridge the gap between computational efficiency and human-like cognition. It’s the dream of creating an AI so versatile that it can seamlessly adapt to any intellectual challenge across diverse domains.

What Exactly is AGI?

AGI isn’t just about making machines smarter in specific ways; it’s about giving them a brainpower equivalent to our own. Imagine an AI that not only plays chess like a grandmaster but also writes poetry, learns to cook, solves intricate physics problems, and holds deep, meaningful conversations—all without needing to be reprogrammed for each task. AGI aspires to be this all-encompassing, adaptable system that can reason, learn, and apply knowledge to new situations, much like a human.

The Difference Between AGI and Narrow AI

To understand AGI, it’s essential to contrast it with what we currently have: “Narrow AI”.

Narrow AI dominates our lives today, powering virtual assistants like Alexa, recommendation algorithms on Netflix, and even self-driving cars. These systems are exceptionally good at what they’re designed to do but lack the ability to generalize or step outside their predefined capabilities. A narrow AI trained to diagnose diseases, for example, can’t suddenly start solving math equations.

AGI, in contrast, has the potential to overcome these constraints. It wouldn’t just perform tasks; it would learn how to approach them, adapt to new ones, and even innovate solutions we humans might never conceive.

The Path to AGI: Still a Theoretical Dream

At present, AGI remains a theoretical concept, with scientists and engineers dedicating their efforts to unraveling the complexities of human-like cognition. Progress is being made in areas like neural networks, reinforcement learning, and natural language processing, but creating a machine that truly “understands” remains elusive.

The challenge isn’t just computational—it’s deeply philosophical. How do we model consciousness, creativity, and abstract thinking? How do we design a machine capable of ethical reasoning or emotional intelligence? AGI isn’t just about programming; it’s about unraveling the mysteries of human thought itself.

The Promise and Peril of AGI

If achieved, AGI could revolutionize every facet of society. It could accelerate scientific discovery, solve complex global challenges like climate change, and redefine education and healthcare. Imagine a world where machines collaborate with humans to unlock limitless potential.

However, this vision isn’t without risks. AGI raises profound ethical questions: How do we ensure it aligns with human values? How do we prevent misuse? And how do we safeguard against scenarios where AGI outpaces our control? These are questions that must be addressed alongside technological progress.

The Road Ahead

AGI represents the culmination of human ambition—a synthesis of technology and intellect that mirrors our own capabilities. While it may still be a distant goal, its pursuit inspires us to explore the very essence of intelligence, creativity, and ethics. The journey to AGI isn’t just about building machines; it’s about redefining what it means to be human in a world of infinite possibilities.

 

The post Exploring Artificial General Intelligence: A Leap Toward Thinking Machines appeared first on ELE Times.

The rapid evolution of consumer electronics has revolutionized how we live and work, but it has also contributed to a growing environmental crisis: electronic waste (e-waste). Globally, millions of tons of e-waste are generated annually, much of which ends up in landfills or incinerators, releasing hazardous materials into the environment. Sustainable electronics and circular design principles offer innovative solutions to mitigate this crisis by extending the lifecycle of devices and promoting resource efficiency.

Understanding the E-Waste Problem

The Scale of E-Waste

E-waste comprises discarded electronic devices, such as smartphones, laptops, televisions, and home appliances. According to the Global E-Waste Monitor, approximately 53.6 million metric tons of e-waste were generated in 2019, a figure expected to rise to 74.7 million metric tons by 2030. However, only 17.4% of this e-waste is formally recycled, leaving the majority untreated and contributing to environmental pollution.

Environmental and Health Impacts

E-waste contains toxic substances like lead, mercury, and cadmium, which can leach into soil and water or be released into the air during improper disposal. This pollution poses severe risks to ecosystems and human health, particularly in regions where informal recycling practices prevail. Moreover, the extraction of raw materials for new electronic devices contributes to resource depletion, energy consumption, and carbon emissions.

The Role of Circular Design in Sustainable Electronics

Circular design is a framework that prioritizes sustainability by minimizing waste, reusing materials, and creating products with extended lifecycles. This approach is particularly relevant to electronics, where rapid obsolescence and limited recycling have exacerbated the e-waste challenge.

Key Principles of Circular Design

1. Design for Longevity: Products are engineered to last longer, with durable components and modular designs that facilitate repairs and upgrades.
2. Design for Disassembly: Devices are built to be easily disassembled, enabling the recovery and reuse of valuable materials.
3. Material Efficiency: Manufacturers prioritize sustainable materials, including recycled or biodegradable options.
4. Product-as-a-Service Models: Instead of selling devices outright, companies provide them as a service, retaining ownership and responsibility for end-of-life management.

Innovations in Sustainable Electronics

Modular Devices

Modular design enables consumers to replace or upgrade specific components rather than discarding an entire device. For example, Fairphone, a company dedicated to sustainable smartphones, offers modular devices that allow users to replace batteries, cameras, and screens independently. This approach not only reduces e-waste but also empowers consumers to extend the useful life of their electronics.

Biodegradable Electronics

Researchers are exploring biodegradable materials for electronic components, such as circuit boards made from cellulose and conductors crafted from natural fibers. These materials can decompose harmlessly at the end of their lifecycle, reducing the environmental impact of discarded devices.

Advanced Recycling Technologies

Innovative recycling methods, such as robotic disassembly and chemical recycling, are improving the efficiency and effectiveness of e-waste processing. These technologies can recover precious metals, rare earth elements, and other valuable materials from discarded electronics, reducing the need for new mining activities.

The Role of Policy and Regulation

Governments and international organizations play a critical role in promoting sustainable electronics through legislation and incentives. Key policy measures include:

1. Extended Producer Responsibility (EPR): Mandating manufacturers to take responsibility for the end-of-life management of their products.
2. Right to Repair Laws: Ensuring consumers have access to tools, parts, and information needed to repair their devices.
3. E-Waste Collection Programs: Establishing systems for the collection, sorting, and recycling of electronic waste.
4. Subsidies for Sustainable Design: Offering financial incentives to companies that adopt circular design principles.

Corporate Initiatives in Sustainable Electronics

Several leading tech companies are embracing circular design to reduce their environmental footprint:

1. Apple: The company has committed to using 100% recycled materials in its products and operates a trade-in program to refurbish old devices.
2. Dell: Dell’s closed-loop recycling program recovers plastics and metals from old devices for use in new products.
3. HP: HP offers cartridge recycling and hardware take-back programs, while also integrating recycled plastics into its product lines.

Consumer Behavior and Its Impact

Consumers play a pivotal role in driving demand for sustainable electronics. By prioritizing repairable, durable, and eco-friendly devices, consumers can encourage manufacturers to adopt circular design principles. Additionally, proper disposal of electronic waste through certified recycling programs ensures that valuable materials are recovered and reused.

Challenges to Adoption

Despite its promise, the widespread adoption of circular design in electronics faces several challenges:

1. Economic Viability: Sustainable materials and processes can be more expensive, deterring manufacturers from adopting them.
2. Technological Barriers: The integration of circular design principles requires innovation in product engineering and materials science.
3. Consumer Awareness: Many consumers are unaware of the environmental impact of their devices or the benefits of sustainable alternatives.
4. Global Disparities: Developing nations often lack the infrastructure for proper e-waste management and recycling.

The Path Forward

Addressing the e-waste crisis through sustainable electronics requires a collaborative effort across stakeholders:

1. Investing in Research: Governments and private entities should fund research into sustainable materials, advanced recycling technologies, and innovative design approaches.
2. Educating Consumers: Public awareness campaigns can inform consumers about the importance of sustainable electronics and proper e-waste disposal.
3. Strengthening Regulations: Policymakers must enforce stricter e-waste management laws and incentivize circular design practices.
4. Fostering Collaboration: Partnerships between manufacturers, recyclers, and policymakers can create a cohesive ecosystem for sustainable electronics.

Conclusion

The integration of circular design principles into the electronics industry offers a transformative approach to reducing e-waste and minimizing environmental impact. By prioritizing longevity, material efficiency, and responsible end-of-life management, manufacturers can shift from a linear to a circular economy. While challenges remain, innovations in technology, supportive policies, and informed consumer behavior can pave the way for a more sustainable future. In the era of rapid technological advancement, sustainable electronics are not just an option—they are a necessity.

The post Sustainable Electronics in Reducing E-Waste Through Circular Design appeared first on ELE Times.

In today’s hyper-connected world, the proliferation of IoT devices and digital systems has transformed industries and redefined modern living. However, this interconnectedness also exposes devices and networks to a broad range of cybersecurity threats. The intersection of Artificial Intelligence (AI) and cybersecurity emerges as a crucial frontier in the effort to protect connected devices from malicious actors.

The Rise of Connected Devices and Their Vulnerabilities

The Internet of Things (IoT) has brought remarkable convenience and efficiency to homes, businesses, and industries. Smart thermostats, wearable health monitors, autonomous vehicles, and industrial control systems are just a few examples of the innovations enabled by IoT. As per estimates, the number of IoT devices globally is expected to exceed 30 billion by 2030.

The rapid adoption of IoT devices necessitates simultaneous advancements in security measures to mitigate emerging vulnerabilities effectively. Many devices are built with minimal security features, lack regular updates, and are often deployed in environments with insufficient cybersecurity protocols. This makes them attractive targets for cybercriminals, who exploit vulnerabilities to launch attacks such as:

DDoS Attacks: Compromised devices can form botnets to overwhelm networks with traffic.

Data Breaches: Sensitive user data collected by IoT devices can be intercepted.

Ransomware: Connected systems, including critical infrastructure, can be locked and held for ransom.

The Role of AI in Cybersecurity

Artificial Intelligence has emerged as a transformative tool in the cybersecurity landscape. By leveraging machine learning (ML) algorithms and deep learning techniques, AI systems can analyze vast amounts of data in real time, identify patterns, and predict potential threats. Artificial Intelligence (AI) is reshaping the cybersecurity landscape by introducing sophisticated tools and methodologies that enhance threat detection, response, and prevention. The following are significant ways AI is being applied to enhance cybersecurity:

1. Threat Detection and Prediction

Conventional cybersecurity solutions typically depend on signature-based detection techniques, which are restricted to identifying previously known threats.  AI enhances threat detection by analyzing behavioral patterns and identifying anomalies that may indicate emerging threats. For instance:

Anomaly Detection: AI can identify irregular network activity or unauthorized access attempts, highlighting potential security threats.

Predictive Analytics: By examining historical attack data, AI can predict the likelihood of future attacks and recommend proactive measures.

2. Automated Incident Response

AI-powered systems can automate responses to cyber incidents, reducing the time between detection and mitigation. For example:

Containment: AI has the potential to quarantine compromised devices, effectively stopping the spread of malware.

Remediation: Automated systems can deploy patches or updates to address vulnerabilities.

3. Behavioral Analytics

AI can establish baseline behavioral profiles for users and devices, enabling the detection of deviations that may indicate compromise. Behavioral analytics is particularly effective in:

• Identifying insider threats
• Detecting credential misuse
• Preventing fraud in financial systems

4. Adaptive Security Measures

AI systems can continuously adapt to evolving threats. Unlike static rule-based systems, AI learns from new data and refines its models to address sophisticated attack techniques.

Challenges in Integrating AI with Cybersecurity

While AI offers transformative potential in cybersecurity, its integration is accompanied by a range of significant challenges.

These include:

Adversarial AI: Cybercriminals can exploit AI systems by using adversarial inputs to deceive models, bypassing detection mechanisms.

High-quality data is essential for AI systems to perform accurately and efficiently. Inaccurate or biased data can undermine the reliability of threat detection, leading to flawed cybersecurity outcomes. Organizations can address these issues by implementing rigorous data validation processes, ensuring diverse and unbiased datasets, and continuously monitoring AI systems to identify and rectify inaccuracies in real time.

Resource Intensity: Training and deploying AI models can be resource-intensive, posing a challenge for organizations with limited budgets.

Privacy Concerns: The use of AI for monitoring and analysis can raise ethical concerns about user privacy and data protection.

Case Studies: AI in Action

1. Securing Smart Cities

Smart city initiatives leverage IoT devices to improve urban living through intelligent traffic management, energy efficiency, and public safety systems. However, the interconnected nature of these systems, such as smart grids, intelligent traffic systems, and healthcare IoT devices, makes them vulnerable to cyberattacks including ransomware, data breaches, and unauthorized control of critical infrastructure. AI-driven cybersecurity solutions are employed to:

• Monitor city-wide networks for anomalies.
• Prevent and respond to ransomware attacks that threaten vital infrastructure systems.
• Protect sensitive citizen data from breaches.

2. Defending Industrial IoT (IIoT)

In industrial and manufacturing settings, IIoT devices are used to operate machinery and oversee various processes. AI is used to:

• Predict and prevent equipment failures caused by cyberattacks.
• Analyze sensor data to detect unauthorized activities.
• Ensure compliance with cybersecurity standards.

3. Healthcare IoT Security

Connected medical devices, such as pacemakers and insulin pumps, are lifesaving but can be exploited by hackers. AI-enhanced systems safeguard healthcare IoT by:

• Identifying unusual device behaviors.
• Protecting patient data from unauthorized access.
• Ensuring devices operate securely in critical conditions.

The Future of AI and Cybersecurity

The partnership between AI and cybersecurity will continue to evolve as threats grow more sophisticated. Emerging trends include:

1. Federated Learning for Privacy-Preserving Security

Federated learning allows AI models to be trained across decentralized data sources without sharing raw data, enhancing privacy while enabling collaborative threat intelligence.

2. AI-Driven Zero Trust Architectures

Zero Trust frameworks operate on the principle that no user or device is inherently trustworthy by default.  AI enhances Zero Trust by continuously monitoring and authenticating access requests in real time.

3. Quantum-Resistant Algorithms

As quantum computing poses a potential threat to encryption, AI is being used to develop and evaluate quantum-resistant cryptographic algorithms to secure connected devices.

Conclusion

The intersection of AI and cybersecurity represents a paradigm shift in how connected devices are protected. By harnessing the power of AI, organizations can stay ahead of evolving cyber threats and safeguard critical systems. However, the journey is not without challenges, requiring collaboration between technologists, policymakers, and industry stakeholders to ensure a secure and resilient digital future. As AI continues to advance, its role in fortifying cybersecurity will undoubtedly expand, paving the way for a safer interconnected world.

The post The Intersection of AI and Cybersecurity: Protecting Connected Devices appeared first on ELE Times.

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By: Javier Valle, General Manager Space Power Products, Texas Instruments

Learn about our collaboration with NASA and industry leaders in developing radiation-hardened, plastic packaging for space electronics, known as QML Class P, to power missions with size, weight and power in mind.

As curiosity and innovation drive space exploration forward, constraints for size, weight and power continue to tighten. To design for space, you have little to no room for error. And increasing space exploration activities by public and private entities, whether in Earth’s orbit or way beyond, requires continued collaboration and improvements.

Recently, our company worked with NASA and other industry experts to lead the development of a new plastic packaging standard for space electronics, known as Qualified Manufacturers List Class P (QML Class P). Electronics in space must meet government standards set forth in the QML, ranging from radiation-tolerant or radiation-hardened devices in either ceramic or plastic packaging. The QML provides assurance that parts will operate as intended in the harsh environments of space.

“The QML Class P packaging standard enables more advanced computing in space, such as how satellites and other spacecraft can autonomously process data and make decisions in orbit as opposed to beaming data back down to Earth,” said Javier Valle, product line manager for space power at our company. “More processing capability also requires greater power. With TI’s QML Class P portfolio, we increase the efficiency of the power supply while reducing the size of the overall package, resulting in much higher power density.”

The QML exists with its many classes to ensure predictability in designs, meeting qualification and certification according to government standards, but new standards such as Class P are introduced as our knowledge and use cases advance. The QML Class P standard enables the use of radiation-hardened plastic packaging for power-management, processor, communications and high-speed integrated circuits (ICs) in satellites, rovers and other spacecraft.

Bring space up to speed through plastic

Ceramic packaging has often been the go-to, reliable option, as it meets a variety of government agency specifications in the United States. Manufacturers of ceramic-packaged space electronics have released ICs to the market under a qualification known as QML Class V.

Until QML Class P, there had been no standardized, radiation-hardened equivalent for plastic packaging.

Earlier forms of plastic packaging standards have also been especially vulnerable to a process known as outgassing. Outgassing describes a process when the harsh temperature and vacuum conditions of space vaporize organic compounds, which can deposit onto electronics causing them to fail. Depending on the severity, the effects of outgassing can interrupt or completely end missions.

Advancements in manufacturing and testing procedures have helped address the consequences of outgassing and other environmental concerns in space. However, these improvements can vary from manufacturer to manufacturer, and consequentially, were not enough to reassure space operators about the reliability of new, unfamiliar technologies without standardization.

In repeatedly hearing from customers that the industry needed a QML standard for plastic packaging, our company assembled a group of more than three dozen experts from industry and standardization bodies.

Looking further ahead with TI

Space operators can now easily transition from a radiation-tolerant electronic design using our Space Enhanced Plastic portfolio to a radiation-hardened design with our QML Class P portfolio, without any hardware change given our pin-to-pin compatibility.

TI’s QML Class P certified portfolio offers solutions across the entire spacecraft electrical power system (EPS), from solar panels all the way to point of load power supplies, and the portfolio is growing.

As we continue to navigate the future of space exploration, designing for space brings unlimited possibilities and solutions as endless as space itself. We have more than six decades of experience in creating solutions for space, and we look forward to helping you engineer the next frontier.

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At Electronica and Productronica 2024, ELE Times caught up with Mr. Babu Ayyappan, Managing Director of Uchi Embedded Solutions. He shared insights about their focus on embedded systems and IoT development, quality assurance, and experiences at the event.

ELE Times: Let’s start by understanding what Uchi Embedded Solutions does and the product portfolio you have displayed at the event this year.

Mr. Babu Ayyappan: Good evening. At Uchi Embedded Solutions, we focus on tools and components for embedded systems and IoT development. It’s a niche field. For instance, an embedded system developer may require tools like debugging and programming tools. We cater to that segment. In IoT development, the process often begins by selecting the chip for development. One of the key products we are promoting is the ESP32 chip, which is widely used in IoT applications. These two segments—embedded systems and IoT—are our primary focus areas.

ELE Times: You’ve mentioned embedded solutions and IoT. Are there any specific trends or changes you’ve observed in these fields over the years?

Mr. Babu Ayyappan: I wouldn’t say there’s anything drastically new, but these fields have always demanded high-quality products. Embedded system developers often face challenges in selecting the right tools because of the plethora of options available in the market. We try to address this by offering global tools that are economical and come from well-known, reliable brands. At events like this, we aim to promote these quality products and grab the audience’s attention.

ELE Times: Can you elaborate on your sales network and distribution channels?

Mr. Babu Ayyappan: Certainly. As a distribution company, we’ve partnered with about 12 vendors from countries like Taiwan, the UK, the US, Germany, and China. We keep our product lines limited to around 12, focusing on quality over quantity. Operating from Bangalore, we manage our sales across India. Thanks to modern connectivity, this model works efficiently. For marketing, we employ a one-man-show approach in major cities like Pune, Mumbai, and Delhi, where we have residential engineers covering the market. This setup works well for us.

ELE Times: Quality and safety are always critical when it comes to components. How does Uchi ensure these aspects in its products?

Mr. Babu Ayyappan: As a distributor, our primary responsibility is to bring quality products to India. We carefully select companies based on their market reputation and business practices. Today, with globalization, anyone can purchase products from anywhere. However, the same product—say, a branded product from Espressif—can be sourced from multiple suppliers. At Uchi, we work directly with authorized distributors. We don’t go through third-party mediators to save costs or speed up imports because we can’t vouch for their practices. By maintaining direct relationships with trusted suppliers, we ensure we import only quality products. That’s the extent of control we have as a distributor in a vast global market.

ELE Times: How has your experience at this year’s event been? Did it meet your expectations, and what are your future plans?

Mr. Babu Ayyappan: Exhibitions serve multiple purposes for us. They allow us to meet customers we might not otherwise encounter, reconnect with existing ones, and engage new prospects. Consistent participation also strengthens our brand reputation, signaling industry commitment. While immediate ROI isn’t always guaranteed, the long-term benefits make the effort worthwhile. Overall, it has been a rewarding experience.

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Edge computing has naturally been a hot topic at CES with companies highlighting a myriad of use cases where the pre-trained edge device runs inference locally to produce the desired output, never once interacting with the cloud. The complexity of these nodes has grown to not only include multimodal support with the fusion and collaboration between sensors for context-aware devices but also multiple cores to ratchet up the compute power.

Naturally, any hardware acceleration has become desirable with embedded engineers craving solutions that ease the design and development burden. The solutions vary where many veer towards developing applications with servers in the cloud that are then virtualized or containerized to run at the edge. Ultimately, there is no one-size-fits-all solution for any edge compute application.

It is clear that support for some kind of hardware acceleration has become paramount for success in breaking into the intelligent embedded edge. Company approaches to the problem run the full gamut from hardware accelerated MCUs with abundant software support and reference code, to an embedded NPU.

Table 1 highlights this with a list of a few companies and their hardware acceleration support.

Company	Hardware acceleration	Implemented in	Throughput	Software
NXP	eIQ Neutron NPU	select MCX, i.MX RT crossover MCUs, and i.MX applications processors	32 Ops/cycle to over 10,000 Ops/cycle	eIQ Toolkit, eIQ Time Series Studio
STMicroelectronics	Neural-ART Accelerator NPU	STM32N6	up to 600 GOPS	ST Edge AI Suite
Renesas	DRP-AI	RZ/V2MA, RZ/V2L, RZ/V2M	–	DRP-AI Translator,  DRP-AI TVM
Silicon Labs	Matrix Vector Processor, AI/ML co-processor	BG24 and MG24	–	MVP Math Library API, partnership with Edge Impulse
TI	NPU	TMS320F28P55x, F29H85x, C2000 and more	Up to 1200 MOPS (on 4bWx8bD) Up to 600 MOPS (on 8bWx8bD)	Model Composer GUI or Tiny ML Modelmaker
Synaptics	NPU	Astra (SL1640, SL1680)	1.6 to 7.9 TOPS	Open software with complete GitHub project
Infineon	Arm Ethos-U55 micro-NPU processor	PSOC Edge MCU series, E81, E83 and E84	–	ModusToolbox
Microchip	AI-accelerated MCU, MPU, DSC, or FPGA	8-, 16- and 32-bit MCUs, MPUs, dsPIC33 DSCs, and FPGAs	–	MPLAB Machine Learning Development Suite, VectorBlox Accelerator Software Development (for FPGAs)
Qualcomm	Hexagon NPU	Oryon CPU, Adreno GPU	45 TOPS	Qualcomm Hexagon SDK

Table 1: Various company’s approaches for hardware acceleration.

Synaptics, for instance, has their Astra platform that is beginning to incorporate Google’s multi-level intermediate representation (MLIR) framework. “The core itself is supposed to take in models and operate in a general-purpose sense. It’s sort of like an open RISC-V core based system but we’re adding an engine alongside it, so the compiler decides whether it goes to the engine or whether it works in a general-purpose sense.” said Vikram Gupta, senior VP and general manager of IoT processors and chief product officer, “We made a conscious choice that we wanted to go with open frameworks. So,whether it’s a Pytorch model or a TFLite model, it doesn’t matter. You can compile it to the MLIR representation, and then from there go to the back end of the engine.” One of their CES demos can be seen in Figure 1.

Figure 1:  A smart camera solution showing the Grinn SoM that uses the Astra SL1680 and software from Arcturus to provide both identification and tracking. New faces are assigned an ID and an associated confidence interval that will adjust according to the distance from the camera itself. 

TI showcased its TMS320F28P55x C2000 real-time controller (RTC) MCU series with an integrated NPU with an arc fault detection solution for solar inverter applications. The system performs power conversion while at the same time doing real-time arc fault detection using AI. The solution follows the standard process of obtaining data, labeling, and training the arc fault models that are then deployed onto the C2000 device (Figure 2).

Figure 2: TI’s solar arc fault detection edge AI solution

One of Microchip’s edge demos detected true touches in the presence water using its mTouch algorithm in combination with their PIC16LF1559 MCU (Figure 3). Another solution highlighted was in partnership with Edge Impulse and used the FOMO ML architecture to perform object detection in a truck loading bay. Other companies, such as Nordic Semiconductor, have also partnered with Edge Impulse to ease the process of labeling, training, and deploying AI to their hardware. The company has also eased the process of leveraging NVIDIA TAO models to adapt well-established AI models to a specific end-application on any Edge-Impulse-supported target hardware. 

Figure 3: Some of Microchip’s edge AI solutions at CES 2025. Truck loading bay augmented by AI in partnership with Edge Impulse (left) and a custom-tailored Microchip solution using their mTouch algorithm to differentiate between touch and water (right).

Aalyia Shaukat, associate editor at EDN, has worked in the design publishing industry for six years. She holds a Bachelor’s degree in electrical engineering from Rochester Institute of Technology, and has published works in major EE journals as well as trade publications.

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I thought it was interesting that my parents' new car has 3 different types of electronic display! Some kind of LED/dot matrix in the center console, LCD on the instrument panel, and a normal (tiny) pixelated screen up on the dash. submitted by /u/AC4401CW [link] [comments]

HaiLa Technologies has introduced the EVAL2000 development board, featuring its BSC2000 passive backscatter Wi-Fi chip and ST’s STM32U0 MCU. The platform empowers developers and researchers to create ultra-low-power connected sensor applications over Wi-Fi.

The BSC2000 is a monolithic chip that combines analog front-end and digital baseband components to implement HaiLa’s backscatter protocol for 802.11 1-Mbps Direct Sequence Spread Spectrum (DSSS) over Wi-Fi. By using backscattering, it enables low-power communication by reflecting existing Wi-Fi signals instead of generating its own. This allows devices to transmit data with minimal energy consumption. Leveraging readily available, standard Wi-Fi infrastructure, the BSC2000 backscatter Wi-Fi chip collects and transmits sensor data with power efficiency that extends the life of battery-operated sensors.

The EVAL2000 development board accelerates prototyping with GPIO, I2C, and SPI sensor interfaces. Sensor integration is handled through firmware on the MCU. The kit also includes an onboard temperature/humidity sensor.

The BSC2000 EVAL2000 development kit is available for preorder, with shipping anticipated for Q1 2025. For more information on the backscatter Wi-Fi chip and development kit, click here.

HaiLa Technology 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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The Talaria 6 family of SoCs from InnoPhase provides Wi-Fi 6, Bluetooth 6.0, Thread, and Zigbee connectivity, along with PSA Level 2 and Level 3 security. Powered by an Arm Cortex-M33 processor and a rich peripheral suite, the SoCs offer the computational performance needed for real-time, on-chip edge AI tasks, including predictive maintenance, sensor analytics, and smart power management.

Talaria 6 wireless SoCs support Wi-Fi 6 (802.11ax) and are Wi-Fi 7 (802.11be) ready, achieving ultra-low power and high-performance connectivity. Integrated digital CMOS radio technology ensures robust throughput in noisy, high-density environments, making them well-suited for smart thermostats, video cameras, and sensors.

Single and dual-band options (2.4 GHz/5 GHz) offer flexible band selection based on use case and network conditions. IEEE 802.11be extensions and multi-link operation improve throughput, lower latency, and increase reliability in congested environments.

Additionally, the SoCs support Bluetooth 6.0, Bluetooth Classic, Thread, and Zigbee mesh networks, enabling seamless integration with a wide range of IoT devices. To protect against cybersecurity threats, Talaria 6 devices feature hardware-based encryption, secure boot, and tamper resistance, safeguarding sensitive data and meeting PSA Level 2 and Level 3 security standards.

The INP6120 2.4-GHz Wi-Fi 6 SoC is expected to sample in Q2 2025, with production starting in Q4 2025. The INP6220 dual-band 2.4/5-GHz Wi-Fi 6 SoC will sample in the second half of 2025, with production beginning in the first half of 2026.

Talaria 6 product page 

InnoPhase IoT

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Synaptics is pairing Google’s ML core with its Astra AI-native hardware and open-source software to simplify context-aware IoT device development. The MLIR-compliant core on Astra hardware accelerates AI processing for vision, image, voice, sound, and other modalities. This combination enables intuitive interaction in wearables, appliances, entertainment systems, embedded hubs, monitoring, and control across consumer, automotive, enterprise, and industrial applications.

 

 

The Astra AI-native compute platform for IoT integrates scalable, low-power edge compute silicon with open-source, user-friendly software, robust tools, a strong partner ecosystem, and wireless connectivity. Built on Synaptics’ expertise in neural networks, proven AI hardware, and compiler design for IoT, the platform also supports a wide range of modalities with refined in-house solutions. Google’s ML core, a highly efficient open-source machine learning core, is MLIR-compliant, enhancing compatibility with modern compilers.

For more information about Synaptics’ Astra embedded processors for AI-native IoT, click here.

Synaptics

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Mitsubishi announced that it has begun shipping samples of two new S1-Series high-voltage IGBT modules rated at 1.7 kV. These two components are useful for large industrial equipment, such as railcars and DC power transmitters. With proprietary IGBT devices and advanced insulation structures, the S1-Series modules enhance reliability, minimize power loss, and reduce thermal resistance, supporting more reliable and efficient operation of inverters in large industrial equipment.

The S1-Series incorporates Mitsubishi’s Relaxed Field of Cathode (RFC) diode, increasing the Reverse Recovery Safe Operating Area (RRSOA) by 2.2 times over previous models, improving inverter reliability. Additionally, an IGBT element with a Carrier Stored Trench Gate Bipolar Transistor (CSTBT) structure reduces power loss and thermal resistance, enabling more efficient inverter operation. The upgraded insulation structure boosts insulation voltage resistance to 6.0 kVRMS—1.5 times higher than earlier products—allowing more flexible insulation designs for compatibility with a broader range of inverter types.

Mitsubishi Electric 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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Nanoelectronics research center imec of Leuven, Belgium has demonstrated electrically driven gallium arsenide (GaAs)-based multi-quantum-well nano-ridge laser diodes fully, monolithically fabricated on 300mm silicon wafers in its CMOS pilot prototyping line. Achieving room-temperature continuous-wave lasing with threshold currents as low as 5mA and output powers exceeding 1mW, the results are said to demonstrate the potential of direct epitaxial growth of high-quality III-V materials on silicon (‘GaAs nano-ridge laser diodes fully fabricated in a 300mm CMOS pilot line’, Yannick De Koninck et al, Nature, volume 637, pages 63–69 (2025)). imec reckons that this provides a pathway to the development of cost-effective, high-performance optical devices for applications in data communications, machine learning and artificial intelligence...

Molecular beam epitaxy (MBE) system maker Riber S.A. of Bezons, France says that long-standing US-based customer Teledyne Imaging Sensors (TIS) has placed a repeat order for an MBE 412 cluster research system (for delivery in 2025) to expand its production capacity and fulfill additional contracts for manufacturing new high-performance infrared devices...

Rohde & Schwarz has upgraded its industry leading R&S SMW200A vector signal generator and its midrange counterpart, the R&S SMM100A. With significant enhancements in error vector magnitude (EVM) performance, the evolved R&S SMW200A is a robust choice for both 5G NR FR3 research and high demand RF applications like power amplifier testing. The instrument now also includes a new RF linearization software option, which uses digital pre distortion to optimize EVM at high output power. The R&S SMM100A has also been upgraded with improved EVM capabilities.

Rohde & Schwarz has introduced the latest evolution of its two vector signal generators, the signature R&S SMW200A for the most demanding applications, and its midrange counterpart, the best-in-class R&S SMM100A. Besides a redesigned front panel and user interface, the R&S SMW200A has been equipped with modified microwave hardware for enhanced error vector magnitude (EVM) performance as well as higher output power in the frequency range above 20 GHz. This addresses the demands of 5G NR FR2 research and RF component and module testing.

This upgrade comes with an RF linearization software option R&S SMW-K575, which utilizes digital pre distortion technology to optimize EVM at high output power. This ensures high accuracy and stability, even for complex modulation schemes in the entire frequency range.

These key upgrades also extend to the R&S SMM100A, the midrange counterpart of the R&S SMW200A. The R&S SMM100A also comes with a new low phase noise option, R&S SMM B709. With this option, the R&S SMM100A can provide, for example, an EVM performance better than –53 dB for an IEEE802.11be signal with a bandwidth of 320 MHz.

Customers with previous models of the R&S SMW200A or R&S SMM100A can also benefit from the new performance enhancements offered by R&S SMx-K575 RF linearization: Rohde & Schwarz offers retrofit options through a simple service and calibration process.

Gerald Tietscher, Vice President of Signal Generators, Power Supplies and Meters at Rohde & Schwarz, says: “With increasing data rates and modulation scheme complexity, achieving low EVM is critical for ensuring stability and robustness in wireless connectivity applications. The latest evolution of our R&S SMW200A and R&S SMM100A vector signal generators is a testament to our commitment to making our art

of signal generation even better. With their superior RF characteristics and exceptional EVM performance, these instruments are a pivotal resource for handling the requirements of the most advanced test applications.”

The post New R&S SMW200A and R&S SMM100A vector signal generators feature significantly improved EVM performance appeared first on ELE Times.

Smart clothes, also known as e-textiles or wearable technology, are garments embedded with sensors, actuators, and other electronic components that enable them to interact with the wearer and environment. These clothes can monitor various health parameters, provide connectivity, and even adapt to the user’s needs.

How Do Smart Clothes Work?

Smart clothes work through integrated sensors and actuators that can detect physical movements, environmental factors, and biological signals. These sensors collect data such as heart rate, body temperature, moisture levels, posture, or even muscle activity. The data is then transmitted to a connected device (like a smartphone or cloud server) for analysis and real-time feedback. Smart fabrics may also have embedded conductive threads that allow them to transmit electrical signals.

Some smart clothes are powered by flexible batteries, solar cells, or energy harvested from movement (like piezoelectric materials), making them lightweight and functional.

Smart Clothes Technology

The core technologies in smart clothes include:

• Conductive fabrics and threads: Materials capable of transmitting electricity, enabling the integration of sensors and circuits into fabrics.
• Flexible sensors: Lightweight sensors that measure things like temperature, pressure, motion, and even muscle activity.
• Wireless communication: Bluetooth, NFC, or Wi-Fi to send data from the clothes to external devices.
• Power sources: Small batteries or energy-harvesting systems like solar cells or kinetic energy converters.

Smart Clothes Applications

1. Health and Fitness Monitoring: Smart clothes like heart rate-monitoring shirts, posture-correcting jackets, and smart sports bras help track and analyze physical activity, vital signs, and performance metrics in real-time.
2. Medical and Rehabilitation: Some garments are designed for patients, offering features like tracking vital signs, muscle movements, and even aiding muscle stimulation.
3. Safety: Smart clothes can include features like LED lights for better visibility for cyclists, workers, and runners, and GPS for tracking.
4. Fashion and Aesthetics: Garments with integrated displays that change patterns or colors based on the environment or user input.
5. Climate Control: Thermal adaptive clothing adjusts to body temperature, providing cooling or heating effects.
6. Workplace Use: In sectors like construction, smart clothing can alert workers about their posture, fatigue, or physical stress.

Smart Clothes Advantages

1. Health Monitoring: They enable continuous monitoring of health metrics like heart rate, blood pressure, and body temperature, which can be used for preventive health care.
2. Improved Performance: Athletes and fitness enthusiasts can track performance and adjust their training based on real-time data.
3. Enhanced Safety: In work environments, smart clothes can provide early warnings about hazardous conditions, track worker location, or improve visibility.
4. Personalized Comfort: With adaptive features, smart clothes can adjust their temperature, moisture level, or fit according to environmental conditions and personal preferences.
5. Convenience: The integration of technology into clothes reduces the need to carry separate gadgets and can be more discreet and comfortable compared to wearables like watches or fitness bands.

Smart clothes continue to evolve, combining the worlds of fashion, health, technology, and convenience into one seamless experience.

 

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Semiconductor innovations in battery systems are leading to energy storage adoption

Takeaways

• Power grids weren’t designed to handle new types of electricity demands and supplies.
• Battery energy storage systems are key to transforming and protecting the grid.
• Innovation in battery-management and high-voltage semiconductors help grids get the most out of battery storage.

The growing adoption of electric vehicles (EVs) and the transition to more renewable energy sources are reducing our more-than-century-long reliance on fossil fuels. Electric utilities are increasingly turning to solar panels and wind turbines rather than natural gas-fueled turbines to generate the electricity needed to charge EVs, as well as help power our homes and businesses. Together, these trends are poised to bring us closer to a sustainable energy future.

Those same trends also pose a big challenge to the electricity grid. Demand can vary throughout the day – and so can supplies of solar and wind energy based on changes in the weather. That’s why batteries are becoming an essential component of the grid.

“Batteries can fill in the gap when it’s cloudy and the wind dies down,” said Richard Zhang, a Virginia Tech professor who teaches power electronics and has worked in the grid and energy industry for 25 years. “And batteries improve the economics of electricity because they can be charged during off-peak times, providing electricity for charging EVs at peak times.”

Getting batteries to safely, reliably and cost-effectively store and release the large amounts of electricity running through the grid is a complex challenge. That’s where our company’s expertise in providing advanced battery-management semiconductor solutions can make a big difference.

“The bigger, higher-voltage batteries used in the grid require better thermal management and more precise monitoring,” said Samuel Wong, our company’s vice president and general manager of Battery Management Solutions. “Effectively managing those batteries requires understanding battery chemistry and adapting high-performance semiconductor devices to safely get the most out of each battery.”

Smoothing out the grid

The adoption of solar and wind generation and EVs is good news for the planet, Richard said. The problem is that power grids weren’t originally designed to handle these new types of electricity demands on available energy.

“Getting people to switch to EVs is easier today than it was just a few years ago,” he said. “Now the growing issue is getting the electricity infrastructure to handle them, alongside other energy demands.”

The challenge, Samuel said, is grid instability – in other words, fluctuations in electricity generation and usage. Variations in energy supply occur in solar and wind generation, especially the complete loss of solar power at night. Supply and demand swings may also occur from the charging routines of EV owners.

“If everyone comes home in the evening and plugs in their EVs for the night, the grid might not be able to handle it,” he said.

Samuel and Richard, like most power experts, agree on the solution to grid instability: energy storage systems (ESS). Storage systems – usually in the form of batteries – can capture and hold excess energy in the grid when supply is high and demand is low, and then make it available at other times. You may be picturing the relatively small, light battery cells used in EVs. But for the grid, an ESS might consist of a railroad-car-sized stack of bigger, heavier cells that each can operate at as much as 4 megawatt-hours (MWh) – enough energy to power thousands of homes.

Staging storage systems at different points in the grid optimizes their ability to distribute enormous amounts of electricity to neighborhoods when and where they’re needed. That can mean placing an ESS alongside a solar panel farm, where it can soak up the excess energy during the day and then pump it back out to the grid at night. Or, an ESS placed in a community can more easily grab energy from local rooftop solar panels and later supply the extra electricity needed to charge nearby EVs. “An ESS can serve as a local reservoir for the community,” Samuel said.

Managing battery and system performance

At the heart of storage systems are high-voltage battery modules – typically lithium-iron phosphate cells – capable of generating enormous amounts of heat if charged or discharged too quickly. These modules can also have shortened lifetimes if completely depleted too often.

Monitoring temperature and charge in these batteries requires extremely precise semiconductors, such as the BQ79616 industrial battery monitor, Samuel said. That’s because even tiny fluctuations in temperature and voltage can signal that a battery may need attention.

“You have to get to millivolt accuracy to know how much charge is left in a battery,” he said.

Our company’s extensive experience in ultra-precise battery monitors is proving essential in helping the ESS industry produce systems that can supply the grid with vital battery-management data. The results can have a big impact on the cost-effectiveness of a grid ESS, Samuel said.

“If you can only measure the charge in a 10-MWh ESS with 5% accuracy, then you can’t safely use more than 9.5 MWh,” he said. “Our battery monitors can get the accuracy measurement to 1%, which enables you to use 9.9 MWh.”

In addition to accurate battery monitoring, grid-scale energy storage systems such as the ones integrated with solar panel farms require efficient high-voltage power conversion that help reduce power losses when transferring power to and from the grid. These systems also rely on sensing and isolation technologies that help maintain system safety and stability, which is critical for managing electricity flow as high as 1500 V.

Impacting the future

For the foreseeable future, innovation in battery ESS looks to be the key to transform and protect the grid from the variabilities coming from solar and wind energy, as well as EV charging.

“It’s really exciting to contribute to strengthening the grid with innovations in energy storage,” Samuel said. “We can already do a lot today, and we’ll be able to do a lot more as we build out tomorrow’s smart grid.”

The post The battery-management technology that will strengthen our grid appeared first on ELE Times.

ROHM has developed the RLD8BQAB3 high-output laser diode for use in ADAS (Advanced Driver Assistance Systems) equipped with LiDAR for distance measurement and spatial recognition. In addition to automotive applications, the firm is also initially supplying samples targeting consumer applications (drones, robot vacuums, golf rangefinders) and industrial applications (AGVs, service robots, and 3D monitoring systems such as sensors for human/object detection)...