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congatec’s aReady.IOT software building blocks offer secure IoT connectivity from the company’s aReady.COM computer-on-modules to the cloud. With aReady.IOT, users can focus on their core competencies while congatec simplifies application development, enabling seamless communication and data transfer between systems and devices.

aReady.IOT allows users to remotely monitor, control, and manage their aReady.COM-based applications, connected peripherals, and sensors. These preconfigured blocks support communication via protocols such as OPCUA, MQTT, and REST. Acquired data can be used for maintenance, management, and predictive maintenance. Additionally, data can be processed at the edge for storage and visualization.

Preconfigured modules in aReady.IOT offer a range of scalable services across both application hardware and software layers. The COM Manager, Application Manager, and Fleet Manager each provide unique capabilities to optimize different aspects of the application. Additionally, congatec can offer bi-directional cloud connectivity via the Cloud Connector, supporting services like AWS, Azure, or Telekom Cloud.

aReady.IOT product page 

aReady.COM product page 

congatec

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Version 2.9 of the Aurora embedded FPGA tool suite from QuickLogic enables seamless integration of block RAM (BRAM) and DSP functions. Along with its new BRAM and DSP IP configurator, the software’s place and route tools improve runtime by up to 2 times.

Other upgrades in Aurora 2.9 include custom function support, which enables the instantiation of lookup table (LUT) macros to create custom functions. The release also introduces interactive path analysis within the new GUI, allowing users to debug design timing in greater detail by highlighting critical path routing. This visibility helps users make informed adjustments to improve timing performance.

Aurora’s inferencing feature streamlines the implementation of reconfigurable computing algorithms by automatically adapting BRAM read/write widths, eliminating the need for manual RTL design modifications.

The Aurora eFPGA development tool suite is now available for Windows 10/11 and major Linux distributions, including CentOS, RedHat, and Ubuntu, via a unified Linux installer. The Aurora Pro version supports Synopsys Synplify for logic synthesis.

Aurora product page

QuickLogic

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New variants in the u-blox Nora-B2 Bluetooth LE 6.0 module family integrate Nordic Semiconductor’s entire nRF54L series of ultra-low power wireless SoCs. Offering a choice of antennas and chipsets, these modules consume up to 50% less current than previous-generation devices while doubling processing capacity.

The NORA-B2 series comprises four variants that differ in memory size, design architecture, and price level. Each variant comes with either an antenna pin or embedded antenna.

• NORA-B20 features an nRF54L15 SoC and integrates a 128-MHz Arm Cortex-M33 processor, a RISC-V coprocessor, and an ultra-low power multiprotocol 2.4-GHz radio. It comes with 1.5 MB of NVM and 256 KB RAM.
• NORA-B21, based on an nRF54L10 SoC, is designed for mid-range applications. It has 1.0 MB of NVM and 192 KB of RAM and handles multiple wireless protocols simultaneously, including Bluetooth LE, Bluetooth Mesh, Thread, Matter, Zigbee, and Amazon Sidewalk.
• NORA-B22 employs an nRF54L05 SoC. It is intended for cost-sensitive applications but still provides access to up to 31 GPIOs. It includes 0.5 MB of NVM and 96 KB of RAM.
• NORA-B26, based on an nRF54L10, is designed for customers using the network coprocessor architecture. It comes pre-flashed with the u-blox u-connectXpress software, allowing customers to easily integrate Bluetooth connectivity into their products with no prior knowledge of Bluetooth LE or wireless security.

All NORA-B2 modules are designed for PSA Certified Level 3 security and meet the Bluetooth Core 6.0 specification, including channel sounding for accurate ranging. They also carry global certification, enabling manufacturers to launch products worldwide with minimal effort.

NORA-B20 samples are available now, while NORA-B21 and B22 are in limited evaluation. A pre-release of u-connectXpress for NORA-B26 is available for early adopters.

NORA-B2 product page 

u-blox 

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An intro to RISC-V

As safety-critical systems become increasingly complex, the choice of processor architecture plays an important role in ensuring functional safety and system reliability. Consider an automotive brake-by-wire system, where sensors detect the pedal position, software interprets the driver’s intent, and electronic controls activate the braking system. Or commercial aircraft relying on flight control computers to interpret pilot inputs and maintain stable flight. Processing latencies or failures in these systems could result in unintended behaviors and degraded modes, potentially leading to fatal accidents.

The RISC-V architecture’s inherent characteristics—modularity, simplicity, and extensibility—align with the demands of functional safety standards like ISO 26262 for automotive applications and DO-178C for aviation software. Unlike proprietary processor architectures, RISC-V is an open standard instruction set architecture (ISA) developed by the University of California, Berkeley, in 2011. The architecture follows reduced instruction set computing (RISC) principles, emphasizing performance and modularity in processor design.

RISC-V is set apart by its open, royalty-free nature combined with a clean-slate design that eliminates the legacy compatibility constraints of traditional architectures. The ISA is structured as a small base integer set with optional extensions, allowing processor designers to implement only the features needed for their specific applications.

This article examines the technical advantages and considerations of implementing RISC-V in safety-critical environments.

Benefits for safety-critical industries

Traditional proprietary architectures, such as Arm, have served safety-critical industries well, but challenges around supplier diversity, customization needs, and safety certification requirements have driven interest in RISC-V.

The following sections describe characteristics of RISC-V that make it a viable option for safety-critical development teams.

Architectural independence

One fundamental challenge in developing safety-critical systems is mitigating supply chain risks. Traditional processor architectures require licensing agreements and create vendor lock-in, which impacts long-term system maintainability and cost.

RISC-V’s open model provides several advantages. The ability to work with multiple silicon vendors reduces single-point-of-failure risks in the supply chain. This is particularly important for long-lifecycle applications in aerospace and automotive, where systems may need to be maintained and supported for decades. When using RISC-V, manufacturers expand their options for semiconductor suppliers and development tool ecosystems, providing flexibility in responding to supply chain issues.

Customization to meet safety-critical requirements

RISC-V’s modular design philosophy allows silicon vendors and system architects to implement custom features at the hardware level. This capability helps address specific safety requirements across mission-specific applications certification standards such as:

• Custom error detection and correction.
• Hardware-level monitoring and diagnostic capabilities.
• Low-latency, deterministic execution features for real-time requirements.

Additionally, RISC-V silicon vendors have products supporting harsh environments, such as processors with radiation hardening and electromagnetic pulse (EMP) protection for space applications.

Memory management

One of RISC-V’s distinguishing features is its approach to cache memory management, helping developers of safety-critical applications requiring deterministic behavior. The ability to implement level 2 cache memory mapping as RAM gives developers greater control over system latency, a crucial factor in real-time safety-critical applications.

This capability addresses challenges covered in aviation safety guidelines like EASA AMC 20-193 and FAA AC 20-193. By providing better solutions for cache contention mitigation than traditional architectures, RISC-V supports more predictable execution timing—a critical requirement for safety certification.

Dissimilar redundancy

Safety-critical systems requiring design assurance level A (DAL-A) certification under DO-178C often implement redundancy to protect against common mode failures. RISC-V’s open architecture provides advantages in implementing dissimilar redundancy strategies:

• Implementation of different processor configurations within the same system.
• Diverse redundancy schemes using different vendor solutions.
• Using different architectures in mixed-criticality systems with varying levels of safety requirements.

Performance considerations

While RISC-V may not always match the raw performance metrics of modern Arm implementations, its architecture provides several advantages specific to safety-critical applications. The ability to implement custom instructions and hardware features allows optimization for specific safety requirements without compromising overall system performance.

Key performance-related features include:

• Deterministic execution paths for real-time applications.
• Custom instructions for safety monitoring.
• Efficient context switching for mixed-criticality systems.
• Configurable memory protection units to minimize stack and data corruption.

RISC-V’s development tool ecosystem

Over the years, the maturation of development tools and verification environments for RISC-V has expanded to cover the entire software lifecycle. For example, LDRA’s target license package (TLP) for RISC-V architectures supports development and on-target testing with multi-core code coverage analysis, worst-case execution time (WCET) measurement for AMC 20-193 compliance, requirements traceability, and integration with major RISC-V development platforms. This TLP makes RISC-V ready for safety and security.

Additionally, LDRA is highly integrated with RISC-V environments, supporting dynamic testing with hardware and commercial and open-source simulation environments, including silicon-level simulation. These environments support comprehensive hardware-accurate testing and verification to develop and test software as the hardware is developed.

Industry momentum around RISC-V

A growing number of safety-certified RISC-V IP cores offer designers pre-verified components that meet stringent safety requirements. Microchip, SiFive, CAST, and other vendors have released specialized RISC-V implementations with integrated safety features, fault detection mechanisms, and redundancy capabilities tailored for automotive and aerospace applications. Vendors such as Frontgrade Gaisler add to this with radiation-hardened microprocessors and IP cores for space-based systems.

The mix of industry support, technical guidelines, and certification tools creates a positive feedback loop that accelerates RISC-V adoption in safety-critical systems, making it increasingly attractive for organizations developing next-generation applications.

Jay Thomas, technical development manager for LDRA Technology, San Bruno, Calif., and has worked on embedded controls simulation, processor simulation, mission- and safety-critical flight software, and communications applications in the aerospace industry. His focus on embedded verification implementation ensures that LDRA clients in aerospace, medical, and industrial sectors are well grounded in safety-, mission-, and security-critical processes. For more information about LDRA, visit http://www.ldra.com.

 Related Content

• Standards, tools address coding and application errors in embedded software
• Software development model for the ISO/SAE 21434 standard
• How ‘shift left’ helps secure today’s connected embedded systems
• CES 2021: RISC-V’s journey from experimentation to commercial processors
• Accelerating RISC-V development with network-on-chip IP
• Developing safety critical ASICs for ADAS and similar automotive systems

The post Why RISC-V is a viable option for safety-critical applications appeared first on EDN.

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Introduction: The Evolution to Industry 5.0 and the Role of Hyper-Accurate Sensors

Industry 5.0 represents the next leap in industrial evolution, emphasizing human-machine collaboration, hyper-connectivity, and AI-driven automation. Unlike Industry 4.0, which focused on full automation and cyber-physical systems, Industry 5.0 integrates human intelligence with advanced technology to achieve greater efficiency, sustainability, and personalization.

At the core of this transformation are hyper-accurate sensors, which provide real-time, high-precision data essential for advanced robotics, AI-driven decision-making, and intelligent manufacturing. These sensors are the backbone of predictive maintenance, digital twins, adaptive production lines, and self-optimizing industrial systems, ensuring unprecedented levels of control, efficiency, and reliability.

Why Hyper-Accurate Sensors Are Critical for Industry 5.0

As manufacturing becomes more sophisticated, the demand for ultra-precise and reliable sensors is at an all-time high. Key drivers include:

• High-Precision Manufacturing – Miniaturization and complex geometries require sensors with nanometer-level accuracy.
• Predictive Maintenance & Self-Healing Systems – Sensors that detect anomalies in real time prevent costly downtime and enable proactive repairs.
• Human-Robot Collaboration (HRC) & Intelligent Automation – Ultra-sensitive sensors ensure safe interaction between humans and machines.
• Autonomous Quality Control & Zero-Defect Manufacturing – AI-driven defect detection improves production efficiency and minimizes waste.
• Sustainability & Energy Efficiency in Smart Factories – Smart sensors optimize energy consumption and reduce environmental impact through adaptive control mechanisms.

Breakthrough Technologies in Hyper-Accurate Sensing for Industry 5.0

1. Quantum Sensors: Unlocking Unprecedented Measurement Precision

Quantum sensors leverage principles of quantum mechanics to achieve unparalleled accuracy in detecting changes in electric, magnetic, or gravitational fields. Applications include:

• Ultra-precise gyroscopes for navigation in GPS-denied environments.
• Magnetometers for non-invasive fault detection in industrial machinery.
• Quantum-enhanced gravimeters for structural health monitoring in factories and critical infrastructure.

2. AI-Enhanced Edge Sensors for Intelligent Decision-Making

Traditional sensors generate raw data, but AI-powered sensors process and analyze this data at the edge, reducing latency and improving response times. Key benefits include:

• Self-learning capabilities to detect micro-level deviations before failures occur.
• Real-time data fusion for complex multi-sensor environments.
• AI-driven self-calibration to enhance long-term accuracy and minimize drift.

3. LiDAR and 3D Vision Sensors for High-Resolution Spatial Awareness

LiDAR (Light Detection and Ranging) is a critical technology in smart factories, offering:

• Millimeter-accurate object detection for precision robotic manipulation.
• 3D mapping of industrial spaces for dynamic logistics and warehouse automation.
• Precision alignment of micro-components in semiconductor and electronics manufacturing.

4. Piezoelectric and Optical Sensors for Sub-Nanometer Accuracy

Advanced piezoelectric and optical interferometric sensors are redefining precision in industrial applications:

• Sub-nanometer resolution for micro-machining and semiconductor fabrication.
• Non-contact displacement sensing for wear monitoring and material integrity assessment.
• Ultra-fast response times for real-time vibration analysis in high-speed machinery.

5. MEMS and NEMS Sensors for Scalable Miniaturized Accuracy

Micro-Electro-Mechanical Systems (MEMS) and Nano-Electro-Mechanical Systems (NEMS) enable:

• Microfluidic sensing for real-time chemical composition monitoring.
• MEMS accelerometers for high-frequency shock and vibration detection in aerospace and defense industries.
• NEMS-based temperature sensors for extreme precision in semiconductor and biotech applications.

Advanced Applications of Hyper-Accurate Sensors in Industry 5.0

1. Predictive Maintenance & Self-Optimizing Machinery

Hyper-accurate sensors detect micro-failures and degradation patterns before catastrophic failures occur, allowing manufacturers to:

• Reduce unplanned downtime by up to 50% through early fault detection.
• Extend machinery lifespan by 30-40% through adaptive maintenance strategies.
• Minimize operational costs by shifting from scheduled maintenance to data-driven predictive servicing.

2. Digital Twins & AI-Powered Real-Time Simulation

A digital twin is a dynamic virtual replica of a physical system, powered by sensor data. Benefits include:

• Continuous real-time performance monitoring for process optimization.
• Virtual simulation of process changes before deployment to mitigate risks.
• AI-driven real-time decision-making for adaptive control of industrial processes.

3. Human-Robot Collaboration (HRC) & Adaptive Safety Mechanisms

For seamless interaction between humans and machines, hyper-accurate sensors enable:

• Proximity detection with sub-millimeter precision to prevent accidents.
• Haptic feedback and force sensing to enhance robotic dexterity.
• Gesture and motion recognition for intuitive human-machine interaction in manufacturing environments.

4. Zero-Defect Manufacturing & Autonomous Quality Control

Advanced sensors revolutionize automated quality inspection with:

• High-resolution optical sensors and X-ray imaging for real-time defect detection.
• AI-driven pattern recognition to identify microscopic production deviations.
• Closed-loop feedback systems that dynamically adjust manufacturing processes to prevent defects in real time.

5. Sustainable Smart Manufacturing & Energy Optimization

Smart sensors contribute to sustainability by:

• Monitoring real-time energy consumption at component and system levels.
• Optimizing heating, cooling, and power distribution for energy efficiency.
• Reducing material waste through precision control and automated resource allocation.

Future Challenges & Research Directions in Hyper-Accurate Sensing

1. Overcoming Data Overload with AI & Edge Computing

With sensors generating terabytes of data per second, real-time processing and intelligent filtering are critical. Future research will focus on:

• AI-enhanced edge computing architectures to reduce latency.
• Neural network-driven anomaly detection for automated decision-making.
• Federated learning models to enable cross-factory data integration without compromising security.

2. Cost & Scalability of Quantum and AI Sensors

While quantum and AI-enhanced sensors offer unmatched precision, their adoption is hindered by high costs and integration complexity. Solutions include:

• Mass-scale nanofabrication for cost-effective sensor production.
• AI model optimization to enable lightweight processing on embedded systems.
• Hybrid sensor architectures that balance cost, accuracy, and efficiency.

3. Cybersecurity & Interoperability in Sensor Networks

With increasing connectivity, sensor networks are vulnerable to cyber threats. Key future developments include:

• Blockchain-secured sensor networks for data integrity.
• Universal communication protocols for seamless cross-industry adoption.
• AI-driven anomaly detection for real-time cyber threat mitigation.

Conclusion: The Future of Industry 5.0 with Hyper-Accurate Sensors

Hyper-accurate sensors are the cornerstone of Industry 5.0, enabling intelligent, efficient, and sustainable industrial ecosystems. As quantum sensing, AI-enhanced analytics, and edge computing converge, we are moving toward a future where factories operate with zero waste, predictive intelligence maximizes uptime, and human-machine collaboration reaches unprecedented synergy. The next decade will witness transformative breakthroughs in sensor technology, shaping the future of smart manufacturing, robotics, and industrial automation.

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