Smart manufacturing is continuing to deploy diverse technologies to improve the performance of automation. This special report on next generation machine control looks at how AI, PLC virtualization, new controllers and software tools will provide solutions for real-time control, data connectivity and secure operations.

The integration of IIoT and connected applications is revolutionizing the manufacturing industry. By leveraging real-time data, predictive analytics, and advanced automation, manufacturers can optimize their operations, improve product quality, and enhance supply chain visibility.

AI technology is helping to empower automation and control engineers by automating routine tasks, improving system efficiency, and enabling smarter decision-making. This allows engineers to focus on innovation and higher-value activities, ultimately driving better outcomes for manufacturing operations

The Cisco 2024 State of Industrial Networking Report for Manufacturing reveals how manufacturing firms worldwide are designing and deploying their operational technologies to improve cybersecurity, quality and efficiency. One of the conclusions of the report is that firms are investing in an AI-ready foundation.

Users can implement automation applications more easily and cost-effectively with new AI tools. The rapid development of these AI tools, combined with a significant acceleration of hardware capabilities and integrated Ethernet interfaces, is opening up both new applications and segments.

Out of the box solutions move data easily and securely from operations to IT. IT specialists don’t always grasp the needs and challenges of their counterparts in OT. To align their needs and objectives with IT, they need surer footing to navigate today’s cybersecurity requirements and quality solutions to bring…

The main application areas for Convolutional Neural Networks (CNN) are pattern recognition and classification of objects contained in input data. This first part of a three part series in this issue explore how CNNs are a type of artificial neural network used in deep learning.

This three part series of articles explains convolutional neural networks (CNNs) and their significance in machine learning within AI systems. CNNs are powerful tools for extracting features from complex data. Part 2 puts the focus on the training process for neural networks.

Previously, AI applications required massive energy consumption. Now, microcontrollers with a dedicated CNN accelerator make it possible to power AI applications from a single battery for extended periods. This breakthrough is making edge-AI accessible and unlocking the potential for exciting edge-AI applications.

Data science is still a young field, but it is being incorporated into systems more each day. The goal is to maximize value by enabling data scientists as many capabilities as possible, and not frustrating them with limited capabilities.