The advances in industrial edge artificial intelligence (AI) are transforming the way industrial equipment and machines interact with the real world, with other machines and humans during manufacturing processes. These advances allow Industrial Internet of Things (IIoT) and edge devices to make decisions during the manufacturing processes using sensors and actuators data. Digital transformation is reshaping the manufacturing industry, and industrial edge AI aims to combine the potential advantages of edge computing (low latency times, reduced bandwidth, distributed architecture, improved trustworthiness, etc.) with the benefits of AI (intelligent processing, predictive solutions, classification, reasoning, etc.). The industrial environments allow the deployment of highly distributed intelligent industrial applications in remote sites that require reliable connectivity over wireless and cellular connections. Intelligent connectivity combines IIoT, wireless/cellular and AI technologies to support new autonomous industrial applications by enabling AI capabilities at the edge and allowing manufacturing companies to improve operational efficiency and reduce risks and costs for industrial applications. There are several critical issues to consider when bringing AI to industrial IoT applications considering training AI models at the edge, the deployment of the AI-trained inferencing models on the target reliable edge hardware platforms and the benchmarking of the solution compared with other implementations. The next-generation trustworthy industrial AI systems offer dependability by design, transparency, explainability, verifiability, and standardised industrial solutions to be implemented into various applications across different industrial sectors. New AI techniques like embedded machine learning (ML) and deep learning (DL) capture edge data, employ AI models and deploy them to hardware target edge devices from ultra-low-power microcontrollers to embedded devices, gateways, and on-premises servers for industrial applications. These techniques reduce latency, increase scalability, reliability, and resilience, and optimise wireless connectivity, greatly expanding IIoT capabilities. This book overviews the latest research results and activities in industrial artificial intelligence technologies and applications based on the innovative research, developments and ideas generated by the ECSEL JU AI4DI, ANDANTE and TEMPO projects. The authors describe industrial AI's challenges, the approaches adopted, and the main industrial systems and applications to give the reader a good insight into the technical essence of the field. The articles provide insightful material on industrial AI technologies and applications.

Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge. Embedded AI combines embedded machine learning (ML) and deep learning (DL or spiking neural network (SNN) algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources. Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations. This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.

The oak tree was a boon companion as humans expanded their presence across much of the globe. While oak woodlands ("Quercus "spp.) come today in stunningly diverse forms, the stately dehesas of Spain and the dramatic oak-dominated ranchlands of California are working landscapes where cultivation and manipulation for a couple of millennia have shaped Mediterranean-type ecosystems into a profoundly modified yet productive environment that is sought-after by every manner ofspecies.

The grazing of wildlife and livestock in oak woodlands yields a remarkable plant and animal biodiversity, creating a mosaic of habitats and visually pleasing savannas. Added products unique to Spain such as Iberian pigs and cork, and in California multiple landowner benefits, include valued ecosystem services that allow owners, visitors, and conservation supporters to experience the benefits of woodland life. With its 15 chapters a decade in the making, this handsomely illustrated book covers key topics in oak woodland policy, ecology, and management in Spain and California, presenting new research results and reviewing an existing expert literature."

En esta Tesis Doctoral se describen arquitecturas neuronales que permiten la segmentacion perceptual y el reconocimiento de imagenes de textura y color sobre Unidades de Procesamiento Grafico (GPUs). El procesamiento de proposito general sobre GPU (GPGPU) se ha revelado en los ultimos anos como uno de las formulas mas atractivas para el procesamiento en paralelo de altas prestaciones y bajo coste. Todas las arquitecturas neuronales desarrolladas en esta Tesis Doctoral utilizan la GPU como plataforma hardware, proporcionando una velocidad de procesamiento decenas de veces superior a la obtenida con implementaciones analogas sobre CPU. La utilizacion de este hardware para la segmentacion y el reconocimiento, etapas fundamentales en vision artificial, hace adecuadas las arquitecturas disenadas para aplicaciones con elevados requerimientos de tiempo. Estas aplicaciones de vision artificial incluyen la monitorizacion de procesos de produccion industrial, la clasificacion y busqueda de contenidos sobre bases de datos multimedia, el analisis y segmentacion de imagenes aereas de alta resolucion o sistemas de video-vigilancia e interfaces hombre-maquina."