English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the past decades, there has been a significant increase in sensor densityand sensor deployment, driven by a significant miniaturization and decrease insize down to the chip level, addressing ubiquitous computing, edge computing,as well as distributed sensor networks. Material-integrated and intelligentsystems (MIIS) provide the next integration and application level, but theycreate new challenges and introduce hard constraints (resources, energy supply,communication, resilience, and security). Commonly, low-resource systems arestatically programmed processors with application-specific software orapplication-specific hardware (FPGA). This work demonstrates the need for andsolution to virtualization in such low-resource and constrained systems towardsresilient distributed sensor and cyber-physical networks using a unifiedlow-resource, customizable, and real-time capable embedded and extensible stackvirtual machine (REXAVM) that can be implemented and cooperate in both softwareand hardware. In a holistic architecture approach, the VM specificallyaddresses digital signal processing and tiny machine learning. The REXAVM ishighly customizable through the use of VM program code generators at compiletime and incremental code processing at run time. The VM uses an integrated,highly efficient just-in-time compiler to create Bytecode from text code. Thispaper shows and evaluates the suitability of the proposed VM architecture foroperationally equivalent software and hardware (FPGA) implementations. Specificcomponents supporting tiny ML and DSP using fixed-point arithmetic with respectto efficiency and accuracy are discussed. An extended use-case sectiondemonstrates the usability of the introduced VM architecture for a broad rangeof applications.

Virtualization of Tiny Embedded Systems with a robust real-time capable  and extensible Stack Virtual Machine REXAVM supporting Material-integrated  Intelligent Systems and Tiny Machine Learning