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Self-Powered Synapse Brings Human-Like Vision to AI Devices Summary: Researchers have developed a self-powered artificial synapse capable of color recognition with near-human precision Unlike traditional systems that demand external energy and massive data processing, this device mimics biological vision and generates its own electricity using solar cells
More efficient machine vision technology modeled on human vision By simply manipulating a camera’s firmware, the technique cuts energy consumption by 80% and has almost no impact upon accuracy when used for practical vision applications, like license plate recognition and facial recognition Called “Digital Foveation,” it’s also faster than conventional methods
Digital Foveation: An Energy-Aware Machine Vision Framework We describe a machine vision energy minimization framework in which imaging hardware and vision algorithms are co-designed and tightly integrated Digital foveation is inspired by the human vision system, which uses a spatially varying sensing architecture to generate oculomotory feedback and capture a series of high-resolution images using the
Vision: Human and Machine - Springer Vision: Human and Machine 8 1 Overview The principal objective of this chapter is to present the principles of computer vision which are applicable in intelligent automation However, vision by machine is essentially a replacement for human vision, hopefully with improvement Thus, we start by explaining how
A Machine Vision-based Cyber-Physical Production System for Energy . . . Machine vision (MV) can help in achieving real-time data analysis in a manufacturing environment This can be implemented in any industry to achieve real-time monitoring of workpieces for geometric defects and material irregularities
Machine Vision in Sustainability: From Theory to Application By leveraging machine vision technology, we can optimize resource utilization, reduce waste, and improve overall efficiency Through the use of AI algorithms, we can enhance productivity and make informed decisions that prioritize sustainability outcomes
Self-powered artificial synapse mimics human color vision Despite advances in machine vision, processing visual data requires substantial computing resources and energy, limiting deployment in edge devices Now, researchers from Japan have developed
EnergyVis: Interactively Tracking and Exploring Energy Consumption for . . . Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints