The rising amount of waste generated worldwide is inducing issues of pollution, waste
management, and recycling, calling for new strategies to improve the waste ecosystem, such …

Increasing generation of municipal solid waste, heterogeneity of waste composition, and
complex processes of waste management and recovery have limited the performance of …

Digital technologies hold enormous potential for improving the performance of future-
generation sorting and processing plants; however, this potential remains largely untapped …

There are several sustainability issues that the linear economy of today's society cannot
adequately tackle (ie resource depletion, waste treatment, etc.). As a result, the scientific …

Waste sorting is highly labor intensive because the wide variety of waste items prohibits
automation. More recently, deep learning (DL) and computer vision technology has …

Waste generation has been increasing drastically based on the world's population and
economic growth. This has significantly affected human health, natural life, and ecology. The …

To achieve recycling of mixed industrial waste toward an advanced sustainable society,
waste sorting automation through robots is crucial and urgent. For this purpose, a robot is …

The great advances produced in the field of artificial intelligence and, more specifically, in
deep learning allow us to classify images automatically with a great margin of reliability. This …

The linear economy of today's society conflicts with many environmental, social, and
economic challenges. Therefore, the implementation of the Circular Economy model is …

With growing understanding of trash impacts on aquatic habitats throughout the world, cities
increasingly face regulatory requirements to reduce trash inputs to local waterways and the …