Recycling is vital for a sustainable and clean environment. Developed and developing countries are both facing the problem of solid management waste and recycling issues. Waste classification is a good solution to separate the waste from the recycle materials. In this work, we propose a cloud based classification algorithm for automated machines in recycling factories using machine learning. We trained an efficient MobileNet model, able to classify five different types of waste. The inference can be performed in real-time on a cloud server. Various techniques are described and used in order to improve the classification accuracy, such as data augmentation and hyper-parameter tuning. Multiple industrial stations are supported and interconnected via custom data transmission protocols, along with security features. Experimental results indicated that our solution can achieve excellent performance with 96.57% accuracy utilizing a cloud server.
In recent years, the production of municipal solid waste has constantly been increasing. Recycling is becoming more and more important, as it is the only way that we can have a clean and sustainable environment. Recycling, however, is a process that is not fully automated; large volumes of waste materials need to be processed manually. New and novel techniques have to be implemented in order to manage the increased volume of waste materials at recycling factories. In this paper, we propose a novel methodology that can identify common waste materials as they are being processed on a moving belt in waste collection facilities. An efficient waste material detection and classification system is proposed, which can be used in real integrated solid waste management systems. This system is based on a convolutional neural network and is trained using a custom dataset of images, taken on site from actual moving belts in waste collection facilities. The experimental results indicate that the proposed system can outperform existing algorithms found in the literature in real-world conditions, with 92.43% accuracy.
On 31 January 2020, the World Health Organization (WHO) declared a global emergency after the discovery of a new pandemic disease that caused severe lung problems. The spread of the disease at an international level drew the attention of many researchers who attempted to find solutions to ameliorate the problem. The implementation of robotics has been one of the proposed solutions, as automated humanoid robots can be used in many situations and limit the exposure of humans to the disease. Many humanoid robot implementations are found in the literature; however, most of them have some distinct drawbacks, such as a high cost and complexity. Our research proposes a novel, secure and efficient programmable system using a humanoid robot that is able to autonomously move and detect survivors in emergency scenarios, with the potential to communicate verbally with victims. The proposed humanoid robot is powered by the cloud and benefits from the powerful storage, computation, and communication resources of a typical modern data center. In order to evaluate the proposed system, we conducted multiple experiments in synthetic hazardous environments.
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