Automation of plastic, metal and glass waste materials segregation using arduino in scrap industry

Rafeeq, Mohammed; Ateequrahman,; Alam, S M Masum; Mikdad,

doi:10.1109/cesys.2016.7889840

Cited by 24 publications

(9 citation statements)

References 4 publications

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“…A second class of works focus on techniques for recognizing and sorting different types of trash, with several approaches. Some works use scalar sensors, such as electromagnetic sensors (capacitive or inductive sensors), which can be utilized for detection of nonferrous metal fractions based upon electrical conductivity of the sample [10,12]. Alternatively, photoelectric sensors (obtained coupling a LED light source and a photodiode as a receiver) can be used to recognize the type of material (especially in presence of transparent wrappings) [18].…”

Section: Related Workmentioning

confidence: 99%

Take the trash out... to the edge. Creating a Smart Waste Bin based on 5G Multi-access Edge Computing

Longo

Sahin

Redondi

et al. 2021

Proceedings of the Conference on Information Technology for Social Good

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The fifth generation of mobile cellular networks (5G) will bring many improvements to our everyday lives. In particular, the interplay between 5G and IoT technologies will pave the way for the realisation of the Smart City concept, providing humans with better, safer and cleaner cities. In this work, we describe the prototype of a Smart Waste Bin, a connected trash bin that automatically sorts garbage with the help of Convolutional Neural Networks algorithms. The bin intelligence runs on the edge of the 5G network exploiting a Mobile Edge Computing (MEC) architecture, enabling a low-latency and energy efficient operation. We describe the design, the implementation and the experimental validation of the prototype, also discussing possible future application scenarios.

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Section: Related Workmentioning

confidence: 99%

Take the trash out... to the edge. Creating a Smart Waste Bin based on 5G Multi-access Edge Computing

Longo

Sahin

Redondi

et al. 2021

Proceedings of the Conference on Information Technology for Social Good

View full text Add to dashboard Cite

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“…This is achieved through the capacitance effect. [6] The machine learning induction algorithm is able to adapt to changing situations through integrating new conditions and findings with those it has already seen. [7] In this paper, they use the Raspberry Pi 3 which is more efficient that Raspberry Pi 1.…”

Section: Related Workmentioning

confidence: 99%

Garbage Segregation System using Support Vector Machine

Akshita¹,

Pushkala²,

Nivetha³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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The garbage bins are full, half full or empty. These bins include various types of garbage ranging from metals, plastics to glasses. The collection of these dump waste are not segregated and so when the eliminating method is proposed the method is not efficient. The bio waste also gets dumped into landfills. To make the system efficient this paper proposes method to segregate system at its early stage. The system uses a Machine Learning Algorithm called Support Vector Machine (SVM) which performs image comparison in the vector form. Also, capacitive proximity sensors is used for next level of segregation which identifies type of waste either wet or dry with dielectric effect. Thus, this system collaboratively separates the waste using Machine learning and capacitance effect. The separated bio waste is converted to bio fuel for economic purposes. The plastic wastes can be given to scrap industries.

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“…Chandramohan et al [12], Donovan et al [13], García [14], and Bhor et al [15] prototyped a system for predicting the type of recycling waste by using machine learning-based image classification research. Their research only uses a camera sensor without using multiple sensors.…”

Section: Related Workmentioning

confidence: 99%

Improved Feature Extraction Method for Sound Recognition Applied to Automatic Sorting of Recycling Wastes

Munaka

Samie

Bauer

et al. 2020

Journal of Information Processing

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As an effort to realize a sustainable society, Tokai University Takanawa Campus has used a smart garbage collection service called BigBelly TM since 2016 for recovering wastes such as cans, bottles, and plastic bottles for recycling. The objective of this demonstration experiment is to clarify how much waste has been correctly separated and collected. As a result of this demonstration about three years, it found that about 30% of the recycling wastes had not correctly sorted. To improve this situation, we propose an automatic sorting function using sound recognition. In many types of research for voice recognition, Mel Frequency Cepstral Coefficient (MFCC) has been used as an algorithm for extracting features used for machine learning Support Vector Machines (SVMs). One reason is that MFCC extracts valuable features that focus on the low dimension for the human voice. However, the sounds of recycling wastes have features of frequency components found in higher dimensions. Based on this characteristic, we propose an improved method of MFCC suitable for sounds, rather than voice recognition for identifying recycling wastes and show the results of the automatic sorting of recycling wastes.

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Automation of plastic, metal and glass waste materials segregation using arduino in scrap industry

Cited by 24 publications

References 4 publications

Take the trash out... to the edge. Creating a Smart Waste Bin based on 5G Multi-access Edge Computing

Take the trash out... to the edge. Creating a Smart Waste Bin based on 5G Multi-access Edge Computing

Garbage Segregation System using Support Vector Machine

Improved Feature Extraction Method for Sound Recognition Applied to Automatic Sorting of Recycling Wastes

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