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Articles 1 - 3 of 3
Full-Text Articles in Engineering
Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh
Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh
Theses: Doctorates and Masters
Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.
Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …
Proactive Content Caching In Future Generation Communication Networks: Energy And Security Considerations, Muhammad Ishtiaque Aziz Zahed
Proactive Content Caching In Future Generation Communication Networks: Energy And Security Considerations, Muhammad Ishtiaque Aziz Zahed
Theses: Doctorates and Masters
The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion.
However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% …
A Sample Weight And Adaboost Cnn-Based Coarse To Fine Classification Of Fruit And Vegetables At A Supermarket Self-Checkout, Khurram Hameed, Douglas Chai, Alexander Rassau
A Sample Weight And Adaboost Cnn-Based Coarse To Fine Classification Of Fruit And Vegetables At A Supermarket Self-Checkout, Khurram Hameed, Douglas Chai, Alexander Rassau
Research outputs 2014 to 2021
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of …