Digital Waste Management - detection technology

In this thesis, the goal has been to improve the data flow from the garbage trucks in Halden municipality. We begin by designing, building, and installing a data capture unit to collect images of the collected waste. Machine learning models were trained to analyze the images and detect whether customers use green bags to dispose of their organic waste. Models were also trained on data from a time of flight camera to measure the volume of the waste. We discover that high accuracy object detection is possible on organic waste. Limitations on the use of time of flight technology are found when it is used in a garbage collection environment. The result is that volume measurement is not possible unless the environment changes

The Whalesong

Opening the door to opportunity: UAS offers new bachelor degrees -- School's out, get a job -- Coming back to the real world -- Lab side show -- Balance -- The nature of UAS: A farewell from Spanish professor -- Crappy situation -- Troops have to deal with spiders? -- Ski season has closed -- KBJZ 94.1 LPFM: Free to mix it up -- Multi-billion dollar idea -- Semester at sea: a floating campus to discover the world -- UAS students bring home first place -- 7th grade geography-fair judges needed at Dryden -- Campus poll -- Osteosarcoma: A risk worth taking? -- Thank you -- A blocked electro-man -- Informed-Traitor advice -- The Learning Center: Building on a successful past -- Arts & entertainment -- Deer dreams: A memoir -- Celebrating 10 years of study abroad at UAS

Searching for Uncollected Litter with Computer Vision

This study combines photo metadata and computer vision to quantify where uncollected litter is present. Images from the Trash Annotations in Context (TACO) dataset were used to teach an algorithm to detect 10 categories of garbage. Although it worked well with smartphone photos, it struggled when trying to process images from vehicle mounted cameras. However, increasing the variety of perspectives and backgrounds in the dataset will help it improve in unfamiliar situations. These data are plotted onto a map which, as accuracy improves, could be used for measuring waste management strategies and quantifying trends.Comment: 17 pages, 6 figure

Parent Resource Packet - A Guide for New Parents

PDF pages: 8

Overview of the work carried out in CleanAtlantic on improving marine litter monitoring: • WP 5.2.1. – Improving methods for marine litter monitoring in the Atlantic Area: seabed, floating and coastal litter • WP 5.2.2. – New tools for the monitoring of marine litter

This report collates the main results delivered in the frame of the CleanAtlantic project, Work package 5.2. Monitoring the presence of marine litter in the marine environment. With this purpose, an overview of new and improved marine litter monitoring methods for seabed, water surface and coastal compartments in the Atlantic Area is presented. Main findings, gaps on monitoring and research as well as potential improvements and recommendations are highlighted. For some of the topics addressed partners produced fully-dedicated reports. In these cases, links to the original reports are included in the reference section for further information

Vision Based Pay-as-you-throw system: motion detection for garbage and recycle - based on faster RCNN

This project implemented the motion detection and tracking, the goal of project is detection, tracking and counting the different types garbage. The Faster Region Convolutional Neural Network (Faster RCNN or FRCNN) is used as classi�er and detector. Because the count for garbage or recycle is the �nal result, therefore, the tracker is developed based Kalman �filte

Identification of residues deposited outside of the deposition equipment, using video analytics

In areas where waste production is excessive, sometimes improper deposition occurs around the garbage equipment, requiring more effort from the waste collection teams. In this dissertation an image recognition system is proposed for the detection and classification of waste outside the existing waste disposal equipment. The main motivation is to facilitate the work of waste collection in the city of Lisbon, which is done by the teams of the Lisbon Waste Collection Centers. In order to help the waste collection planning, the collection team inspectors in partnership with the Lisbon City Council created a repository with several datasets, which they named, 'LxDataLab'. The collected images go through the pre-processing process and finally are submitted to waste detection and classification, through deep learning networks. In this sense, a classification and identification method using neural networks for image analysis is proposed: the first approach consisted in training a deep learning convolutional neural network (CNN) specifically developed to classify residues; in a second approach a CNN was trained using a pre-trained MobileNetV2 model, which only the last layer was trained. The training in this approach was faster compared to the previous approach, as were the performance values in detecting the class and the amount of residues in the images. The hit rate for the classification of the selected debris varied between 80%, for test set. After the detection and classification of the residues in the images are recognized, annotations are generated on the images.Nas áreas onde a produção de resíduos é excessiva, por vezes ocorre a deposição indevida em torno dos equipamentos de deposição de lixo, exigindo mais esforço por parte das equipas de recolha destes resíduos. Nesta dissertação é proposto um sistema de reconhecimento de imagem para a deteção e classificação de resíduos fora dos equipamentos de deposição existentes para o mesmo. A principal motivação é facilitar o trabalho de recolha dos resíduos na cidade de Lisboa. De forma a possibilitar o desenvolvimento de algoritmos que possam vir a ser úteis na automatização de tarefas em diferentes áreas de intervenção, a Câmara Municipal de Lisboa criou um repositório, denominado ‘LxDataLab’, contendo vários conjuntos de dados. Estes dados, por sua vez são submetidos a um processo pré-processamento e por fim são submetidas para deteção e classificação dos resíduos. Assim é proposto um método de classificação e identificação de resíduos utilizando redes neuronais para análise de imagens: a primeira abordagem consistiu no treino de uma rede neuronal convolucional de aprendizagem profunda (CNN) desenvolvida especificamente para classificar resíduos; numa segunda abordagem foi treinada uma CNN utilizando um modelo pré-treinado MobileNetV2. Nesta última abordagem, o treino foi mais rápido em relação à abordagem anterior, e o desempenho na deteção da classe e da quantidade de resíduos nas imagens foi superior. A taxa de acerto para as classes de resíduos selecionadas variou nos 80% para o conjunto de teste. Após a deteção e classificação dos resíduos nas imagens são geradas anotações nas mesmas

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested

Respiratory

The purpose of this sequence is to teach the aspects of basic science related to the respiratory system, building on the anatomy, physiology, and biochemistry taught in year 1. Clinical examples of applied basic science are based on common lung diseases including: pneumonia, emphysema, asthma, cancer, trauma, ARDS, and respiratory diseases of the newborn.http://deepblue.lib.umich.edu/bitstream/2027.42/120538/1/medical_m2_curriculum_respiratory-March10.zi