A bibliography of six years (1951-1956) research in arithmetic

Thesis (Ed.M.)--Boston Universit

Vacancy state detector oriented to convolutional neural network, background subtraction and embedded systems

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáMuch has been discussed recently related to population ascension, the reasons for this event, and, in particular, the aspects of society affected. Over the years, the city governments realized a higher level of growth, mainly in terms of urban scale, technology, and individuals numbers. It comprises improvements and investments in their structure and policies, motivated by improving conditions in population live quality and reduce environmental, energy, fuel, time, and money resources, besides population living costs, including the increasing demand for parking structures accessible to the general or private-public, and a waste of substantial daily time and fuel, disturbing the population routinely. Therefore, one way to achieve that challenge is focused on reducing energy, money, and time costs to travel to work or travel to another substantial location. That work presents a robust, and low computational power Smart Parking system adaptive to several environments changes to detect and report vacancy states in a parking space oriented to Deep Learning, and Embedded Systems. This project consists of determining the parking vacancy status through statistical and image processing methods, creates a robust image data set, and the Convolutional Neural Network model focused on predict three final classes. In order to save computational power, this approach uses the Background Subtraction based on the Mixture of Gaussian method, only updating parking space status, in which large levels of motion are detected. The proposed model presents 94 percent of precision at the designed domain.Muito se discutiu recentemente sobre a ascensão populacional, as razões deste evento e, em particular, os aspectos da sociedade afetados. Ao longo dos anos, os governos perceberam um grande nível de crescimento, principalmente em termos de escala urbana, tecnologia e número de indivíduos. Este fato deve-se a melhorias e investimentos na estrutura urbana e políticas motivados por melhorar as condições de qualidade de vida da população e reduzir a utilização de recursos ambientais, energéticos, combustíveis, temporais e monetários, além dos custos de vida da população, incluindo a crescente demanda por estruturas de estacionamento acessíveis ao público em geral ou público-privado. Portanto, uma maneira de alcançar esse desafio é manter a atenção na redução de custos de energia, dinheiro e tempo para viajar para o trabalho ou para outro local substancial. Esse trabalho apresenta um sistema robusto de Smart Parking, com baixo consumo computacional, adaptável a diversas mudanças no ambiente observado para detectar e relatar os estados das vagas de estacionamento, orientado por Deep Learning e Embedded Systems. Este projeto consiste em determinar o status da vaga de estacionamento por meio de métodos estatísticos e de processamento de imagem, criando um conjunto robusto de dados e um modelo de Rede Neuronal Convolucional com foco na previsão de três classes finais. A fim de reduzir consumo computacional, essa abordagem usa o método de Background Subtraction, somente atualizando o status do espaço de estacionamento em que grandes níveis de movimento são detectados. O modelo proposto apresenta 94 porcento da precisão no domínio projetado

Visualizing the Motion Flow of Crowds

In modern cities, massive population causes problems, like congestion, accident, violence and crime everywhere. Video surveillance system such as closed-circuit television cameras is widely used by security guards to monitor human behaviors and activities to manage, direct, or protect people. With the quantity and prolonged duration of the recorded videos, it requires a huge amount of human resources to examine these video recordings and keep track of activities and events. In recent years, new techniques in computer vision field reduce the barrier of entry, allowing developers to experiment more with intelligent surveillance video system. Different from previous research, this dissertation does not address any algorithm design concerns related to object detection or object tracking. This study will put efforts on the technological side and executing methodologies in data visualization to find the model of detecting anomalies. It would like to provide an understanding of how to detect the behavior of the pedestrians in the video and find out anomalies or abnormal cases by using techniques of data visualization

Identification and Classification of Moving Vehicles on Road

It is important to know the road traffic density real time especially in cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. The image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles, rejecting background and tracks vehicles over a specific period of time. Once the (object) vehicle is tracked, the attributes of the vehicle like width, length, perimeter, area etc are extracted by image process feature extraction techniques. These features will be used in classification of vehicle as big or small using neural networks classification technique of data mining. In proposed system we use LABVIEW and Vision assistant module for image processing and feature extraction.  A feed-forward neural network is trained to classify vehicles using data mining WEKA toolbox. The system will solve major problems of human effort and errors in traffic monitoring and time consumption in conducting survey and analysis of data. The project will benefit to reduce cost of traffic monitoring system and complete automation of traffic monitoring system. Keywords: Image processing, Feature extraction, Segmentation, Threshold, Filter, Morphology, Blob, LABVIEW, NI, VI, Vision assistant, Data mining, Machine learning, Neural network, Back propagation, Multi layer perception, Classification, WEK

Summary report of work on ten tasks

There are no author-identified significant results in this report