Impact of Stratigraphic and Sedimentological Heterogeneity on Hydrocarbon Recovery in Carbonate Reservoirs

Imperial Users onl

Communication Systems of Smart Agriculture Based on Wireless Sensor Networks in IoT

As technology develops, major countries have begun to implement the Smart Agriculture system and Internet of Things to facilitate farmers in managing their agricultural land. This study discusses the communication system of Smart Agriculture based on Internet of Things. Data from the sensor will be sent by Wireless Sensor Network to Raspberry Pi and send it to the database server which can then be accessed via the internet using android applications. Android applications can be used to monitor soil pH sensors and moisture. In addition, the control of sluice gates and water pumps can also be done manually and automatically. So that water can be controlled through applications and the web remotely. The success percentage of the communication system of Smart Agriculture based on the Internet of Things is 100% because all data from the sensor are successfully received by the Raspberry Pi and sent to the database so it can be accessed through the built-in android application and website

Industrial networks and IIoT: Now and future trends

Connectivity is the one word summary for Industry 4.0 revolution. The importance of Internet of Things (IoT) and Industrial IoT (IIoT) have been increased dramatically with the rise of industrialization and industry 4.0. As new opportunities bring their own challenges, with the massive interconnected devices of the IIoT, cyber security of those networks and privacy of their users have become an important aspect. Specifically, intrusion detection for industrial networks (IIoT) has great importance. For instance, it is a key factor in improving the safe operation of the smart grid systems yet protecting the privacy of the consumers at the same time. In the same manner, data streaming is a valid option when the analysis is to be pushed from the cloud to the fog for industrial networks to provide agile response, since it brings the advantage of fast action on intrusion detection and also can buy time for intrusion mitigation. In order to dive deep in industrial networks, basic ground needs to be settled. Hence, this chapter serves in this manner, by presenting basic and emerging technologies along with ideas and discussions: First, an introduction of semiconductor evolution is provided along with the up-to-date hi-tech wired/wireless communication solutions for industrial networks. This is followed by a thorough representation of future trends in industrial environments. More importantly, enabling technologies for industrial networks is also presented. Finally, the chapter is concluded with a summary of the presentations along with future projections of IIoT networks

WearIoT: swearable IoT human emergency system

A área da saúde foi uma das muitas beneficiadas com a evolução tecnológica, dando origem a novos conceitos que visam melhorar ou mesmo prolongar a vida das pessoas. Os sistemas de monitorização vestíveis, juntamente com as comunicações sem fios, são a base de uma classe emergente de redes de sensores. Estas tecnologias de informação permitem a deteção precoce de condições anormais e ajudam na sua prevenção. O objetivo é criar um destes sistemas compostos por uma rede de sensores que é implementada numa peça de roupa através de fios condutores com sensores conectados. Em contato com o corpo humano tem a função de fazer várias leituras, e.g., temperatura corporal, pulsação, entre outras. Outro objetivo é detetar quedas do utilizador. A deteção de quedas é cada vez mais importante para o utilizador, pois é uma situação que pode colocar em risco a sua saúde. Para o desenvolvimento deste conceito, são utilizadas Comunicações Móveis e o Sistema de Posicionamento Global. A primeira é uma tecnologia que permite criar chamadas de emergência em resposta a alarmes do sistema, o segundo indica qual a sua posição geográfica. Para complementar o sistema, existe uma plataforma online que regista a posição do utilizador tal como os seus dados. Tem também uma área de alertas no qual o utilizador pode verificar os seus valores preocupantes. Em caso de emergência o sistema contacta os serviços de emergência ou em casos especiais a ajuda pode ser obtida através de um UAV.The health area was one of the many beneficiaries of technological evolution, giving rise to new concepts that aim to improve or even prolong people’s lives. Wearable monitoring systems, along with wireless communications, form the basis of an emerging class of sensor networks. These information technologies enable the early detection of abnormal conditions and help in their prevention. The goal is to create one of these systems composed by a network of sensors that is implemented in a garment through conductive wires with connected sensors. In contact with the human body it has the function of doing several readings, e.g., body temperature, heartbeat, among others. Another goal is to detect user falls. The detection of falls is increasingly important for the user, as it is a situation that can endanger people’s health. For the development of this concept, Mobile Communications and the Global Positioning System are used. The first is a technology that allows to create emergency calls in response to system alarms, the second indicates the geographical location. To complement the system there is an online platform that registers the position of the user as well as his data. There is also an alert area in which the user can check his alarming values. In case of emergency the system contacts the emergency services or in special cases help can be obtained through an UAV

Distributed deep neural networks

Deep neural networks have become popular for solving machine learning problems in the field of computer vision. Although computers have reached parity in the task of image classification in machine learning competitions, the task of mining massive training data often takes expensive hardware a long time to process. Distributed protocol for model training can be attractive because less powerful distributed nodes are cheaper to operate than specialized high-performance cluster. Stochastic gradient descent (SGD) is a popular optimizer at the heart of many deep learning systems. To investigate the performance of distributed asynchronous SGD, Tensorflow deep learning framework was tested with Downpour SGD and Delay Compensated SGD to see effect of model training in typical commercial environments. Experimental results show that both Downpour and Delay Compensated SGD are viable protocols for distributed deep learning.Electrical and Computer Engineerin

Large Scale Feature Extraction from Linked Web Data

Veebiandmed on ajas muutuvad ning viis, kuidas neid esitatakse muutub samuti. Linkandmed on muutnud veebis leiduva info masinloetavaks. Selles töös esitame kontseptsioonitõenduseks lahenduse, mis võtab veebisorimise andmetest linkandmed ja teostab nende peal tunnusehõivet. Esitletud lahenduse eesmärgiks on luua sisendeid masinõppe mudelite treenimiseks, mida kasutatakse firmade krediidiskoori hindamiseks. Meie näitelahendus keskendub toote linkandmetele. Me proovime ühendadatoodete linkandmed, mis esitavad sama toodet, aga pärinevad erinevatelt veebilehtedelt.Toodete linkandmed ühendatakse firmadega, mille lehelt tooted pärit on. Informatsioon firmadest ja nende toodetest moodustab graafi, millel arvutame graafimeetrikuid.Erinevate ajahetketede veebisorimisandmetel arvutatud graafimeetrikud moodustavad ajaseeria, mis näitab graafi muutusi läbi aja. Saadud ajaseeriatel rakendame tunnushõive arvutamist.Loodud lahendus on planeeritud suurte andmete jaoks ning ehitatud ja disainitud skaleeruvust silmas pidades. Me kasutame Apache Sparki, et töödelda suurt hulka andmeid kiiresti ning olla valmis, kui sisendandmete hulk suureneb 100 korda.Data available on the web is evolving, and the way it is represented is changing as well.Linked data has made information on the web understandable to machines. In this thesis we develop a proof of concept pipeline that extracts linked data from web crawling and performs feature extraction on it. The end goal of this pipeline is to provide input to machine learning models that are used for credit scoring. The use case focuses on extracting product linked data and connecting it with the company that offers it. Built solution attempts to detect if two products from different web sites are the same in order to use one representation for both. Information about companies and products is represented as a graph on which network metrics are calculated. Network metrics from multiple different web crawls are stored in time series that shows changes in graph over time. We then calculate derivatives on the values in time series.The developed pipeline is designed to handle data in terabytes and built with scalability in mind. We use Apache Spark to process huge amounts of data and to be ready if input data increases 100 times