Праћење параметара физичког окружења применом Интернета интелигентних уређаја у циљу анализе њиховог утицаја на квалитет предавања

Предмет овог истраживања је праћење параметара физичког окружења применом Интернета интелигентних уређаја и анализа њиховог утицаја на квалитет предавања. Главна хипотеза од које се полази и која је доказана је да се применом технологије Интернета интелигентних уређаја у настави може побољшати процес одвијања наставе. Задатак интелигентних уређаја је да мере релевантне параметре физичке средине, као и активности предавача и студената, који ће потом бити послати на обраду. Побољшање процеса одвијања наставе остварено је имплементацијом система за одређивање квалитета предавања који у готово реалном времену омогућава анализу прикупљених података и приказује обрађене резултате. Како би се реализовао систем који је у могућности да у реалном времену пружи информацију о квалитету предавања, урађен је преглед релевантних истраживања и достигнућа у области техничких и друштвених наука. Урађен је преглед и класификација постојећих имплементација паметних учионица. Да би се идентификовали параметри физичке средине који утичу на квалитет предавања, проучена је литература релевантних области, спроведена анкета и обрађени добијени резултати, па су на основу тога издвојени они који би могли утицати на квалитет предавања. С обзиром на то да се систем бави и анализом предавачевог понашања, проучени су и радови из области образаца понашања и социолошких сигнала као и њихова веза са технологијама које се баве препознавањем активности посматраних субјеката...The subject of this dissertation is monitoring the parameters of the physical environment and analysing their impact on the lecture quality using Internet of Things. This research has proved the main hypothesis that using Internet of Things during lectures can enhance the learning process. The aim of the Internet of Things is to measure relevant parameters of the physical environment, as well as the lecturer's and students' activity which will then be sent for further analysis. Enhancement of the learning process is achieved through the system which is able to analyse the collected data in order to distinguish the lecture quality in almost real time. To this end and for the purpose of developing such system, relevant researches and achievements from both technical and social sciences were reviewed. Existing smart classroom systems were reviewed and classified. In addition, relevant researches from related fields were also studied and leveraged with the results obtained from the questionnaire analysis, in order to identify parameters of the physical environment that could have impact on the lecture quality. Bearing in mind that the aim of such system to analyse lecturer’s behaviour, researches related to behaviour pattern and social signals analysis were also examined together with their relationship to the technologies dealing with recognizing subject’s activities. After defining system requirements, the system architecture that meets the specified requirements was presented. The system is essentially based on the classifier that is trained to determine if students are satisfied with the lecture quality at a given moment. The classifier’s accuracy on the training dataset was up to 93.2%. The system is entirely implemented in Matlab and has the ability to process digital signal in order to extract different sound features, as well as to analyse data received using the accelerometer. These values are later used as the input for the classifier with the aim to..

SensX: About Sensing and Assessment of Complex Human Motion

The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without professional supervision which may lead to suboptimal training success or even serious injuries. We believe, that automated supervision and realtime feedback during a workout may help to solve these issues. Therefore we introduce four fundamental steps for complex human motion assessment and present SensX, a sensor-based architecture for monitoring, recording, and analyzing complex and multi-dimensional motion chains. We provide the results of our preliminary study encompassing 8 different body weight exercises, 20 participants, and more than 9,220 recorded exercise repetitions. Furthermore, insights into SensXs classification capabilities and the impact of specific sensor configurations onto the analysis process are given.Comment: Published within the Proceedings of 14th IEEE International Conference on Networking, Sensing and Control (ICNSC), May 16th-18th, 2017, Calabria Italy 6 pages, 5 figure

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

Tracking energy fluctuations from fragment partitions in the Lattice Gas model

Partial energy fluctuations are known tools to reconstruct microcanonical heat capacities. For experimental applications, approximations have been developed to infer fluctuations at freeze out from the observed fragment partitions. The accuracy of this procedure as well as the underlying independent fragment approximation is under debate already at the level of equilibrated systems. Using a well controlled computer experiment, the Lattice Gas model, we critically discuss the thermodynamic conditions under which fragment partitions can be used to reconstruct the thermodynamics of an equilibrated system.Comment: version accepted for publication in Phys.Rev.

Measurement with Persons: A European Network

The European ‘Measuring the Impossible’ Network MINET promotes new research activities in measurement dependent on human perception and/or interpretation. This includes the perceived attributes of products and services, such as quality or desirability, and societal parameters such as security and well-being. Work has aimed at consensus about four ‘generic’ metrological issues: (1) Measurement Concepts & Terminology; (2) Measurement Techniques: (3) Measurement Uncertainty; and (4) Decision-making & Impact Assessment, and how these can be applied specificallyto the ‘Measurement of Persons’ in terms of ‘Man as a Measurement Instrument’ and ‘Measuring Man.’ Some of the main achievements of MINET include a research repository with glossary; training course; book; series of workshops;think tanks and study visits, which have brought together a unique constellation of researchers from physics, metrology,physiology, psychophysics, psychology and sociology. Metrology (quality-assured measurement) in this area is relativelyunderdeveloped, despite great potential for innovation, and extends beyond traditional physiological metrology in thatit also deals with measurement with all human senses as well as mental and behavioral processes. This is particularlyrelevant in applications where humans are an important component of critical systems, where for instance health andsafety are at stake

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development