The design of an indirect method for the human presence monitoring in the intelligent building

This article describes the design and verification of the indirect method of predicting the course of CO2 concentration (ppm) from the measured temperature variables Tindoor (degrees C) and the relative humidity rH(indoor) (%) and the temperature T-outdoor (degrees C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to February 28, 2015, from June 1 to June 28, 2015 and from February 8 to February 14, 2015. For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 92%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored IAB premises for monitoring. The designed indirect method of CO2 prediction has potential for reducing the investment and operating costs of the IAB in relation to the reduction of the number of implemented sensors in the IAB within the process of management of operational and technical functions in the IAB. The article also describes the design and implementation of the FEIVISUAL visualization application for mobile devices, which monitors the technological processes in the IAB. This application is optimized for Android devices and is platform independent. The application requires implementation of an application server that communicates with the data server and the application developed. The data of the application developed is obtained from the data storage of the PI System via a PI Web REST API (Application Programming Integration) client.Web of Science8art. no. 2

Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

Testing of the voice communication in smart home care

This article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.Web of Science5art. no. 1

Comparison of two approaches for web-based 3D visualization of smart building sensor data

Abstract. This thesis presents a comparative study on two different approaches for visualizing sensor data collected from smart buildings on the web using 3D virtual environments. The sensor data is provided by sensors that are deployed in real buildings to measure several environmental parameters including temperature, humidity, air quality and air pressure. The first approach uses the three.js WebGL framework to create the 3D model of a smart apartment where sensor data is illustrated with point and wall visualizations. Point visualizations show sensor values at the real locations of the sensors using text, icons or a mixture of the two. Wall visualizations display sensor values inside panels placed on the interior walls of the apartment. The second approach uses the Unity game engine to create the 3D model of a 4-floored hospice where sensor data is illustrated with aforementioned point visualizations and floor visualizations, where the sensor values are shown on the floor around the location of the sensors in form of color or other effects. The two approaches are compared with respect to their technical performance in terms of rendering speed, model size and request size, and with respect to the relative advantages and disadvantages of the two development environments as experienced in this thesis

Immunochromatographic diagnostic test analysis using Google Glass.

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.e., the user) and also stored on a central server along with the RDT image, QR code, and other related information (e.g., demographic data). The same server also provides a dynamic spatiotemporal map and real-time statistics for uploaded RDT results accessible through Internet browsers. We tested this Google Glass-based diagnostic platform using qualitative (i.e., yes/no) human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) tests. For the quantitative RDTs, we measured activated tests at various concentrations ranging from 0 to 200 ng/mL for free and total PSA. This wearable RDT reader platform running on Google Glass combines a hands-free sensing and image capture interface with powerful servers running our custom image processing codes, and it can be quite useful for real-time spatiotemporal tracking of various diseases and personal medical conditions, providing a valuable tool for epidemiology and mobile health

Smart Solutions: Smart Grid Demokit

Treball desenvolupat dins el marc del programa 'European Project Semester'.The purpose of this report is to justify the design choices of the smart grid demo kit. Something had to be designed to make a smart grid clear for people who have little knowledge about smart grids. The product had to be appealing and clear for people to understand. And eventually should be usable, for example, on an information market. The first part of the research consisted of looking how to shape the whole system. How the 'tiles' had to look to be interactive for users and what they should feature. One part of this was doing research to get to know more about the already existing knowledge amount users. Another research investigated what appeals the most to the users. After this, a concept was created in compliance with the group and the client. The concept consists of hexagonal tiles, each with a different function: houses, solar panels, wind turbines, factories and energy storages. These tiles are all different parts of a smart grid. When combining these tiles, it can be made clear to users how smart grids work. The tiles are fabricated using a combination of 3D printing and laser cutting. The tiles have laser cut symbols on top of them to show what part of the smart grid they are. Digital LED strips are on top of the tiles to show the direction of the energy flow, and the colors indicate if the tile is producing or consuming power from the grid. The tiles are connected to each other by the so called “grid blocks”. These blocks make up the central power grid and are also lighting up by LED strips. Each tile is equipped with a microcontroller which controls the LED strips and makes it possible for the different tiles to “talk” with each other. Using this, the central tile knows which tiles are connected to the system. The central tile controls all tiles and runs the simulation of the smart grid. For further development of the project, it can be investigated how to control and adjust the system from an external system, for example by a tablet. The final product consists of five tiles connected by seven grid blocks which show how a smart grid works