AVIR – Audio-Visual Indexing and Retrieval for Non IT Expert Users

The AVIR proposal originates from the demand for new solutions allowing common users to easily access, store and retrieve relevant audio-visual information from the vast amounts of resources at their disposal. The next generation of television systems will be connected to many sources of information and entertainment (TV-and radio from air, cable or satellite, video and audio libraries, video tape/disk recorders, Internet). Literally hundreds of channels will soon be offered to the user, which could be disoriented by this overload of information. Users will not pay for just more extra channels, but will appreciate if the content in the channels is easily accessible and, more importantly, can be easily selected according to the user's personal interest. This can only be achieved if the broadcaster delivers meta-data describing the actual content in sufficient detail enabling for automatic handling by agents residing on the end user's system. AVIR investigates on novel procedures for automatic analysis and indexing of audio-visual information, specifically meant to support consumer services. The objective of this project is to investigate and experiment end-to-end solutions for delivering new added value services on top of digital video broadcast services, which will enable a better exploitation of multimedia information resources by non-IT experts. As a result the project is building a prototype service user platform and will demonstrate its feasibility on a broadcast delivery chain. It takes into account extraction of high quality meta-data and electronic delivery of meta-data associated to audio-visual content, including adaptation of consumer receivers and recorders towards a personalized multimedia repository. Intelligent agents based on a user interest profile will help the user to browse and access most relevant programmes via an intelligent, personal electronic guide. A low cost, high capacity home storage device, will also be used to increment the capabilities of the consumer system. Thanks to the received descriptors, advanced retrieval features can be implemented on the stored assets and, in combination with the user’s profile, automatic recording feature is possible. A visual navigation system, a search engine and agents will help the user identifyvideo material of interest on the home video-recorder, transfo rming it into a personal multimedia repository

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

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes