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Internet of Things (IoT) devices are big part of concept, which is called by electronic producers and many others Smart Home. Authors of this paper have decided to take a look at it and as a result of this analysis propose and implement (in a form of working prototype) a system that could manage different kinds of devices. The main objective of the project presented in this paper is a device that looks like a mirror and it is known to most people also is an interactive center, a place to obtain information of the devices that surround us and various parameters coming from sensors. Authors have prepared a prototype, that will be the central point of the apartment and will allow users to control all devices connected to this main system. Prototype allows to connect external sensors over GPIO (general purpose input/output) interface or Internet connection. There is also a possibility to add another communication interfaces such as 433MHz radio module, which is very cheap and works great in small areas or bluetooth module. [ABSTRACT FROM AUTHOR]

The paper presents a web application for navigation through the university campus, which is based on Global Positioning System GPS and OpenStreetMap. The application has a multilayer structure and multi-labelling support. The proposed solution ensures better management of visual data and more efficient image processing comparing to the other known methods. With the new search system, users can place a lot of information on one layer without losing the legibility of displayed data. All the information that was displayed on the map was grouped and assigned to the appropriate categories.Therefore a map contains a lot of related information that needs to be linked to each other. The system has been divided into modules that ensure the integrity of the displayed things. Presenting so much information at the same time is managed by modules. Their main job is to provide results that is then segregated and grouped. The system presented in this paper was applied for Lodz University of Technology. [ABSTRACT FROM AUTHOR]

Electrical Capacitance Tomography is a non-invasive imaging technique, which allows visualization of the industrial processes interior and can be applied to many branches of the industry. Image reconstruction process, especially in case of 3D images, is a very time consuming task (when using classic processors and algorithms), which in turn leads to an unacceptable waiting time and currently limits the use of 3D Electrical Capacitance Tomography. Reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem must be able to transform capacitance data into images in a fraction of a second. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms, time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for further optimizations of previously developed algorithms. [ABSTRACT FROM AUTHOR]

3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems. [ABSTRACT FROM AUTHOR]

3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems. [ABSTRACT FROM AUTHOR]