Respirable particulate matter present in outdoor and indoor environments is a health hazard. The particle concentrations can quickly change, with steep gradients on short temporal and spatial scales, and their chemical composition and physical properties vary considerably. Existing networks of aerosol particle measurements consist of limited number of monitoring stations, and mostly aim at assessment of compliance with air quality legislation regulating mass of particles of varying sizes. These networks can now be supplemented using small portable devices with low-cost sensors for assessment of particle mass that may provide higher temporal and spatial resolution if we understand the capabilities and characteristics of the data they provide. This paper overviews typical currently available devices and their characteristics. In addition it is presented original results of measurement and modelling in the aim of one low-cost PM monitor validation.

The paper describes an approach to integration of physical and digital worlds through aggregation of Internet of Things (IoT) service with the Augmented Reality (AR) platform, AR Genie. The solution is generally applicable, but concretely in this paper the IoT service is provided by ekoNET platform which provides the environmental data through AR based applications. The ekoNET solution is developed for a real-time monitoring of air quality and other atmospheric condition parameters such as temperature, air pressure and humidity. AR Genie is Software as a Service (SaaS) and Platform as a Service (PaaS) solution offering features and functionalities for creation and deployment of AR applications using simple and intuitive drag and drop interface requiring no programming knowledge. By extending the AR Genie platform with the ekoNET IoT service we were able to demonstrate usage of a real-time environmental data within AR mobile applications as well as to enable a new, more engaging way of IoT data visualization utilizing serious gaming and AR technologies.

This paper describes a novel approach in engaging citizen communities based on serious gaming incorporating integration of the physical and digital worlds through aggregation of Internet of Things (IoT) service with Augmented Reality (AR) data visualization. The IoT service is provided by the ekoNET solution providing real-time monitoring of air quality and other atmospheric condition environmental data. Data visualization is based on markerless AR methods optimized and adapted for the mobile platforms. Using these technologies we were able to demonstrate usage of real-time environmental data within ARvatar serious game developed for the purpose of enabling a novel as well as more entertaining and engaging way of raising awareness of environmental issues.

This paper presents the environmental monitoring solution ekoNET, developed for a real-time monitoring of air pollution and other atmospheric condition parameters such as temperature, air pressure and humidity. The system is based on low-cost gas, PM and meteorological sensors providing cost-efficient, simple to deploy, use and maintain solution targeted for the usage within the Internet of Things domain of smart cities and smart enterprises. The paper gives an overview of the system architecture, encompassing the ekoNET device, back-end cloud infrastructure, data handling and visualization engine as well as the application-level components and modules. Furthermore, initial field trial data of twelve ekoNET devices is presented, enabling the overall system operation performance testing.

Internet of Things (IoT) domain has attracted a lot of interest over the last few years, to a large extent due to its applicability across a plethora of application domains. This variety of application domains resulted in a variety of requirements that IoT systems should comply with. Due to the heterogeneity of the domains, the requirements varied significantly, and demanding more or less complex systems with varied performance expectations. This situation affected the architecture design and resulted in a range of IoT architectures with not only varied set of components and functionalities, but also varied terminologies used. It resulted in limited interoperability between the systems which in turn hampered development of the complete domain. To address these issues, to ensure a common understanding by providing a framework catering for different applications and eventually enable reuse of the existing work across the domains, reference architectures are an appropriate tool. This paper presents an overview of the activities done in Europe towards definition of such a common framework together with how it is being used and a potential outlook for these efforts.

As the effects of global warming are spreading all over the planet, the activities on monitoring the environment are getting on importance and are becoming the focus of many projects, in particular those targeting development of smart cities and smart societies in general. Development of technology is enabling implementation of low-cost environment monitoring systems that can be installed across the cities utilizing the existing city infrastructure (for example attached to lampposts or installed on public transport vehicles) or even carried around by individuals contributing to crowd sourced based monitoring solutions. However, even though the costs are significantly lower than just a few years ago, it is still prohibitive to deploy such systems on a very large scale. This paper presents a possible approach towards significant reduction of the number of measurement points as well as the number of required sensors while still providing reliable estimations of environment parameters across an area. The approach is based on the mathematical statistics methods. It is shown that the proposed solution works well in the areas with stable and slowly-changing weather conditions, i.e. in cases when there are no sudden climate changes. DOI: http://dx.doi.org/10.5755/j01.eee.20.6.7269

This paper presents an application of Augmented Reality (AR) within a smart city service to be deployed in the domain of public transport in the city of Novi Sad in Serbia. The described solution is focused on providing a simple and efficient method to citizens for accessing important information such as bus arrival times, bus routes and tourist landmarks using smart phones and AR technology. The AR information is triggered by image and geo-location markers and the data is provided via secure IoT infrastructure. The IoT infrastructure is based on bus-mounted IoT devices which utilize secure CoAP software protocol to transmit the data to the associated cloud servers. Description of the complete end-to-end solution is presented, providing the overall system set-up, user experience aspects and the security of the overall system, focusing on the lightweight encryption used within the low-powered IoT devices.

IoT6 is a research project on the future Internet of Things. It aims at exploiting the potential of IPv6 and related standards to overcome current shortcomings and fragmentation of the Internet of Things. The main challenges and objectives of IoT6 are to research, design and develop a highly scalable IPv6-based Service-Oriented Architecture to achieve interoperability, mobility, cloud computing integration and intelligence distribution among heterogeneous smart things components, applications and services. The present article starts by a short introduction on IPv6 capabilities for the Internet of Things and information on the current deployment of IPv6 in the world. It continues with a presentation of the IoT6 architecture model and its concept of service discovery. Finally, it illustrates the potential of such IPv6-based architecture by presenting the integration of building automation components using legacy protocols.

A novel area detector has been designed for material science SR studies, capable of simultaneously collecting the diffraction data in two angular regimes. The detector for collecting wide-angle X-ray scattering (WAXS) data consists of four taper-coupled CCDs arranged as a 2×2 mosaic with a central aperture about 40mm in diameter, so permitting the inclusion of a distant on-axis CCD detector for small-angle X-ray scattering (SAXS). The distance of the SAXS detector from the sample can be varied over the range 0.27m to about 2m. The overall aperture of WAXS detector is approximately 200×200mm2 allowing the measurement of the diffraction patterns from 5° to 45° with an average angular resolution of 0.05°. The parallax error for large angles is substantially reduced as the individual WAXS CCDs are tilted towards the specimen location. Both WAXS and SAXS diffraction data are simultaneously collected at 30MB/s data rate, which is equivalent to 6 complete frames per second. Each pixel value is digitised using low- and high gain Analogue-to-Digital Converters (ADCs) which effectively increase the detector's overall dynamic range. The detector will be used in the study of a whole range of time-dependent phenomena, most importantly reaction kinetics, materials processing and real-time deformation studies. This paper discusses the unusual geometry of the system, how it relates to design optimisation and the techniques for recovering combined SAXS/WAXS patterns.

This paper presents a novel approach for intensity calculation of X-ray diffraction spots based on a two-stage radial basis function (RBF) network. The first stage uses pre-determined reference profiles from a database as basis functions in order to locate the diffraction spots and identify any overlapping regions. The second-stage RBF network employs narrow basis functions capable of local modifications of the reference profiles leading to a more accurate observed diffraction spot approximation and therefore accurate determination of spot positions and integrated intensities.

A fundamental limitation to the use of single-point absorbance detection for capillary electrophoresis is irradiance, since it is not possible to create an image at the detection point on capillary that is brighter than the light source. This limitation may be overcome by illuminating a length of the capillary using a fiber-optic bundle and using a charge coupled device (CCD) camera that can image the full length of the illuminated zone. The present paper describes design and development of a CCD detector for UV absorbance that can be used in both multiwavelength and single-wavelength modes. The CCD camera images analyte peaks in the capillary dimension, together with wavelength-resolved absorbance in the dimension perpendicular to the capillary. Successive snapshots of the peaks are added together, after appropriate correction for time-dependent peak displacement, without sacrificing spatial resolution. Measured baseline rms noise values at 200 mu are 34 mu AU using a holographic grating in multiwavelength mode and 8 mu AU with the addition of a band-pass filter. Both values are in excellent agreement with calculations of limiting shot noise. Performance in multiwavelength mode is constrained by the 470-ms readout time of the CCD used, which sets a maximum duty cycle of 2.3%, Noise contributions from source intensity fluctuations are reduced by using a portion of the CCD image to provide a baseline reference signal. With 4-hydroxybenzoate as test analyte, the linear dynamic range in multiwavelength mode is shown to be between 3 and 4 orders of magnitude. High-quality spectra of 2-, 3-, and 4-methylbenzoates are obtained on capillary and used in deconvolution of closely migrating peaks of the 2- and 3-isomers.

A method for real-time processing of snapshot data obtained from a capillary electrophoresis instrument using laser-induced fluorescence detection is described. The detector is based on a charge coupled device imaging 25 mm of the capillary length. Output data from the camera consists of a series of snapshot images of the analyte bands moving across the capillary window. The snapshot images are first pre-processed in order to obtain spatial profiles of fluorescence intensity. Each profile from successive snapshot images is processed on-line using a neural network, allowing the real-time measurement of separation parameters such as the velocity and width for each peak. This approach enables dynamic information to be extracted during the separation and the reduction of storage requirements, since the profiles where there are no peaks need not be recorded. The signal processing is based on profile approximation using a radial basis neural network which provides accurate and sensitive peak determination for each camera snapshot. The high spatial resolution of the system and accurate fluorescence profile approximation leads to good peak shape description and therefore to improved deconvolution of overlapping peaks. Examples of real-time analysis of complex peak patterns in separations of sulfonated aluminium phthalocyanines are given.

The efficient method presented for the accurate approximation of signal profiles corrupted by noise is based on a principled combination of linear and nonlinear processing. The nonlinear processing is realised using a radial basis network which is designed, trained and validated within the strict time constraints set by instrumentation requirements. The quality of profile approximation and the decision to use either linear or nonlinear processing are set by confidence limits which, in turn, are set by the best estimate of current system noise. The approach is described in terms of a novel capillary electrophoresis instrument with all processing implemented on a dedicated DSP subsystem.