Missing values handling in any collected data is one of the first issues that must be resolved to be able to use that data. This paper presents an approach used for missing values interpolation in PurpleAir particle pollution sensor data, based on a correlation of the measurements from the observed locations with the measurements from its neighboring locations, using KNIME Analytics Platform. Results of our experiments with data from five locations in Bosnia & Herzegovina, presented in this paper, show that this approach, which is relatively simple to implement, gives good results. All modeling and experiments were conducted using KNIME Analytics Platform.

Visual impairments often pose serious restrictions on a visually impaired person and there is a considerable number of persons, especially among aging population, which depend on assistive technology to sustain their quality of life. Development and testing of assistive technology for visually impaired requires gathering information and conducting studies on both healthy and visually impaired individuals in a controlled environment. We propose test setup for visually impaired persons by creating RFID based assistive environment – Visual Impairment Friendly RFID Room. The test setup can be used to evaluate RFID object localization and its use by visually impaired persons. To certain extent every impairment has individual characteristics as different individuals may better respond to different subsets of visual information. We use virtual reality prototype to both simulate visual impairment and map full visual information to the subset that visually impaired person can perceive. Time-domain color mapping real-time image processing is used to evaluate the virtual reality prototype targeting color vision deficiency.

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented—two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast.

There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generationmethodsrelatedtounstablemixtures.Inthisresearchweredevelopedalgorithmsusingfusionofartificialintelligencemethodstopredicttarconcentration.Outputsofdevelopmentarethreefuzzystructuresoptimizedwithgeneticalgorithmsresultingingeneticalgorithm(GA)-FUZZY,GA-adaptiveneurofuzzyinferencesystem(ANFIS),GA-GA-FUZZYalgorithms.Proposedalgorithmsareusedforthetarpredictioninthecigaretteproductionprocess.Theresultsofpredictionarecomparedwithgaschromatograph(high-performanceliquidchromatography(HPLC))readings.

Visually impaired person might find it very difficult to locate an object that has been even slightly misplaced from its usual position. Unfortunately this is very common situation in a shared environment where multiple individuals can affect object’s position and where visually impaired person cannot rely on object’s position remaining unchanged since the last interaction with the object. In order to independently localize the object of its interest visually impaired person must rely on assistive technology. It is yet very unlikely that any single wearable assistive device will encompass the whole range of object localization scenarios and be universally adoptable to a broad range of environments. In this paper we propose indoors test setup for visually impaired persons by creating RFID based assistive environment – Visual Impairment Friendly RFID Room. The test setup can be used to evaluate RFID object localization and its use by visually impaired persons.