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.

This paper examines possibilities for improving the existing strategies of consistency management for highly-distributed transactional database in a hybrid cloud environment. With a detailed analysis of the existing consistency models for distributed database and standard strategies including Classic, Quorum and Tree Based Consistency (TBC), it is concluded that an improved advanced model of so-called visible adaptive consistency needs to be applied in a highly-distributed cloud environment, as necessary and sufficient degree of synchronization of all replicas. Along with the proposed model, research and development of an advanced novel strategy for consistency management Rose TBC (R-TBC) approach has been conducted, by improving standard TBC approach. Regarding implementation, a specific agglomerative Rose Tree Algorithm (RTA) has been developed, based on Bayesian hierarchical clustering and Graph Partitioning Algorithm - Multidimensional Data Clustering (GPA-MDC) intelligent partitioning of transactional Cloud Database Management System (CDBMS). The final result is constructed R-TBC model that changes in accordance with dynamic changes of entire heterogeneous CDBMS environment.

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.

Organizations can improve efficiency of process execution through a correct resource allocation, as well as increase income, improve client satisfaction, and so on. This work presents a novel approach for solving problems of resource allocation in business processes which combines process mining, statistical techniques, and metaheuristic algorithms for optimization. In order to get more reliable results of the simulation, in this paper, we use process mining analysis and statistical techniques for building a simulation model. For finding optimal human resource allocation in business processes, we use the improved differential evolution algorithm with population adaptation. Because of the use of a stochastic simulation model, noise appears in the output of the model. The differential evolution algorithm is modified in order to include uncertainty in the fitness function. In the end, validation of the model was done on three different data sets in order to demonstrate the generality of the approach, and the comparison with the standard approach from the literature was done. The results have shown that this novel approach gives solutions which are better than the existing model from literature.