— Cause-effect graphing is a commonly used black-box technique with many applications in practice. It is important to be able to create accurate cause-effect graph specifications from system requirements before converting them to test case tables used for black-box testing. In this paper, a new graphical software tool for creating cause-effect graph specifications is presented. The tool uses standardized graphical notation for describing different types of nodes, logical relations and constraints, resulting in a visual representation of the desired cause-effect graph which can be exported for later usage and imported in the tool. The purpose of this work is to make the cause-effect graph specification process easier for users in order to solve some of the problems which arise due to the insufficient amount of understanding of cause-effect graph elements. The proposed tool was successfully used for creating cause-effect graph specifications for small, medium and large graphs. It was also successfully used for performing different types of tasks by users without any prior knowledge of the functionalities of the tool, indicating that the tool is easy to use, helpful and intuitive. The results indicate that the usage of standardized notation is easier to understand than non-standardized approaches from other tools.

Cause-effect graphs are a popular black-box testing technique. The most commonly used approach for generating test cases from cause-effect graph specifications uses backward-propagation of forced effect activations through the graph in order to get the values of causes for the desired test case. Many drawbacks have been identified when using this approach for different testing requirements. Several algorithms for automatically generating test case suites from cause-effect graph specifications have been proposed. However, many of these algorithms do not solve the main drawbacks of the initial back-propagation approach and offer only minor improvements for specific purposes. This work proposes two new algorithms for deriving test cases from cause-effect graph representations. Forward-propagation of cause values is used for generating the full feasible test case suite, whereas multiple effect activations are taken into account for reducing the feasible test case suite size. Evaluation of the test case suites generated by using the proposed algorithms was performed by using the newly introduced test effect coverage and fault detection rate effectiveness metrics. The evaluation shows that the proposed algorithms work in real time even for a very large number of cause nodes. The results also indicate that the proposed algorithm for generating all feasible test cases generates a larger test case suite, whereas the proposed algorithm for test case suite minimization generates a smaller test case subset than the originally proposed approaches while ensuring the maximum effect coverage, fault detection rate effectiveness and a better test effect coverage ratio.

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.

Automated detection of asphalt pavement distresses is a very popular computer vision and image processing problem. In recent years, automated detection is an essential part of every pavement management system, since it allows very fast detection of distresses on the road. This is important because timely detection can prevent many road accidents, and hence it has potential to save lives. In this paper, we presented a new unsupervised image processing method for segmentation of the most common road distresses-pavement cracks. The method first performs slicing of an image into M×N sub-images, and then removes sub-images without cracks based on empirically defined threshold. Analysis is then carried out only on a small number of sub-images, which significantly reduces computation time. Then, a series of images processing tasks are performed to select only pixels with pavement cracks. The method is suitable as a pre-processing step in a number of computer vision tasks, and can provide rough estimation of damaged area in an image.

Visual impairment severely constraints the ability to independently conduct many everyday tasks that we usually do not consider challenging. Although some types of visual impairment can be treated efficiently there is still a considerable number of visually impaired persons, especially among aging population, which depend on help of others or assistive technology to sustain their life quality. Visually impaired person cannot perceive the full extent of surrounding information due to the lack of visual details. However great progress can be achieved if surrounding information can be somehow visually transformed to the subset of visual information that visually impaired person can perceive. To certain extent every impairment has individual characteristics, as different individuals may better respond to different subsets of visual information. Thus any assistive solution aiming to visually transform surrounding information to accommodate broad range of impairment conditions must be personalized in order to be effective. Virtual reality enables individuals to experience imaginary surroundings by tricking their visual senses and such virtual surroundings can be personalized to any extent desired. We use virtual reality, image processing, and RFID to create a test setup able to simulate visual impairment and visually transformed surroundings suitable for visual Impairment studies. The test setup enables gathering information and conducting studies on both healthy and visually impaired individuals in a controlled environment enabling reliable assistive technology development and testing.

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.

Team performance depends on both individual and collaborative skills. This dependence creates increasing education and training demand while striving to improve teams’ efficiency. Consequentially, training and education systems emerge with new capabilities that are changing the learning landscape. With increasingly disperse and mobile teams it can be very inefficient and costly to provide training and education in a centralized instructor-led classes’ manner. Remote solutions are able to reach far more potential users at any moment, and tend to be satisfactory and possibly preferred in many different training and education areas. Computer Aided Design (CAD) requires high quality graphics for positive impact and high satisfaction. Software tools used for hands-on CAD training exercises depend on dedicated Graphics Processing Unit (GPU) to deal with complex graphics processing needed to visualize virtual models in real-time. It is challenging for the remote training system to offer this high level of experience to remote users. In this paper, synergy of conventional CAD laboratory workstations into cells is proposed in order to create a cost-effective team training remote system. The system utilizes existing capabilities of dedicated GPUs and custom software modules to capture video, perform hardware HEVC encoding, and stream the video at low bitrates and sub-second latency to remote team members.

Color vision deficiency is a surprisingly frequent vision impairment, but not considered to be a mayor eye disease due to being inherited condition and not progressive condition. However it poses serious restrictions on a visually impaired person because vision deficiency tests are commonly used to disqualify individuals affected by color vision deficiency from certain occupations. Color vision deficiency cannot be cured, thus it is important to develop suitable assistive technology to overcome the restrictions it poses. Virtual reality can project custom and separate images to both eyes in a real-time and thus enabling a new class of assistive technology that can deliver visual information in a highly customized manner. Virtual reality based assistive technology is promising for age-related macular degeneration, diabetic retinopathy and particularly for color vision deficiency. Virtual reality prototype is created based on a video see-through setup using commercial virtual reality headset and stereo camera. The prototype uses custom image processing to transform visual information from the camera to color vision deficiency friendly form. Time-domain color mapping real-time image processing is proposed to improve scores on standard color vision deficiency tests - Ishihara tests. Experiment is conducted to evaluate a protanope time-domain color mapping with sinusoidal envelope.

Online courses have potential to reach broader audience when compared to traditional learning methods, as they can be made instantly available to groups of students dispersed over wide range of locations and time zones. Some learning topics are easily adopted to this online environment, while others may be challenging to implement as online courses due to their specific requirements and associated cost. If mastering certain topic requires hands-on experience then online course must be made interactive to match the experience of a traditional instructor-led class. Otherwise, online course will be perceived as unsatisfactory and lacking positive impact. Computer Aided Design (CAD) training requires both high performance Graphics Processing Unit (GPU) and hands-on experience with specific CAD software. Both these requirements are difficult to meet on a remote student computer without additional cost. In this paper, grouping CAD workstations into cells is proposed in order to perform required graphics processing using existing hardware and software resources available in an average CAD laboratory. Proposed cell framework uses video capture and GPU hardware encoding to stream the content to the remote students while capturing their interactive feedback for the near real-time hands-on experience. The framework requires single capture card per cell and utilizes 20% of the resources for the cell overhead processing. Remaining 80% of workstations are fully available to the online students and instructors.

E-Learning solutions for Computer Aided Design (CAD) require high quality graphics for positive impact and high satisfaction. Software tools used for hands-on CAD laboratory exercises depend on dedicated Graphics Processing Unit (GPU) to deal with complex graphics processing needed to visualize virtual models in real-time. It is challenging to offer the same level of experience to remote users as they usually cannot afford laboratory-level hardware with dedicated GPU nor such hardware can be provided to them as part of their e-Learning experience. In this paper, grouping of average CAD laboratory workstations in pairs is proposed in order to create remote stations capable of performing required CAD graphics processing for remote users while streaming it over the network for a near real-time experience. Remote station captures video and utilizes hardware HEVC encoding, as common capability of high-end dedicated GPUs, to perform low bitrate video streaming with sub-second latency. The remote station concept enables cost-effective extension of average conventional CAD laboratory to an e-Learning remote laboratory for up to 50% additional remote users in respect to the total number of the laboratory workstations.

Development of a spatial decision support system requires integration of various spatial data sets coming from different information systems of possibly more than one organization. The spatial decision support system development for spatial planning and environmental protection is discussed and spatial data integration is described. The heterogeneity of information systems from which spatial data come is reflected through their purpose. Utility and real estate cadaster systems imply services to citizens as part of spatial data infrastructure in the Federation of Bosnia and Herzegovina. The spatial planning information system is intended for registration of environmental changes and spatial decision making. Utility cadaster information system development is described along with important aspects on how to satisfy both functional cadastral services and spatial planning experts needs to analyze information related to land use and network supply systems. Particular attention is given to spatial data transformation for utility cadaster database development in accordance with a prescribed data model.