Counting the number of objects that are transported on a conveyor belt is frequently encountered in production facilities, airports or post offices. Although most of these tasks may usually be solved by using common photoelectric or inductive sensors, there are cases when objects have to be counted using more complex sensing systems based on machine vision. In this paper, an image-processing algorithm for segmenting, detecting and counting rectangular objects which are being transported on a conveyor belt is presented. The method is specifically designed to detect rectangular objects that can be partly occluded. The application is implemented using OpenCV/C++ library. Two different test scenarios are analyzed in the paper. Experimental results suggest that the proposed method has promising accuracy and it is applicable in real-world applications.

Car gates can be found in many private and business facilities. Typically, gates are controlled by commercially available electronic systems that allow users to remotely operate them. Most of those systems are based on robust RF 315/433MHz transmitters for remote control. These communication modules suffer from limited range and allow the user to establish only simplex communication. Today, with the rapid growth of the Internet of Things, not only that every driver has an Internet-enabled smartphone, but most modern cars are equipped with such systems as well. This paper proposes a prototype of an electronic gate control structure that allows users, in addition to the common gate-panel and an RF-based remote, to control and supervise the gate using an Internet connection (e.g. with a smartphone). Both hardware and software parts that are required to operate the gate are designed, developed, and presented in this paper. Experimental tests on the small-scale model are conducted to point out the device's advantages and disadvantages and propose guidelines for future work and development.

Purpose: Research has suggested that in female athletes from aesthetic sports the prevalence of disordered eating attitudes is higher than in female athletes from other sports, mainly due to sport related factors like extreme training and practicing sports associated with high pressure and the idea that “being thin leads to success”. The study was conducted to examine the prevalence of disturbed eating attitudes and their relationship with body image concerns in aesthetic and non-aesthetic female athletes. Methods: 54 female athletes from aesthetic sports (synchronized swimming, artistic and rhythmic gymnastics, and dance), as well as 66 female athletes from non-aesthetic sports (volleyball, track and field, and soccer), completed the Eating Attitudes Test, the Body Shape Questionnaire, and the Figure Rating Scale (a visual scale used to assess body image dissatisfaction and body image dissatisfaction in relation to sport). Results: The results indicated that aesthetic athletes scored significantly higher than those involved in non-aesthetic sports in Dieting, and in Body Image Dissatisfaction. Moreover, aesthetic athletes demonstrated significantly lower BMI mean scores. Significant correlations were found between Body Mass Index and Oral Control, Body Image Dissatisfaction and Body Image Dissatisfaction in relation to Sport, and between Eating Attitudes Test and the Body Shape Questionnaire results in aesthetic athletes. Furthermore, significant associations were found between Body Mass Index and Body Shape Questionnaire, Body Image Dissatisfaction and Body Image Dissatisfaction in relation to Sport, and Eating Attitudes Test and Body Shape Questionnaire in non-aesthetic athletes. Conclusion: The study confirmed the relationship between body image concerns and pathological eating attitudes among female aesthetic sport athletes.

Descending necrotizing mediastinitis (DNM) is a rare, life-threatening form of mediastinitis caused by odontogenic, pharyngeal, or cervical infections. The retropharyngeal space is the most common primary site of infection. Given the fulminant course and high mortality rate, early diagnosis and prompt treatment are important predictors of survival in patients with DNM. Appropriate empirical antibiotic treatment, prompt surgical intervention, and proper management of patients in the intensive care unit can be of vital importance. We present the case of a previously healthy 20-year-old male patient who was successfully cured and discharged from the Clinical Center University of Sarajevo after suffering from a severe form of mediastinitis as a complication of the retropharyngeal abscess caused by anaerobes.

Let M be a finite volume, non-compact hyperbolic Riemann surface, possibly with elliptic fixed points, and let $$\chi $$ χ denote a finite dimensional unitary representation of the fundamental group of M . Let $$\Delta $$ Δ denote the hyperbolic Laplacian which acts on smooth sections of the flat bundle over M associated with $$\chi $$ χ . From the spectral theory of $$\Delta $$ Δ , there are three distinct sequences of numbers: the first coming from the eigenvalues of $$L^{2}$$ L 2 eigenfunctions, the second coming from resonances associated with the continuous spectrum, and the third being the set of negative integers. Using these sequences of spectral data, we employ the super-zeta approach to regularization and introduce two super-zeta functions, $$\mathcal {Z}_-(s,z)$$ Z - ( s , z ) and $$\mathcal {Z}_+(s,z)$$ Z + ( s , z ) that encode the spectrum of $$\Delta $$ Δ in such a way that they can be used to define the regularized determinant of $$\Delta -z(1-z)I$$ Δ - z ( 1 - z ) I . The resulting formula for the regularized determinant of $$\Delta -z(1-z)I$$ Δ - z ( 1 - z ) I in terms of the Selberg zeta function, see Theorem 5.3, encodes the symmetry $$z\leftrightarrow 1-z$$ z ↔ 1 - z .

The physical and mechanical properties of a polycrystalline material depend on its microstructure characteristics such as the size and morphology of grains. In practice, different imaging methods are used to visualize the grain structure of such materials. To analyze microstructural changes in case of applied stress and to predict its performance in a given application, the quantitative information about the grain structure must be taken into account. In this work, an effcient and reproducible algorithm, which automatically detects grains in different types of microstructure images, is proposed. Due to the diversity between the analyzed images and a limited number of labeled data, a clustering patch-based approach is followed. The algorithm aims to distinguish between patches in homogeneous grain areas and those lying on grain boundaries through Gaussian Mixture Modeling. The identified groups of grain patches are used to create the seed image for a Seeded Region Growing algorithm, enabling nally a pixelwise image segmentation.

ABSTRACT The Online Biomass Potential Atlas is a tool primarily intended for geo-visualisation of biomass data from the Biomass Potential Monitoring System in Bosnia and Herzegovina. However, its role does not have to end here. By developing a functional extension, it can offer an environment for the location analysis of potential biomass users and sources of unused biomass potential. This paper describes an approach for developing tool with such functionality, based on spatial interaction modelling. Determining the optimal location for biogas plants in the region covered by the administrative units of two cantons in Bosnia and Herzegovina is considered as a case study. Based on the analysis conducted in the case study, the feasibility of applying this tool has been demonstrated.

Flexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

Value iteration (VI) is a ubiquitous algorithm for optimal control, planning, and reinforcement learning schemes. Under the right assumptions, VI is a vital tool to generate inputs with desirable properties for the controlled system, like optimality and Lyapunov stability. As VI usually requires an infinite number of iterations to solve general nonlinear optimal control problems, a key question is when to terminate the algorithm to produce a "good" solution, with a measurable impact on optimality and stability guarantees. By carefully analysing VI under general stabilizability and detectability properties, we provide explicit and novel relationships of the stopping criterion's impact on near-optimality, stability and performance, thus allowing to tune these desirable properties against the induced computational cost. The considered class of stopping criteria encompasses those encountered in the control, dynamic programming and reinforcement learning literature and it allows considering new ones, which may be useful to further reduce the computational cost while endowing and satisfying stability and near-optimality properties. We therefore lay a foundation to endow machine learning schemes based on VI with stability and performance guarantees, while reducing computational complexity.