This article addresses the problem of finding patterns in graphs. This is formally defined as the subgraph isomorphism problem and is one of the core problems in theoretical computer science. We consider the counting variation of this problem. The task is to count all instances of the pattern G occurring in a (usually larger) graph H. The vast majority of algorithms for this problem use a variation of backtracking. Most commonly they exhaustively search through the space of all possible monomorphisms between G and H. The size of the search tree depends heavily on the choice of the ordering of vertices of G, which are systematically assigned to the vertices of H. We use a method called heuristic sampling to estimate the size of the search tree for each ordering in advance. We use this estimation to select the most suitable order of vertices of G which minimizes the expected tree size. This approach is empirically evaluated on a set of instances, showing the practical potential of the method.

In this paper, characterization and realisation of planar thick film rain sensor based on capacitive principle is presented. By using three different pastes (inks) for realisation of capacitive sensor it was found out that sensor produced by carbon ink is not suitable for use in this application because it shows no change in measured capacitance when temperature or humidity level varies. The best characteristics were obtained by using silver conductive paste. Capacity of capacitive sensor realized by polymer silver paste shows rising tendency when temperature and humidity values rises.

Development of algorithms for solving various kinds of computer related problems consists of several consecutive and possibly repetitive phases. The final and very important step in this process is to implement the developed algorithm in a selected programming language, to test its behavior on some real-world test cases and to compare the results with the results of other algorithms. This evaluation can be done by comparing different execution indicators among which the time consumption is usually considered to be the most relevant. On the other hand, timing the algorithms in practice is very difficult since it is hard to ensure a fair and reproducible environment in which algorithm’s implementations can be compared. To overcome this barrier, we introduce a system called ALGATOR that was developed to facilitate the algorithm evaluation process. Besides the time complexity and the project-specific indicators, ALGATOR also measures the counters of Java code and Java bytecode usage. The measurement of the former is implemented by using special tags that are to be inserted in the appropriate lines of Java code while the measurement of the latest is enabled by using an adapted Java virtual machine, which counts the Java bytecode usage and reports the statistics. By using this counters new timingindependent criteria for algorithm assessment can be derived. In this paper we present some basic concepts of the ALGATOR system and give some examples of how to use the system in practice. We show the distribution of the usage of Java bytecode instruction for the sorting problem and the usage of the Java bytecode indicators in the time-complexity prediction for the matrix multiplication algorithm. The examples presented in this article show how the classic time measurement methods can be replaced by measuring some other more reliable indicators, and how this measurements can help to asses the quality of our algorithms.

This paper investigates the value of depth modality in object classification in RGB-D images. We use a simple model based on a multi-layered convolutional neural network which we train on a dataset of segmented RGB-D images of household and office objects. We evaluate and quantify the benefit of additional depth modality and its effect on classification accuracy on this dataset. Also, we compare the benefit of depth channel against the addition of color to grayscale image. Our experimental results support a conclusion, that for these categories of objects the depth modality provides a significant benefit to classification, which also outweighs the benefit of color information. Similar supporting evidence found in recent research is shown in comparison along with the resulting quantified benefit of depth modality.

This paper deals with the evaluation of the amount of filler influence and the ultraviolet (UV) radiation influence on selected dielectric properties of the innovative type of composites. These composites consist of a linear low-density polyethylene matrix and a clay filler (with different filling levels) that contains halloysite nanotubes (HNT) primarily. The intended application of tested composites in the field of electrical engineering is in insulating systems of power cables. Attention is paid to the analysis of the behaviour of composites in their charging, which is caused by the connection with the direct voltage source. The volume resistivity and polarization indexes are determined based on this analysis. Analysis of charging based on the measurement of the current, which flows through the sample under constant applied voltage, is supplemented with the short evaluation of the material behaviour after the disconnecting of the voltage source and after the electrode shorting when the material is discharged. Furthermore, the measurement of dielectric strength under an alternating electric field as another key parameter of electrical insulating materials is determined. Results of analyses of absorption and resorption processes and the dielectric strength measurement of materials in the delivered state prove a significant effect of the filler percentage in the composite. Subsequently, markedly different values of selected parameters because of material exposure to UV radiation are also determined. Observed changes in dielectric properties caused by UV irradiation are supported by the analysis of the chemical structure of materials via Fourier transform infrared spectroscopy.

The submitted contribution is devoted to the issue of the existence of latent losses on the overhead lines. Quantification of latent losses of electricity is a very complicated task, which has been solved in practice for several years. In their assessment, however, often appear some inconsistencies which cause is often unknown. The magnitude of these losses in some cases cannot be negligible. From the point of view of maintaining an efficient and reliable operation of distribution and transmission systems in the future, attention will still need to be paid to the problem of latent loss analysis.

This paper presents the performance evaluation of a developed multiple transform watermarking system for privacy protection of medical data using Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NC). The PSNR was used to evaluate the imperceptibility of the system, while the NC was used to evaluate the robustness of the system under the various attacks include: Gaussian noise, pepper and salt noise, sharp enhancing, image cutting, image compression, low pass filter and image rotation. The obtained result showed that the similarity between original image and watermarked image has PSNR of 52.4595dB as compared to the existing system of 50.0285dB. This indicates that the proposed scheme can conceal the watermark better, and as well retains the image quality.

The suitability of 2-dimensional Discrete Wavelet Transform (2D DWT) as a tool in image and video compression is nowadays indisputable. Wavelet based techniques such as JPEG2000 for image compression has a lot more to offer than conventional methods in terms of compression ratio. This paper presents DWT and the use of its compression properties in image processing and briefly describes convolutional implementation of the discrete wavelet transform. More specifically, it deals with lifting implementations of the discrete wavelet transform (LDWT). The main feature of the lifting scheme is that all constructions are derived in the spatial domain. LDWT does not require complex mathematical calculations unlike traditional methods and does not depend on Fourier transforms. The lifting scheme is used to generate second-generation wavelets, which are not necessarily translation and dilation of one particular function. This paper compares two most common types of implementation of LDWT, which are LDWT using a filter banks (FB) 5/3 and 9/7. Within the experiment, the compression properties of the LDWT implementations are compared.

The aim of this article is to present how we processed and analysed data from a logistics company using various Business Intelligence tools. The theoretical part of the article is therefore focused on defining Business Intelligence concepts and data warehouses that are relevant to the issue. The practical part of the article focuses on editing data, creating dimension tables and facts. The data were collected from the Dynafleet system and originated from a shipping company. The data provided are for the period from 2013 to 2016. We design user scenarios to help the company's manager in making an efficient assignment of drivers to planned delivery routes. The research is focused on design and creation of a logistics system based on data analytics that can continuously analyze the incoming data and generate current decision support reports. The created user scenarios have a wide range of uses and can also be helpful in assessing the performance of individual drivers and their workloads. Using a logistics system, the logistics manager can get the valuable and useful information needed to effectively operate the business.