Epilepsy represents a neurological disorder of the brain characterized by repeated seizures. These are sudden abnormality in the brain’s electrical activities that temporarily affect normal brain function. Electroencephalogram (EEG) is one of the main diagnostic tools for monitoring the brain activity of patients with epilepsy. Typically, the detection of epileptic activity is carried out by an expert by analyzing the EEG recordings, but this is a difficult, error prone and time-consuming task. In order to get timely and accurate automatic detection of seizure, various approaches based on both conventional and deep learning techniques were proposed in the literature. The aim of this paper is to present a framework for the automatic detection of epileptic seizure based on the functional connectivity matrix obtained from EEG signals and deep learning. Convolutional neural networks (CNN) were employed because of their capability to learn patterns of neural activities based on brain connectivity represented by connectivity matrix. Obtained results are very promising indicating a potential of this approach as an efficient tool for automated seizure detection based on EEG data.

Abstract The security of using applications in cloud services and on the Internet is an important topic in the field of engineering. In this paper, two laboratory tests for data transmission protection, specifically designed for different security analysis techniques, are presented and explained. During lab tests on public Wi-Fi networks from the MIDM (“Man in the Middle”) attacks, various monitoring techniques were applied, using a special lab test scenario with Kali Linux penetration tools by creating an SSH tunnel on an Android mobile device. These test benches allow easy data capturing, and the captured data is processed using available software programs. Expected outcomes, practical improvement and security performance assessment are presented in detail, and considered in terms of their value in security engineering. The aim of this paper is to detect and overcome some of the weaknesses of the application of security protocols in a Wi-Fi network environment.

Artefacts caused by the presence of metallic implants and prosthesis appear as dark and bright streaks in computed tomography (CT) images, that obscure the information about underlying anatomical structures. These phenomena can severely degrade the image quality and hinder the correct diagnostic interpretation. Although many techniques for the reduction of metal artefacts have been proposed in literature, their effectiveness is still limited. In this paper, an application of a convolutional neural networks (CNN) to the problem of metal artefact reduction (MAR) in the image domain is investigated. Experimental results show that image-domain CNN can substantially suppresses streaking artefacts in the reconstructed images.

Signal, image and Synthetic Aperture Radar imagery algorithms in recent time are used in a daily routine. Due to huge data and complexity, their processing is almost impossible in a real time. Often image processing algorithms are inherently parallel in nature, so they fit nicely into parallel architectures multicore Central Processing Unit (CPU) and Graphics Processing Unit GPUs. In this paper image processing algorithms were evaluated, which are capable to execute in parallel manner on several platforms CPU and GPU. All algorithms were tested in TensorFlow, which is a novel framework for deep learning, but also for image processing. Relative speedups compared to CPU were given for all algorithms. TensorFlow GPU implementation can outperform multi-core CPUs for tested algorithms, obtained speedups range from 3.6 to 15 times.

Medical image registration plays an important part in most today’s clinical procedures. Registration goal is to find transformation which warps one image into the space of another. Registration of moving organs in human body has a significant part in therapy planning. This task is harder in cases when one organ (tissue) slides along another, i.e. in a case of discontinuities in the motion field. Discontinuities introduce unwanted transformations which often lead to poor or unsatisfied registration results. In this paper we evaluate one form of discontinuities for two well-known and used registration algorithms namely Free Form Deformation and Demons.

In this paper is presented Model Reference Fuzzy-Neuro Text Matching System. By using user-friendly interface in the form of if-then rules, syntax and semantics rule base, and English word base, the input text has been transformed to an array of potentially important terms (entities). Entities, that are characterized with their names and attributes that have been extracted from the text, are inputs into the Fuzzy Inference System (FIS). The output from FIS is “term importance” for given text which is regarded as the coordinate of input text in term space. In order to simplify tuning of parameters of the FIS, FIS is transformed to neural network. Neural network parameters are adjusted offline using some reference knowledge that has been a prior given by some experts. Matching of the input text against other texts is performed using Euclidean norm in the term space. Due to the fact that output term base is organized in the term-wise form, text matching is simplified since it is performed only against those texts that the given term belongs to. By using user-friendly interface in the form of if-then rules, the overall system is open for future inclusion of new syntax and semantics rules.

Computer aided diagnosis of lung CT image has been a revolutionary step in the early diagnosing of lung diseases. The best method of implementing computer aided diagnosis for medical image analysis is first to preprocess the image in order to segment it. The first step in computer aided diagnosis of lung computed tomography patient image is generally to first segment the region of interest, in this case lung, and then analyze separately each area obtained, for a tumor, cancer, node detection or other pathology for diagnosis. This is generally much easier approach, because the area used for setting the right diagnosis, is getting smaller with the process of segmentation, so the radiologist can focus his observation only on specific data inside the specific region. In this paper we proposed lung segmentation technique to accurately segment the lung parenchyma of lung CT images, which can help radiologist in early diagnosing lung diseases, but the algorithm can also be used to early diagnose other benign or malignant pathologies in other organs, such as liver, brain or spine.

An excessive power consumption characterizes all industries, even IT. Today, different methods of energy savings are in use, due to the pollution of the environment and the greenhouse effect. One of the newest methods in the IT is green IT that is directly connected with the virtualization of hardware resources. This study illustrates the procedure of hardware virtualization using real-world example and the analyses of the results in power consumption before and after the virtualization.