FaSTExt: Fast and Small Text Extractor

Text detection in natural images is a challenging but necessary task for many applications. Existing approaches utilize large deep convolutional neural networks making it difficult to use them in real-world tasks. We propose a small yet relatively precise text extraction method. The basic component of it is a convolutional neural network which works in a fully-convolutional manner and produces results at multiple scales. Each scale output predicts whether a pixel is a part of some word, its geometry, and its relation to neighbors at the same scale and between scales. The key factor of reducing the complexity of the model was the utilization of depthwise separable convolution, linear bottlenecks, and inverted residuals. Experiments on public datasets show that the proposed network can effectively detect text while keeping the number of parameters in the range of 1.58 to 10.59 million in different configurations.Comment: 6 pages, 8th International Workshop on Camera-Based Document Analysis & Recognitio

Thermal analysis of IRT-T reactor fuel elements

The article describes the method and results of thermo-physical calculations of IRT-T reactor core. Heat fluxes, temperatures of cladding, fuel meat and coolant were calculated for height of core, azimuth directions of FA and each fuel elements in FA. Average calculated values of uniformity factor of energy release distribution for height of fuel assemblies were shown in this research. Onset nucleate boiling temperature and ONB-ratio were calculated. Shows that temperature regimes of fuel elements at rated power of the reactor keep within limit values and meet safety requirements. Obtained results could be applied at feasibility study of increasing thermal power of IRT-T research reactor to 12 MW level

Design and implementation of the first aid assistance service based on Smart-M3 platform

Smart technologies may be successfully applied in healthcare for creation of an IoT-enabled proactive pre-hospital and first aid assistance mobile services. A variety of smart services for the m-Health scenarios may be constructed by interaction of multiple knowledge processors (software agents) running on devices of the IoT environment. Thus, IoT-enabled m-Health applications should provide connection with smart space. It is possible to build such kind of services with Smart-M3 platform. The ontology describes interaction rules and the high-level design of the service. The first aid assistance scenario was chosen as a basic one. According to this scenario, sympathetic people provide first aid to patients in case of emergency. The study is focused on the implementation of the first aid assistance service consists of knowledge processors running on Linux servers and Android mobile devices. Such service should be scalable with adding new modules, sensors or participants. The purpose is to evaluate a possibility of application of a smart spaces approach for implementation mobile first aid services. Besides, implementation issues of server and client sides are discussed

Modeling of ultrasound tomographic imaging for non-destructive inspection of underwater structures

In non-destructive inspection regular ultrasound techniques provide detection and estimate size of specific defects or undesired objects in different structures. The method of tomographic imaging modeled in this paper can provide the correct position and shape of the object inside underwater structures to be inspected. The proposed method is based on 2-D full waveform inversion of reflected ultrasound field registered by the array of sensors at a short distance from the target object. The computationally intensive algorithm for tomographic image reconstruction is described and tested with focus on underwater pipeline ultrasound inspection. The results of numerical modeling using parallel calculations and physical modeling are presented and discussed

Modeling of operating history of the research nuclear reactor

The results of simulation of the IRT-T reactor operation history from 2012 to 2014 are presented. Calculations are performed using continuous energy Monte Carlo code MCU-PTR. Comparison is made between calculation and experimental data for the critical reactor

Resting-state EEG recorded with gel-based vs. consumer dry electrodes: spectral characteristics and across-device correlations

IntroductionRecordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes.MethodsHere we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications.ResultsDistinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low-frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality.DiscussionWe suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system