Wyniki 1-3 spośród 3 dla zapytania: authorDesc:"Bogdan P. KNYSH"

The enhanced method of a spectrum’s window estimation DOI:10.15199/48.2019.04.27

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Constunt increasing computer technologies' and the systems' implementation takes place in the geometrical progression's order, and the key feature of the computer's system development is the creation of the highly efficient and highly reliable methods of a data transmitting and receiving. Among all computer's networks the important place take the networks, that can provide the good work's results in the environment, that has a high level of the noises. Those are the industrial and the radio networks. In the industry the authenticity of the transmitted information is more important, than the speed of transmitting and very often there is the necessity to provide information transmitting in any conditions of the environment. In the Wi-Fi networks, when the error probability is 0.1, the loss of the transmitted information is 40%, and when the level of the noises is higher, the transmitting is impossible at all [1]. According to the radio networks standards [2], especially the Wi-Fi, WiMAX networks, the level of signal/noise should be more than 57 Db. In the networks on the base of the CAN С 2.0 technology, when the error counter reaches 256 in case of the concrete node, the shift Bus Off takes place, and in this work state the information transmitting is provided by the simplex mode of work [3]. There are many methods of data analything, but the traditional methods are not always be efficient in the case of the high level of transmitting noises, so the need of improvement of the current methods is always important [1,2,3]. One of such methods is the method of window spectrum estimation, which uses the mean estimation of the log spectrum of a signal. The main misfit of this method is decreasing of his implementation efficiency in the high noises environment. So, it is proposed to use the multiwindow mean estimation of the signal's log spectrum in order to get more precise estimatio[...]

Low computational complexity algorithm for recognition highly corrupted QR codes based on Hamming-Lippmann neural network DOI:10.15199/48.2019.04.29

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The beginning of the neural networks history is connected with the "connectionist models" or "parallel distributed processing", that are considered in the publication in 1943 of McCulloch and Pitts. These scientists proposed general approaches and specific mathematical models of the biological neural networks and their components - neurons, that became fundamental in the artificial neural networks theory. These neurons were presented as models of the biological neurons and as conceptual components for circuits, that performed computational tasks. They used threshold elements with two stable states, which are called “McCallock-Pitts neurons" [1]. The task of developing models and systems, which are based on the threshold elements was so unusual and complex, that only in 1956 was appeared the first capable artificial neural network - Rosenblatt perceptron. Its demonstrated the possibility of creating technical patterns, based on the models of the human brain, for image recognition. However, further researches of perceptrons showed, that their usage in the recognition systems is associated with many difficulties. In 1969 Minsky and Papert published their book, in which they described the deficiencies of the perceptron model and showed their fundamental nature. The negative prognosis of the authoritative scientists caused a decline in the interest in neural networks, which lasted more than ten years, but in the 80's after some important theoretical results, the neural networks began to rebound. The renewed interest is reflected in many researches, the amounts of funding, the number of conferences and journals associated with neural networks [2,3]. At the same time, neurocomputing begins to develop, which allows solving problems from different areas of knowledge using neural networks, which are modelled on ordinary computers [4]. The machine interpretation of the neural network came into the world of Computer[...]

Experimental research of turbo-codes application in telemedicine systems with wireless body area sensor networks DOI:10.15199/48.2019.04.30

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The evolution of science and technics has allowed to create a promising application of wireless sensor networks, that is called wireless body area sensor networks (WBAN). It has a huge potential for the revolutionary transformation of medical technologies [1-3]. WBAN can be used to provide assistance to automatic medical treatment, automatic dosing, and vital signal monitoring. Fig. 1 shows an intuitive view of automatic medical treatment process (closed loop control) [2]. At the first step various vital data are collected using different sensors attached to a person. These data are sent to the command unit. At the second step the command unit decides the corresponding treatment method or correct dosing based on the received vital parameters. After this the command unit sends a command to the action unit. At the third step the action unit applies the treatment or dosing to objectives. When the treatment or dosing is finished, sensors will collect updated vital data and the process enters another circulation. WBAN can provide a healthcare service in a more comfortable, convenient and economical way, than other conventional methods. WBAN provide the ability to broadcast multiple vital parameters in “online" mode, that provides an indispensable aid for people, who suffer from chronic diseases and acute attacks, allowing to react to the worsening of the disease. This is a key technology of “online" (contrast to the Holter "offline" monitoring) prevention of cardiovascular diseases (myocardial infarction or other abnormal conditions). The tumors diagnostics without biopsy may be done on the basis of the work of many miniature sensors, that can detect cancer cells. WBAN can help people with asthma by the way of monitoring airborne allergens and providing medics some "alert signals“ in real time mode. Also this technology can be integrated into the telemedicine system, that provides unobtrusive ambulatory monito[...]

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