141 research outputs found

    Research and Design of an X-Band UHF Power Amplifier

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    Introduction. A method for designing power amplifiers for use in the transmitting channels of X-band transceiver modules is investigated. The design process was aimed at optimizing the relationship between the basic amplifier characteristics, including the operating frequency band, output power level, output linearity, high harmonics suppression, etc. Β Aim. Investigation of a method for designing an X-band UHF power amplifier, which is capable of optimizing the relationship between its main characteristics. Β Materials and methods. Theoretical calculations were combined with experimental studies into the design of a UHF power amplifier. The stages of the design process are described in detail, including major ideas, principal circuits, and strip circuits. Evaluations were conducted using the Keysight ADS high frequency circuit simulation tool. Β Results. A method for designing X-band UHF power amplifiers on the basis of a close combination of theory, simulation, and experimental adjustment was described in detail. Β Conclusion. A prototype of an X-band PA was developed; an approach to developing a methodology for manufacturing, measuring, and testing X-band PAs is described.Introduction. A method for designing power amplifiers for use in the transmitting channels of X-band transceiver modules is investigated. The design process was aimed at optimizing the relationship between the basic amplifier characteristics, including the operating frequency band, output power level, output linearity, high harmonics suppression, etc. Β Aim. Investigation of a method for designing an X-band UHF power amplifier, which is capable of optimizing the relationship between its main characteristics. Β Materials and methods. Theoretical calculations were combined with experimental studies into the design of a UHF power amplifier. The stages of the design process are described in detail, including major ideas, principal circuits, and strip circuits. Evaluations were conducted using the Keysight ADS high frequency circuit simulation tool. Β Results. A method for designing X-band UHF power amplifiers on the basis of a close combination of theory, simulation, and experimental adjustment was described in detail. Β Conclusion. A prototype of an X-band PA was developed; an approach to developing a methodology for manufacturing, measuring, and testing X-band PAs is described

    Impact of foaming conditions on quality for foam-mat drying of Butterfly pea flower by multiple regression analysis

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    In recent years, the Butterfly pea flower has been increasingly interested in its color and function. However, the preservation of the extract faced many difficulties; therefore, foam drying technology was applied to solve this problem. The study was conducted to determine the effect of foaming conditions, including albumin ratio, carboxymethyl cellulose (CMC) ratio, and whipping time on foam characteristics. At the same time, the multi-dimensional regression method was also used to determine the most suitable foaming conditions for the following process. The research results showed that all 3 factors strongly influenced the foaming process of pea flower extract. It could be concluded that the most suitable condition for foaming is to use 9.3% albumin, 0.79% CMC and stir for 19 min. Under these conditions, the foam expansion and stability were 584.79% and 96.44% respectively. The powder obtained from the foam drying of Butterfly pea flower extract was also analyzed for quality. The temperature of 65 oC for 4 hrs gave relatively high-quality powder with protein content, anthocyanin and antioxidant activity of 9.89 g/100g, 1.15 mg/g and 87.34% respectively. In conclusion, the foam-mat dried powder from butterfly pea flower extract is suitable for other processing processes, especially in the processing of folk cakes, pasta and bread industry

    Influence Of Fabrication Condition on the Microstructural and Optical Properties of Lead-Free Ferroelectric Bi0.5_{0.5}Na0.5_{0.5}TiO3_{3} Materials

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    Lead-free ferroelectric materials have attracted considerable attention due to the increasing potential application in environmental benign materials. Among lead-free ferroelectric materials, the Bi0.5Na0.5TiO3 (BNT) materials were more studied because it exhibited the good ferroelectric and piezoelectric properties which could be promising candidate materials replacing Pb(Zr,Ti)O3. In this work, the lead-free ferroelectric BNT materials were synthesized by sol-gel method. The effects of fabrication process to microstructural and optical properties were studied which includes Na precursor concentration and calcining temperature. The result indicated that the Na precursor concentration were higher 40 mol.% and the calcining temperature

    Π‘ΠΈΠ½Ρ‚Π΅Π· Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€Π½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ Ρ‚Π΅ΠΎΡ€ΠΈΠΈ кластСризации Π΄Π°Π½Π½Ρ‹Ρ…

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    Introduction.Β  Requirements for the quality of information about the trajectory of moving objects provided by sensor networks are increasingly becoming more stringent. For Information and Data Processing Centers (DPC) at control and management command posts, the issue of information mapping and forming the true trajectories of moving objects in the area of intersection of network detection zones is of particular importance. The use of conventional approaches to solving this problem involves issuesΒ  related to ensuring the efficient provision of users with complete and reliable information about trajectories in real time. In this article, wee propose a new approach to solving this problem using data mining theory, in particular, the methods of data clustering theory. Based on an analysis of the process of processing radar data in a DPC and its similarity with that of data clustering, we synthesized an algorithm for processing the trajectories of moving objects. The algorithm was verified by modelling and experimental research.Aim.Β  To develop a generalized scheme for processing object trajectories (TP) in a DPC and to synthesized a TP algorithm using the methods of data clustering theory.MaterialsΒ  andΒ  methods.Β  DataΒ  ClusteringΒ  theory,Β  SystemsΒ Β  EngineeringΒ  theory,Β  RadarΒ  DataΒ  processingΒ  theory (RD), methods of mathematical modelling and experimental research.Results.Β  Based on an analysis of the essence of radar data processing (RD) in a DPC and its similarity with the process of data clustering,Β  an algorithm for processing the trajectories of moving objects was synthesized and verified by modelling and experimental research. A generalized scheme for processing the trajectories of moving objects in a DPC and a TP algorithm for a DPC were synthesized.Conclusions.Β  An algorithm for processing object trajectories was proposed based on a new approach of data clustering theory. A generalized scheme and an algorithm for processing object trajectories (TP) in a DPC were suggested. These developments can beΒ  effectively applied in various models, e.g. centralized, hierarchical and decentralized. The synthesized algorithm can provide output information about the true identified trajectories in terms of various indicators of data processing systems (DPS).Π’Π²Π΅Π΄Π΅Π½ΠΈΠ΅.Β  Π‘Β  ΠΊΠ°ΠΆΠ΄Ρ‹ΠΌΒ  Π³ΠΎΠ΄ΠΎΠΌΒ  становится  слоТнСС  ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Ρ‚ΡŒΒ  процСсс  ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈΒ  ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈΒ  ΠΎ траСкториях двиТСния ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ², ΠΏΠΎΠ»ΡƒΡ‡Π°Π΅ΠΌΠΎΠΉΒ  ΠΈΠ· сСнсорной сСти. Π­Ρ‚ΠΎΡ‚ процСсс ΠΈΠΌΠ΅Π΅Ρ‚ особоС Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ для  Ρ†Π΅Π½Ρ‚Ρ€ΠΎΠ² ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈ Π΄Π°Π½Π½Ρ‹Ρ… (Π¦ΠžΠ˜Π”), Π² ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… осущСствляСтся ΠΊΠΎΠ½Ρ‚Ρ€ΠΎΠ»ΡŒ ΠΈ ΡƒΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠ΅, Ρ‚Π°ΠΊ ΠΊΠ°ΠΊ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π΅Ρ‚ вопрос сопоставлСния ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈ формирования истинных Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΉ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² Π² области пСрСсСчСния Π·ΠΎΠ½ обнаруТСния сСти. ΠŸΡ€ΠΈ использовании Ρ‚Ρ€Π°Π΄ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ²Β  ΠΊΒ  Ρ€Π΅ΡˆΠ΅Π½ΠΈΡŽΒ  Π΄Π°Π½Π½ΠΎΠΉΒ  ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹Β  Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡŽΡ‚Β  вопросы,Β  связанныС  с  обСспСчСниСм  эффСктивного  прСдоставлСния  ΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚Π΅Π»Π΅ΠΌΒ  Π²Ρ‹Ρ…ΠΎΠ΄Π½ΠΎΠΉΒ  достовСрной  Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€Π½ΠΎΠΉΒ  ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈΒ  Π²Β  Ρ€Π΅Π°Π»ΡŒΠ½ΠΎΠΌΒ  Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ.Β  Π’Β  Π΄Π°Π½Π½ΠΎΠΉ ΡΡ‚Π°Ρ‚ΡŒΠ΅ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ Π½ΠΎΠ²Ρ‹ΠΉ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ ΠΊ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡŽ этого вопроса Π½Π° основС Ρ‚Π΅ΠΎΡ€ΠΈΠΈ ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ… (data mining) с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² кластСризации Π΄Π°Π½Π½Ρ‹Ρ….ЦСль Ρ€Π°Π±ΠΎΡ‚Ρ‹. Π Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½ΠΎΠΉ схСмы Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€Π½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ (ВО) Π² Π¦ΠžΠ˜Π” ΠΈ синтСз Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° ВО с использованиСм ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² кластСризации Π΄Π°Π½Π½Ρ‹Ρ….ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹.Β  ВСория кластСризации Π΄Π°Π½Π½Ρ‹Ρ…, тСория систСмотСхники,Β  тСория ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Ρ€Π°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉΒ  ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈΒ  (Π Π›Π˜), ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ матСматичСского модСлирования ΠΈ практичСского исслСдования.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹.Β  На основС Π°Π½Π°Π»ΠΈΠ·Π° сущности процСсса ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈΒ  Π Π›Π˜Β  Π² Π¦ΠžΠ˜Π” ΠΈ Π΅Π³ΠΎ сходства с процСссом кластСризации  Π΄Π°Π½Π½Ρ‹Ρ…Β  синтСзирован  Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΒ  Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€Π½ΠΎΠΉΒ  ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈΒ  ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ²,Β  ΠΏΡ€ΠΎΠ²Π΅Ρ€Π΅Π½Π½Ρ‹ΠΉΒ  ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌΒ  ΠΈΒ  ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½ΠΎ.Β  Помимо  Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ°Β  синтСзирована  структурная  схСма  ВО  для  Π¦ΠžΠ˜Π”, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½ΠΎΠΉ ΠΈΠ· сСнсорной сСти.Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅.Β  ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ‹ обобщСнная  структурная  схСма  ΠΈΒ  Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΒ  ВО  для  Π¦ΠžΠ˜Π”.Β  Они  ΠΌΠΎΠ³ΡƒΡ‚Β  эффСктивно ΠΏΡ€ΠΈΠΌΠ΅Π½ΡΡ‚ΡŒΡΡ для Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… систСмных ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ВО, Ρ‚Π°ΠΊΠΈΡ…, ΠΊΠ°ΠΊΒ  цСнтрализованная, иСрархичСская ΠΈ дСцСнтрализованная.Β  Π‘ΠΈΠ½Ρ‚Π΅Π·ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹ΠΉΒ  Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΒ  ΠΌΠΎΠΆΠ΅Ρ‚Β  ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°Ρ‚ΡŒΒ  прСдоставлСниС  Π²Ρ‹Ρ…ΠΎΠ΄Π½Ρ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ… ΠΎΠ±Β  истинных отоТдСствлСнных траСкториях ΠΏΠΎ ΠΌΠ½ΠΎΠ³ΠΈΠΌ показатСлям систСмы ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ (БОИ)

    Introduction: special issue of selected papers from ACML 2014

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    An Aquaculture Water Checker--design and Manufacture

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    A real-time, mobile aquaculture water checker is presented. The configuration of double integrating spheres is developed for simultaneously measuring backward scattering RdR_d, forward scattering TdT_d Β and transmitted light TcT_c . Based on Kubelka-Munk model, a set of optical parameters including absorption coefficient ΞΌa\mu _a , scattering coefficient ΞΌs\mu_s and anisotropy Β gg Β are calculated. The obtained results for diluted milk standard samples with different milk concentrations and aquaculture water samples with different densities of Psexdo-Nitzschia-delicatissium algae are also reported

    Autologous Transplantation of Adipose-Derived Stem Cells to Treat Acute Spinal Cord Injury: Evaluation of Clinical Signs, Mental Signs, and Quality of Life

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    BACKGROUD: Spinal cord injury (SCI) is damage that can cause a temporary or permanent change in spinal cord functions AIM: This work evaluates clinical signs, mental signs, and quality of life (QoL) after autologous adipose-derived stem cells (ADSCs) transplantation to treat acute spinal cord injury (SCI). MATERIAL AND METHODS: In this study, 47 SCI patients were recruited and divided into two groups: intervention and control. ADSCs were isolated and cultured under the cell culture quality control procedure. All patients in both groups underwent neurosurgery with or without ADSC transplantation. The recovery regarding neurological muscle, QoL, neurogenic bladder, and mental improvement was assessed after transplantation. RESULTS: All patients had improved in terms of motor function, bladder function, and daily living. No patients reported any side effect. MRI imaging showed significant changes in the lesion length of the spinal canal and the thickening of the spinal cord. Mental improvement was highest at six months after transplantation and lowest at one month after transplantation. The proportion of patients whose quality of life improved after treatment was 100%, while 80% of patients were satisfied with treatment outcomes. CONCLUSIONS: Thus, our data suggested that ADSCs transplantation was safe and effective for the treatment of SCI patients. Neurological muscle and neurogenic bladder were improved significantly after transplantation

    Π‘ΠΈΠ½Ρ‚Π΅Π· ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈ формирования Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠΎ ΠΎΡ‚Ρ€Π°ΠΆΠ΅Π½Π½Ρ‹ΠΌ сигналам ΠΎΡ‚ слоТных Ρ†Π΅Π»Π΅ΠΉ

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    Introduction. The quality of input information for trajectory processing (TP) systems can be improved by increasing the measurement accuracy of radar sensors (RS). However, in such a case, radar targets acquire the characteristics of complex targets having several marks at the output of the detector. This makes it difficult to accurately assess the kinetic parameters of targets in a TP system. In this respect, the development of a generalized algorithm for processing and generating data from the reflected signals of complex targets seems a relevant research task.Aim. To investigate reasons for the formation of complex targets and, using the theory of radar image processing, to synthesize an algorithm for processing and generating data on reflected signals from a complex target.Materials and methods. The following methodological approaches were used: the theory of digital signal processing; applied theory of radar image processing; MATLAB Simulink Toolboxes for simulating radar image processing; some prerequisites for fuzzy clustering methods.Results. Following an analysis of some characteristics of complex targets and the theory of radar image processing, an generalized algorithm was synthesized for processing and generating data of reflected signals from this class of targets. The results can be used to improve the measurement accuracy of their representative point when solving the TP problem.Conclusion. Reasons for the formation of complex targets in radar technology were analyzed. Their specific features consist in the need to accurately assess a true mark. A generalized algorithm for processing and generating these signals reflected from complex targets was proposed. The results can serve as a basis for solving the TP problem.Π’Π²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠŸΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ качСства Π²Ρ…ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ для систСмы Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€Π½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ (ВО) Π½Π° основС ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ точности ΠΈΠ·ΠΌΠ΅Ρ€Π΅Π½ΠΈΠΉ Ρ€Π°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… (Π Π›) сСнсоров являСтся ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΎΡ‡Π΅Π²ΠΈΠ΄Π½Ρ‹Ρ… ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ². Однако ΠΏΡ€ΠΈ этом Π Π›-Ρ†Π΅Π»ΠΈ ΠΌΠΎΠ³ΡƒΡ‚ ΡΡ‚Π°Ρ‚ΡŒ "слоТными цСлями", ΠΈΠΌΠ΅ΡŽΡ‰ΠΈΠΌΠΈ нСсколько ΠΎΡ‚ΠΌΠ΅Ρ‚ΠΎΠΊ Π½Π° Π²Ρ‹Ρ…ΠΎΠ΄Π΅ обнаруТитСля. Π­Ρ‚ΠΎ затрудняСт Ρ‚ΠΎΡ‡Π½ΡƒΡŽ ΠΎΡ†Π΅Π½ΠΊΡƒ кинСтичСских ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Ρ†Π΅Π»Π΅ΠΉ Π² систСмС ВО. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ прСдставлСны Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ синтСза ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈ формирования Π΄Π°Π½Π½Ρ‹Ρ… ΠΈΠ· ΠΎΡ‚Ρ€Π°ΠΆΠ΅Π½Π½Ρ‹Ρ… сигналов слоТных Ρ†Π΅Π»Π΅ΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰Π΅Π³ΠΎ Ρ‚ΠΎΡ‡Π½ΠΎ ΠΎΡ†Π΅Π½ΠΈΡ‚ΡŒ кинСтичСскиС ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ для Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ‡ΠΈ ВО.ЦСль Ρ€Π°Π±ΠΎΡ‚Ρ‹. ΠšΡ€Π°Ρ‚ΠΊΠΎΠ΅ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ ΠΏΡ€ΠΈΡ‡ΠΈΠ½ формирования "слоТных Ρ†Π΅Π»Π΅ΠΉ". Π‘ΠΈΠ½Ρ‚Π΅Π· ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈ формирования Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠΎ ΠΎΡ‚Ρ€Π°ΠΆΠ΅Π½Π½Ρ‹ΠΌ сигналам ΠΎΡ‚ слоТных Ρ†Π΅Π»Π΅ΠΉ Π½Π° основС Ρ‚Π΅ΠΎΡ€ΠΈΠΈ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π Π›-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹. ВСория Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ сигналов; прикладная тСория ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π Π›-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ; MATLAB Simulink Toolboxes для модСлирования ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π Π›-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ; ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΎΠΉ кластСризации.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. На основС Π°Π½Π°Π»ΠΈΠ·Π° Π½Π΅ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… характСристик слоТных Ρ†Π΅Π»Π΅ΠΉ ΠΈ Ρ‚Π΅ΠΎΡ€ΠΈΠΈ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π Π›-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ синтСзирован ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½Ρ‹ΠΉ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈ формирования Π΄Π°Π½Π½Ρ‹Ρ… ΠΎΡ‚Ρ€Π°ΠΆΠ΅Π½Π½Ρ‹Ρ… сигналов ΠΎΡ‚ этого класса Ρ†Π΅Π»Π΅ΠΉ, ΡΠ²Π»ΡΡŽΡ‰ΠΈΡ…ΡΡ прСдпосылкой для Ρ‚ΠΎΡ‡Π½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΈΡ… "ΠΏΡ€Π΅Π΄ΡΡ‚Π°Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΎΡ‚ΠΌΠ΅Ρ‚ΠΊΠΈ" ΠΏΡ€ΠΈ Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΈ Π·Π°Π΄Π°Ρ‡ΠΈ ВО.Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· особСнностСй формирования слоТных Ρ†Π΅Π»Π΅ΠΉ Π² Π Π›-Ρ‚Π΅Ρ…Π½ΠΈΠΊΠ΅ ΠΈ ΠΈΡ… особСнностСй ΠΏΡ€ΠΈ Ρ‚ΠΎΡ‡Π½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠ΅ истинной ΠΎΡ‚ΠΌΠ΅Ρ‚ΠΊΠΈ; синтСзирован ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½Ρ‹ΠΉ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈ формирования Π Π›-сигналов, ΠΎΡ‚Ρ€Π°ΠΆΠ΅Π½Π½Ρ‹Ρ… ΠΎΡ‚ слоТных Ρ†Π΅Π»Π΅ΠΉ, ΡΠ²Π»ΡΡŽΡ‰ΠΈΠΉΡΡ основой ΠΏΡ€ΠΈ Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΈ Π·Π°Π΄Π°Ρ‡ ВО
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