Recognition Of Sources Research Articles

An Advanced Phase Synchronization Scheme Based on Coherent Integration and Waveform Diversity for Bistatic SAR

In the bistatic synthetic aperture radar (BiSAR) system, the deviation between two oscillators in different platforms will cause an additional modulation of BiSAR echoes. Therefore, phase synchronization is one of the key issues that must be addressed for the BiSAR system. The oscillator phase error model and the principle of phase synchronization are firstly described. The waveform diversity technology has been widely used in many fields, for example, the hearing aids device and the recognition of auditory input source in cocktail party problem. Inspired by this, an advanced phase synchronization scheme based on coherent integration and waveform diversity is proposed. The synchronization signal and radar signal are orthogonal signals which can be separated by using waveform diversity technique. After extracting the synchronization signal, the phase synchronization accuracy can be further improved by coherent integration. The transmission of synchronization signals between two synchronization antennas is analyzed, followed by the theoretical error analysis. Then, the processing of separating the echo signal and synchronization signal is described in detail. The simulation experiments are performed. The accuracy of phase synchronization can reach 1 degree, which verifies the effectiveness of the proposed synchronization scheme.

Evolution of deformation mechanism and fluid flow in two pre-rift siliciclastic deposits (Pannonian Basin, Hungary)

Abstract Distinct stages of deformation and fluid flow-related diagenetic alterations are recorded in Lower Miocene sandstone and conglomerate of the Pannonian Basin, Central Europe. Multiple generations of structural elements (deformation bands, calcite dominoes and veins) as well as host rocks were investigated using petrographical, elemental and stable isotope geochemical methods together with fluid inclusion petrography and microthermometry. The integration of acquired structural and diagenetic data into a subsidence model constrains the spatial and temporal evolution of deformation mechanisms and diagenetic processes. The six investigated sites represent central and marginal areas during both the pre-rift and syn-rift phases of basin evolution. The elements of pre-rift phases were preserved at the eastern margin, where eogenetic calcite precipitated in the host rocks and in the early types of deformation bands. Their δ18OPDB isotope values from −4.3‰ to −1.9‰ paired with δ13CPDB isotope values from −3.8‰ to 1.8‰ refer to precipitation from connate marine and mixed marine and meteoric pore water. The syn-rift phases are represented by eogenetic calcite and start of shallow mesogenetic diagenetic alterations in marginal position as well as in the basin centre. The δ18OPDB and δ13CPDB isotope ratios in these calcites yielded values from −15.2‰ to −5.7‰ and from to −18.5‰ to -–1.6‰, respectively. The gradual depletion in heavy isotopes shows positive covariance as the deformation progressed in time. This trend is attributed to an increasing proportion of deeply circulating meteoric fluid. The sporadic fluid inclusion data confirm meteoric fluid contribution to certain carbonate cement phases. The distinguished calcite generations in pre-rift and syn-rift structural elements and host rocks were mainly related to the phases of intense subsidence that, together with the increased rift-related heat flow, warmed up the circulating fluids. The isotope values deviating from the general trend allow the recognition of local source of light carbon contribution to basin-wide fluid-flow evolution, and cannot be tied to the switch in tectonic settings from pre-rift compression to syn-rift extension.

FINANCIAL LITERACY OF THE MANAGEMENT STUDENTS – CZECH AND SLOVAK EXPERIENCE

Financial literacy is becoming one of the key competencies in the 21st century. In its absence, it is virtually impossible to navigate the market for financial products and services and thus ensure financial stability throughout the whole life. The author presents the results of a survey of the financial literacy of young future managers who are currently university students. Based on a questionnaire survey among full-time and part-time students, the author detects the positive impact of the practical experience of external students on their financial literacy. The author uses an innovative metric in the form of a personal finance index to assess financial literacy. In addition to overall success, this approach also makes it possible to analyze knowledge from the eight components of financial literacy. By comparing the results in individual areas, the author reveals that risk management is Achyla's heel of financial literacy. In addition to the knowledge component, the author also deals with the respondents' access to information sources. Based on these findings, the author formulates conclusions for the content of financial education. There is a need for a stronger link between the development of probabilistic thinking and the development of general numeracy with practical financial subjects. An important part of financial education is also the recognition of credible information sources.

Bacteriophages in dairy plants.

Bacteriophages represent the main microbiological threat for the manufacture of fermented foods. The dairy industry is the most affected by this problem, as phages are naturally present in raw milk, surfaces, vats, tanks, floors, and distributed by air displacements. Cheese whey may also contain high phage concentrations. Prophages harbored by lysogenic strains could be induced, generating new lytic phages. In this context, where phages cannot be eradicated from dairies, methods of phage monitoring are mandatory. These are mainly based in microbiological features, like classical methods, that are the most used, economic and simple to carry out. Phage DNA detection and quantification by PCR and qPCR, more complex and expensive, are faster, although not able to discern between viable and non-viable virions. Electron microscopy allows direct visualization and characterization of phage morphology, but the apparatus is expensive. Alternative methods based in other phage traits also exist, though less studied and not applicable on a daily basis. Recognition of contamination sources and correct phage monitoring in dairy factories allow a correct application of control measures. These include general measures such as proper factory design, efficient programs of sanitization, good treatment of raw materials, especially milk, and careful handling of by-products. Additionally, the use of starts cultures should be adequate, with application of rotation schemes when possible. Finally, the selection of bacteriophage insensitive mutants (BIM) is essential, and can be achieved simply and empirically, though the study of CRISPR-Cas and other newly discovered mechanisms provide a more rational basis to obtain BIMs with optimized features.

A Theoretical Basis for the Application of Neuromorphic Concepts to Polychronous Wavefront Computation

Polychronous Wavefront Computation (PWC) defines a simple model of spiking neural networks based on spatially positioned transponders stimulated by signal wavefronts. The simplicity of the concept and elimination of explicit connections between transponders can potentially provide a practical basis for construction of large scale, complex pattern recognition systems. Previous work has shown how PWC transponders can be used to perform basic computations and be organized into simple pattern recognition configurations but the creation of complex pattern recognition behavior remains a difficult problem. The purpose of this work is to identify the key characteristics of complex biological and artificial neural networks and explore the application of those characteristics to the PWC model. The analysis includes a review of neuromorphic processes such as spike-timing-dependent plasticity, synaptic fatigue and potentiation decay as well as biologically inspired artificial neural network structures such as multi-layer perceptrons and convolutional neural networks. Recognition of stimulus sources and use of inhibitive stimulus are identified as key characteristics not addressed by PWC. Approaches to incorporate these key characteristics into the PWC model are discussed with the objective of creating transponder configurations that use unsupervised learning to perform complex pattern recognition.

Application of light gradient boosting machine in mine water inrush source type online discriminant

Water inrush is a kind of mine geological disaster that threatens mining safety. Type recognition of water inrush sources is an effective auxiliary method to forecast water inrush disaster. Compared with the current hydro-chemistry methodology, it spends a large amount of time on sample collection. Considering this problem, it is urgent to propose a novel method to discriminate water inrush source types online, and further to strive to create more time for evacuation before the disaster. The paper proposes an in-situ mine water sources discrimination model based on light gradient boosting machine (LightGBM). This method combined light gradient boosting (GB) with the decision tree (DT) to improve the network integrated learning ability and enhance model generalisation. The data were collected from in-situ sensors such as pH, conductivity, Ca, Na, Mg and CO3 components in different water bodies of LiJiaZui Coal Mine in HuaiNan. The results illustrate that the accuracy of proposed method achieves 99.63% to recognise water sources in the mine. Thus, the proposed discriminant model is a timely and an effective way to recognise source types of water in a mine online.

Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Data assaults from unauthorized access to the Internet of Things will induce severe intrusion and hazard to the whole network. Employing only traditional application layer password authentication approaches cannot guarantee the security of the communication system. Therefore, it is critical to develop a capable and efficient radio frequency fingerprints based physical layer authentication system. To incorporate the domain knowledge in more capable feature extracting and reduce information loss caused by converting RF baseband I/Q signals, we propose a novel differential complex-valued convolutional neural network based individual recognition approach of communication radiation sources in the paper. The proposed method can fully capture the nonlinearity of the RF baseband I/Q signals while decreasing the unfavorable impact of phase rotation induced by carrier frequency offset, which also significantly reduces the required data length of the collected steady-state data transmission section. The recognition performance evaluation on 20 wireless network card devices with duplicate batch, type, and manufacturer shows that the proposed approach has the best recognition performance compared with two conventional approaches whose recognition accuracies are lower than 95%, achieving the total recognition accuracy of 99.7%. Moreover, compared with constellation based approaches, which require at least 5,000 to 10,000 data points as input parameters, the proposed method can reduce the required data length of the collected steady-state data transmission section effectively, which is easier to implement in practical applications.

Application of light gradient boosting machine in mine water inrush source type online discriminant

Water inrush is a kind of mine geological disaster that threatens mining safety. Type recognition of water inrush sources is an effective auxiliary method to forecast water inrush disaster. Compared with the current hydro-chemistry methodology, it spends a large amount of time on sample collection. Considering this problem, it is urgent to propose a novel method to discriminate water inrush source types online, and further to strive to create more time for evacuation before the disaster. The paper proposes an in-situ mine water sources discrimination model based on light gradient boosting machine (LightGBM). This method combined light gradient boosting (GB) with the decision tree (DT) to improve the network integrated learning ability and enhance model generalisation. The data were collected from in-situ sensors such as pH, conductivity, Ca, Na, Mg and CO3 components in different water bodies of LiJiaZui Coal Mine in HuaiNan. The results illustrate that the accuracy of proposed method achieves 99.63% to recognise water sources in the mine. Thus, the proposed discriminant model is a timely and an effective way to recognise source types of water in a mine online.

The influence of the MEX manufacturing parameters on the tensile elastic response of printed elements

PurposeThis paper aims to investigate the influence of the manufacturing stages on three-dimensionally printed elements via material extrusion (MEX).Design/methodology/approachThree factors were identified: the printer, slicer and filament. The impact of each of them was considered by analysing the results from tensile tests, performed on the printed specimens. Using the Prusa MK3S and Anet A6 printers, Cura and PrusaSlicer software as well as Prusa and Plast-Spaw filaments allowed to determine the main factor which affected the differences in the observed sample responses. Additionally, two manufacturing parameters were considered: the infill density and layer height.FindingsThe obtained results showed that the experimental results depend on the process parameters. Firstly, the slicer and next, the printer, had the biggest impact on the mechanical behaviour of the samples, where the first determined the stiffness of the investigated parameter and the second determined its distribution. Finally, the presented results showed that the algorithms implemented in the slicers can have a greater significance that the defined inputs value.Originality/valueThe paper’s aim is recognition of the main sources of inaccuracies in elements produced via the MEX technology. The presented results showed that slicer inputs influenced the mean values of the printed elements’ mechanical parameters, while the printer features affected their distribution. Moreover, the results showed that mechanical behaviours of printed elements can differ significantly depending on the software used. It could explain the discrepancies presented in the conclusions by various researchers.

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

Computer-aided security assessment of water networks monitoring platforms

Monitoring platforms are widely employed for protecting critical systems. The accurate design of these infrastructures against accidental and malicious threats is a crucial activity of their life-cycle. The early recognition of possible attack sources is still an open challenge for the current research.This work aims to exploit model-driven practices and Bayesian Networks to evaluate the impact of monitoring platforms over the protection of modern transport networks and to recognize the most probable attack source during operational phases. As this approach is generally applicable to different transport systems, to better highlight the potentialities of the approach, an instance within the domain of the water distribution networks is presented. Threats against the quality of water, such as chemical and bacteriological attacks aiming at introducing dangerous substances in the transport system, are considered in this work.

Methodology of classification and recognition of the radar emission sources based on Bayesian programming

This study considers the Bayesian programming methodology for recognition and classification of radio emission sources. A mathematical model of Bayesian programming proposes forming a family of probability distributions based on known parameters contained in a training sample (database). The correlations between the object classes have been estimated according discussed methodology. The received assessment has been used to separate procedures of recognition and classification of radar emission sources. The simulation of methodology has carried out for four parameters of radar signals (frequency range, pulse width, pulse repetition interval and radar rotation frequency) by used database with 346 classes and 16 types of radar. Based on the existing database of radar emission sources, it is possible to predict the probability of class recognition for the general population of objects, if its distribution is known. This study demonstrates the consistency of the Bayesian programming methodology for object identification in ELINT systems.

The Bayes Recognition Model for Mine Water Inrush Source Based on Multiple Logistic Regression Analysis

Accurate recognition of water inrush sources is important in mine water hazard control. In this study, 118 water samples of four water types in the Qinan coal mine were analysed by multiple logistic regression, and the 12 water samples that did not meet the requirements were removed. The remaining 106 were used as training samples to establish a Bayes recognition model (BRM). In addition, the BRM was used to complement multiple logistic regression analysis (MLRA) to discriminate other water samples in the mining area. The recognition accuracy of the combined model was 95.28%. The results from the model were consistent with the field water samples and showed that the combined MLRA-BRM approach fully considers the hydraulic relationships between different aquifers and a mixed water inrush source. Moreover, the MLRA-BRM combination improved water inrush source recognition accuracy and was more reliable than using MLRA or BRM alone.

Parallel heuristic search strategy based on a Bayesian approach for simultaneous recognition of contaminant sources and aquifer parameters at DNAPL-contaminated sites

In this study, we develop a parallel heuristic search strategy based on a Bayesian approach for simultaneously recognizing groundwater contaminant sources and aquifer parameters (unknown variables) at sites contaminated with dense non-aqueous phase liquids (DNAPLs). The parallel search strategy is time-consuming because thousands of simulation models must run in order to calculate the likelihood. Various stand-alone surrogate systems for the simulation models have been established, but they also have unavoidable limitations. Thus, we develop an optimal combined surrogate system by combining Gaussian process, kernel extreme learning machine, and support vector regression methods using a differential evolution algorithm with a variable mutation rate based on the rand-to-best/1/bin strategy, thereby improving the approximation accuracy of the surrogate system to the simulation model and significantly decreasing the high computational cost. Utilizing the optimal combined surrogate system reduced the CPU time by more than 400 times. In the iterative parallel heuristic search process, each round of iteration involves determining the candidate points and state transitions. The Monte Carlo approach is used widely for selecting candidate point, but this approach does not readily converge to the posterior distribution for unknown variables when the probability density function types are complex with weak search ergodicity. In order to improve the search ergodicity, we develop a particle swarm optimization algorithm with a non-linear decreasing inertia weight and Metropolis criterion, which is more suitable for unknown variables with complex probability density functions. The recognition results are obtained simultaneously when the iterative process terminates. We assess our proposed approaches based on a hypothetical case study at a three-dimensional site contaminated with DNAPLs. The results demonstrate that the parallel heuristic search strategy is helpful for the simultaneous recognition of DNAPL contaminant sources in groundwater and aquifer parameters.

Thoracic Outlet Syndrome in the Overhead Athlete: Diagnosis and Treatment Recommendations.

Neurovascular compression in the upper extremity is rare but can affect even those participating in high-level competitive athletics. To assess optimal approaches to treatment, in this review, we evaluate the current literature on neurovascular compressive syndromes affecting the upper extremity, with a special focus on the thoracic outlet syndrome (TOS). Neurovascular compression at the thoracic outlet can involve the brachial plexus, subclavian artery, or subclavian vein, each with distinct clinical manifestations. Neurogenic TOS is best treated with surgical decompression, if physical therapy has not improved symptoms. Venous TOS results in acute thrombosis superimposed on chronic venous compression. Treatment is best directed at early anticoagulation, catheter-directed thrombolysis, and surgical decompression, with most patients able to discontinue anticoagulation and return to high-level athletic activity. Arterial TOS is related to aneurysmal degeneration of the subclavian artery with distal embolization, leading to limb-threatening ischemia. This should be aggressively treated with surgery. Similar degenerative changes can occur in the axillary artery and its branches, leading to distal embolization. Prompt recognition of these potential sources of limb-threatening ischemia is critical to limb preservation. TOS includes rare but important conditions in the overhead athlete. Recent advances in physical therapy and image-guided diagnostic techniques have facilitated more accurate diagnosis. Surgical treatment remains the gold standard to maximize function or for limb preservation, and future research is needed to clarify optimal pain and physiotherapy regimens, as well as to examine novel approaches to neurovascular decompression.

Удосконалена методика раціонального розподілу інформації за важливістю і кількістю джерел радіомоніторингу в АСУ військами та зброєю

When designing automated systems for command and control of troops and weapons, an important role is given to the form and volume of information transmitted information. Information in the control loop comes from sensors that make up the monitoring system and continuously monitor the sources and objects of monitoring. The control and communication system of a group object of radio monitoring is a complex hierarchical structure. The number of sources that operate in the interests of providing a control system exceeds the number of available forces and means that carry out radio monitoring. There is a discrepancy in the recognition of objects and sources of radio monitoring: identification of the object of observation, the number of signs. Therefore, it is necessary to solve the multicriteria optimization problem for identifying the operating mode of radio monitoring sources for guaranteed determination of the state of the control system and communication of the group object of radio monitoring, which consists in choosing those sources whose utility is greatest. The proposed methodology for the rational distribution of information in automated control systems by troops and weapons, by selecting the most important sources of radio monitoring and rationalizing the number of gradations of radio monitoring objects to ensure a given probability of correct recognition and guaranteed determination of their condition.

Energy Distribution of Optical Radiation Emitted by Electrical Discharges in Insulating Liquids

This article presents the results of the analysis of energy distribution of optical radiation emitted by electrical discharges in insulating liquids, such as synthetic ester, natural ester, and mineral oil. The measurements of optical radiation were carried out on a system of needle–needle type electrodes and on a system for surface discharges, which were immersed in brand new insulating liquids. Optical radiation was recorded using optical spectrophotometry method. On the basis of the obtained results, potential possibilities of using the analysis of the energy distribution of optical radiation as an additional descriptor for the recognition of individual sources of electric discharges were indicated. The results can also be used in the design of various types of detectors, as well as high-voltage diagnostic systems and arc protection systems.

Remarkable nucleation and growth of ultrafine particles from vehicular exhaust

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem.

ІНФОРМАЦІЙНА СИСТЕМА ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕНЬ АВТОМАТИЗОВАНОЇ СИСТЕМИ УПРАВЛІННЯ РАДІОМОНІТОРИНГОМ

Armed aggression of the Russian Federation against Ukraine, loss of important industrial potentials, as Topaz state-owned holding company, which specialized in the development and production of complex radio engineering systems and complexes, including unique long-range radio intelligence systems and early warning of air defense systems, Kolchuga radio intelligence has given a significant impetus to the development of new approaches to conducting radio monitoring of the situation, as one of the main tion method of obtaining information. Such a complex type of technical activity as radio monitoring requires considerable effort to maintain it in readiness. The continuous provision of radio monitoring by the required number of highly qualified specialists with extensive experience in determining the type of radio source requires considerable effort to find and train them. Training of a specialist in this class takes at least 1-1.5 years and significant financial costs. Solving the scientific task of automating the process of detection and recognition is one of the priority areas for the development of radio monitoring. Modern radio monitoring character of functioning is divided into 2 groups: active, which allows to identify and detect non-functioning sources of radio emission; passive, allowing you to identify and recognize functioning radio sources. The basis for the operation of passive radio monitoring is the detection of signal transmitting messages (such as air traffic control signal) and the recognition of the parameters of the powerful signals of onboard, ship and ground radar stations. The informative nature of the radio source signal for radio monitoring facilities depends on how reliably the signal is detected and how accurately (accurately) its parameters are determined, which are useful for the intelligence of the message or value. Since signal monitoring always occurs against a background of all sorts of obstacles, the fact of detecting a signal, as well as errors in measuring signal parameters and message selection are always random. The article deals with the actual scientific task of automating the process of detection and recognition of radio sources, by developing a decision support system for determining the type of sources of radio radiation by its signal parameters in the interests of the state monitoring system, which provides guidance to the MTS-Kolchuga RTR operators the choice of the most likely variant of the decision that will reduce the qualification requirements for the operators, which will significantly reduce the financial and time costs for their preparation.

Antibody validation for Western blot: By the user, for the user

Antibody validation for Western blot: By the user, for the user