Акустооптическая вейвлет-обработка биоэлектрических сигналов

Wavelet analysis is one of the most efficient methods of the informative signals characteristics investigation. First shown the possibility of informative signals wavelet processing by means of the acousto-optic processor with time integration. The possibility of the realization of both power spectrum calculation and wavelet transform performance of bioelectric signals in the real time mode has been proved. The analysis is listed which describes its operation in different modes

Research of oscillation mode in automated pulsed eddy current testing systems

The formation and analysis of eddy current probe signals obtained in pulsed excitation mode is considered. The proposed method of implementing pulsed eddy current testing with the formation of attenuating harmonic oscillations is more resistant to the effects of noise and interference that accompany the process of inspected object parameters evaluation. The equivalent scheme of the system “test object–eddy current probe” is developed and analyzed. The obtained mathematical model of the eddy current probe signals allowed proposing the natural frequency and the attenuation as informative signals parameters, which are determined from signals phase and amplitude characteristics. Developed algorithm and the proposed methodology was implemented for evaluation of eddy current signals parameters and related characteristics of testing objects. This method was experimentally verified on a series of different test specimens. The obtained results confirm the possibility to apply the proposed informative signals to solve some problems concerned with automated eddy current testing. The formation and analysis of eddy current probe signals obtained in pulsed excitation mode are considered. The proposed method of implementing pulsed eddy current testing with the formation of attenuating harmonic oscillations is more resistant to the effects of noise and interference that accompany the process of automated eddy current testing. The equivalent scheme of the system “test object–eddy current probe” is developed and analyzed. The obtained mathematical model of the eddy current probe signals allows proposing the natural frequency and the attenuation as informative signals parameters, which are determined from signal phase and amplitude characteristics. Methods of increasing the accuracy of determining the eddy current probe signals attenuation and frequency using trends of signals phase and amplitude characteristics are considered. The proposed signal processing method was verified by modeling the process of determining the eddy current probe signals attenuation and the frequency from the signal with Gaussian noise. Algorithmic and software were developed based on the simulation results and the proposed improved methodology was implemented for determining signals parameters and related parameters and characteristics of testing objects.

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

The microbiome emits informative signals of biological organization and environmental pressure that aid ecosystem monitoring and prediction. Are the many signals reducible to a habitat-specific portfolio that characterizes ecosystem health? Does an optimally structured microbiome imply a resilient microbiome? To answer these questions, we applied our novel Eco-Evo Mandala to bacterioplankton data from four habitats within the Great Barrier Reef, to explore how patterns in community structure, function and genetics signal habitat-specific organization and departures from theoretical optimality. The Mandala revealed communities departing from optimality in habitat-specific ways, mostly along structural and functional traits related to bacterioplankton abundance and interaction distributions (reflected by ϵ and λ as power law and exponential distribution parameters), which are not linearly associated with each other. River and reef communities were similar in their relatively low abundance and interaction disorganization (low ϵ and λ) due to their protective structured habitats. On the contrary, lagoon and estuarine inshore reefs appeared the most disorganized due to the ocean temperature and biogeochemical stress. Phylogenetic distances (D) were minimally informative in characterizing bacterioplankton organization. However, dominant populations, such as Proteobacteria, Bacteroidetes, and Cyanobacteria, were largely responsible for community patterns, being generalists with a large functional gene repertoire (high D) that increases resilience. The relative balance of these populations was found to be habitat-specific and likely related to systemic environmental stress. The position on the Mandala along the three fundamental traits, as well as fluctuations in this ecological state, conveys information about the microbiome’s health (and likely ecosystem health considering bacteria-based multitrophic dependencies) as divergence from the expected relative optimality. The Eco-Evo Mandala emphasizes how habitat and the microbiome’s interaction network topology are first- and second-order factors for ecosystem health evaluation over taxonomic species richness. Unhealthy microbiome communities and unbalanced microbes are identified not by macroecological indicators but by mapping their impact on the collective proportion and distribution of interactions, which regulates the microbiome’s ecosystem function.

Private Monitoring and Communication in the Repeated Prisoner’s Dilemma

This paper provides a model of the repeated prisoner’s dilemma in which cheap-talk communication is necessary in order to achieve cooperative outcomes in a long-term relationship. The model is one of complete information. I consider a continuous time repeated prisoner’s dilemma game where informative signals about another player’s past actions arrive following a Poisson process; actions have to be held fixed for a certain time. I assume that signals are privately observed by players. I consider an environment where signals are noisy, and the correlation of signals is higher if both players cooperate. We show that, provided that players can change their actions arbitrary frequently, there exists an equilibrium with communication that strictly Pareto-dominates all equilibria without communication.

Automated Robust Interpretation of Intraoperative Electrophysiological Signals – A Bayesian Deep Learning Approach

Intraoperative neurophysiological monitoring (IONM) is an essential tool during numerous surgical interventions to assess and monitor the functional integrity of neural structures at risk. A reliable signal interpretation is of importance to support medical staff by reducing manual evaluation. Deep learning (DL) techniques proved to be a robust tool for the analysis of neurophysiological data. The large amount of required manually labeled data as well as the lack of interpretability of the results however often limit the use of DL in medical scenarios. A possible way to tackle these obstacles is the utilization of Bayesian deep learning (BDL) methods. The modelling of uncertainties in the network parameters and the thereby possible quantification of predictive uncertainties allows both the identification of potential erroneous predictions as well as the targeted selection of informative signals in the context of active learning. To evaluate the applicability of BDL for the analysis of electrophysiological data as well as to increase the training efficiency by active learning, we implemented a multi-task Bayesian Convolutional Neural Network (BCNN) for the simultaneous classification of action potentials and the assessment of relevant signal characteristics (latency, maximum, minimum). We compare the results for electromyographical signals (EMG), containing in total approximately twelve thousand signals from 34 patients, with both a traditional non-Bayesian single-task and multi-task CNN. For all models, including the BCNN, we could achieve similar performances with detection rates over 97% accuracy. Further, we could improve training efficiency of the BCNN using pool-based active learning and therefore significantly reduce the required amount of manual labeling. The evaluated predictive uncertainties of the BCNN prove useful both for the efficient selection of informative signals in the context of active learning as well as the interpretation of the predictive posterior distribution and therefore trustworthiness of the classifications.

Modeling and analysis of the states of objects and subjects of monitoring by means of secure sensor networks and IoT networks with a supercomputer

The paper proposes an information technology for evidence-based monitoring of the states of remote and mobile objects and subjects. The proposed method for the effective implementation of long-term monitoring of a large number of objects based on modeling information states of objects by means of aperture or zone control of changes in selected indicators and calculated signal characteristics. Taking into account the minimization of computations with performance-limited processor facilities of the object systems of secure wireless monitoring networks at the places of introduction of monitoring signals, it is proposed to form logical and statistical information models of the behavior of objects that correspond to the current functional and operating states of objects of long-term monitoring. To identify the most informative signals and characteristics of the states of objects, it is proposed to calculate and analyze the relative and normalized indicators and characteristics of signals. Information technology is focused on long-term monitoring of objects and subjects in various spheres of human activity.

Models for Generating and Processing of Signals of the Panoramic Sensor of Aerodynamic Angle and True Airspeed

The importance of information about the true airspeed and aerodynamic angles of aircraft and replenishment of arsenal of their measuring means with only electronic design scheme, low weight and cost, providing a panoramic measurement of the gliding angle is noted. It is shown that traditional measuring means of true airspeed of AP, which implement the aerodynamic and vane measuring methods of parameters of incoming air flow, using receivers and sensors distributed over the fuselage, have a complex design, significant weight and cost, and limited ranges of measuring aerodynamic angles, which limits their use on small-sized aircraft plane. The integrated sensor of aerodynamic angle and true airspeed, which implements a vortex method for measuring the parameters of incoming air flow, is considered. A single fixed flow receiver simplifies the design, and the time-frequency primary informative signals reduce the errors of instrumentation channel. The limited range of measurement of the gliding angle limits the use of the sensor on small AP. The integrated sensor of aerodynamic angle and true airspeed, which implements the ion-mark method for measuring the parameters of incoming air flow, is considered. The sensor provides a panoramic measurement of aerodynamic angle using receivers distributed in the measurement plane. But the multichannel measuring circuit significantly complicates the design, increases the weight and cost of the sensor, which limits its use on small-sized aircraft plane. The functional scheme of the original panoramic purely electronic sensor of the aerodynamic angle and true airspeed with one fixed receiver of the incoming air flow and ultrasonic instrumentation channels is revealed. Analytical models of the formation, processing and determination of the aerodynamic angle and true airspeed using frequency, time-pulse and phase informative signals are obtained. The analysis of the variants of used informative signals determines the prospects of using of the panoramic sensor with frequency informative signals on small-sized aircraft plane, in which there are no methodological errors from the influence of the ambient temperature when changing the flight altitude.

Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

Virtual reality (VR) in retailing (V-commerce) has been proven to enhance the consumer experience. Thus, this technology is beneficial to study behavioral patterns by offering the opportunity to infer customers’ personality traits based on their behavior. This study aims to recognize impulsivity using behavioral patterns. For this goal, 60 subjects performed three tasks—one exploration task and two planned tasks—in a virtual market. Four noninvasive signals (eye-tracking, navigation, posture, and interactions), which are available in commercial VR devices, were recorded, and a set of features were extracted and categorized into zonal, general, kinematic, temporal, and spatial types. They were input into a support vector machine classifier to recognize the impulsivity of the subjects based on the I-8 questionnaire, achieving an accuracy of 87%. The results suggest that, while the exploration task can reveal general impulsivity, other subscales such as perseverance and sensation-seeking are more related to planned tasks. The results also show that posture and interaction are the most informative signals. Our findings validate the recognition of customer impulsivity using sensors incorporated into commercial VR devices. Such information can provide a personalized shopping experience in future virtual shops.

Back to the Present: Learning about the Euro Area through a Now-casting Model

We build a model for simultaneously now-casting economic conditions in the euro area and its three largest member countries|Germany, France, and Italy. The model formalizes how market participants and policymakers monitor the euro area by incorporating all market moving indicators in real time. We find that area wide and country-specific data provide informative signals to now-cast the economic conditions in the euro area and member countries. The model provides accurate predictions of economic conditions in real time over a period that covers the past three recessions.