Bolide Energetics and Infrasound Propagation: Exploring the 18 December 2018 Bering Sea Event to Identify Limitations of Empirical and Numerical Models

Infrasound observations are an important tool in assessing the energetics of bolides and can help quantify the flux of meteoroids through Earth’s atmosphere. Bolides are also important atmospheric sources for assessing long-range infrasound propagation models and can be used as benchmark events for validating the International Monitoring System (IMS) infrasound network, which is designed to detect nuclear tests in the atmosphere. This article exploits unique infrasound observations from a large bolide recorded on IMS infrasound arrays and high-density infrasound deployments in the United States to assess limitations in infrasound source scaling relationships. The observations provide an unprecedented sampling of infrasound propagation along a transect at an azimuth of 60° from the source to a distance of ∼8000 km. Widely used empirical laws for assessing bolide energetics and state-of-the-art numerical models for simulating infrasound propagation are assessed to quantify important discrepancies with the observations. In particular, empirical laws for equivalent yield, which are based on signal period and are assumed to be relatively unaffected by propagation effects, can be heavily contaminated by site noise. In addition, by modeling infrasound propagation over a range of ∼8000 km, we show that state-of-the-art models do not reproduce the observed amplitude decay over this long range (which decays by a rate of at least 2 higher than can be modeled).

Devising methods to synthesize discrete complex signals with required properties for application in modern information and communication systems

Information and communication systems (ICSs) must comply with increasingly stringent requirements to ensure the reliability and speed of information transmission, noise immunity, information security. This paper reports the methods to synthesize discrete complex cryptographic signals, underlying the construction of which are random (pseudo-random) processes; the methods for synthesizing characteristic discrete complex signals whose construction is based on using the nature of the multiplicative group of a finite field; the results of studying the properties of the specified signal systems. It is shown that the methods built provide a higher synthesis performance than known methods and make it possible to algorithmize the synthesis processes for the construction of software and hardware devices to form such signals. The win in the time when synthesizing nonlinear signals in finite fields using the devised method is, compared to the known method, for the period of 9,972 elements is 1,039.6 times. The proposed method for synthesizing the entire system of such signals, based on decimation operation, outperforms the known method of difference sets in performance. Thus, for a signal period of 2,380 elements, the win in time exceeds 28 times. It has also been shown that the application of such systems of complex signals could improve the efficiency indicators of modern ICSs. Thus, the imitation resistance of the system, when using complex discrete cryptographic signals with a signal period of 1,023 elements, is four orders of magnitude higher than when applying the linear signal classes (for example, M-sequences). For a signal period of 1,023 elements, the win (in terms of structural secrecy) when using the signal systems reported in this work exceeds 300 times at a period of 8,192, compared to the signals of the linear form (M-sequences)

Using Repetitive Control with Force Feedback to Reduce Impedance of Exoskeletons for Gait Training

Transparent interaction, or the reduction of human-robot interaction forces, is an important quality of gait training exoskeletons. In this paper, we investigate the feasibility of using a repetitive controller for reducing impedance of gait training exoskeletons using force feedback. We used a two-mass spring damper model system, and simulated the application of repetitive force controllers with the objective of reducing the end-point impedance of the distal mass. We designed and applied three repetitive controllers: a 1st order, a 2nd order designed for random signal period error, and a 2nd order designed for constant signal period error. We compared these three repetitive controllers subject to plant model parameter error, random signal period error, and constant signal period error. Numerical simulations under nominal conditions show that via repetitive force control, it is possible to reduce the endpoint impedance to the targeted magnitude and RMSE force below the limit achievable with force controllers while guaranteeing passivity. Furthermore, we established that the application of a 2nd order repetitive controller designed for random period error is highly robust to random period error - exceeding the performance of the passive proportional controller up to 30% error of nominal frequency. Furthermore, this 2nd order repetitive controller designed for random period error maintains a 100% convergence rate through 60% plant parameter error.

Verification of the Propagation Range of Respiratory Strain Using Signal Waveform Measured by FBG Sensors

The FBG (Fiber Bragg grating) sensor is an optical fiber type strain sensor. When a person breathes, strain occurs in the lungs and diaphragm. This was verified using an FBG sensor to which part of the living body this respiratory strain propagates. When measured in the abdomen, the signal waveforms were significantly different between breathing and apnea. The respiratory cycle measured by the temperature sensor attached to the mask and the strain cycle measured by the FBG sensor almost matched. Respiratory strain was measured in the abdomen, chest, and shoulder, and the signal amplitude decreased with distance from the abdomen. In addition, the respiratory rate could be calculated from the measured strain signal. On the other hand, respiratory strain did not propagate to the elbows and wrists, which were off the trunk, and the respiratory time, based on the signal period, could not be calculated at these parts. Therefore, it was shown that respiratory strain propagated in the trunk from the abdomen to the shoulder, but not in the peripheral parts of the elbow and wrist.

Signal periodicity detection using Ramanujan subspace projection

Signal periodic decomposition and periodic estimation are two crucial problems in the signal processing domain. Due to its significance, the applications have been extended to fields like periodic sequence analysis of biomolecules, stock market predictions, speech signal processing, and musical pitch analysis. The recently proposed Ramanujan sums (RS) based transforms are very useful in analysing the periodicity of signals. This paper proposes a method for periodicity detection of signals with multiple periods based on autocorrelation and Ramanujan subspace projection with low computational complexity. The proposed method is compared with other signal periodicity detection methods and the results show that the proposed method detects the signal period correctly in less time.

A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.

Usage of phase portraits in analysis of doppler signals reflected from drone rotors

Subject and Purpose. Comparative analysis of the shapes of phase portraits of Doppler signals (DS) reflected from drone rotating rotors is given with regard to different time delays. The shapes of DS phase portraits are examined for various rotational velocities and numbers of the rotating rotors. Methods and Methodology. A method using phase portraits is suggested for analysis of Doppler signals reflected from drone rotating rotors. The method determines the number of the rotors and estimates their rotational velocities. Results. It has been found that the shape of the phase portrait of the baseline signal is practically independent of the time delay and can be described as an occasional movement of the image point following the phase trajectory in the center of the phase portrait. The appearance of characteristic regions on the periphery of the phase portrait allows separating baseline and sounding signals. It has been shown that the shape of the DS phase portrait of the rotating rotor during the flight movement of the drone depends on the time delay value. With a larger delay, phase portraits similar in shape appear at regular intervals equal to the Doppler signal period. With a larger rotational velocity of the rotor, the rate of similar phase portrait appearance diminishes. Conclusion. During the sounding of drone rotating rotors, the shapes of DS phase portraits depend on the value of time delay. With a larger number of rotors in the drone flight, the periodic change character as to the DS phase portrait shape remains unchanged. In this case, DS phase portrait shapes differ substantially for different numbers of drone rotors

ALGORITHM FOR DETERMINING PHASE SHIFT OF HARMONIC SIGNALS USING STOCHASTIC DISCRETIZATION

The article focuses on the problem of estimating basic parameters of harmonic signals. An algorithm for determining the phase shift of such signals by the probabilistic-statistical method is proposed. An important advantage of the algorithm is the fact that the circulation time is aliquant to the signal period or shorter. It is important when measuring parameters of infra-low-frequency radio signals. Mathematical modeling of the algorithm was carried out to study errors and assess the possibility of practical application of the algorithm for processing infra-low-frequency radio signals at stochastic discretization. The results of a numerical experiment show that the error of determining the phase shift of harmonic signals using the developed algorithm on the basis of integral samples with stochastic discretization of time intervals according to the uniform distribution law is 1/100 of a percent, and little depends on the accuracy of signal discretization by level. The error obtained corresponds to the accuracy of discretization when translating the 12-bit ADCs into analog-digital converters. The proposed algorithm can be applied for processing infra-low-frequency radio signals in acoustics and communication, as well as in geophysical and biomedical research.