MEMS Accelerometer Noises Analysis Based on Triple Estimation Fractional Order Algorithm

This paper is devoted to identifying parameters of fractional order noises with application to noises obtained from MEMS accelerometer. The analysis and parameters estimation will be based on the Triple Estimation algorithm, which can simultaneously estimate state, fractional order, and parameter estimates. The capability of the Triple Estimation algorithm to fractional noises estimation will be confirmed by the sets of numerical analyses for fractional constant and variable order systems with Gaussian noise input signal. For experimental data analysis, the MEMS sensor SparkFun MPU9250 Inertial Measurement Unit (IMU) was used with data obtained from the accelerometer in x, y and z-axes. The experimental results clearly show the existence of fractional noise in this MEMS’ noise, which can be essential information in the design of filtering algorithms, for example, in inertial navigation.

Soundscapes in the German Baltic Sea Before and During the Covid-19 Pandemic

Anthropogenic underwater noise has been identified as one of the main pressures on the marine environment. Considerable research efforts have been made to quantify acoustic soundscapes on different spatial and temporal scales in order to identify trends and investigate how this may impact the marine environment. Measures to reduce noise input into the seas from anthropogenic sources are under discussion, including the reduction of vessel speed or re-routing of shipping lanes. The decline in maritime transport as a consequence of the Covid-19 pandemic provides an opportunity to examine the associated extent of noise reduction. Here, we present the results of a “Before-After-Control-Impact” study where we analyzed acoustic data sets from two monitoring stations in the German Baltic Sea. Data were collected between 2013 and 2020. As part of an international initiative, coordinated by the International Quiet Ocean Experiment, monthly statistics (20 average sound pressure levels per 1/3 octave bands) were calculated from acoustic data collected during the pre-pandemic period (2013–2019), and were compared with data from the year 2020, during the Covid-19 pandemic. To account for varying natural conditions the measurements were sorted into categories of same prevailing sea state. Through this approach, measurements with equivalent natural noise impact are compared and any resulting differences are likely due to the variability in the anthropogenic noise. A decline in sound pressure of 13% (1.2 dB) for low frequencies (10 Hz–1 kHz) was observed at both stations, which corresponds to the reduced level of shipping activity.

Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs

According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the “common-noise” groups compared to the “independent-noise” groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.

Flexural Band Gaps and Vibration Control of a Periodic Railway Track

Periodic structures exhibit unique band gap characteristics by virtue of which they behave as vibro-acoustic filters thereby allowing only waves within a certain frequency range to pass through. In this paper, both lateral and vertical flexural wave propagation and vibration control of a periodic railway track are studied in depth. More precisely, a rail fastened on rigid sleeper blocks is modeled with an Euler-Bernoulli beam. The dispersion relations in both lateral and vertical directions are obtained using the Floquet-Bloch theorem and the resulting dispersion curves are verified using finite element (FE) models. Afterwards, tuned mass dampers (TMDs) with different mass ratios are designed to control vibrations of the examined rail along both lateral and vertical directions. Moreover, the influence of damping of rail and resonators on band structures is investigated. As a replacement to the conventional TMD, a novel possibility to control vibrations relies on using another rail as a lateral distributed resonator (LDR). Although the effectiveness of LDR is lower than that of localized resonators, the former represents a simple and promising way to control vibrations. Efficacy of the proposed control methods is finally verified using the results of transient simulation based on a random Gaussian white noise input.

Cortical resonance selects coherent input

SummarySynchronization has been implicated in neuronal communication, but causal evidence remains indirect. We used optogenetics to generate depolarizing currents in pyramidal neurons of cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. Cortex transformed constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increased the strength and frequency of synchronization. Slow, symmetric excitation profiles revealed hysteresis of power and frequency. Crucially, white-noise input sequences enabled causal analysis of network transmission, establishing that cortical resonance selectively transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncovered a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.

A study on shape of electromagnetic guideway for continuous steel plates by analysis of multi-body dynamics

To obtain the static shape of the continuous steel plate and vibration characteristic, we performed the analysis of the multi-body dynamics in which the continuous steel plate was discretized in many solid bodies. The shape of the steel plate obtained by the measurement experiment was agreement with the static shape of the steel plate obtained by the analysis. Then, dynamical analysis was performed when white noise input to the steel plate. It was confirmed that the vibration could be suppressed when the electromagnets were installed in consideration of the static shape of the steel plate.

Performance Investigation of Hydraulic Actuator Based Mass Lift System using MPC and LQR Controllers

A hydraulic actuator is a system that can provide a large power amplification in industries and factories. In this paper, mass lifter hydraulic actuator system to a desired displacement is designed using optimal control theory. MPC and LQR controllers are used to design and improve the performance of the hydraulic actuator. The hydraulic actuator system is linearized using Taylor series linearization method and designed using Matlab/Simulink tool. Comparison of the hydraulic actuator with MPC and LQR controllers using three desired output displacement signals (step, sine wave and white noise) is done and simulation results have been analyzed successfully. For the desired step input signal, the hydraulic actuator system with MPC controller lower rise and settling times with small percentage overshoot as compared to the hydraulic actuator system with LQR controller and for the desired sine wave signal, the hydraulic actuator system with MPC controller almost track the desired sine wave input signal correctly as compared to the hydraulic actuator system with LQR controller. While for the desired white noise input signal, the hydraulic actuator system with MPC controller have tried to track the desired white noise input signal with small variation in amplitude as compared to the hydraulic actuator system with LQR controller. Finally the comparative simulation results prove the effectiveness of the proposed hydraulic actuator system with MPC controller.

Identification of Stochastic Process in MATLAB

The system identification toolbox in MATLAB has been successfully used to compare model identification of a first order system subjected to high and low disturbances. The model structures used are FIR, ARX, AMX, OE and BJ. The obtained Model was validated using data generated from the actual process. It shows that the more the variance of the noise input into the system, the more difficult it is for the model identified to reproduce that validation data obtained from process response. Also when the measurement noise has zero mean and low variance, the effect on the steady state gain and other process parameters is negligible.