Optimal Signal Research Articles

Washing and Abrasion Resistance of Textile Electrodes for ECG Measurements

Electrocardiogram (ECG) signals are often measured for medical purposes and in sports. While common Ag/AgCl glued gel electrodes enable good electrode skin contact, even during movements, they are not comfortable and can irritate the skin during long-term measurements. A possible alternative is textile electrodes, which have been investigated extensively during the last years. These electrodes, however, are usually not able to provide reliable, constant skin contact, resulting in reduced signal quality. Another important problem is the modification of the electrode surface due to washing or abrasion, which may impede the long-term use of such textile electrodes. Here, we report a study of washing and abrasion resistance of different ECG electrodes based on an isolating woven fabric with conductive embroidery and two conductive coatings, showing unexpectedly high abrasion resistance of the silver-coated yarn and optimum ECG signal quality for an additional coating with a conductive silicone rubber. Sheet resistances of the as-prepared electrodes were in the range of 20–30 Ω, which was increased to the range of 25–40 Ω after five washing cycles and up to approximately 50 Ω after Martindale abrasion tests. ECG measurements during different movements revealed reduced motion artifacts for the electrodes with conductive silicone rubber as compared to glued electrodes, suggesting that electronic filtering of such noise may even be easier for textile electrodes than for commercial electrodes.

Careful feedback active noise and vibration control algorithm robust to large secondary path changes

Feedback active noise and vibration control (ANVC) reduces narrowband noise and vibration when there is no reference signal. However, most ANVC algorithms require a secondary path (plant) model and can become unstable when this model is inaccurate. This work proposes an algorithm less sensitive to plant modeling errors and can cope with large and fast plant changes. This is achieved by modeling the plant and the noise using an autoregressive exogenous input (ARX) model. This model allows obtaining the expected values of the future residual noise and determining the optimal control signal. Since the model coefficient uncertainty (variance) is considered when calculating the expected value, the resulting controller is a careful controller. This allows solving problems due to significant model estimation errors when information is insufficient to estimate the full ARX model.

Automation in solid state NMR

Automation in solid state NMR (ssNMR) requires appropriate hardware, from rotor loading mechanisms over highly stable rf-transmitters and probe circuitry to automatic tuning and matching capabilities including automatic magic angle adjustment for ssNMR probes. While these hardware capabilities are highly desirable and are, to various degrees, provided by manufacturers, we focus herein on automating experiment setup using radio frequency (rf) fields, which are key parameters in solid state NMR experiments. Specifically, these include spinlock fields during cross polarization (CP), or rf-fields for homo- or heteronuclear spin recoupling or decoupling. Often, these fields have specific relationships to the magic angle spinning (MAS) frequency. Relying on a well-maintained spectrometer, the experiment setup shifts from traditionally required optimization of rf-power values for each element of an experiment sequence to automatically setting all parameters correctly without any need for optimization. The proposed approach allows executing an experiment by reading its rf-amplitude requirements based on the actual MAS rotation frequency just before starting data acquisition, while all other hardware-related parameters are automatically provided through global tables and scripts. Under modest MAS frequencies, no further rf-power optimization is required while providing optimal sensitivity of better than 90% of the optimal signal to noise. Any optional parameter optimization relates only to adjusting rf-nutation frequencies to the requirements of the sample and the sample rotation frequency rather than the spectrometer hardware. Fast MAS CP experiments with MAS frequencies above 40 kHz require a semi-automated setup by optimizing Hartmann-Hahn (HH) matched rf-fields that are synchronously varied relative to the MAS-frequency. This allows for a significant reduction of setup steps by up to one order of magnitude for such experiments, avoiding the traditional grid search for optimal CPMAS conditions. The approach presented here can also be applied to decoupling or recoupling sequences, requiring rotor synchronized rf-fields, reducing the setup to a few steps addressing the spin system’s properties rather than the spectrometer hardware. Our approach permits automating all basic solid state NMR experiments for high throughput analytical tasks.

A model predictive control approach for energy saving optimization of an electronic assembly line

The energy-saving potential of original equipment manufacturers (OEMs) for electronic products is substantial owing to the huge market size of assembling industry in China. A semi-automatic electronic assembly line (SAEAL) for smart-phones is introduced, and its digital twin (DT) enabled energy-saving platform is developed as the hardware foundation. We propose a stochastic dynamics model via max-plus algebra to characterize the spatio-temporal nature of state transition, which provides dynamical opportunity windows for further energy-saving control strategy. Accordingly, model linearization is conducted to obtain linear state and output equations. Then, a model predictive control (MPC) approach for energy saving optimization is provided to determine the optimal start-stop control signal. The dynamics modeling and MPC approach have been applied and verified in the case of assembly line. The results show that the optimization approach could reach a proportion of 49% for the equipment sleep time of total running time and save a considerable amount of energy for normal production of OEMs.

Intelligent traffic signal controller for heterogeneous traffic using reinforcement learning

A traffic signal controller is an essential part of a signalized intersection to alleviate congestion and pollution by ensuring safety. However, the available research solutions are focused on homogeneous traffic scenarios, whereas heterogeneous traffic is the reality in most countries. Hence, a traffic signal control scheme suitable for heterogeneous traffic conditions is proposed in the current study using Reinforcement Learning. A novel reward function with an objective to reduce the traffic residual is defined and a combination of exploration and exploitation optimal policy is applied which made the system learn quickly. The proposed scheme can choose the appropriate phase sequence with optimal signal lengths based on traffic demand on each approaching road. The simulation results proved that the proposed model is well-suited for heterogeneous traffic conditions and its performance against the actuated traffic signal controller is significant in reducing the green time wastage and mean waiting time at the intersection.

Robust Self-Learning Fault-Tolerant Control for Hypersonic Flight Vehicle Based on ADHDP

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action–critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

Stability analysis and optimal control strategies of a fractional-order monkeypox virus infection model

In this study, the spread of the monkeypox virus is investigated through the dynamical study of a novel Caputo fractional order monkeypox epidemic model. The interaction between human and rodent populations along with the effects of control signals are considered in the model. These control signals are established through the optimal control strategy. Furthermore, the effect of memory is examined via varying fractional order parameters in the model. The influences of other parameters are also examined. The positivity and boundness of the solution are verified through theoretical analysis. In addition, the equilibrium points for the system are obtained for both the free and endemic cases, and the local stability has been studied. To verify the theoretical findings, numerical experiments are conducted. The optimal control signals are obtained and verified through numerical simulations of different configurations of control parameters. From these simulations, it is found that the optimal control scheme can help in reducing the size of the infected, quarantined, and exposed categories while increasing the susceptible and recovered categories. These acquired results can provide some assistance to governments in providing some preventive control to suppress the spread of the virus.

Reinforcement learning for traffic signal control: Incorporating a virtual mesoscopic model for depicting oversaturated traffic conditions

Recently, with increasing urban traffic congestion, there has been an upsurge in studies on reinforcement learning for traffic signal control (RL-TSC), which enables efficient traffic management. However, most existing RL-TSC research relies on simulation, and instances of real-world application are relatively scarce. Furthermore, existing RL-TSC methods employ a step-based approach, controlling traffic signals every short step. This approach can be inefficient in oversaturated traffic conditions, where large deviations in traffic demand between movements prevent traffic signals from being effectively managed based on the overall traffic situation. In this study, we aim to transform simulation-based RL-TSC into a more practical and applicable model. We have developed a RL-TSC method capable of responding to oversaturated traffic conditions, designing an action space that enables the agent to derive an optimal signal set for every cycle length, thereby understanding the traffic situation across all movements. During each cycle length, our proposed model conducts traffic signal optimization and identifies the optimal signal through an iterative exploration. To facilitate a fast and accurate strategy search process, we developed a kinematic wave-based mesoscopic model. This model estimates the density across the entire link, based on collected traffic data and derives the state and reward values. We have validated the field applicability of the proposed RL-TSC method through a real-world demonstration at a congested intersection in Seoul, Korea. The results indicated a significant improvement in traffic congestion, with the average queue length at the intersection reduced by up to 11.4%.

Optimal phase-selective entrainment of heterogeneous oscillator ensembles.

We develop a framework to design optimal entrainment signals that entrain an ensemble of heterogeneous nonlinear oscillators, described by phase models, at desired phases. We explicitly take into account heterogeneity in both oscillation frequency and the type of oscillators characterized by different Phase Response Curves. The central idea is to leverage the Fourier series representation of periodic functions to decode a phase-selective entrainment task into a quadratic program. We demonstrate our approach using a variety of phase models, where we entrain the oscillators into distinct phase patterns. Also, we show how the generalizability gained from our formulation enables us to meet a wide range of design objectives and constraints, such as minimum-power, fast entrainment, and charge-balanced controls.

Features of the trajectory signal processing in the distributed radar when rotating the joint set of transmitting and receiving antennas

Increasing the resolution of ground survey radar systems (radars) makes it possible to obtain images of ground objects and the observed surface with high quality. At the same time, as it is known, the most difficult is to provide high resolution radar by angular coordinate. The method based on synthesis of artificial aperture allows to increase significantly the angular resolution of airborne radar. The capabilities of a radar station with synthesis of antenna aperture (SAR) in angular resolution are determined by the structure of the spatial and temporal trajectory signal formed in the process of synthesis. In this case, the resolution is determined by the angular size of the synthesized aperture. The purpose of this article is to consider the features of the trajectory signal structure and its processing during synthesis of an artificial antenna aperture in a distributed radar station consisting of a set of two receiving and one receiving-transmitting modules rotating in a circle. It is required to analyze the trajectory signal structure and evaluate the potentially achievable angular resolution capabilities of a distributed radar station when synthesizing an artificial antenna aperture under conditions of rotation of a spatially spaced set of phase centers of real antennas. To analyze the structure of the trajectory signal generated in a distributed radar station, we have considered the geometry of the problem, because it determines the change in the current distance between the phase center of the antennas and the observation object and is the main generating factor of the arising modulation of this signal. An informative tool for analyzing the characteristics of radar systems with synthesis of antenna aperture by potential resolution, is the analysis of the output response of the optimal trajectory signal processing system. The direct convolution method has been used to obtain the responses. Evaluation of the efficiency of coherent processing of the trajectory signals formed at the separated reception points has been carried out by computer simulation. From the simulation results presented in the figures in the article, it can be seen that the coherent processing of the trajectory signal formed at the separated receiving points during rotation of the phase center of real antennas, allows to increase the azimuth resolution of the radar. Thus, the results presented in the article show that rotation of the separated phase centers of the antennas of the receiving and receiving and transmitting modules within a range of up to 120 degrees provides the formation of a synthesized aperture when processing the trajectory signal from the observed target at each receiving point. The best azimuth resolution is obtained by processing the trajectory signal at the location of the transceiver module antenna. When coherent processing of trajectory signals formed at separated receiving points is performed together, the azimuth resolution of the radar station increases by about two times compared to the case of their separate handling.

Improving power system low-frequency oscillations damping based on multiple-model optimal control strategy using polynomial combination algorithm

In this paper, the damping of low-frequency oscillations (LFOs) in a power system containing Static VAR Compensator (SVC) is investigated based on Multiple-Model Linear Optimal Control (MMLOC) Strategy. The performance of MMLOC is enhanced using Polynomial Combination Algorithm (PCA). A linear optimal controller (LOC) generates the optimal control signal with linearization the nonlinear state equations of the system and solving the Riccati equation. One LOC generates synchronized control signals for the synchronous generator excitation system as well as SVC. The LOC is assigned to generate each model’s control signal. The final control signal is made by interpolating the LOC output signals obtained from each model using PCA. Considering a three-phase symmetrical fault on a near bus to the generator, the proposed control strategy is evaluated. This strategy not only maintains stability but also reduces LFO effectively. Furthermore, steady state error of rotor speed and rotor angle tend to zero favorably. Simulations are done for single- and multi-machine power systems via a MATLAB m-code.

Microphone utility estimation in acoustic sensor networks using single-channel signal features

In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone signals, i.e., microphone utility, for coherent array processing, but its estimation requires that the uncoded signals are synchronized and transmitted between nodes. In resource-constrained environments like acoustic sensor networks, low data transmission rates often make transmission of all observed signals to the centralized location infeasible, thus discouraging direct estimation of signal cross-correlation. Instead, we employ characteristic features of the recorded signals to estimate the usefulness of individual microphone signals using the Magnitude-Squared Coherence (MSC) between the source and respective microphone signal as ground-truth metric. In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features. The performance of both approaches is validated experimentally using both simulated and recorded acoustic data, comprising a variety of realistic and practically relevant acoustic scenarios including moving and static sources.

Hierarchical Structure-Based Fixed-Time Optimal Fault-Tolerant Time-Varying Output Formation Control for Heterogeneous Multiagent Systems

This article studies the issue of distributed hierarchical fixed-time optimal fault-tolerant output formation for heterogeneous multiagent systems (MASs). A two-layer formation control framework is proposed by using distributed optimization and cooperative fault-tolerant output regulation approaches. The upper layer includes a virtual system and a distributed fixed-time optimization control algorithm to produce a global optimal reference signal, which minimizes the global objective function in a fixed time. The lower layer includes an actual agent system and an adaptive fixed-time fault-tolerant tracking control protocol to compensate for the actuator faults and ensure the fixed-time tracking of the optimal formation trajectory composed of the optimal reference signal and the expected time-varying formation vector. Different from the relevant works, this framework can avoid the phenomenon of fault propagation between neighboring agents by the interaction network. Furthermore, the convergence time of the formation error is not relied on the initial conditions of MASs. Finally, two simulation experiments are designed to prove the effectiveness of the developed theoretics.

Distributed Stochastic MPC Traffic Signal Control for Urban Networks

In this paper, we design a stochastic model predictive control (MPC)-based traffic signal control method for urban networks when the uncertainties of the traffic model parameters (including the exogenous traffic flows and the turning ratios of downstream traffic flows) are taken into account. Considering that the traffic model parameters are random variables with known expectations and variances, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole network into multiple subproblems. By applying an Alternating Direction Method of Multipliers (ADMM) scheme, the optimal stochastic traffic signal splits are found in a distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.

The selection of the control unit optimal signalling sequence in containers stack handling at sea port terminal

The possibility of increasing the productivity of technological operations performed in the storage area of a container terminal due to the introduction of automation elements into mechanized warehouse cargo handling has been researched. As one of the possible automation options, a method for transporting and storing containers with the implementation of a loader control unit in the form of a deterministic finite automaton is proposed. The sequence of actions of the loader is simulated in the control unit by a line of encoded symbols generated by the transition diagram of the Mealy automaton. In this case, the transition diagram is an automatic programming tool allowed mathematically describing the logic of controlling the actions of the loader when it processes a stack of containers. As a result of generation at the output of the machine, several valid loader control lines are got. Each symbol of the control line corresponds to one action from the loader working cycle. It is required to find the control line that will bring the loader to the state, when the picking up of containers from the stack is completed, in the minimum number of actions. The object of the research is a variable sequence of loader actions during partial disassembly of the stack with its corresponding control line. To reduce the complexity of the task being solved, each control line is divided into sections consisting of symbols responsible for the following actions of the loader: moving when changing the disassembled row, disassembling the row with the picking up and movement of target containers, returning blocking containers to the stacks where they were initially placed. As a result of the comparison of the corresponding symbolic sections of different lines, an algorithm that allows choosing the optimal sequence of signals for the control unit of the technological automaton in terms of the number of symbols is developed.

Active fault diagnosis for linear stochastic systems subject to chance constraints

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of linear stochastic systems subject to chance constraints, where the input signals are specifically designed to enhance diagnosis performance by discriminating between multi-fault modes. As the misdiagnosis probability cannot be expressed in the closed form, a reachable upper bound on it is derived to construct a novel input design criterion for AFD based on the Cauchy–Schwarz divergence. A sufficient condition is provided to decompose the multivariate chance constraints into a set of univariate ones. A three-stage optimization approach is proposed to solve the input design problem incorporating chance constraints for AFD. In the upper stage, the best iteration parameter is determined for subsequent iterative optimization to obtain the least conservative risk allocation scheme. In the middle stage, the total risk of violating the multivariate constraints is optimally allocated to a set of univariate ones through iterative optimization, which can improve diagnosis performance. In the lower stage, a set of univariate chance constraints is equivalently converted into expectation constraints under the allocated risk, and then the input design problem under the constructed design criterion is solved to global optimality. The optimal input signals are injected, and the AFD decision is made based on real-time measurement information according to the minimum probability of misdiagnosis decision principle. Finally, simulation results on a four-tank system demonstrate the effectiveness and superiority of the proposed AFD method.

Covert transmission performance for visible light communications under optical intensity and covert constraints

This paper investigates the fundamental performance limit for an indoor covert visible light communication (VLC) system consisting of a transmitter, a receiver, and a warden. In the system, the input signal of VLC is constrained by nonnegativity, peak optical intensity constraint, average optical intensity constraint and covertness constraint. We derive the optimal distribution of the input signal by using the variational method and the entropy power inequality. The allowable maximum peak optical intensity is obtained by substituting the optimal input signal into the covertness constraint. Then, by substituting the maximum peak optical intensity into the lower bound of the maximum mutual information, we derive an upper bound of the information bits that can be covertly transmitted. The asymptotic analysis shows that the upper bound on the transmittable covert information bits tends to a constant when the number of channel uses tends to infinity. These conclusions are also confirmed by numerical results.

Integrated fire/flight control coupler design for armed helicopters based on improved non-monotone adaptive trust region algorithm with distributionally robust optimization

Aiming at enhancing the extent of military automatization and the effectiveness of armed helicopters in complex battlefield environment, integrated fire/flight control (IFFC) coupler connects the fire control system and the flight control system which is used to generate the optimal flight command signals. However, it is difficult to obtain satisfactory flight command signals within a certain firing accuracy due to the unavoidable uncertain information in battlefield environment. In this paper, a new IFFC scheme is proposed for armed helicopters against complex combat by using the idea of distributionally robust optimization combined with the framework of trust region. Initially, a vector equation and coordinate transformation are employed to acquire the relative motion model of the IFFC coupler problem in complex battlefield environment. Moreover, an improved performance index is designed according to the constraints, and the original non-convex problem is handled by the Taylor formula combined with an improved BFGS (Broyden–Fletcher–Goldfard–Shanno) algorithm. Considering the uncertain parameters during optimization, the non-linear distributionally robust optimization is transformed into a linear matrix inequality optimization problem based on the worst-case conditional value-at-risk (WC-CVaR) approximation. Then, an algorithmic framework with a non-monotone adaptive trust region algorithm is given to obtain the optimal flight command signals. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach.

Multidimensional-Taylor-network-based robust optimal tracking control for MIMO nonlinear discrete-time systems

In practical engineering, system control is indispensable. However, due to the influence of model uncertainty, speed unavailability, input nonlinearity (such as actuator dead zone/fault), and multi-input coupling, the control results are not satisfactory. In this paper, a robust optimal tracking control strategy is proposed for a class of nonlinear multi-input-multi-output discrete-time systems with unknown uncertainties. This control strategy is to minimize the cost function in the process of uncertainty processing and stabilize the closed-loop system by establishing an adaptive controlling approach based on a combination of actor MTN and critic MTN based on the Multi-dimensional Taylor Network (MTN). By using the approximation property of MTN, the optimal control signal is generated by action MTN, which is used to approach the controller, and the cost function is approximated by critic MTN, which is tuned online because the cost function cannot be obtained in hands-on experience. By designing a new cost function, the amount of calculation in the control process is reduced, and the adaptive critic design control idea is integrated into the controller design to deal with the uncertainty of the system. The simulation results verify the effectiveness of the control strategy proposed in the essay.

Optimal acknowledgment signal attacks against remote estimation

In this paper, we consider a malicious attack issue against remote state estimation in cyber-physical systems. Due to the limited energy, the sensor adopts an acknowledgment-based (ACK-based) online power schedule to improve the remote state estimation. However, the feedback channel will also increase the risk of being attacked. The malicious attacker has the ability to intercept the ACK information and modify the ACK signals (ACKs) from the remote estimator. It could induce the sensor to make poor decisions while maintaining the observed data packet acceptance rate to keep the attacker undetected. To maximize the estimation error, the attacker will select appropriate attack times so that the sensor makes bad decisions. The optimal attack strategy based on the true ACKs and the corrosion ACKs is analytically proposed. The optimal attack time to modify the ACKs is the time when the sensor’s tolerance, i.e., the number of consecutive data packet losses allowed, is about to reach the maximum. In addition, such an optimal attack strategy is independent of the system parameters. Numerical simulations are provided to demonstrate the analytical results.