Sensor Saturation Research Articles

The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments

The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF converts a continuous-time high-dynamic-range (HDR) signal into folded, low-dynamic-range, modulo samples and allows the recovery of the HDR signal via algorithmic unfolding. In hardware, however, implementing ideal modulo folding requires careful calibration, analog design and high precision. At the interface of theory and practice, this paper explores a computational sampling strategy that relaxes strict hardware requirements via a novel, mathematically guaranteed reconstruction. We start with a generalized model for USF with two new parameters modeling <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hysteresis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">folding transients</i> in addition to the modulo threshold. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hysteresis</i> accounts for mismatches between the reset threshold and the amplitude displacement at the folding time and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">folding transient</i> is a continuous transition period in the implementation of a reset. Both these effects are motivated by our hardware experiments and also occur in previous, domain-specific applications. We show that hysteresis is beneficial for USF and leverage it to derive the first recovery guarantees in the context of our generalized USF model for a certain sampling rate regime. Additionally, we show how the sampling rate requirement can be greatly reduced via a direct generalization of the proposed recovery. Our theoretical work is corroborated by hardware experiments with a hysteresis enabled, modulo ADC testbed comprising off-the-shelf electronic components. Thus, by capitalizing on a collaboration between hardware and algorithms, our paper enables an end-to-end pipeline for HDR sampling allowing more flexible hardware implementations.

A Dynamic Event-Triggered Approach to H∞ Control for Discrete-Time Singularly Perturbed Systems With Time-Delays and Sensor Saturations

In this article, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control problem is investigated for a class of discrete-time singularly perturbed systems (DTSPSs) with time-delays and sensor saturations. In order to save the network bandwidth with satisfactory communication reliability during the signal transmissions, a dynamic event-triggered mechanism (DETM) is put forward to regulate the data-packet transmissions from the saturated sensors to the proposed controller. By constructing a novel Lyapunov–Krasovskii functional dependent on the singular perturbation parameter (SPP), the dynamics of the DTSPSs under the DETM are rigorously analyzed, and an output-feedback controller design scheme is then developed to ensure that, for any SPP not exceeding a prescribed upper bound, the closed-loop system is asymptotically stable with a guaranteed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance index. By resorting to the convex programming techniques, the desired controller gain is parameterized in light of the feasible solutions to a set of matrix inequalities. Finally, the effectiveness and merits of the proposed algorithm are demonstrated by a simulation example.

Observed-Based Finite-Time Control of Nonlinear Semi-Markovian Jump Systems With Saturation Constraint

This article is concerned with the issues of finite-time control for a class of continuous-time nonlinear semi-Markovian jump systems (SMJSs) with saturation constraint via observed-based control. Both sensor and actuator saturations, and unknown nonlinearities are considered simultaneously. The main purpose of this article is to derive parameter selection sufficient conditions by designing an observed-based controller. These conditions ensure that the system is finite-time boundedness (FTB). By employing some reasonable assumptions and constructing an appropriate semi-Markovian Lyapunov function, some novel finite-time stabilization criteria are obtained, which guarantee the FTB for the underlying systems over the whole finite-time interval. Meanwhile, the observed-based controller is designed. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

Optimal PD-type networked iterative learning algorithm based fault estimation for repetitive systems with delays, packet losses, sensor saturation and sensor failure

Optimal PD-type networked iterative learning algorithm based fault estimation for repetitive systems with delays, packet losses, sensor saturation and sensor failure

Probabilistic-constrained tracking control for stochastic time-varying systems under deception attacks: A Round-Robin protocol

The probabilistic-constrained tracking control issue is investigated for a class of time-varying nonlinear stochastic systems with sensor saturation, deception attacks and limited bandwidth in an unified framework. The saturation of sensors is quantified by a sector-bound-based function satisfying certain conditions, and the random deception attacks are considered and modeled by a random indicator variable. To gain more efficient utilization of communication channels, a Round-Robin (RR) protocol is utilized to orchestrate the transmission order of measurements. The main purposes of this study aim to plan an observer-based tracking controller to achieve the following goals: (1) the related performance indicators of the estimation error is less than given bound at each time step; and (2) the violation probability of the tracking error confined in a predefined scope is supposed to be higher than a prescribed scalar and the area is minimized at each instant. In order to reach these requirements, a group of recursive linear matrix inequalities (RLMIs) are developed to estimate the state and design the tracking controller at the same time. Finally, two simulation examples are exploited to illustrate the availability and flexibility of the proposed scheme.

Dynamic-transmission-based recursive filtering algorithm for microseismic event detection under sensor saturations

This paper is dedicated to dealing with the recursive filtering problem for a class of microseismic signal arrival time picking with dynamic transmission mechanism (DTM) and sensor saturation (SS). Firstly, the microseismic wave and its environmental noise are established as an exponentially decaying cyclic wave and a Markov process, respectively, and then the state space expression of the microseismic system is exhibited. Next, the phenomenon of the SS is taken into account to describe the practical microseismic system. Simultaneously, the DTM is introduced to avoid the waste of communication resources and energy, where more valuable measurements are transmitted to the remote filter. The purpose of this paper is to design a filter such that the arrival time of P wave and S wave (ATPSW) of microseismic signals is picked automatically in consideration of the SS and the DTM, and an upper bound of the filtering error covariance matrix is optimized by the designed filter gain at each time instant. Subsequently, a sufficient condition is given to ensure that the filtering error is exponential bounded in the sense of mean square. Finally, the effectiveness of the proposed algorithm is illustrated by a simulation experiment.

Robust Asynchronous H∞ Observer-Based Control Design for Discrete-Time Switched Singular Systems with Time-Varying Delay and Sensor Saturation: An Average Dwell Time Approach

This work discuss the robust stabilization problem for discrete-time switched singular systems with simultaneous presence of time-varying delay and sensor nonlinearity. To this end, an observer-based controller was synthesized that works under asynchronous switching signals. Investigating the average dwell time approach and using a Lyapunov–Krasovskii functional with triple sum terms, sufficient conditions were derived for achieving the existence of such asynchronous controller and guaranteeing the resulting closed-loop system to be exponentially admissible with H∞ performance level. Subsequently, the effectiveness of the proposed control scheme was verified through two numerical examples.

Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI

Envisat’s MERIS and its successor Sentinel OLCI have proven invaluable for documenting algal bloom conditions in coastal and inland waters. Observations over turbid eutrophic waters, in particular, have benefited from the band at 708 nm, which captures the reflectance peak associated with intense algal blooms and is key to line-height algorithms such as the Maximum Chlorophyll Index (MCI). With the MERIS mission ending in early 2012 and OLCI launched in 2016, however, time-series studies relying on these two sensors have to contend with an observation gap spanning four years. Alternate sensors, such as MODIS Aqua, offering neither the same spectral band configuration nor consistent spatial resolution, present challenges in ensuring continuity in derived bloom products. This study explores a neural network (NN) solution to fill the observation gap between MERIS and OLCI with MODIS Aqua data, delivering consistent algal bloom spatial extent products from 2002 to 2020 using these three sensors. With 14 bands of MODIS level 2 partially atmospherically corrected spectral reflectance as the NN input, the missing MERIS/OLCI band at 708 nm required for the MCI is simulated. The resulting NN-derived MODIS MCI (NNMCI) is shown to be in good agreement with MERIS and OLCI MCI in 2011 and 2017 respectively over the western basin of Lake Erie (R2 = 0.84, RMSE = 0.0032). To overcome the impact of MODIS sensor saturation over bright water targets, which otherwise renders pixels unusable for bloom detection using R-NIR wavebands, a variant NN model is employed which uses the 9 MODIS bands with the lowest probability of saturation to simulate the MCI. This variant NN predicts MCI with only a small increase in uncertainty (R2 = 0.73, RMSE = 0.005) allowing reliable estimates of bloom conditions in those previously unreported pixels. The NNMCI is shown to be robust when applied beyond the initial training dataset on Lake Erie, and when re-trained on different geographic areas (Lake Winnipeg and Lake of the Woods). Despite differences in spatial, temporal, and spectral resolution, MODIS algal bloom presence/absence was correctly classified in &gt;92% of cases and bloom spatial extent derived within 25% uncertainty, allowing the application to the 2012–2015 time period to form a continuous and consistent multi-mission monitoring dataset from 2002 to 2020.

H∞ Reliable Dynamic Output-Feedback Controller Design for Discrete-Time Singular Systems with Sensor Saturation

In this study, we investigate the H∞ fault-tolerant control problem for a discrete-time singular system which is subject to external disturbances, actuator faults, and sensor saturation. By assuming that the state variable of the system is unavailable for measurement, and the actuator fault can be described by a Markovian jump process, attention is mainly focused on designing a reliable dynamic output-feedback (DOF) controller able to compensate for the effects of the aforementioned factors on the system stability and performance. Based on the sector non-linear approach to handle the sensor saturation, a new criterion is established to ensure that the closed-loop system is stochastically admissible with a γ level of the H∞ disturbance rejection performance. The main aim of this work is to develop a procedure for synthesizing the controller gains without any model transformation or decomposition of the output matrix. Therefore, by introducing a slack variable, the H∞ admissibility criterion is successfully transformed in terms of strict linear matrix inequalities (LMIs). Three practical examples are exploited to test the feasibility and effectiveness of the proposed approach.

Wearable light spectral sensor optimized for measuring daily α-opic light exposure.

Light has many non-visual effects on human physiology, including alterations in sleep, mood, and alertness. These effects are mainly mediated by photoreceptors containing the photopigment melanopsin, which has a peak sensitivity to short wavelength ('blue') light. Commercially available light sensors are commonly wrist-worn and report photopic illuminance and are calibrated to perceive visual brightness and hence cannot be used to investigate the non-visual impacts of light. In this paper, we report the development of a wearable spectrophotometer designed to be worn as a pendant or affixed to clothing to capture spectral power density data close to eye level in the visible wavelength range 380-780 nm. From this, the relative impact of a given light stimulus can be determined for each photoreceptive input in the human eye by calculating effective illuminances. This device showed high accuracy for all effective illuminances while measuring a range of commonly encountered light sources by calibrating for directional response, dark noise, sensor saturation, non-linearity, stray-light and spectral response. Features of the device include IoT-integration, onboard data storage and processing, Bluetooth Low Energy (BLE) enabled data transfer, and cloud storage in one cohesive unit.

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

AbstractThis article proposes a distributed model‐free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete‐time multi‐agent systems (MASs) with sensor saturation and measurement disturbance to perform consensus tracking tasks. Meanwhile, both fixed and switching topologies are considered, where only a subset of agents can acquire the desired trajectory information in each topology. A time‐varying linear data model for each agent is first established by utilizing the dynamic linearization method to formulate this algorithm. Merely, the input data and the saturated output data with measurement disturbance of each agent are applied to construct the DMFACTC algorithm without employing any dynamics model information of MASs. The convergence of the designed scheme is strictly proved. It illustrates that the output saturation and switching topologies do not affect the stability of MASs. Moreover, even if the sensor saturation, measurement disturbance, and switching topologies happen simultaneously, the DMFACTC also guarantees that the tracking errors of MASs converge to a small range around the origin. Furthermore, two numerical simulations and a realistic filling system simulation further verify the correctness and effectiveness of the theoretical results.

Distributed state estimation for renewable energy microgrids with sensor saturations

In this paper, the distributed state estimation problem is studied for renewable energy microgrids with sensor saturations. A system model for the microgrids with sensor saturations is proposed. Attention is focused on the design of a distributed recursive estimation scheme such that, in the presence of the sensor saturations, an upper bound of the estimation error covariance is guaranteed. Subsequently, such an upper bound is minimized by appropriately designing the gain matrices of the corresponding state estimator. In particular, the sparsity of the gain matrices resulting from network topology is handled by using a matrix simplification method. Moreover, the performance evaluation of the designed distributed state estimator is conducted by analyzing the exponential boundedness of the estimation error in the mean square sense. Finally, simulation experiments under two cases are carried out on a renewable energy microgrid which contains two distributed generation units. The simulation results demonstrate that the developed state estimation scheme is effective.

Evaluation of smartphone-integrated magnetometers in detection of safe electromagnetic devices for use near programmable shunt valves: a proof-of-concept study.

External magnetic forces can have an impact on programmable valve mechanisms and potentially alter the opening pressure. As wearable technology has begun to permeate mainstream living, there is a clear need to provide information regarding safety of these devices for use near a programmable valve (PV). The aim of this study was to evaluate the magnetic fields of reference devices using smartphone-integrated magnetometers and compare the results with published shunt tolerances. Five smartphones from different manufacturers were used to evaluate the magnetic properties of various commonly used (n = 6) and newer-generation (n = 10) devices using measurements generated from the internal smartphone magnetometers. PV tolerance testing using calibrated magnets of varying field strengths was also performed by smartphone magnetometers. All tested smartphone-integrated magnetometers had a factory sensor saturation point at around 5000 µT or 50 Gauss (G). This is well below the threshold at which a magnet can potentially deprogram a shunt, based on manufacturer reports as well as the authors' experimental data with a threshold of more than 300 G. While many of the devices did saturate the smartphone sensors at the source, the magnetic flux density of the objects decreases significantly at 2 inches. The existence of an upper limit on the magnetometers of all the smartphones used, although well below the published deprogramming threshold for modern programmable valves, does not allow us to give precise recommendations on those devices that saturate the sensor. Based on the authors' experimental data using smartphone-integrated magnetometers, they concluded that devices that measure < 40 G can be used safely close to a PV.

Neural-Network Based Adaptive Self-Triggered Consensus of Nonlinear Multi-Agent Systems With Sensor Saturation

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.

Event-triggered [formula omitted] state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations

In this paper, the H∞ state estimation problem is considered for a class of the discrete-time delayed switched stochastic neural networks with sensor saturations. In order to improve the resource utilization efficiency, an event-triggered H∞ state estimator is designed to regulate the transmission of the measurement outputs. The persistent dwell-time switching strategy is adopted in this paper which is more general and less conservative. By utilizing a proper Lyapunov–Krasovskii functional, some criteria are presented to ensure the exponential mean square stability of the estimation error system and the prespecified H∞ level of disturbance attenuation. Finally, a numerical example is given to illustrate the effectiveness of our results.

Resilient H-infinity filtering for networked nonlinear Markovian jump systems with randomly occurring distributed delay and sensor saturation

The H∞ filtering problem for a class of networked nonlinear Markovian jump systems subject to randomly occurring distributed delays, nonlinearities, quantization effects, missing measurements and sensor saturation is investigated in this paper. The measurement missing phenomenon is characterized via a random variable obeying the Bernoulli stochastic distribution. Moreover, due to bandwidth limitations, the measurement output is quantized using a logarithmic quantizer and then transmitted to the filter. Further, the output measurements are affected by sensor saturation since the communication links between the system and the filter are unreliable and is described by sector nonlinearities. The objective of this work is to design a quantized resilient filter that guarantees not only the stochastic stability of the augmented filtering error system but also a prespecified level of H∞ performance. Sufficient conditions for the existence of desired filter are established with the aid of proper Lyapunov–Krasovskii functional and linear matrix inequality approach together with stochastic analysis theory. Finally, a numerical example is presented to validate the developed theoretical results.

Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations.

This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.

Central Monitor Based on Personal Computer Design with SpO2 and Body Temperature Parameters Using Wireless Xbee Pro

Central patient monitor that is not real-time and continues will cause inaccuracies monitoring results and also sending data that is still using cable will cause limited distance. The purpose of this research is to design a central monitoring based personal computer via Xbee Pro. The contribution of this research is, the system works in real-time and continues, more parameters, using wireless, longer transmission distances. So that monitoring can be done in real-time and continue via wireless with more distance, then the wireless system uses the Xbee Pro module which has larger output power and uses the same number of wireless modules between transmitter and receiver. Body temperature was measured using the LM35 sensor and oxygen saturation in the blood was measured using the MAX30100 sensor. Data is sent using Xbee Pro and displayed on a personal computer. At the distance of receiving data approximately 25 meters with a wall divider, obtained results of smooth monitoring without any loss of data. The results showed that the average SpO2 error value was 0.34% in module 1 and 0.68% in module 2. The average value of body temperature error was 0.46% in module 1 and 0.72% in module 2. The results of this research can be implemented in a centralized patient monitoring system at the hospital, making it easier for health workers to monitor multiple patients, with the results of monitoring in real-time and continue, more parameters, via wireless with greater distance.

Resilient Asynchronous State Estimation for Markovian Jump Neural Networks Subject to Stochastic Nonlinearities and Sensor Saturations.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.

Iterative Learning Control for Switched Systems with Sensor Saturation Constraints

To solve trajectory tracking problem of switched system with sensor saturation, an iterative learning control algorithm is proposed. The method uses actual measurement error to modify the control variable of system on the premise that switched rule does not change along iteration axis, but it randomly changes along time axis. Moreover, by dealing with the saturation via diagonal matrix method, the convergence of the algorithm is strictly proved in the sense of λ‐norm, and the convergence condition is derived. The algorithm can achieve complete tracking of desired trajectory in the finite time interval under the random switched rule, as iterations increase. The simulation example verifies the validity of the proposed algorithm.