Stochastic Sensor Research Articles

Fast Analysis of Humic Acid in Mud and Water Samples

A broad spectrum of pharmacological activity of mud is related to humic acid, creating a need for fast reliable methods for its assay in mud and water samples. This paper proposes an electroanalytical method for the determination of humic acid content using a stochastic sensor, a method that does not involve the extraction of active ingredients prior to the determination. The method has proven to be a very good choice for the analysis as no sample processing is needed, the sample matrix does not influence the analysis results, and reliable quantitative and qualitative analyses can be performed. High sensitivity (1.61 × 109 s−1g−1ml) and very low limit of determination (1 × 10−18 g ml−1) were recorded. Recoveries higher than 95.00% of humic acid, with percentages of RSD lower than 1.00 determined, when the stochastic sensor was used for the electroanalysis of humic acid in mud.

Two-Dimensional Combined Stochastic Sensor for the Determination of Aflatoxin B1 in Bovine and Vegan Milk

Aflatoxin B1 is a carcinogenic mycotoxin responsible for hepatocellular carcinoma in humans and animals. A two-dimensional combined stochastic sensor based on the physical modification of a gold dot with N-[5-methylpyridin-2yl] octadec-9-enamide was designed and validated for aflatoxin B1 determination in bovine and vegan milk. The working concentration range was between 0.1 fmol L−1 and 0.1 µmol L−1 with a sensitivity of 2.70 × 109 s−1mol−1L. The recovery values for aflatoxin B1 in the milk samples exceeded 95.00%, with relative standard deviations lower than 0.10%.

Fast screening of Milk for Deoxynivalenol

A 2D platform was designed and validated for fast screening of milk for deoxynivalenol. The qualitative and quantitative assay of deoxynivalenol in the milk samples was done using a 2D stochastic sensor based on single walled carbon nanotubes modified with [N-(pyridine-4-yl-methyl)]octadec-9-enamide. The working concentration range was between 10−15 and 10−6 g mL−1, with a sensitivity of 5.48 × 105 s−1g−1mL, and a limit of determination of 1 fg mL−1. Recovery values higher than 99.00 % with RSD values lower than 1.00 % were recorded for the determination of deoxynivalenol in cow's and in the vegetarian (coconut milk, soy milk, almond milk) milk.

The portable stochastic sensor as a screening tool for simultaneous determination of HER-1 and CA 125 - a key factor in the rapid recognition of gastric cancer.

The significance of HER-1 and CA 125 lies in their ability to guide cancer diagnosis, treatment, and monitoring, improving personalized care and enhancing prognostic accuracy. The utilization of HER-1 and CA 125 as screening biomarkers for the anticipation of early-stage cancer and monitoring cancer progression is expanding due to the invasive and costly nature of present techniques.In this study, a novel stochastic sensor was developed forthe simultaneous determination of HER-1 and CA 125 in whole blood, saliva, and gastric tumor tissue samples usinga fast, easy, inexpensive, and portable method.The stochastic sensor was prepared by electropolymerization of cysteine on the surface of the Au-TiO2@rGO/SPCE sensor. The Au-TiO2@rGO nanocomposite was synthesized using a simple chemical reduction process. The proposed sensor showed wide linear concentration ranges and very low limits of quantification (LOQ). The concentration ranges were from 3.9 × 10-14 to 3.9 × 10-8µgmL-1, with a LOQ of 3.9 × 10-14µgmL-1 for HER-1. For CA 125, the concentration ranges were from 8.3 × 10-14 to 8.3 × 10-10 U mL-1, with a LOQ of 8.3 × 10-14 U mL-1. Both biomarkers exhibit precise discrimination in different biological samples, with recoveries above 96.78% and RSD values below 0.04%. With a confidence level of 99%, the Student t-test revealed that there is no statistically significant difference between the outcomes obtained by using the poly-Cys/Au-TiO2@rGO/SPCE sensor for screening examinations of biological samples. This was determined because the results were not significantly different from one another.

A Smartphone Operated Intelligent Stochastic Miniplatform for On-Site Screening of Milk for Fast Determination of Ochratoxin A

Ochratoxine A is a very stable mycotoxin which cannot be destroyed below 250°C. Found in grains, coffee, grapes, wine, milk, and meat, it can produce kidney damage and cancer. Therefore, a smartphone operated intellingent stochastic miniplatform was designed, characterised, and validated for the on-site screening of milk for fast determination of ochratoxine A. (Z)-N-[2-(4-hydroxyphenyl) ethyl]octadec-9-enamide was used as modifier of a gold matrix for a screen printed stochastic sensor used as sensing tool in the design of the miniplatform. A wide linear concentration range (1.0 × 10−15 − 1.0 × 10−7 mol l‒1) and a low limit of quantification of 1 fmol l‒1 were achieved for the assay of ochratoxin A. Recovery values higher than 99.00% were obtained for ochratoxine A, when the miniplatform was used for the screening of cow’s milk and vegetarian milk.

An enantioselective portable stochastic platform for quick screening of whole blood for methionine enantiomers. A new approach for early diagnosis of lung cancer

ObjectivesIn order to perform the enantioanalysis of L- and D-methionine in whole blood samples, this research suggests a 3D enantioselective stochastic platform. Materials and MethodsThe platform contains a 3D-printed stochastic sensor designed by mixing carbon nanopowder and europium oxide, modified with N-[5-methylpyridin-2-yl] octadec-9-enamide (namely N10-Eu2O3/CNP), an auxiliary Pt electrode, and an Ag/Ag Cl reference electrode. Stochastic method was applied for all measurements. ResultsThe quantification limits of L- and D-methionine were of 10-16 and 10−18 mol/L magnitude order, respectively. High sensitivities and large working concentration ranges were recorded. In whole blood, the enantiomers could be identified with recoveries greater than 99.00 %, and RSD% less than 1.00 %. ConclusionsThe proposed platform can be reliable used for enantioanalysis of methionine in whole blood samples. The preliminary results of the screening tests of whole blood shown that the D-methionine enantiomer is only present in the whole blood of the patients diagnosed with lung carcinoma.

Molecular Recognition of VSIG1 in Biological Samples for Fast Diagnosis of Gastric Cancer

VSIG1 is a new biomarker member of the JAM family relevant in gastric cancer diagnostics. Due to its detection and quantification impact for fast and early diagnosis of gastric cancer, two types of intelligent miniplatforms based on stochastic sensors as detection tools, were designed and validated using real samples. A 3D stochastic microsensor based on Nitrogen and Sulfur dopped graphene paste modified with calix[4]arene-25,26,27,28-tetrol, and a 2D disposable screen-printed stochastic sensor based on thin film gold modified with calix[4]arene-25,26,27,28-tetrol were constructed and inserted as working sensors into the miniplatforms. The proposed intelligent miniplatforms shown sensitivities as high as 1.12 × 1010 s−1g−1ml, limits of determination of 1 × 10−23g ml−1, and working concentration ranges between 1 × 10−23 and 1 × 10−8 g ml−1. Recoveries higher than 99.30% with % RSD values lower than 0.05% were obtained when used for screening test of biological samples, for VSIG1.

UAV autonomous source seeking with cumulative exposure minimization in complex hazardous environments

Considering autonomous searching for an unknown source in complex hazardous environments while balancing search efficiency and flight safety, this paper proposes a framework called source seeking with cumulative exposure minimization (SSCEM) for an unmanned aerial vehicle (UAV) equipped with gas sensors. First, the hazardous substance dispersion is formulated by using an isotropic plume model, and the stochastic sensor measurement in turbulent environments is modeled as a Poisson distribution. Second, the source term including the source location and release rate, is estimated by using a Bayesian inference framework. A particle filter is adopted to reduce the computational burden. Third, as cumulative exposure to hazardous fields can lead to airborne device failures and endanger flight safety, to achieve a reasonable trade-off among exploitation, exploration and flight safety, SSCEM for UAVs is proposed based on information theory and an interfered fluid dynamical system (IFDS), where heuristic action candidates in a continuous domain and a cost function considering cumulative exposure collectively drive the UAV to approach the unknown source location while reducing cumulative exposure, and no-fly zone avoidance is realized using the IFDS. Eventually, source seeking is efficiently performed by the UAV with no-fly zone avoidance and kinematic requirements being met, and comparative simulation results demonstrate the performance of SSCEM.

Dynamic event-triggered mean-square consensus of positive multi-agent systems via impulsive control under false data injection attacks

This paper investigates the mean-square impulsive consensus problem of positive multi-agent systems (MASs) under stochastic sensor false data injection attacks (FDIA). False data injection attacks are mortal to a positive system since the system positivity could be destroyed with ease (by tampering with the sampled positive state value to a negative one, for instance). To deal with this issue, a data compensator is proposed which preprocesses deliberately distorted state values to reduce their adverse impacts on the positivity of the positive MASs. On the other hand, a novel sampled-data-based dynamic event-triggered mechanism (SDB-DETM) is tailored to generate impulsive control time sequences, save control costs, and promise a desired control performance. A maximum event-trigger time interval is introduced as a substitute for the assumption of the average time interval when considering the randomness of FDIA. Furthermore, the dynamics of the auxiliary dynamic variable are improved to adapt the novel SDB-DETM with mixed signals. A sufficient and necessary condition of the positivity of the positive MASs under FDIA is presented. The commonly used positive constraints on the error system are removed. By constructing a quadratic Lyapunov function, a sufficient condition of the mean-square consensus of the positive MASs is derived. Two numerical examples are provided to illustrate the effectiveness of theoretical results.

A Concentration-Based Approach for Optimizing the Estimation Performance in Stochastic Sensor Selection

A Concentration-Based Approach for Optimizing the Estimation Performance in Stochastic Sensor Selection

Adaptive Optimal Control of Networked Nonlinear Systems With Stochastic Sensor and Actuator Dropouts Based on Reinforcement Learning.

This article investigates the adaptive optimal control problem for networked discrete-time nonlinear systems with stochastic packet dropouts in both controller-to-actuator and sensor-to-controller channels. A Bernoulli model-based Hamilton-Jacobi-Bellman (BHJB) equation is first developed to deal with the corresponding nonadaptive optimal control problem with known system dynamics and probability models of packet dropouts. The solvability of the nonadaptive optimal control problem is analyzed, and the stability and optimality of the resulting closed-loop system are proven. Two reinforcement learning (RL)-based policy iteration (PI) and value iteration (VI) algorithms are further developed to obtain the solution to the BHJB equation, and their convergence analysis is also provided. Furthermore, in the absence of a priori knowledge of partial system dynamics and probabilities of packet dropouts, two more online RL-based PI and VI algorithms are developed by using critic-actor approximators and packet dropout probability estimator. It is shown that the concerned adaptive optimal control problem can be solved by the proposed online RL-based PI and VI algorithms. Finally, simulation studies of a single-link manipulator are provided to illustrate the effectiveness of the proposed approaches.

Disposable Stochastic Miniplatforms for Simultaneous Recognition and Determination of Vitamins B5, B7, and B9 in Food and Pharmaceutical Compounds.

Vitamins are essential for supporting daily physiological functions and are crucial for metabolic processes, including preventing vascular events and delaying the progression of diabetic nephropathy. The specific assessment of food and pharmaceutical compound quality requires the highly sensitive evaluation of vitamins B5, B7, and B9. Therefore, it is necessary to have a fast screening method for multivitamin tablets, pharmaceutical tablets, water, food, including raw egg yolk, and biological fluids, including urine, in order to ensure the precise identification and measurement of vitamins B5, B7, and B9. This paper introduces a novel disposable stochastic miniplatform that utilizes an incorporated 2D stochastic sensor to be able to simultaneously assay the three vitamins. Cobalt-phthalocyanine was used to modify the carbon film of the 2D sensor. High sensitivities (of 102-108 s-1 g-1 mL magnitude order) and low detection limits (of pg mL-1 magnitude order) were recorded for the disposable stochastic miniplatforms. The validation procedure involved the utilization of pharmaceutical tablets, supplement tablets, water samples, food samples, including raw egg yolk, and biological samples, specifically urine. The disposable stochastic miniplatforms demonstrate cost-effectiveness and suitability for the fast screening tests, with a time frame of 6 min, across a range of samples, a capacity of over 150 measurements, and an endurance of up to one month. Recovery values higher than 94.00% with RSD (%) values lower than 1.00 were recorded.

Distributed resilient fault-tolerant cooperative automatic generation control and multi-event triggering mechanism co-design for wind energy conversion power system

In wind energy conversion power system, some sensor and actuator fall into abnormal condition which may lead to lower wind energy utilization rate and poor system performance or even system collapse. Meanwhile, with the increasing complexity of wind energy conversion power system, more device nodes compete for limited network communication resources, there are time-delays and packets dropout in data transmission, and these may reduce the efficiency of wind energy conversion. Accordingtoaboveconsideration, the multi-event triggering mechanism based distributed resilient fault-tolerant cooperative control scheme for wind energy conversion power system is put forward. A novel model of the wind energy conversion power system is established when the sensor and actuator faults simultaneously. According to Lyapunov stability theory, the sufficient condition for system exponential mean-square stable is obtained. Different from previous fault-tolerant control method, the proposed control strategy considered the wind energy conversion power system with data transmission management under simultaneous stochastic sensor and actuator faults. The simulation demonstrates that the method proposed in this paper is effective and the multi-event triggering mechanism based distributed resilient fault-tolerant controller provides stronger reliability and higher wind energy utilization efficiency.

Adaptive convolutional dictionary learning for denoising seismocardiogram to enhance the classification performance of aortic stenosis

Background:Aortic stenosis (AS) is the most prevalent type of valvular heart disease (VHD), traditionally diagnosed using echocardiogram or phonocardiogram. Seismocardiogram (SCG), an emerging wearable cardiac monitoring modality, is proved to be feasible in non-invasive and cost-effective AS diagnosis. However, SCG waveforms acquired from patients with heart diseases are typically weak, making them more susceptible to noise contamination. While most related researches focus on motion artifacts, sensor noise and quantization noise have been mostly overlooked. These noises pose additional challenges for extracting features from the SCG, especially impeding accurate AS classification. Method:To address this challenge, we present a convolutional dictionary learning-based method. Based on sparse modeling of SCG, the proposed method generates a personalized adaptive-size dictionary from noisy measurements. The dictionary is used for sparse coding of the noisy SCG into a transform domain. Reconstruction from the domain removes the noise while preserving the individual waveform pattern of SCG. Results:Using two self-collected SCG datasets, we established optimal dictionary learning parameters and validated the denoising performance. Subsequently, the proposed method denoised SCG from 50 subjects (25 AS and 25 non-AS). Leave-one-subject-out cross-validation (LOOCV) was applied to 5 machine learning classifiers. Among the classifiers, a bi-layer neural network achieved a moderate accuracy of 90.2%, with an improvement of 13.8% from the denoising. Conclusions:The proposed sparsity-based denoising technique effectively removes stochastic sensor noise and quantization noise from SCG, consequently improving AS classification performance. This approach shows promise for overcoming instrumentation constraints of SCG-based diagnosis.

Fog Node Planning With Stochastic Sensor Traffic in Dynamic Industrial Environment

The emergence of Industrial Internet of Things (IIoT) along with Fog Computing (FC) has brought great benefits in the industry field through real-time monitoring, resource optimization configuration and intelligent cloud control. The deployment of fog nodes in industrial plants is the essential precondition for FC implementation. Existing works are mainly based on pre-given sensor traffic in the deployment of fog nodes, which largely ignores the stochastic uncertainty imposed by the dynamic industrial environment. In this paper, without requiring a priori knowledge of sensor traffic pattern, we establish a mathematical model based on uncertain programming to formulate the fog nodes location determination and the sensor association problem. Due to the complexity of the model, we introduce a learning-based algorithm to effectively solve the problem with an acceleration mechanism. Finally, intensive simulations are implemented to verify the performance of the algorithm. The results indicate that our approach outperforms other benchmarks in terms of transmission energy on planning fog nodes in industrial plants with stochastic sensor traffic.

KUBAI: Sensor Fusion for Non-Invasive Fetal Heart Rate Tracking.

Fetal heart rate (FHR) is critical for perinatal fetal monitoring. However, motions, contractions and other dynamics may substantially degrade the quality of acquired signals, hindering robust tracking of FHR. We aim to demonstrate how use of multiple sensors can help overcome these challenges. We develop KUBAI1, a novel stochastic sensor fusion algorithm, to improve FHR monitoring accuracy. To demonstrate the efficacy of our approach, we evaluate it on data collected from gold standard large pregnant animal models, using a novel non-invasive fetal pulse oximeter. The accuracy of the proposed method is evaluated against invasive ground-truth measurements. We obtained below 6 beats-per-minute (BPM) root-mean-square error (RMSE) with KUBAI, on five different datasets. KUBAI's performance is also compared against a single-sensor version of the algorithm to demonstrate the robustness due to sensor fusion. KUBAI's multi-sensor estimates are found to give overall 23.5% to 84% lower RMSE than single-sensor FHR estimates. The mean ± SD of improvement in RMSE is 11.95 ±9.62 BPM across five experiments. Furthermore, KUBAI is shown to have 84% lower RMSE and ∼3 times higher R2 correlation with reference compared to another multi-sensor FHR tracking method found in literature. The results support the effectiveness of KUBAI, the proposed sensor fusion algorithm, to non-invasively and accurately estimate fetal heart rate with varying levels of noise in the measurements. The presented method can benefit other multi-sensor measurement setups, which may be challenged by low measurement frequency, low signal-to-noise ratio, or intermittent loss of measured signal.

Stabilization of Triangular Nonlinear Systems With Multiplicative Stochastic State Sensing Noise

We present new state-feedback control designs for lower-triangular/upper-triangular nonlinear systems with multiplicative stochastic sensor uncertainty. For lower-triangular nonlinear systems with small sensor noise, we develop a novel control design where the control gains are suitably constructed to simultaneously dominate the nonlinear functions and sensor noise of sufficiently small multiplicative gain. For upper-triangular nonlinear systems, we propose a new low-gain domination design, the advantage of which is that it can effectively deal with the sensor noise with arbitrarily large intensities. These two designs can both ensure that closed-loop system has an almost surely unique global solution, the origin of the closed-loop system is mean-square stable, and the states can be regulated to zero almost surely. Finally, two simulation examples are given to illustrate the designs.

2D Enantioselective Disposable Stochastic Sensor for Fast Real-Time Enantioanalysis of Glutamine in Biological Samples

As protein is both a structural component and a metabolic intermediary, amino acids play a crucial function in the body. When it comes to proteins, only the L-configuration of chiral amino acids is found. At the molecular level, symmetry is disrupted; however, the scientific basis for this symmetry breaking is not yet known. Enantioanalysis of chiral compounds such as amino acids plays a very important role in the correct diagnosis of illnesses, such as cancer. The enantiomers of glutamine—a chiral amino acid—were investigated in biological samples using a disposable stochastic sensor. The disposable stochastic sensor based on immobilization of maltodextrin (DE 4.0–7.0) on the surface of a disposable sensor based on graphene decorated with Ag was designed, characterized, and validated for screening tests of whole blood and tissue samples. The stochastic sensor was designed using cold plasma deposition of graphene decorated with Ag on plastic material. The sensor was enantioselective, being able to discriminate between the enantiomers of glutamine. High sensitivities were recorded for both enantiomers, while the limits of determination were 100 fmol L−1 for L-glutamine and 1 fmol L−1 for D-glutamine. High recoveries were determined for the assay of one enantiomer in the presence of the other, despite the ratio between the two enantiomers.

Theoretical upper bound of multiplexing in biological sensory receptors

Biological sensory receptors provide excellent examples of microscopic scale information transduction amidst stochastic noise. We argue that stochasticity is not always a hindrance to sensing. Instead, it could allow a single stochastic sensor to perform multiplexing: simultaneously transducing multiple types of environmental information to the downstream sensory network. Through a Langevin dynamics simulation of a ligand-receptor sensor in a bath of ligands, we demonstrate that a binary-state receptor can simultaneously encode multiple independent environmental variables, such as ligand concentration and the speed of media flow. We develop a general theory of stochastic sensory multiplexing and suggest two theoretical upper bounds. Furthermore, we conjecture that randomly generated sensors typically saturate the tighter upper bound. The theoretical framework developed in this study, which involves a rank-deficient maximum likelihood analysis (rd-MLE), provides a systematic approach to comprehensively assess a sensor's sensory ability without any initial assumptions. This theoretical framework can inspire the design of more efficient artificial sensors.

Learning Optimal Stochastic Sensor Scheduling for Remote Estimation With Channel Capacity Constraint

Scheduling for multiple sensors to observe multiple systems is investigated. Only one sensor can transmit a measurement to the remote estimator over a Markovian fading channel at each time instant. A stochastic scheduling protocol is proposed, which first chooses the system to be observed via a probability distribution, and then chooses the sensor to transmit the measurement via another distribution. The stochastic sensor scheduling is modeled as a Markov decision process (MDP). A sufficient condition is derived to ensure the stability of remote estimation error covariance by a contraction mapping operator. In addition, the existence of an optimal deterministic and stationary policy is proved. To overcome the curse of dimensionality, the deep deterministic policy gradient, a recent deep reinforcement learning algorithm, is utilized to obtain an optimal policy for the MDP. Finally, a practical example is given to demonstrate that the developed scheduling algorithm significantly outperforms other policies.