Stochastic Sensor Research Articles

Protein Detection by Nanopores Equipped with Aptamers

Protein nanopores have been used as stochastic sensors for the detection of analytes that range from small molecules to proteins. In this approach, individual analyte molecules modulate the ionic current flowing through a single nanopore. Here, a new type of stochastic sensor based on an αHL pore modified with an aptamer is described. The aptamer is bound to the pore by hybridization to an oligonucleotide that is attached covalently through a disulfide bond to a single cysteine residue near a mouth of the pore. We show that the binding of thrombin to a 15-mer DNA aptamer, which forms a cation-stabilized quadruplex, alters the ionic current through the pore. The approach allows the quantification of nanomolar concentrations of thrombin, and provides association and dissociation rate constants and equilibrium dissociation constants for thrombin·aptamer interactions. Aptamer-based nanopores have the potential to be integrated into arrays for the parallel detection of multiple analytes.

Stochastic MEMS Sensor Using White Noise Oscillation for High Temperature Environment

Stochastic MEMS Sensor Using White Noise Oscillation for High Temperature Environment

Finite-horizon ℋ 2/ℋ ∞ control for a class of nonlinear Markovian jump systems with probabilistic sensor failures

This article is concerned with the mixed ℋ2/ℋ∞ control problem over a finite horizon for a class of nonlinear Markovian jump systems with both stochastic nonlinearities and probabilistic sensor failures. The stochastic nonlinearities described by statistical means could cover several types of well-studied nonlinearities, and the failure probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over a given interval. The purpose of the addressed problem is to design state feedback controllers such that the closed-loop system achieves the expected ℋ2 performance requirement with a guaranteed ℋ∞ disturbance attenuation level. The solvability of the addressed control problem is expressed as the feasibility of certain coupled matrix equations. The controller gain at each time instant k can be obtained by solving the corresponding set of matrix equations. A numerical example is given to illustrate the effectiveness and applicability of the proposed algorithm.

Stochastic Sensor Scheduling for Energy Constrained Estimation in Multi-Hop Wireless Sensor Networks

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense a dynamic process and transmit measured information over a shared channel to a central fusion center. The fusion center computes an estimate of the process state by means of a Kalman filter. In this technical note we assume that the WSN admits a tree topology with one fusion center at the root. At each time step only a subset of sensors can be selected to transmit observations to the fusion center due to a limited energy budget. We propose a stochastic sensor selection algorithm that randomly selects a subset of sensors according to a certain probability distribution, which is opportunely designed to minimize the asymptotic expected covariance matrix of the estimation error. We show that the optimal stochastic sensor selection problem can be relaxed into a convex optimization problem and thus efficiently solved. We also provide a possible implementation of our algorithm which does not introduce any communication overhead. The technical note ends with some numerical examples that show the effectiveness of the proposed approach.

ERROR ANALYSIS OF THE INERTIAL MEASUREMENT UNIT / INERCINIO MATAVIMŲ BLOKO KLAIDŲ ANALIZĖ

The paper describes the results of error analysis of the new inertial measurement unit ADIS16364 produced by Analog Devices. This error analysis concerns stochastic sensor errors identified by the Allan variance method. In order to improve the performance of the inertial sensors, the users are keen to know more details about the noise components in each axis for a better modelling of the stochastic parts to improve the navigation solution. The main contribution of this paper is to present data necessary for further inertial sensors signal processing by means of Kalman filtering. Santrauka Straipsnyje pateikiami naujojo inercinio matavimų bloko ADIS16364 klaidų analizės rezultatai. Aprašyta klaidų analizė yra susijusi su stochastinio jutiklio klaidomis, nustatytomis Allano variacijos metodu. Siekiant pagerinti inercinių jutiklių našumą, naudotojai yra linkę daugiau sužinoti apie kiekvienoje ašyje esančius komponentus, kad būtų pagerintas stochastinių dalių modeliavimas bei rasti pažangesni navigacijos sprendimai. Šiuo darbu siekiama pristatyti duomenis, kurie yra reikalingi tolimesniam inercinių jutiklių signalų apdorojimui panaudojant Kalmano filtravimą.

Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System

Due to their complementary characteristics, Global Positioning System (GPS) is integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low-cost. In this study, a low cost reduced inertial sensor system (RISS) is considered. It consists of a MEMS-grade gyroscope and the vehicle built-in odometer. The system works together with GPS to provide 2D navigation for land vehicles. With adequate accuracy, Kalman filter (KF) is the commonly used estimation technique to achieve the data fusion of GPS and inertial sensors in case of high-end IMUs. However, due to the inherent error characteristics of MEMS grade devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be handled by KF and its linear error models. To overcome the problem, Fast Orthogonal Search (FOS), a nonlinear system identification technique, is suggested for modeling the higher order RISS errors. As a general-purpose numerical method, FOS algorithm has the ability to figure out the system nonlinearity efficiently with a tolerance of arbitrary stochastic system noise. Even using online short-term training data, this method is still able to build an accurate nonlinear model that predicts the system dynamics. Motivated by the above merits, an augmented KF/FOS module is proposed by cascading FOS algorithm to a traditional KF structure. By estimating and reducing both linear and nonlinear RISS errors, the proposed method is supposed to offer substantial enhancement on the positioning accuracy of MEMS-based RISS during GPS outages. In order to examine the effectiveness of the proposed technique, the KF/FOS module is applied on the low cost RISS together with GPS in a land vehicle for several road test trajectories. The performance of the proposed method is compared to KF-only solution, both assessed with respect to a reference offered by a high-end solution. The experimental results confirm that KF/FOS module outperforms KF-only method. The results also show the applicability of the proposed method for real-time vehicle applications.

高温環境で用いるための雑音振動を利用した確率型MEMSセンサ

高温環境で用いるための雑音振動を利用した確率型MEMSセンサ

Electrophysiological Method for Quantification of the Number of phi29 DNA Packaging Nanopore in Planar Bilayer Membrane

Bacterial virus phi29 uses one of the strongest DNA packaging nanomotors to package its micron-length genomes into a pro-capsid. After re-engineering, whether the DNA nano-motor can be used to pump drugs, DNA, RNA or other therapeutic molecules into specifically targeted cells represent a great challenge in nanomedicine. We have recently successfully embedded the connector, a core component of phi29 DNA package motor, into a planar bilayer membrane (BLM). Under an electric field, double-stranded DNA translocated through the connector channel.The application of phi29 connector array as a stochastic sensor requires knowledge of the number of channels on each membrane. We herein report a method for precise counting of the number of channels on each membrane by electrophysiological approach. Generally, the number of channels is determined by the conductance of total channels and conductance per single channel. Using a derived empirical equation, we can calculate the conductance per single channel at any salt concentration using conductivity of respective conducting buffer. The total conductance of total channels can be measured by ionic current through all the channels under an applied ramp voltage. Comparison of calculated and true values established this as a feasible, reliable and reproducible approach.

Fluctuation-enhanced sensing of bacterium odors

The goal of this paper is to explore the possibility to detect and identify bacteria by sensing their odor via fluctuation-enhanced sensing with commercial Taguchi sensors. The fluctuations of the electrical resistance during exposure to different bacterial odors, Escherichia coli and anthrax-surrogate Bacillus subtilis, have been measured and analyzed. In the present study, the simplest method, the measurement and analysis of power density spectra was used. The sensors were run in the normal-heated and the sampling-and-hold working modes, respectively. The results indicate that Taguchi sensors used in these fluctuation-enhanced modes are effective tools of bacterium detection and identification even when they are utilizing only the power density spectrum of the stochastic sensor signal.

Robust Filtering With Randomly Varying Sensor Delay: The Finite-Horizon Case

In this paper, we consider the robust filtering problem for discrete time-varying systems with delayed sensor measurement subject to norm-bounded parameter uncertainties. The delayed sensor measurement is assumed to be a linear function of a stochastic variable that satisfies the Bernoulli random binary distribution law. An upper bound for the actual covariance of the uncertain stochastic parameter system is derived and used for estimation variance constraints. Such an upper bound is then minimized over the filter parameters for all stochastic sensor delays and admissible deterministic uncertainties. It is shown that the desired filter can be obtained in terms of solutions to two discrete Riccati difference equations of a form suitable for recursive computation in online applications. An illustrative example is presented to show the applicability of the proposed method.

Pulverized Fuel Control using Biased Flow Measurements

Abstract This paper describes the design, implementation, and use of a pulverized fuel flow observer for a coal fired power plant. The observer is based on a state of the art pulverized fuel flow sensor, which adds transient information previously unavailable. Unfortunately, the flow measurement is subject to an uncertain and slowly changing bias, which makes it unsuitable for direct feedback control. The effect of the bias is effectively removed by combining a stochastic sensor error model with a simple coal mill model in a state observer. The designed observer is implemented on 12 coal mills at two Danish coal fired power plants, and the results show that it can be used to effectively attenuate firing disturbances, when used in a feedback control loop around the coal mills. Focus is kept on building a simple observer and control structure to accommodate easy implementation and maintenance.

Sensitivity and Signal Complexity as a Function of the Number of Ion Channels in a Stochastic Sensor

Alternating current, phase-sensitive stochastic detection using between 1 and 26 alpha-hemolysin ion channels reconstituted in a lipid bilayer, suspended over a 160-nm-radius orifice glass nanopore, is reported. As predicted by the binomial distribution, simultaneous analyte detection at large numbers of channels is effectively zero, independent of the number of ion channels. The results indicate that alphaHL channels are noninteracting and that significant gains in sensitivity are possible without sacrificing the simplicity of single-molecule detection strategies.

Stochastic sensing of biomolecules in a nanopore sensor array

In this study, we demonstrate that a pattern-recognition stochastic sensor can beconstructed by employing an array of protein pores modified with a variety of non-covalentbonding sites as effective sensing elements. The collective responses of each of theindividual component nanopores to a compound produce diagnostic patternscharacterized by event dwell time, amplitude, and voltage dependence, which canindependently or collectively serve as (an) analyte signature(s). With an increase inthe dimensionality of the signal, the nanopore sensor array provides enhancedresolution for the differentiation of analytes compared to a single-pore configuration.This allows identification of a target analyte from a mixture or the potential forsimultaneous detection. The pattern-recognition nanopore method is envisaged for furtherdevelopment as a miniaturized and automated sensing technique, which could findpotential use as a laboratory or clinical tool for routine sensor applications, includingenvironmental monitoring, drug discovery, medical diagnosis, and homeland security.

Nanoprecipitation-assisted ion current oscillations

Nanoscale pores exhibit transport properties that are not seen in micrometre-scale pores, such as increased ionic concentrations inside the pore relative to the bulk solution, ionic selectivity and ionic rectification. These nanoscale effects are all caused by the presence of permanent surface charges on the walls of the pore. Here we report a new phenomenon in which the addition of small amounts of divalent cations to a buffered monovalent ionic solution results in an oscillating ionic current through a conical nanopore. This behaviour is caused by the transient formation and redissolution of nanoprecipitates, which temporarily block the ionic current through the pore. The frequency and character of ionic current instabilities are regulated by the potential across the membrane and the chemistry of the precipitate. We discuss how oscillating nanopores could be used as model systems for studying nonlinear electrochemical processes and the early stages of crystallization in sub-femtolitre volumes. Such nanopore systems might also form the basis for a stochastic sensor.

Stochastic Sensing on a Modular Chip Containing a Single-Ion Channel

Engineered protein channels have many potential applications in biosensing at the single-molecule level. A future generation of biosensor could be an array of target-specific ion channels, where each protein pore acts as a sensor element. An important step toward this goal is to create a portable, durable, single-protein channel-integrated chip device. Here we report a versatile, modular chip that contains a single-ion channel for single-molecular biosensing. The core of the device is a long-lived lipid membrane that has been sandwiched between two air-insulated agarose layers which gel in situ. A single-protein pore embedded in the membrane serves as the sensor element. The modular device is highly portable, allowing a single-ion channel to continuously function following detachment of the chip from the instrument and independent transportation of the device. The chip also exhibits high durability, which is evidenced from long-duration continuous observation of single-channel dynamics. Once engineered protein pores are installed, the chip becomes a robust stochastic sensor for real-time targeting such as detection of the second messenger IP3. This pluggable biochip could be incorporated with many applicable devices, such as a microfluidic system, and be made into a microarray for both biomedical detection and membrane protein research.

A Genetically Encoded Pore for the Stochastic Detection of a Protein Kinase

Stochastic sensing is an emerging approach for the detection of a wide variety of analytes at the level of individual molecules. Detection is accomplished by observing the modulation of the current that flows through a single protein pore that has been engineered to bind an analyte of interest. Previously, protein analytes have been detected by using pores to which ligands have been appended at specific sites by targeted chemical modification. Here, we report the first genetically encoded stochastic sensor element for detecting a protein. A protein kinase inhibitor peptide sequence was incorporated into the alpha-hemolysin polypeptide, which was used to form a heteroheptameric pore containing a single copy of the inhibitor sequence. With this pore, the successful detection of the catalytic subunit of protein kinase A was demonstrated. This development should greatly facilitate the detection of active kinase subunits by stochastic sensing and the rapid screening of kinase inhibitors by an approach that yields kinetic information.

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.

ROBUST FILTERING FOR RANDOM SENSOR DELAY SYSTEMS

In this paper, we consider the robust filtering problem for discrete time-varying systems with sensor delayed measurement subject to norm-bounded parameter uncertainties. The sensor delayed measurement is assumed to be a linear function of a stochastic variable that satisfies Bernoulli random binary distribution. An upper bound for the actual covariance of uncertain stochastic parameter system is derived, and is used for the estimation variance constraints. Such an upper bound is then minimized over the filter parameters for all stochastic sensor delays and admissible deterministic uncertainties. It is shown that the desired filter can be obtained in terms of solutions to two discrete Riccati difference equations, which are of a form suitable for recursive computation in online applications. An illustrative example is presented to show the applicability of the proposed method.

ON A STOCHASTIC ALGORITHM FOR SENSOR SCHEDULING

A stochastic algorithm for solving the sensor selection problem is presented. The problem arises when many sensors are jointly trying to estimate a process but only a subset of them can take measurements at any time step. The proposed stochastic sensor selection strategy is easy to implement and is computationally tractable. The algorithm is illustrated through simple examples of sensor scheduling and dynamic sensor coverage.