Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks(WSNs)where the battery capacity and energy sources are very restricted.Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features.This paper suggests a Hybrid Sensor Selection(HSS)technique that combines filter-wrappermethod to acquire a rich-informational subset of sensors in a reasonable time.HSS aims to increase the lifetime of WSNs by using the optimal number of sensors.At the same time,HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy.The evaluation of the HSS technique has adopted four experiments by using four different datasets.These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting theWSNfunctionality.Furthermore,to ensure HSS credibility and reliability,the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.

Parametric study on contact sensors for MASW measurement-based interfacial debonding detection for SCCS

Steel-concrete composite structures(SCCS)have been widely used as primary load-bearing components in large-scale civil infrastructures.As the basis of the co-working ability of steel plate and concrete,the bonding status plays an essential role in guaranteeing the structural performance of SCCS.Accordingly,efficient non-destructive testing(NDT)on interfacial debondings in SCCS has become a prominent research area.Multi-channel analysis of surface waves(MASW)has been validated as an effective NDT technique for interfacial debonding detection for SCCS.However,the feasibility of MASW must be validated using experimental measurements.This study establishes a high-frequency data synchronous acquisition system with 32 channels to perform comparative verification experiments in depth.First,the current sensing approaches for high-frequency vibration and stress waves are summarized.Secondly,three types of contact sensors,namely,piezoelectric lead-zirconate-titanate(PZT)patches,accelerometers,and ultrasonic transducers,are selected for MASW measurement.Then,the selection and optimization of the force hammer head are performed.Comparative experiments are carried out for the optimal selection of ultrasonic transducers,PZT patches,and accelerometers for MASW measurement.In addition,the influence of different pasting methods on the output signal of the sensor array is discussed.Experimental results indicate that optimized PZT patches,acceleration sensors,and ultrasonic transducers can provide efficient data acquisition for MASW-based non-destructive experiments.The research findings in this study lay a solid foundation for analyzing the recognition accuracy of contact MASW measurement using different sensor arrays.

Sensor selection of helicopter transmission systems based on physical model and sensitivity analysis

In the helicopter transmission systems, it is important to monitor and track the tooth damage evolution using lots of sensors and detection methods. This paper develops a novel approach for sensor selection based on physical model and sensitivity analysis. Firstly, a physical model of tooth damage and mesh stiffness is built. Secondly, some effective condition indicators （Cls） are presented, and the optimal Cls set is selected by comparing their test statistics according to Mann-Kendall test. Afterwards, the selected CIs are used to generate a health indicator （HI） through sen slop estimator. Then, the sensors are selected according to the monotonic relevance and sensitivity to the damage levels. Finally, the proposed method is verified by the simulation and experimental data. The results show that the approach can provide a guide for health monitor- ing of helicopter transmission systems, and it is effective to reduce the test cost and improve the system＇s reliability.

Sensor selection for parameterized random field estimation in wireless sensor networks

We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based incremental sensor node selection through information quality computation for and message exchange among neighboring sensors. Simulation results show the good performance of the proposed algorithms.

Sensor selection for received signal strength-based source localization in wireless sensor networks

Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained. A sensor selection method is proposed to improve the localization accuracy as well as to save energy consumption. By selecting only a limited number of sensor nodes based on the model accuracy and geometry structure analysis, localization performance is improved, and energy consumption is reduced. In addition, extensive simulations are presented to demonstrate that the estimation performance with the proposed sensor selection method is better than that without sensor selection.

A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors

The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.

Accurately tracking hypersonic gliding vehicles via an LEO mega-constellation in relay tracking mode

In order to effectively defend against the threats of the hypersonic gliding vehicles(HGVs),HGVs should be tracked as early as possible,which is beyond the capability of the ground-based radars.Being benefited by the developing megaconstellations in low-Earth orbit,this paper proposes a relay tracking mode to track HGVs to overcome the above problem.The whole tracking mission is composed of several tracking intervals with the same duration.Within each tracking interval,several appropriate satellites are dispatched to track the HGV.Satellites that are planned to take part in the tracking mission are selected by a new derived observability criterion.The tracking performances of the proposed tracking mode and the other two traditional tracking modes,including the stare and track-rate modes,are compared by simulation.The results show that the relay tracking mode can track the whole trajectory of a HGV,while the stare mode can only provide a very short tracking arc.Moreover,the relay tracking mode achieve higher tracking accuracy with fewer attitude controls than the track-rate mode.

Concepts for Sensor Matching in Mechatronic Systems

A typical mechatronic system consists of a multitude of components,and the sensors belong to an important and crucial class of such components.Optimal matching of the system components is implicit in the current definition of a mechatronic system.The focus of the present paper is the optimal matching of sensors with other hardware in the system.Sensor matching may be based on several concepts such as the operating frequency range(operating bandwidth),speed of response(and the corresponding rate of data sampling in digital conversion),the device sensitivity(or gain or data amplification),and the effect of component acc uracy on the overall accuracy of the system.The present paper explores all these concepts and presents suit able approaches for sensor matching through those criteria.The relevant procedures are illustrated using case studies.

Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement

A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches:a greedy method based on D-optimality and a noise-robust greedy method in this paper.Extensions of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement.

Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

The performance of data-driven fault detection and diagnostics(FDD)is heavily dependent on sensors.However,sensor inaccuracy and sensor faults are pervasive in building operation:inaccurate and missing sensor readings deteriorate FDD performance;sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process,which is another key factor of data-driven FDD performance.Sensor accuracy and sensor selection individually are well-studied research topics in this field,but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified.In this paper,we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy.Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy.This methodology focuses on the net impact of fault states across a full sensor set.The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance.A case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory,USA.

Highly sensitive hybrid nanofiber-based room-temperature CO sensors： Experiments and density functional theory simulations

Chemical sensors （CSs） are an emerging area in nanoscience research, which focuses on the highly sensitive detection of toxic and hazardous gases and disease- related volatile organics. While the field has advanced rapidly in recent years, it lacks the theoretical support required for the rational design of innovative materials with tunable measurement responses. Herein, we present a one-dimensional （1D） hybrid nanofiber decorated with ultrafine NiO nanoparticles （NiO NPs） as an efficient active component for CSs. Highly dispersed （110）-facet NiO NPs with a high percentage of Ni2~ active sites with unsaturated coordination were confined in a TiO2 nanofiber （TiO2 NF） matrix that is favorable for surface catalytic reactions. The CSs constructed using the 1D heterostructure NiO/TiO2 nanofibers （NiOdrio2 HNFs） exhibited a highly selective response to trace CO gas molecules （1 ppm） with high sensitivity （AR/Ro = 1.02）, ultrafast response/ recovery time （T 〈 20 s）, and remarkable reproducibility at room tem- perature. The density functional theory （DFT） simulations and experimental results confirmed that the selective response could be attributed to the high molecular adsorption energy of the NiO nanoparticles with （110） facets and abundant interfaces, which act synergistically to promote CO adsorption and facilitate charge transfer.

Enhanced flow injection analysis for measurements of S-nitrosothiols species in biological samples using highly selective amperometric nitric oxide sensor

A highly selective nitric oxide（NO） sensor is fabricated and applied to devise an enhanced flow injection analysis（FIA） system for S-nitrosothiols（RSNOs） measurement in biological samples.The NO sensor is prepared using a polytetrafluoroethylene（PTFE） gas-permeable membrane loaded with Teflon AF？ solution,a copolymer of tetrafluoroethylene and 2,2-bis（trifluoroethylene）-4,5-difluoro -l,3-dioxole,to improve selectivity.This method is much simpler and possesses good performance over a wide range of RSNOs concentrations.Standard deviation for three parallel measurements of blood plasma is 4.0%.The use of the gas sensing configuration as the detector enhances selectivity of the FIA measurement vs.using less selective electrochemical detectors that do not use PTFE/Teflon type outer membranes.

Remaining useful life estimation for deteriorating systems with time-varying operational conditions and condition-specific failure zones

Dynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life （RUL） for the deteriorating systems. This paper presents a method based on probabilistic and stochastic approaches to estimate system RUL for periodically moni- tored degradation processes with dynamic time-varying operational conditions and condition- specific failure zones. The method assumes that the degradation rate is influenced by specific oper- ational condition and moreover, the transition between different operational conditions plays the most important role in affecting the degradation process. These operational conditioqs are assumed to evolve as a discrete-time Markov chain （DTMC）. The failure thresholds are also determined by specific operational conditions and described as different failure zones. The 2008 PHM Conference Challenge Data is utilized to illustrate our method, which contains mass sensory signals related to the degradation process of a commercial turbofan engine. The RUE estimation method using the sensor measurements of a single sensor was first developed, and then multiple vital sensors were selected through a particular optimization procedure in order to increase the prediction accuracy. The effectiveness and advantages of the proposed method are presented in a comparison with exist- ing methods for the same dataset.

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.

A WO3 nanorod-Cr2O3 nanoparticle composite for selective gas sensing of 2-butanone

The hexagonal （h）-WO3-Cr2o3 nanocomposites with different W/Cr molar ratio of 4：1,10：1 and 40：1 were prepared by a facile two-step hydrothermal method, and its gas sensing properties were investigated under optimum working temperature. X-ray diffraction （XRD）, scanning electron microscopy （SEM）, transmission electron microscopy （TEM）, high resolution transmission electron microscopy （HRTEM） were used to characterize the morphology, microstructure and crystallinity of the as-synthesized samples. The hexagonal WO3 nanorods show a better crystallinity than Cr2O3 nanoparticles. When the molar ratio of W/Cr is 10：1, the hexagonal WO3-Cr2O3 nanocomposite shows obvious selectivity toward 2-butanone at 205℃ compared with other typical reducing gases, and the response value to 100 ppm 2- butanone can reach 5.6. However, there is no selectivity toward 2-butanone when the Cr/W molar ratio is 1：4 and 1：40. Furthermore, hexagonal WO3-Cr2O3 nanocomposites have a short response and recovery time to 5ppm 2-butanone, which is lOs and 80s, respectively. The measured results indicate that hexagonal WO3-Cr2O3 nanocomposite is a potential gas sensing material for monitoring volatile organic compounds （VOCs）.

Pseudo-crown ether having AIE and PET effects from a TPE-CD conjugate for highly selective detection of mercury ions

A new tetraphenylethylene-cyclodextrin (TPE-CD) conjugate with a linkage composed of long triethylene glycol chain and triazole ring on the CD rim has been designed and synthesized. The TPE-CD conjugate exists in a stretched form in DMSO and enhances its fluorescence after addition of a small amount of water due to aggregation-induced emission (AIE) effect. However, in the presence of a large amount of water, the TPE unit will enter the cyclodextrin cavity to form a folded self-inclusion compound. In the self-inclusion compound, not only nitrogen-containing pseudo-crown ether is formed but also arouses photo-induced electron transfer (PET) process from nitrogen atoms of triazole ring to TPE unit and quenches the fluorescence although more aggregation occurs in more water. This is the first finding that TPE-macrocycle conjugate can form pseudo-crown ether and has both the AIE phenomenon and the PET effect. Interestingly, only mercury ion arouses the fluorescence recover of the self-inclusion compound by entering the pseudo-crown ether cavity and blocking the PET process by binding to the nitrogen atoms, while other tested metal ions almost have no effect on the fluorescence. Therefore, the TPE-CD conjugate can be used for the highly selective fluorescence "Turn-On" detection of Hg^(2+).

Effect of TiO_2 Functionalization on Nano-Porous Silicon for Selective Alcohol Sensing at Room Temperature

Room temperature operated sensor for detection of alcohol vapours in low ppm range based on TiO_2 functionalized nano-porous silicon（PSi） is demonstrated. The effect of functionalization by TiO_2 on PSi is investigated using SEM, EDX, Raman spectroscopy, XRD and contact angle measurements. Sensing is accomplished by measuring change in resistance of the sensing layer using Cr-Au inter-digitatedelectrode（IDE） structure formed on top of the functionalized PSi layer. The sensors were tested for volatile organic compounds（VOCs） and water vapours in the wide range of 5–500 ppm concentration at room temperature. Functionalization of the nanostructured PSi by sputter deposited TiO_2 results in significant enhancement of sensitivity and inverse change in selectivity. PSi sensors have displayed strong response to water vapours whereas after functionalization, selective sensing to ethanol is depicted. Minimum detection by PSi sensors is portrayed at 100 ppm and that of functionalized sensors is at 10 ppm. Sensing mechanism is explained on the basis of surfaces and structures of both PSi and TiO_2. This study incites the importance of surface treatment of PSi for tuning the sensing properties and is useful in the development of selective alcohol sensors.