Sensor Density Research Articles

Influence of the Particle Shape and Density of Self-Assembled Gold Nanoparticle Sensors on LSPR and SERS

A low-cost methodology to prepare highly sensitive sensors is reported with different nanoparticles for LSPR and SERS measurements. The LSPR and SERS sensor was self-assembled as an array of polystyrene-capped gold nanoparticles (Au NPs), produced by drop-coating a Au NP suspension on a glass surface followed by plasma etching of the polystyrene. The procedure was applied to different sizes and shapes of Au NPs, primarily 60 nm gold nanospheres and 80 nm gold nanoraspberries, created with precise control on the packing density of the array. The LSPR sensitivity and figure of merit (FOM) for the arrays of gold nanoraspberries were found to be almost 2-fold better than for the nanospheres and had a shorter penetration depth, a consequence of their small-sized surface asperities. The average measured SERS enhancement factor (EF) for the raspberries was also higher than for spheres and control over the EF was achieved using different packing densities which allowed control of the interparticle coupling enhanc...

An integrated multivariate empirical mode decomposition method towards modal identification of structures

In this paper, a hybrid empirical mode decomposition (EMD) method is proposed to undertake ambient modal identification of civil structures. Unlike univariate EMD that uses single channel measurement independently, multivariate EMD (MEMD) is employed to estimate the joint information of multichannel vibration measurements of structural systems. The mode mixing in the resulting modal responses from MEMD is then circumvented using ensemble EMD (EEMD). The proposed hybrid MEMD method is validated using a suite of numerical models and experimental studies including the presence of low energy modes, closely-spaced frequencies, measurement noise and reduced sensor densities. The results show the improved performance of the proposed method over the traditional EMD method and reveal the potential of MEMD as a possible candidate for the ambient modal identification method.

Furnace tests on unprotected and protected concrete filled structural hollow sections

The accurate prediction of cross-sectional temperatures within concrete filled steel hollow (CFS) sections is critical for the accurate prediction of fire resistance. Whilst there have been many thermal and structural tests conducted on CFS columns, there are few that report the full cross-sectional thermal profile, and when they are reported, the sensor density is low, hindering the ability to validate models. This paper presents furnace tests and thermal modelling on 14 unprotected and 20 protected CFS sections, and examines the effect of several parameters on cross-sectional thermal profiles, as well as assessing the accuracy of both Eurocode thermal analysis guidance and intumescent fire protection design guidance. This paper shows that; (a) the assumptions within the Eurocode guidance can lead to large over-estimations in cross-sectional temperatures; (b) proposes new thermal modelling assumptions in three key areas; and (c) shows that the current intumescent fire protection design guidance is very conservative.

Robust tracking of piecewise linear trajectories with binary sensor networks

We present a novel approach to the problem of tracking objects moving along smooth trajectories using a network of simple inexpensive binary sensors. Specifically, we consider object trajectories that can be well-approximated by piecewise linear curves and sensors that can only detect whether an object is in their sensing range. We start by considering objects moving along straight-line trajectories with an unknown speed and show that such objects can be tracked using the measurements of just three generically placed binary sensors whose sensing ranges intersect the trajectory. Next, we present an asymptotic analysis that shows that a trajectory consisting of a finite number of straight line segments can be recovered with high probability using an arbitrarily low spatial density of sensors in the limit when the area to be covered gets larger and larger. We also present efficient algorithms that effectively recover piecewise linear trajectories. Finally we present analysis and simulations to demonstrate the high tracking accuracy of our approach and its robustness to sensing errors.

EEG source localization: Sensor density and head surface coverage

BackgroundThe accuracy of EEG source localization depends on a sufficient sampling of the surface potential field, an accurate conducting volume estimation (head model), and a suitable and well-understood inverse technique. The goal of the present study is to examine the effect of sampling density and coverage on the ability to accurately localize sources, using common linear inverse weight techniques, at different depths. Several inverse methods are examined, using the popular head conductivity. New methodSimulation studies were employed to examine the effect of spatial sampling of the potential field at the head surface, in terms of sensor density and coverage of the inferior and superior head regions. In addition, the effects of sensor density and coverage are investigated in the source localization of epileptiform EEG. ResultsGreater sensor density improves source localization accuracy. Moreover, across all sampling density and inverse methods, adding samples on the inferior surface improves the accuracy of source estimates at all depths. Comparison with existing methodsMore accurate source localization of EEG data can be achieved with high spatial sampling of the head surface electrodes. ConclusionsThe most accurate source localization is obtained when the voltage surface is densely sampled over both the superior and inferior surfaces.

Built to fight: variable loading conditions and stress distribution in stag beetle jaws

Designing very robust structures in an efficient way is a reoccurring challenge in engineering. For male stag beetle weaponry, the solution to this problem was evolved by natural and sexual selection. Stag beetle armature is adapted to perform under extreme circumstances: male stag beetles fight pugnacious battles over females, by using their extremely large jaws as ferocious weapons. During violent encounters, these jaws have to withstand forces with a wide range of unpredictable directions at several application points. We constructed 1020 finite element models with different input forces to investigate how the male jaws are structurally adapted to avoid failure. The cross-sectional shape of the jaw is adapted to provide robustness against the reaction forces of biting. Nevertheless, the jaw’s shape cannot prevent the fact that bite forces induce relatively high material stresses compared to other force directions. Also, males do not confine themselves in combats to bite with the most robust jaw regions. Both observations emphasize the usefulness of bite force modulation to avoid jaw failure. This is likely effectuated by a sensory network in the jaw exoskeleton, as sensor densities are nicely correlated to the maximal material stress caused by 510 different loading directions. Probably, stag beetles use this sensory information to adjust their fighting strategy as well. Finally, male jaws also need to resist the forceful bites inflicted by opponents. Even though this loading applies at other locations along the jaw, and bends the jaw in the opposite direction, our models show that the jaws are equally robust against these external forces as they are against the forces caused by their own biting.

Low Frequency Noise Characterization and Signal-to-Noise Ratio Optimization for Silicon Hall Cross Sensors

In this paper, silicon-based Hall cross sensors with different device dimensions and carrier densities are designed and fabricated. Low frequency noise behavior regarding to the device dimension and carrier density of the Hall cross sensors has been successfully characterized. Based on the measured $1/f$ -dependent low frequency noise spectral density and the theoretically calculated geometrical factors using a finite-element analysis tool, the noise voltage is calculated and Hooge's constant is reliably extracted. The signal-to-noise ratio is thus calculated and experimentally verified. This paper provides a guidance for the optimal design of Hall cross sensors.

Optimized Random Deployment of Energy Harvesting Sensors for Field Reconstruction in Analog and Digital Forwarding Systems

This work examines the large-scale deployment of energy harvesting sensors for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are powered solely by energy harvested from the environment and are deployed randomly according to a spatially nonhomogeneous Poisson point process whose density depends on the energy arrival statistics at different locations. Random deployment is suitable for applications that require deployment over a wide and/or hostile area. During an observation period, each sensor takes a local sample of the random field and reports the data to the closest data-gathering node if sufficient energy is available for transmission. The realization of the random field is then reconstructed at the fusion center based on the reported sensor measurements. For the purpose of field reconstruction, the sensors should, on the one hand, be more spread out over the field to gather more informative samples, but should, on the other hand, be more concentrated at locations with high energy arrival rates or large channel gains toward the closest data-gathering node. This tradeoff is exploited in the optimization of the random sensor deployment in both analog and digital forwarding systems. More specifically, given the statistics of the energy arrival at different locations and a constraint on the average number of sensors, the spatially-dependent sensor density and the energy-aware transmission policy at the sensors are determined for both cases by minimizing an upper bound on the average mean-square reconstruction error. The efficacy of the proposed schemes are demonstrated through numerical simulations.

Distributed Detection of Binary Decisions with Collisions in a Large, Random Network

We consider the problem of distributed detection in a large network of sensors. A random number of sensor nodes are randomly deployed. Sensor nodes perform local detection tests and communicate detections over a multiple-access channel to a fusion center. The fusion center can recognize both successful communications and communication collisions in the channel. We derive decision rules for both perfect communications and a delay-constrained communications protocol, and show that each are functions of count statistics only. We derive analytical expressions that characterize the performance of the system in terms of detection performance. We analyze performance with respect to sensor density and with respect to communications delay. Simulation examples validate theoretical predictions with numerical results. We show that the detection performance improves with network density despite increasing communication collisions. In addition, we show that detection performance using the protocol model, with imperfect communications, rapidly converges to the perfect communications case as the number of communication slots increase.

Fault Data Diagnosis by Cluster Computing in Wireless Sensor and Actuator Networks

Since sensors and communication links are prone to fail, to propose an efficient fault diagnosis algorithm becomes an important issue in wireless sensor and actuators networks. However, most of researches focused on the link fault tolerance without considering the sensing fault tolerance. Actuators may perform incorrect actions on receiving the fault data because the sensing function is on malfunction but the communication function is capable. Therefore, a fault data diagnosis by cluster computing (FDDCC) was proposed in this paper. In FDDCC, each sensor was clustered according to an actuator. Each actuator acted as a cluster head by a clustering mechanism. Each actuator selected the correct data and detected the fault data by cluster computing in a distributed manner. Simulation results showed that FDDCC had the better performance than other fault diagnosis algorithms, such as distributed fault detection method (DFDM), even if the ratio of the fault data to all data or the sensor density varied.

IJARCCE - Computer and Communication Engineering

2 Abstract: In the proposed paper the major issue is the target tracking in wireless sensor networks and to obtain exact position of the target as to be measured so that we can expect improvement in the tracking resolution. so there is need of finding accurately sensor and targets trajectory path as to be studied with special care and captured at regular basis. We also studied correlation properties and sensitivity in mobile sensors from the system parameters and maintained good resolution for tracking of mobile sensors at different speeds. Our simulation results gave satisfactory system parameters like sensor density, sensor and target mobility compared static sensor network environment.

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

Abstract Between September 5, 2008 and September 5,2009, near-surface soil moisture time series were collectedin the northern part of a 1.7 km2 watershed in SWHungaryat 14 monitoring locations using a portable TDR-300 soilmoisture sensor. The objectives of this study are to increasethe accuracy of soil moisture measurement at watershedscale, to improve flood forecasting accuracy, and to optimizesoil moisture sensor density. According to our results, in 10 of 13 cases, a strong correlationexists between the measured soil moisture dataof Station 5 and all other monitoring stations; Station 5is considered representative for the entire watershed. Logically,the selection of the location of the representativemeasurement point(s) is essential for obtaining representativeand accurate soil moisture values for the given watershed.This could be done by (i) employing monitoringstations of higher number at the exploratory phase of themonitoring, (ii) mapping soil physical properties at watershedscale, and (iii) running cross-relational statisticalanalyses on the obtained data. Our findings indicate that increasing the number of soilmoisture data points available for interpolation increasesthe accuracy of watershed-scale soil moisture estimation.The data set used for interpolation (and estimation ofmean antecedent soil moisture values) could be improved(thus, having a higher number of data points) by selectingpoints of similar properties to the measurement pointsfrom the DEM and soil databases. By using a higher numberof data points for interpolation, both interpolation accuracyand spatial resolution have increased for the measuredsoil moisture values for the Pósa Valley.

The Wireless Sensor Networks Base Layout and Density Optimization Oriented towards Traffic Information Collection

Wireless sensor networks (WSN) are applied in Intelligent Transport System for data collection. For the low redundancy rate of the wireless sensor networks nodes of traffic information collection, the senor nodes should be deployed reasonably for the WSN nodes to work effectively, and, thus, the base network structure and the density optimization of the sensor network are one of the main problems of WSN application. This paper establishes the wireless sensor networks design optimization model oriented to the traffic information collection, solving the design optimization model with the chemical reaction optimization (CRO) algorithm. The experimental results show that CRO algorithm outperforms the traditional particle swarm optimization (PSO) in solving the wireless sensor network design optimization oriented to the traffic information collection, capable of optimizing the wireless sensor network deployment of traffic information collection to contribute to the great improvement of the comprehensive value of the network performance. The reasonable design of the wireless sensor network nodes has great significance for the information collection, post-maintenance-and-extension, and cost saving of a monitoring system.

Optimum Sensor Placement for Impact Location Using Trilateration

AbstractA key problem associated with structural health monitoring (SHM) is the placement of sensors upon a structure to detect the existence, location, and the extent of any damage. Because input data coming from the sensors are groups of measurements, it is arguable that the most widely used approach to SHM nowadays is to consider it as a statistical pattern recognition problem. Artificial neural networks have made a great impact on pattern recognition practice. A problem associated with this monitoring strategy is to find a good compromise between the quality of information achieved by the sensor network, increasing with the sensor density, and the need to keep the minimum weight and instrumentation cost. Thus, the number of sensors must be kept under control, and a search of the optimal location of such sensors needs to be performed.All these aspects have been taken into account in the present work, dealing with the problem of optimum sensor placement for impact location on a multilayered composite structure.Multilayered composite structures may suffer particularly relevant trauma when subject to low‐velocity impacts, as they may produce non‐visible or barely visible damage on the structure surface, while remarkable subsurface delaminations may be present. Such hidden damage, when remaining undetected, may grow to catastrophic failure. To overcome this issue, a neural network approach has been used here to predict the impact locations on a composite panel from time‐dependent data recorded on a set of surface‐mounted piezoelectric sensors during an experimental impact test. A genetic algorithm has been used to find the optimal sensor layout that minimised the error in predicting the impact location. A new approach, based on trilateration, is discussed and compared with the traditional one and is shown to provide the same degree of accuracy at reduced computational cost.

Particle-Filter-Based Radio Localization for Mobile Robots in the Environments With Low-Density WLAN APs

This paper proposes a new localization method for mobile robots based on received signal strength (RSS) in indoor wireless local area networks (WLANs). In indoor wireless networks, propagation conditions are very difficult to predict due to interference, reflection, and fading effects. As a result, an explicit measurement equation is not available. In this paper, an observation likelihood model is accomplished using kernel density estimation to characterize the dependence of location and RSS. Based on the measured RSS, the robot's location is dynamically estimated using the proposed adaptive local search particle filter (ALSPF), which adopts the covariance adaptation for correcting the system states and updating the motion uncertainty. To deal with low sensor density in large-space environments, we present a strategy based on the strongest signal with minimum variance to choose a subset of detectable access points (APs) for enhancing robot localization and reducing the computational burden. The proposed approaches are verified by realistic low-density WLAN APs to demonstrate the feasibility and suitability. Experimental results indicate that the proposed ALSPF provides approximately 1-m error and significant improvements over particle filtering.

사물인터넷에서 가중치 기반 클러스터 헤드 교체 알고리즘

Since the sensors of Internet of Things (IOT) collect various data, the lifetime of sensor network is very important and the data should be aggregated efficiently. The contiguous collection by the certain sensors occurs an excessive battery consumption and successive transmission of same value of data should be avoided. To solve these things, we propose an weight-based cluster head replacement method that divides whole network into several grids and cluster head is selected by remaining energy, density of alive sensors and location of sensor. The aim of algorithm maximizes the lifetime of network. Our simulation results shows that the proposed method is very simple as well as balances energy consumption. ▸

Optical fiber sensors-based temperature distribution measurement in ex vivo radiofrequency ablation with submillimeter resolution.

Radiofrequency thermal ablation (RFTA) induces a high-temperature field in a biological tissue having steep spatial (up to 6°C∕mm) and temporal (up to 1°C∕s) gradients. Applied in cancer care, RFTA produces a localized heating, cytotoxic for tumor cells, and is able to treat tumors with sizes up to 3 to 5 cm in diameter. The online measurement of temperature distribution at the RFTA point of care has been previously carried out with miniature thermocouples and optical fiber sensors, which exhibit problems of size, alteration of RFTA pattern, hysteresis, and sensor density worse than 1 sensor∕cm. In this work, we apply a distributed temperature sensor (DTS) with a submillimeter spatial resolution for the monitoring of RFTA in porcine liver tissue. The DTS demodulates the chaotic Rayleigh backscattering pattern with an interferometric setup to obtain the real-time temperature distribution. A measurement chamber has been set up with the fiber crossing the tissue along different diameters. Several experiments have been carried out measuring the space-time evolution of temperature during RFTA. The present work showcases the temperature monitoring in RFTA with an unprecedented spatial resolution and is exportable to in vivo measurement; the acquired data can be particularly useful for the validation of RFTA computational models.

무선 센서 액터 네트워크에서 액터의 연결성과 커버리지를 향상시키기 위한 클러스터 구성

본 논문은 싱크 기반 무선 센서 액터 네트워크에서 액터의 연결성과 커버리지를 향상시키기 위하여 홉 거리를 기반으로 클러스터를 구성하는 알고리즘을 제안한다. 제안한 알고리즘은 싱크로부터 일정한 홉 간격으로 CH(Cluster Head)를 선출함으로써 목표영역에 고르게 분포된 센서 클러스터를 생성한다. CH 선출은 CH 위치에 배치되는 액터들과 싱크의 연결성을 보장하기 위해서 싱크로부터 순차적으로 진행된다. 또한 액터의 커버리지를 향상시키기 위해서 센서 밀도가 높은 영역부터 우선적으로 이루어진다. 제안한 알고리즘을 통하여 일정한 분포로 클러스터를 구성하여 중첩을 최소화함으로써 목표영역에서 생성되는 클러스터 수와 CH 위치에 배치되는 액터 수를 감축한다. 시뮬레이션을 수행하여 제안한 알고리즘이 싱크에 연결이 보장된 액터 네트워크를 형성함을 검증하였다. 그리고 기존의 IDSC 알고리즘에 비해 액터 커버리지를 향상시킴으로써 배치되는 액터 수를 9~20% 감축함을 확인하였다. This paper proposes an algorithm that forms the clusters on the basis of hop distance in order to improve the actor coverage and connectivity in the sink-based wireless sensor and actor networks. The proposed algorithm forms the clusters that are distributed evenly in the target area by electing the CHs(Cluster Heads) at regular hop intervals from a sink. The CHs are elected sequentially from the sink in order to ensure the connectivity between the sink and the actors that are located on the CHs. Additionally, the electing are achieved from the area of the higher rate of the sensors density in order to improve the actor coverage. The number of clusters that are created in the target area and the number of the actors that are placed on the positions of the CHs are reduced by forming the clusters with regular distribution and minimizing the overlap of them through the proposed algorithm. Simulations are performed to verify that the proposed algorithm constructs the actor network that is connected to the sink. Moreover, we shows that the proposed algorithm improves the actor coverage and, therefore, reduces the amount of the actors that will be deployed in the region by 9~20% compared to the IDSC algorithm.

Sensor requirements for in situ imaging of multiple damage by Lamb waves

Lamb waves are guided waves that propagate in plate-like structures. These waves are well suited for wide-area inspection, such as aircraft wing skins, because of their relatively slow geometrical decay with propagation distance. Recently, a computationally efficient algorithm for in situ imaging was derived based on the multi-static data matrix. Here, previous work on imaging a single circular inhomogeneity is extended to the case of structural damage spread over two distinct sites. Detailed results are presented for the rank and bandwidth of the data matrix, showing the dependence on the overall size of the damage cluster. The implications of these results for the practically important problem of determining minimum sensor requirements for image reconstruction are discussed. References C. T. Ng and M. Veidt. A Lamb-wave-based technique for damage detection in composite laminates. Smart Mater. Struct. , 18(7):074006, 2009. doi:10.1088/0964-1726/18/7/074006 J. E. Michaels and T. E. Michaels. Guided wave signal processing and image fusion for in situ damage localization in plates. Wave Motion , 44(6):482–192, 2007. doi:10.1016/j.wavemoti.2007.02.008 J. S. Hall and J. E. Michaels. Multi-path guided wave imaging for in situ monitoring of complex structures. AIP Conf. Proc. 1511:707-713, 2013. doi:10.1063/1.4789115 Z. Q. Su, L. Cheng, X. M. Wang and L. Yu. Diagnostic imaging for structural damage. Adv. Mater. Res. , 47–50:1157–1160, 2008. doi:10.4028/www.scientific.net/AMR.47-50.1157 L. R. F. Rose and C. H. Wang. Mindlin plate theory for damage detection: Source solutions. J. Acoust. Soc. Am. , 116(1):154–171, 2004. doi:10.1121/1.1739482 L. R. F. Rose and C. H. Wang. Mindlin plate theory for damage detection: Imaging of flexural inhomogeneities. J. Acoust. Soc. Am. , 127(2):754–763, 2010. doi:10.1121/1.3277217 C. H. Wang and L. R. F. Rose. Imaging damage using Mixed Passive and Active Sensors. 4th Asia-Pacific Workshop on Structural Health Monitoring, 2012 , Melbourne, Australia. C. H. Wang and L. R. F. Rose. Minimum Sensor Density for Quantitative Damage Imaging. 9th International Workshop on Structural Health Monitoring, 2013, Standford, USA . E. Chan, C. H. Wang and L. R. F. Rose. Characterisation of Part-through Damage in a Stiffened Panel by a Time-reversal Based Imaging Method. 9th International Workshop on Structural Health Monitoring, 2013, Standford, USA . Abaqus/Explicit 6.10 Dassault Systemes http://www.3ds.com/products-services/simulia/portfolio/abaqus/abaqus-portfolio/abaqusexplicit/

Boundary tracking and estimation of pollutant plumes with a mobile sensor in a low-density static sensor network

Static sensor networks are used to detect concentrations of pollutants exceeding critical thresholds or to delineate the boundaries of contaminated regions in the air or in water. As the density of sensors deployed in the field may be locally insufficient to accomplish this task, it is necessary to add adaptive mobile sensors to the permanent sensing infrastructure. We propose a method for tracking and estimating the boundary of a dynamic plume with a mobile sensor in addition to binary below/above threshold signals from a low-density static sensor network. It is a heuristic method with low computational effort, which is efficient in terms of sensor movement. Neither prior information about the plume nor meteorological data is required. Our approach is applicable for dynamic plumes and operating with a small number of static sensors. We evaluate several variations of the method and parameter combinations using a plume simulated with the atmospheric dispersion model RIMPUFF. We give a number of practical recommendations.