Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

NASA Technical Reports Server (NTRS)

Matthies, Larry; Grandjean, Pierrick

1993-01-01

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors.

Acoustic/seismic signal propagation and sensor performance modeling

NASA Astrophysics Data System (ADS)

Wilson, D. Keith; Marlin, David H.; Mackay, Sean

2007-04-01

Performance, optimal employment, and interpretation of data from acoustic and seismic sensors depend strongly and in complex ways on the environment in which they operate. Software tools for guiding non-expert users of acoustic and seismic sensors are therefore much needed. However, such tools require that many individual components be constructed and correctly connected together. These components include the source signature and directionality, representation of the atmospheric and terrain environment, calculation of the signal propagation, characterization of the sensor response, and mimicking of the data processing at the sensor. Selection of an appropriate signal propagation model is particularly important, as there are significant trade-offs between output fidelity and computation speed. Attenuation of signal energy, random fading, and (for array systems) variations in wavefront angle-of-arrival should all be considered. Characterization of the complex operational environment is often the weak link in sensor modeling: important issues for acoustic and seismic modeling activities include the temporal/spatial resolution of the atmospheric data, knowledge of the surface and subsurface terrain properties, and representation of ambient background noise and vibrations. Design of software tools that address these challenges is illustrated with two examples: a detailed target-to-sensor calculation application called the Sensor Performance Evaluator for Battlefield Environments (SPEBE) and a GIS-embedded approach called Battlefield Terrain Reasoning and Awareness (BTRA).

Active imaging system performance model for target acquisition

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Teaney, Brian; Nguyen, Quang; Jacobs, Eddie L.; Halford, Carl E.; Tofsted, David H.

2007-04-01

The U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate has developed a laser-range-gated imaging system performance model for the detection, recognition, and identification of vehicle targets. The model is based on the established US Army RDECOM CERDEC NVESD sensor performance models of the human system response through an imaging system. The Java-based model, called NVLRG, accounts for the effect of active illumination, atmospheric attenuation, and turbulence effects relevant to LRG imagers, such as speckle and scintillation, and for the critical sensor and display components. This model can be used to assess the performance of recently proposed active SWIR systems through various trade studies. This paper will describe the NVLRG model in detail, discuss the validation of recent model components, present initial trade study results, and outline plans to validate and calibrate the end-to-end model with field data through human perception testing.

Flight model performances of HISUI hyperspectral sensor onboard ISS (International Space Station)

NASA Astrophysics Data System (ADS)

Tanii, Jun; Kashimura, Osamu; Ito, Yoshiyuki; Iwasaki, Akira

2016-10-01

Hyperspectral Imager Suite (HISUI) is a next-generation Japanese sensor that will be mounted on Japanese Experiment Module (JEM) of ISS (International Space Station) in 2019 as timeframe. HISUI hyperspectral sensor obtains spectral images of 185 bands with the ground sampling distance of 20x31 meter from the visible to shortwave-infrared region. The sensor system is the follow-on mission of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) in the visible to shortwave infrared region. The critical design review of the instrument was accomplished in 2014. Integration and tests of an flight model of HISUI hyperspectral sensor is being carried out. Simultaneously, the development of JEM-External Facility (EF) Payload system for the instrument started. The system includes the structure, the thermal control system, the electrical system and the pointing mechanism. The development status and the performances including some of the tests results of Instrument flight model, such as optical performance, optical distortion and radiometric performance are reported.

LANDSAT-D conical scanner evaluation plan

NASA Technical Reports Server (NTRS)

Bilanow, S.; Chen, L. C. (Principal Investigator)

1982-01-01

The planned activities involved in the inflight sensor calibration and performance evaluation are discussed and the supporting software requirements are specified. The possible sensor error sources and their effects on sensor measurements are summarized. The methods by which the inflight sensor performance will be analyzed and the sensor modeling parameters will be calibrated are presented. In addition, a brief discussion on the data requirement for the study is provided.

Probability and Statistics in Sensor Performance Modeling

DTIC Science & Technology

2010-12-01

language software program is called Environmental Awareness for Sensor and Emitter Employment. Some important numerical issues in the implementation...3 Statistical analysis for measuring sensor performance...complementary cumulative distribution function cdf cumulative distribution function DST decision-support tool EASEE Environmental Awareness of

Apparatus for sensor failure detection and correction in a gas turbine engine control system

NASA Technical Reports Server (NTRS)

Spang, H. A., III; Wanger, R. P. (Inventor)

1981-01-01

A gas turbine engine control system maintains a selected level of engine performance despite the failure or abnormal operation of one or more engine parameter sensors. The control system employs a continuously updated engine model which simulates engine performance and generates signals representing real time estimates of the engine parameter sensor signals. The estimate signals are transmitted to a control computational unit which utilizes them in lieu of the actual engine parameter sensor signals to control the operation of the engine. The estimate signals are also compared with the corresponding actual engine parameter sensor signals and the resulting difference signals are utilized to update the engine model. If a particular difference signal exceeds specific tolerance limits, the difference signal is inhibited from updating the model and a sensor failure indication is provided to the engine operator.

Mathematical models and photogrammetric exploitation of image sensing

NASA Astrophysics Data System (ADS)

Puatanachokchai, Chokchai

Mathematical models of image sensing are generally categorized into physical/geometrical sensor models and replacement sensor models. While the former is determined from image sensing geometry, the latter is based on knowledge of the physical/geometric sensor models and on using such models for its implementation. The main thrust of this research is in replacement sensor models which have three important characteristics: (1) Highly accurate ground-to-image functions; (2) Rigorous error propagation that is essentially of the same accuracy as the physical model; and, (3) Adjustability, or the ability to upgrade the replacement sensor model parameters when additional control information becomes available after the replacement sensor model has replaced the physical model. In this research, such replacement sensor models are considered as True Replacement Models or TRMs. TRMs provide a significant advantage of universality, particularly for image exploitation functions. There have been several writings about replacement sensor models, and except for the so called RSM (Replacement Sensor Model as a product described in the Manual of Photogrammetry), almost all of them pay very little or no attention to errors and their propagation. This is because, it is suspected, the few physical sensor parameters are usually replaced by many more parameters, thus presenting a potential error estimation difficulty. The third characteristic, adjustability, is perhaps the most demanding. It provides an equivalent flexibility to that of triangulation using the physical model. Primary contributions of this thesis include not only "the eigen-approach", a novel means of replacing the original sensor parameter covariance matrices at the time of estimating the TRM, but also the implementation of the hybrid approach that combines the eigen-approach with the added parameters approach used in the RSM. Using either the eigen-approach or the hybrid approach, rigorous error propagation can be performed during image exploitation. Further, adjustability can be performed when additional control information becomes available after the TRM has been implemented. The TRM is shown to apply to imagery from sensors having different geometries, including an aerial frame camera, a spaceborne linear array sensor, an airborne pushbroom sensor, and an airborne whiskbroom sensor. TRM results show essentially negligible differences as compared to those from rigorous physical sensor models, both for geopositioning from single and overlapping images. Simulated as well as real image data are used to address all three characteristics of the TRM.

Model development and system performance optimization for staring infrared search and track (IRST) sensors

NASA Astrophysics Data System (ADS)

Olson, Craig; Theisen, Michael; Pace, Teresa; Halford, Carl; Driggers, Ronald

2016-05-01

The mission of an Infrared Search and Track (IRST) system is to detect and locate (sometimes called find and fix) enemy aircraft at significant ranges. Two extreme opposite examples of IRST applications are 1) long range offensive aircraft detection when electronic warfare equipment is jammed, compromised, or intentionally turned off, and 2) distributed aperture systems where enemy aircraft may be in the proximity of the host aircraft. Past IRST systems have been primarily long range offensive systems that were based on the LWIR second generation thermal imager. The new IRST systems are primarily based on staring infrared focal planes and sensors. In the same manner that FLIR92 did not work well in the design of staring infrared cameras (NVTherm was developed to address staring infrared sensor performance), current modeling techniques do not adequately describe the performance of a staring IRST sensor. There are no standard military IRST models (per AFRL and NAVAIR), and each program appears to perform their own modeling. For this reason, L-3 has decided to develop a corporate model, working with AFRL and NAVAIR, for the analysis, design, and evaluation of IRST concepts, programs, and solutions. This paper provides some of the first analyses in the L-3 IRST model development program for the optimization of staring IRST sensors.

Impact of Isolation and Immobilization Layers on the Electro-Mechanical Response of Piezoresistive Nano Cantilever Sensors.

PubMed

Mathew, Ribu; Sankar, A Ravi

2018-03-01

In the last decade, piezoresistive nano cantilever sensors have been extensively explored, especially for chemical and biological sensing applications. Piezoresistive cantilever sensors are multi-layer structures with different constituent materials. Performance of such sensors is a function of their geometry and constituent materials. For a fixed material set, the pre-requisite for optimizing the performance of a composite piezoresistive cantilever sensor is careful geometrical design of its constituent layers. Even though, treatise encompasses various designs of such sensors, typically for computational simplicity the functional layers i.e., the isolation and immobilization layers are neglected in the modeling stages. In this paper, we elucidate the impact of the functional layers on the electro-mechanical response of composite piezoresistive nano cantilever sensors. Systematic and detailed computations are performed using theoretical models and numerical simulations. Results show that both the isolation and immobilization layers play a critical role in governing the sensor performance. Simulation results depict that compared to a sensor with an isolation layer of thickness 100 nm, a sensor without isolation layer has 36.29% and 42.51% better deflection sensitivity and electrical sensitivity respectively. Furthermore, it is found that when an immobilization layer of thickness 40 nm is added atop the isolation layer, the deflection sensitivity and electrical sensitivity reduces by 12.98% and 15.83% respectively. Through our investigation it is shown that the isolation and immobilization layers not only play a vital role in determining the stability and electro-mechanical response of the sensor but their negligence in the design stages can be detrimental. Apart from investigating the impact of the immobilization layer thickness, to model the sensor closer to real time operational conditions, we have performed analysis to understand the impact of non-uniformity in the immobilization layer thickness and non-uniform surface stress loading on the electro-mechanical response of the sensor. Results and inferences obtained from this study will help NEMS engineers to optimize the performance of piezoresistive nano cantilever sensors and to design multi-layer cantilever platform structures for other transducers.

Sensors and Clinical Mastitis—The Quest for the Perfect Alert

PubMed Central

Hogeveen, Henk; Kamphuis, Claudia; Steeneveld, Wilma; Mollenhorst, Herman

2010-01-01

When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models. PMID:22163637

Sensors and clinical mastitis--the quest for the perfect alert.

PubMed

Hogeveen, Henk; Kamphuis, Claudia; Steeneveld, Wilma; Mollenhorst, Herman

2010-01-01

When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models.

Human activity discrimination for maritime application

NASA Astrophysics Data System (ADS)

Boettcher, Evelyn; Deaver, Dawne M.; Krapels, Keith

2008-04-01

The US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) is investigating how motion affects the target acquisition model (NVThermIP) sensor performance estimates. This paper looks specifically at estimating sensor performance for the task of discriminating human activities on watercraft, and was sponsored by the Office of Naval Research (ONR). Traditionally, sensor models were calibrated using still images. While that approach is sufficient for static targets, video allows one to use motion cues to aid in discerning the type of human activity more quickly and accurately. This, in turn, will affect estimated sensor performance and these effects are measured in order to calibrate current target acquisition models for this task. The study employed an eleven alternative forced choice (11AFC) human perception experiment to measure the task difficulty of discriminating unique human activities on watercrafts. A mid-wave infrared camera was used to collect video at night. A description of the construction of this experiment is given, including: the data collection, image processing, perception testing and how contrast was defined for video. These results are applicable to evaluate sensor field performance for Anti-Terrorism and Force Protection (AT/FP) tasks for the U.S. Navy.

A trust-based sensor allocation algorithm in cooperative space search problems

NASA Astrophysics Data System (ADS)

Shen, Dan; Chen, Genshe; Pham, Khanh; Blasch, Erik

2011-06-01

Sensor allocation is an important and challenging problem within the field of multi-agent systems. The sensor allocation problem involves deciding how to assign a number of targets or cells to a set of agents according to some allocation protocol. Generally, in order to make efficient allocations, we need to design mechanisms that consider both the task performers' costs for the service and the associated probability of success (POS). In our problem, the costs are the used sensor resource, and the POS is the target tracking performance. Usually, POS may be perceived differently by different agents because they typically have different standards or means of evaluating the performance of their counterparts (other sensors in the search and tracking problem). Given this, we turn to the notion of trust to capture such subjective perceptions. In our approach, we develop a trust model to construct a novel mechanism that motivates sensor agents to limit their greediness or selfishness. Then we model the sensor allocation optimization problem with trust-in-loop negotiation game and solve it using a sub-game perfect equilibrium. Numerical simulations are performed to demonstrate the trust-based sensor allocation algorithm in cooperative space situation awareness (SSA) search problems.

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

NASA Astrophysics Data System (ADS)

Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

2009-05-01

A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

Modeling and simulation of soft sensor design for real-time speed and position estimation of PMSM.

PubMed

Omrane, Ines; Etien, Erik; Dib, Wissam; Bachelier, Olivier

2015-07-01

This paper deals with the design of a speed soft sensor for permanent magnet synchronous motor. At high speed, model-based soft sensor is used and it gives excellent results. However, it fails to deliver satisfactory performance at zero or very low speed. High-frequency soft sensor is used at low speed. We suggest to use a model-based soft sensor together with the high-frequency soft sensor to overcome the limitations of the first one at low speed range. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Review of infrared technology in The Netherlands

NASA Astrophysics Data System (ADS)

de Jong, Arie N.

1993-11-01

The use of infrared sensors in the Netherlands is substantial. Users can be found in a variety of disciplines, military as well as civil. This need for IR sensors implied a long history on IR technology and development. The result was a large technological-capability allowing the realization of IR hardware: specialized measuring equipment, engineering development models, prototype and production sensors for different applications. These applications range from small size, local radiometry up to large space-borne imaging. Large scale production of IR sensors has been realized for army vehicles. IR sensors have been introduced now in all of the armed forces. Facilities have been built to test the performance of these sensors. Models have been developed to predict the performance of a new sensor. A great effort has been spent on atmospheric research, leading to knowledge upon atmospheric- and background limitations of IR sensors.

Infrared horizon sensor modeling for attitude determination and control: Analysis and mission experience

NASA Technical Reports Server (NTRS)

Phenneger, M. C.; Singhal, S. P.; Lee, T. H.; Stengle, T. H.

1985-01-01

The work performed by the Attitude Determination and Control Section at the National Aeronautics and Space Administration/Goddard Space Flight Center in analyzing and evaluating the performance of infrared horizon sensors is presented. The results of studies performed during the 1960s are reviewed; several models for generating the Earth's infrared radiance profiles are presented; and the Horizon Radiance Modeling Utility, the software used to model the horizon sensor optics and electronics processing to computer radiance-dependent attitude errors, is briefly discussed. Also provided is mission experience from 12 spaceflight missions spanning the period from 1973 to 1984 and using a variety of horizon sensing hardware. Recommendations are presented for future directions for the infrared horizon sensing technology.

Transient multivariable sensor evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vilim, Richard B.; Heifetz, Alexander

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

PubMed

Marschollek, Michael; Rehwald, Anja; Wolf, Klaus-Hendrik; Gietzelt, Matthias; Nemitz, Gerhard; zu Schwabedissen, Hubertus Meyer; Schulze, Mareike

2011-06-28

Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

PubMed Central

2011-01-01

Background Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. Methods In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Results Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Conclusions Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach. PMID:21711504

Fiber-optic epoxy composite cure sensor. II. Performance characteristics

NASA Astrophysics Data System (ADS)

Lam, Kai-Yuen; Afromowitz, Martin A.

1995-09-01

The performance of a fiber-optic epoxy composite cure sensor, as previously proposed, depends on the optical properties and the reaction kinetics of the epoxy. The reaction kinetics of a typical epoxy system are presented. It is a third-order autocatalytic reaction with a peak observed in each isothermal reaction-rate curve. A model is derived to describe the performance characteristics of the epoxy cure sensor. If a composite coupon is cured at an isothermal temperature, the sensor signal can be used to predict the time when the gel point occurs and to monitor the cure process. The sensor is also shown to perform well in nonstoichiometric epoxy matrices. In addition the sensor can detect the end of the cure without calibration.

Multi-type sensor placement and response reconstruction for building structures: Experimental investigations

NASA Astrophysics Data System (ADS)

Hu, Rong-Pan; Xu, You-Lin; Zhan, Sheng

2018-01-01

Estimation of lateral displacement and acceleration responses is essential to assess safety and serviceability of high-rise buildings under dynamic loadings including earthquake excitations. However, the measurement information from the limited number of sensors installed in a building structure is often insufficient for the complete structural performance assessment. An integrated multi-type sensor placement and response reconstruction method has thus been proposed by the authors to tackle this problem. To validate the feasibility and effectiveness of the proposed method, an experimental investigation using a cantilever beam with multi-type sensors is performed and reported in this paper. The experimental setup is first introduced. The finite element modelling and model updating of the cantilever beam are then performed. The optimal sensor placement for the best response reconstruction is determined by the proposed method based on the updated FE model of the beam. After the sensors are installed on the physical cantilever beam, a number of experiments are carried out. The responses at key locations are reconstructed and compared with the measured ones. The reconstructed responses achieve a good match with the measured ones, manifesting the feasibility and effectiveness of the proposed method. Besides, the proposed method is also examined for the cases of different excitations and unknown excitation, and the results prove the proposed method to be robust and effective. The superiority of the optimized sensor placement scheme is finally demonstrated through comparison with two other different sensor placement schemes: the accelerometer-only scheme and non-optimal sensor placement scheme. The proposed method can be applied to high-rise buildings for seismic performance assessment.

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2015-01-01

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2016-01-01

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Detection of person borne IEDs using multiple cooperative sensors

NASA Astrophysics Data System (ADS)

MacIntosh, Scott; Deming, Ross; Hansen, Thorkild; Kishan, Neel; Tang, Ling; Shea, Jing; Lang, Stephen

2011-06-01

The use of multiple cooperative sensors for the detection of person borne IEDs is investigated. The purpose of the effort is to evaluate the performance benefits of adding multiple sensor data streams into an aided threat detection algorithm, and a quantitative analysis of which sensor data combinations improve overall detection performance. Testing includes both mannequins and human subjects with simulated suicide bomb devices of various configurations, materials, sizes and metal content. Aided threat recognition algorithms are being developed to test detection performance of individual sensors against combined fused sensors inputs. Sensors investigated include active and passive millimeter wave imaging systems, passive infrared, 3-D profiling sensors and acoustic imaging. The paper describes the experimental set-up and outlines the methodology behind a decision fusion algorithm-based on the concept of a "body model".

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Small pixel cross-talk MTF and its impact on MWIR sensor performance

NASA Astrophysics Data System (ADS)

Goss, Tristan M.; Willers, Cornelius J.

2017-05-01

As pixel sizes reduce in the development of modern High Definition (HD) Mid Wave Infrared (MWIR) detectors the interpixel cross-talk becomes increasingly difficult to regulate. The diffusion lengths required to achieve the quantum efficiency and sensitivity of MWIR detectors are typically longer than the pixel pitch dimension, and the probability of inter-pixel cross-talk increases as the pixel pitch/diffusion length fraction decreases. Inter-pixel cross-talk is most conveniently quantified by the focal plane array sampling Modulation Transfer Function (MTF). Cross-talk MTF will reduce the ideal sinc square pixel MTF that is commonly used when modelling sensor performance. However, cross-talk MTF data is not always readily available from detector suppliers, and since the origins of inter-pixel cross-talk are uniquely device and manufacturing process specific, no generic MTF models appear to satisfy the needs of the sensor designers and analysts. In this paper cross-talk MTF data has been collected from recent publications and the development for a generic cross-talk MTF model to fit this data is investigated. The resulting cross-talk MTF model is then included in a MWIR sensor model and the impact on sensor performance is evaluated in terms of the National Imagery Interoperability Rating Scale's (NIIRS) General Image Quality Equation (GIQE) metric for a range of fnumber/ detector pitch Fλ/d configurations and operating environments. By applying non-linear boost transfer functions in the signal processing chain, the contrast losses due to cross-talk may be compensated for. Boost transfer functions, however, also reduce the signal to noise ratio of the sensor. In this paper boost function limits are investigated and included in the sensor performance assessments.

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

NASA Astrophysics Data System (ADS)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

A Personal Navigation System Based on Inertial and Magnetic Field Measurements

DTIC Science & Technology

2010-09-01

MATLAB IMPLEMENTATION.................................................................74 G. A MODEL FOR PENDULUM MOTION SENSOR DATA...76 1. Pendulum Model for MATLAB Simulation....................................76 2. Sensor Data Generated with the Pendulum Model... PENDULUM ..................................................................................................88 I. FILTER PERFORMANCE WITH REAL PENDULUM DATA

Optical fiber sensors and signal processing for intelligent structure monitoring

NASA Technical Reports Server (NTRS)

Rogowski, Robert; Claus, R. O.; Lindner, D. K.; Thomas, Daniel; Cox, Dave

1988-01-01

The analytic and experimental performance of optical fiber sensors for the control of vibration of large aerospace and other structures are investigated. In particular, model domain optical fiber sensor systems, are being studied due to their apparent potential as distributed, low mass sensors of vibration over appropriate ranges of both low frequency and low amplitude displacements. Progress during the past three months is outlined. Progress since September is divided into work in the areas of experimental hardware development, analytical analysis, control design and sensor development. During the next six months, tests of a prototype closed-loop control system for a beam are planned which will demonstrate the solution of several optical fiber instrumentation device problems, the performance of the control system theory which incorporates the model of the modal domain sensor, and the potential for distributed control which this sensor approach offers.

Identification of ground targets from airborne platforms

NASA Astrophysics Data System (ADS)

Doe, Josh; Boettcher, Evelyn; Miller, Brian

2009-05-01

The US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) sensor performance models predict the ability of soldiers to perform a specified military discrimination task using an EO/IR sensor system. Increasingly EO/IR systems are being used on manned and un-manned aircraft for surveillance and target acquisition tasks. In response to this emerging requirement, the NVESD Modeling and Simulation division has been tasked to compare target identification performance between ground-to-ground and air-to-ground platforms for both IR and visible spectra for a set of wheeled utility vehicles. To measure performance, several forced choice experiments were designed and administered and the results analyzed. This paper describes these experiments and reports the results as well as the NVTherm model calibration factors derived for the infrared imagery.

Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks

NASA Astrophysics Data System (ADS)

Gosangi, Rakesh; Gutierrez-Osuna, Ricardo

2011-09-01

We present a data-driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico-chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data-driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients.

Improving Planetary Rover Attitude Estimation via MEMS Sensor Characterization

PubMed Central

Hidalgo, Javier; Poulakis, Pantelis; Köhler, Johan; Del-Cerro, Jaime; Barrientos, Antonio

2012-01-01

Micro Electro-Mechanical Systems (MEMS) are currently being considered in the space sector due to its suitable level of performance for spacecrafts in terms of mechanical robustness with low power consumption, small mass and size, and significant advantage in system design and accommodation. However, there is still a lack of understanding regarding the performance and testing of these new sensors, especially in planetary robotics. This paper presents what is missing in the field: a complete methodology regarding the characterization and modeling of MEMS sensors with direct application. A reproducible and complete approach including all the intermediate steps, tools and laboratory equipment is described. The process of sensor error characterization and modeling through to the final integration in the sensor fusion scheme is explained with detail. Although the concept of fusion is relatively easy to comprehend, carefully characterizing and filtering sensor information is not an easy task and is essential for good performance. The strength of the approach has been verified with representative tests of novel high-grade MEMS inertia sensors and exemplary planetary rover platforms with promising results. PMID:22438761

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Design Considerations For Imaging Charge-Coupled Device (ICCD) Star Sensors

NASA Astrophysics Data System (ADS)

McAloon, K. J.

1981-04-01

A development program is currently underway to produce a precision star sensor using imaging charge coupled device (ICCD) technology. The effort is the critical component development phase for the Air Force Multi-Mission Attitude Determination and Autonomous Navigation System (MADAN). A number of unique considerations have evolved in designing an arcsecond accuracy sensor around an ICCD detector. Three tiers of performance criteria are involved: at the spacecraft attitude determination system level, at the star sensor level, and at the detector level. Optimum attitude determination system performance involves a tradeoff between Kalman filter iteration time and sensor ICCD integration time. The ICCD star sensor lends itself to the use of a new approach in the functional interface between the attitude determination system and the sensor. At the sensor level image data processing tradeoffs are important for optimum sensor performance. These tradeoffs involve the sensor optic configuration, the optical point spread function (PSF) size and shape, the PSF position locator, and the microprocessor locator algorithm. Performance modelling of the sensor mandates the use of computer simulation programs. Five key performance parameters at the ICCD detector level are defined. ICCD error characteristics have also been isolated to five key parameters.

Disbonding effects on elastic wave generation and reception by bonded piezoelectric sensor systems

NASA Astrophysics Data System (ADS)

Blackshire, James L.; Martin, Steven A.; Na, Jeong K.

2007-04-01

Durable integrated sensor systems are needed for long-term health monitoring evaluations of aerospace systems. For legacy aircraft the primary means of implementing a sensor system will be through surface mounting or bonding of the sensors to the structure. Previous work has shown that the performance of surface-bonded piezo sensors can degrade due to environmental effects such as vibrations, temperature fluctuations, and substrate flexure motions. This performance degradation included sensor cracking, disbonding, and general loss of efficiency over time. In this research effort, the bonding state of a piezo sensor system was systematically studied to understand and improve the long-term durability and survivability of the sensor system. Analytic and computational models were developed and used to understand elastic wave generation and reception performance for various states of sensor disbond. Experimental studies were also conducted using scanning laser vibrometry, pitch-catch ultrasound, and pulse-echo ultrasound methods to understand elastic wave propagation effects in thin plate materials. Significant performance loss was observed for increasing levels of sensor disbond as well as characteristic frequency signatures which may be useful in understanding sensor performance levels for future structural health monitoring systems.

An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud

PubMed Central

Dinh, Thanh; Kim, Younghan

2016-01-01

This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud. PMID:27367689

An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud.

PubMed

Dinh, Thanh; Kim, Younghan

2016-06-28

This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud.

Distributed fiber optic sensor-enhanced detection and prediction of shrinkage-induced delamination of ultra-high-performance concrete overlay

NASA Astrophysics Data System (ADS)

Bao, Yi; Valipour, Mahdi; Meng, Weina; Khayat, Kamal H.; Chen, Genda

2017-08-01

This study develops a delamination detection system for smart ultra-high-performance concrete (UHPC) overlays using a fully distributed fiber optic sensor. Three 450 mm (length) × 200 mm (width) × 25 mm (thickness) UHPC overlays were cast over an existing 200 mm thick concrete substrate. The initiation and propagation of delamination due to early-age shrinkage of the UHPC overlay were detected as sudden increases and their extension in spatial distribution of shrinkage-induced strains measured from the sensor based on pulse pre-pump Brillouin optical time domain analysis. The distributed sensor is demonstrated effective in detecting delamination openings from microns to hundreds of microns. A three-dimensional finite element model with experimental material properties is proposed to understand the complete delamination process measured from the distributed sensor. The model is validated using the distributed sensor data. The finite element model with cohesive elements for the overlay-substrate interface can predict the complete delamination process.

Automated Synthetic Scene Generation

DTIC Science & Technology

2014-07-01

Using the Beard-Maxwell BRDF model , the BRDF from Equations (3.3) and (3.4) is composed of specular, diffuse, and volumetric terms such that x y zSun... models help organizations developing new remote sensing instruments anticipate sensor performance by enabling the ability to create synthetic imagery...for proposed sensor before a sensor is built. One of the largest challenges in modeling realistic synthetic imagery, however, is generating the

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

High-Temperature Strain Sensing for Aerospace Applications

NASA Technical Reports Server (NTRS)

Piazza, Anthony; Richards, Lance W.; Hudson, Larry D.

2008-01-01

Thermal protection systems (TPS) and hot structures are utilizing advanced materials that operate at temperatures that exceed abilities to measure structural performance. Robust strain sensors that operate accurately and reliably beyond 1800 F are needed but do not exist. These shortcomings hinder the ability to validate analysis and modeling techniques and hinders the ability to optimize structural designs. This presentation examines high-temperature strain sensing for aerospace applications and, more specifically, seeks to provide strain data for validating finite element models and thermal-structural analyses. Efforts have been made to develop sensor attachment techniques for relevant structural materials at the small test specimen level and to perform laboratory tests to characterize sensor and generate corrections to apply to indicated strains. Areas highlighted in this presentation include sensors, sensor attachment techniques, laboratory evaluation/characterization of strain measurement, and sensor use in large-scale structures.

A comparison of long-term parallel measurements of sunshine duration obtained with a Campbell-Stokes sunshine recorder and two automated sunshine sensors

NASA Astrophysics Data System (ADS)

Baumgartner, D. J.; Pötzi, W.; Freislich, H.; Strutzmann, H.; Veronig, A. M.; Foelsche, U.; Rieder, H. E.

2017-06-01

In recent decades, automated sensors for sunshine duration (SD) measurements have been introduced in meteorological networks, thereby replacing traditional instruments, most prominently the Campbell-Stokes (CS) sunshine recorder. Parallel records of automated and traditional SD recording systems are rare. Nevertheless, such records are important to understand the differences/similarities in SD totals obtained with different instruments and how changes in monitoring device type affect the homogeneity of SD records. This study investigates the differences/similarities in parallel SD records obtained with a CS and two automated SD sensors between 2007 and 2016 at the Kanzelhöhe Observatory, Austria. Comparing individual records of daily SD totals, we find differences of both positive and negative sign, with smallest differences between the automated sensors. The larger differences between CS-derived SD totals and those from automated sensors can be attributed (largely) to the higher sensitivity threshold of the CS instrument. Correspondingly, the closest agreement among all sensors is found during summer, the time of year when sensitivity thresholds are least critical. Furthermore, we investigate the performance of various models to create the so-called sensor-type-equivalent (STE) SD records. Our analysis shows that regression models including all available data on daily (or monthly) time scale perform better than simple three- (or four-) point regression models. Despite general good performance, none of the considered regression models (of linear or quadratic form) emerges as the "optimal" model. Although STEs prove useful for relating SD records of individual sensors on daily/monthly time scales, this does not ensure that STE (or joint) records can be used for trend analysis.

Error Modeling and Experimental Study of a Flexible Joint 6-UPUR Parallel Six-Axis Force Sensor.

PubMed

Zhao, Yanzhi; Cao, Yachao; Zhang, Caifeng; Zhang, Dan; Zhang, Jie

2017-09-29

By combining a parallel mechanism with integrated flexible joints, a large measurement range and high accuracy sensor is realized. However, the main errors of the sensor involve not only assembly errors, but also deformation errors of its flexible leg. Based on a flexible joint 6-UPUR (a kind of mechanism configuration where U-universal joint, P-prismatic joint, R-revolute joint) parallel six-axis force sensor developed during the prephase, assembly and deformation error modeling and analysis of the resulting sensors with a large measurement range and high accuracy are made in this paper. First, an assembly error model is established based on the imaginary kinematic joint method and the Denavit-Hartenberg (D-H) method. Next, a stiffness model is built to solve the stiffness matrix. The deformation error model of the sensor is obtained. Then, the first order kinematic influence coefficient matrix when the synthetic error is taken into account is solved. Finally, measurement and calibration experiments of the sensor composed of the hardware and software system are performed. Forced deformation of the force-measuring platform is detected by using laser interferometry and analyzed to verify the correctness of the synthetic error model. In addition, the first order kinematic influence coefficient matrix in actual circumstances is calculated. By comparing the condition numbers and square norms of the coefficient matrices, the conclusion is drawn theoretically that it is very important to take into account the synthetic error for design stage of the sensor and helpful to improve performance of the sensor in order to meet needs of actual working environments.

Error Modeling and Experimental Study of a Flexible Joint 6-UPUR Parallel Six-Axis Force Sensor

PubMed Central

Zhao, Yanzhi; Cao, Yachao; Zhang, Caifeng; Zhang, Dan; Zhang, Jie

2017-01-01

By combining a parallel mechanism with integrated flexible joints, a large measurement range and high accuracy sensor is realized. However, the main errors of the sensor involve not only assembly errors, but also deformation errors of its flexible leg. Based on a flexible joint 6-UPUR (a kind of mechanism configuration where U-universal joint, P-prismatic joint, R-revolute joint) parallel six-axis force sensor developed during the prephase, assembly and deformation error modeling and analysis of the resulting sensors with a large measurement range and high accuracy are made in this paper. First, an assembly error model is established based on the imaginary kinematic joint method and the Denavit-Hartenberg (D-H) method. Next, a stiffness model is built to solve the stiffness matrix. The deformation error model of the sensor is obtained. Then, the first order kinematic influence coefficient matrix when the synthetic error is taken into account is solved. Finally, measurement and calibration experiments of the sensor composed of the hardware and software system are performed. Forced deformation of the force-measuring platform is detected by using laser interferometry and analyzed to verify the correctness of the synthetic error model. In addition, the first order kinematic influence coefficient matrix in actual circumstances is calculated. By comparing the condition numbers and square norms of the coefficient matrices, the conclusion is drawn theoretically that it is very important to take into account the synthetic error for design stage of the sensor and helpful to improve performance of the sensor in order to meet needs of actual working environments. PMID:28961209

3D environment modeling and location tracking using off-the-shelf components

NASA Astrophysics Data System (ADS)

Luke, Robert H.

2016-05-01

The remarkable popularity of smartphones over the past decade has led to a technological race for dominance in market share. This has resulted in a flood of new processors and sensors that are inexpensive, low power and high performance. These sensors include accelerometers, gyroscope, barometers and most importantly cameras. This sensor suite, coupled with multicore processors, allows a new community of researchers to build small, high performance platforms for low cost. This paper describes a system using off-the-shelf components to perform position tracking as well as environment modeling. The system relies on tracking using stereo vision and inertial navigation to determine movement of the system as well as create a model of the environment sensed by the system.

Defining the uncertainty of electro-optical identification system performance estimates using a 3D optical environment derived from satellite

NASA Astrophysics Data System (ADS)

Ladner, S. D.; Arnone, R.; Casey, B.; Weidemann, A.; Gray, D.; Shulman, I.; Mahoney, K.; Giddings, T.; Shirron, J.

2009-05-01

Current United States Navy Mine-Counter-Measure (MCM) operations primarily use electro-optical identification (EOID) sensors to identify underwater targets after detection via acoustic sensors. These EOID sensors which are based on laser underwater imaging by design work best in "clear" waters and are limited in coastal waters especially with strong optical layers. Optical properties and in particular scattering and absorption play an important role on systems performance. Surface optical properties alone from satellite are not adequate to determine how well a system will perform at depth due to the existence of optical layers. The spatial and temporal characteristics of the 3d optical variability of the coastal waters along with strength and location of subsurface optical layers maximize chances of identifying underwater targets by exploiting optimum sensor deployment. Advanced methods have been developed to fuse the optical measurements from gliders, optical properties from "surface" satellite snapshot and 3-D ocean circulation models to extend the two-dimensional (2-D) surface satellite optical image into a three-dimensional (3-D) optical volume with subsurface optical layers. Modifications were made to an EOID performance model to integrate a 3-D optical volume covering an entire region of interest as input and derive system performance field. These enhancements extend present capability based on glider optics and EOID sensor models to estimate the system's "image quality". This only yields system performance information for a single glider profile location in a very large operational region. Finally, we define the uncertainty of the system performance by coupling the EOID performance model with the 3-D optical volume uncertainties. Knowing the ensemble spread of EOID performance field provides a new and unique capability for tactical decision makers and Navy Operations.

A Micro-Force Sensor with Slotted-Quad-Beam Structure for Measuring the Friction in MEMS Bearings

PubMed Central

Liu, Huan; Yang, Shuming; Zhao, Yulong; Jiang, Zhuangde; Liu, Yan; Tian, Bian

2013-01-01

Presented here is a slotted-quad-beam structure sensor for the measurement of friction in micro bearings. Stress concentration slots are incorporated into a conventional quad-beam structure to improve the sensitivity of force measurements. The performance comparison between the quad-beam structure sensor and the slotted-quad-beam structure sensor are performed by theoretical modeling and finite element (FE) analysis. A hollow stainless steel probe is attached to the mesa of the sensor chip by a tailor-made organic glass fixture. Concerning the overload protection of the fragile beams, a glass wafer is bonded onto the bottom of sensor chip to limit the displacement of the mesa. The calibration of the packaged device is experimentally performed by a tri-dimensional positioning stage, a precision piezoelectric ceramic and an electronic analytical balance, which indicates its favorable sensitivity and overload protection. To verify the potential of the proposed sensor being applied in micro friction measurement, a measurement platform is established. The output of the sensor reflects the friction of bearing resulting from dry friction and solid lubrication. The results accord with the theoretical modeling and demonstrate that the sensor has the potential application in measuring the micro friction force under stable stage in MEMS machines. PMID:24084112

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

NASA Astrophysics Data System (ADS)

Kerekes, John P.; Presnar, Michael D.; Fourspring, Kenneth D.; Ninkov, Zoran; Pogorzala, David R.; Raisanen, Alan D.; Rice, Andrew C.; Vasquez, Juan R.; Patel, Jeffrey P.; MacIntyre, Robert T.; Brown, Scott D.

2009-05-01

A novel multi-object spectrometer (MOS) is being explored for use as an adaptive performance-driven sensor that tracks moving targets. Developed originally for astronomical applications, the instrument utilizes an array of micromirrors to reflect light to a panchromatic imaging array. When an object of interest is detected the individual micromirrors imaging the object are tilted to reflect the light to a spectrometer to collect a full spectrum. This paper will present example sensor performance from empirical data collected in laboratory experiments, as well as our approach in designing optical and radiometric models of the MOS channels and the micromirror array. Simulation of moving vehicles in a highfidelity, hyperspectral scene is used to generate a dynamic video input for the adaptive sensor. Performance-driven algorithms for feature-aided target tracking and modality selection exploit multiple electromagnetic observables to track moving vehicle targets.

Sensor trustworthiness in uncertain time varying stochastic environments

NASA Astrophysics Data System (ADS)

Verma, Ajay; Fernandes, Ronald; Vadakkeveedu, Kalyan

2011-06-01

Persistent surveillance applications require unattended sensors deployed in remote regions to track and monitor some physical stimulant of interest that can be modeled as output of time varying stochastic process. However, the accuracy or the trustworthiness of the information received through a remote and unattended sensor and sensor network cannot be readily assumed, since sensors may get disabled, corrupted, or even compromised, resulting in unreliable information. The aim of this paper is to develop information theory based metric to determine sensor trustworthiness from the sensor data in an uncertain and time varying stochastic environment. In this paper we show an information theory based determination of sensor data trustworthiness using an adaptive stochastic reference sensor model that tracks the sensor performance for the time varying physical feature, and provides a baseline model that is used to compare and analyze the observed sensor output. We present an approach in which relative entropy is used for reference model adaptation and determination of divergence of the sensor signal from the estimated reference baseline. We show that that KL-divergence is a useful metric that can be successfully used in determination of sensor failures or sensor malice of various types.

Abstract - Cooperative Research and Development Agreement between Ames National Laboratory and National Energy Technology Laboratory AGMT-0609

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bryden, Mark; Tucker, David A.

The goal of this project is to develop a merged environment for simulation and analysis (MESA) at the National Energy Technology Laboratory’s (NETL) Hybrid Performance (Hyper) project laboratory. The MESA sensor lab developed as a component of this research will provide a development platform for investigating: 1) advanced control strategies, 2) testing and development of sensor hardware, 3) various modeling in-the-loop algorithms and 4) other advanced computational algorithms for improved plant performance using sensors, real-time models, and complex systems tools.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network

PubMed Central

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish–Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection. PMID:26447696

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

PubMed

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California.

PubMed

Mukherjee, Anondo; Stanton, Levi G; Graham, Ashley R; Roberts, Paul T

2017-08-05

The use of low-cost air quality sensors has proliferated among non-profits and citizen scientists, due to their portability, affordability, and ease of use. Researchers are examining the sensors for their potential use in a wide range of applications, including the examination of the spatial and temporal variability of particulate matter (PM). However, few studies have quantified the performance (e.g., accuracy, precision, and reliability) of the sensors under real-world conditions. This study examined the performance of two models of PM sensors, the AirBeam and the Alphasense Optical Particle Counter (OPC-N2), over a 12-week period in the Cuyama Valley of California, where PM concentrations are impacted by wind-blown dust events and regional transport. The sensor measurements were compared with observations from two well-characterized instruments: the GRIMM 11-R optical particle counter, and the Met One beta attenuation monitor (BAM). Both sensor models demonstrated a high degree of collocated precision (R² = 0.8-0.99), and a moderate degree of correlation against the reference instruments (R² = 0.6-0.76). Sensor measurements were influenced by the meteorological environment and the aerosol size distribution. Quantifying the performance of sensors in real-world conditions is a requisite step to ensuring that sensors will be used in ways commensurate with their data quality.

Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California

PubMed Central

Roberts, Paul T.

2017-01-01

The use of low-cost air quality sensors has proliferated among non-profits and citizen scientists, due to their portability, affordability, and ease of use. Researchers are examining the sensors for their potential use in a wide range of applications, including the examination of the spatial and temporal variability of particulate matter (PM). However, few studies have quantified the performance (e.g., accuracy, precision, and reliability) of the sensors under real-world conditions. This study examined the performance of two models of PM sensors, the AirBeam and the Alphasense Optical Particle Counter (OPC-N2), over a 12-week period in the Cuyama Valley of California, where PM concentrations are impacted by wind-blown dust events and regional transport. The sensor measurements were compared with observations from two well-characterized instruments: the GRIMM 11-R optical particle counter, and the Met One beta attenuation monitor (BAM). Both sensor models demonstrated a high degree of collocated precision (R2 = 0.8–0.99), and a moderate degree of correlation against the reference instruments (R2 = 0.6–0.76). Sensor measurements were influenced by the meteorological environment and the aerosol size distribution. Quantifying the performance of sensors in real-world conditions is a requisite step to ensuring that sensors will be used in ways commensurate with their data quality. PMID:28783065

Scalable Deployment of Advanced Building Energy Management Systems

DTIC Science & Technology

2013-05-01

150  Figure J.5 Sensor Schema...151  Figure J.6 Temperature Sensor Schema...augments an existing BMS with additional sensors /meters and uses a reduced-order model and diagnostic software to make performance deviations visible

TRAC performance estimates

NASA Technical Reports Server (NTRS)

Everett, L.

1992-01-01

This report documents the performance characteristics of a Targeting Reflective Alignment Concept (TRAC) sensor. The performance will be documented for both short and long ranges. For long ranges, the sensor is used without the flat mirror attached to the target. To better understand the capabilities of the TRAC based sensors, an engineering model is required. The model can be used to better design the system for a particular application. This is necessary because there are many interrelated design variables in application. These include lense parameters, camera, and target configuration. The report presents first an analytical development of the performance, and second an experimental verification of the equations. In the analytical presentation it is assumed that the best vision resolution is a single pixel element. The experimental results suggest however that the resolution is better than 1 pixel. Hence the analytical results should be considered worst case conditions. The report also discusses advantages and limitations of the TRAC sensor in light of the performance estimates. Finally the report discusses potential improvements.

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

2015-12-31

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Assessing Performance Tradeoffs in Undersea Distributed Sensor Networks

DTIC Science & Technology

2006-09-01

time. We refer to this process as track - before - detect (see [5] for a description), since the final determination of a target presence is not made until...expressions for probability of successful search and probability of false search for modeling the track - before - detect process. We then describe a numerical...random manner (randomly sampled from a uniform distribution). II. SENSOR NETWORK PERFORMANCE MODELS We model the process of track - before - detect by

Nanostructure based EO/IR sensor development for homeland security applications

NASA Astrophysics Data System (ADS)

Sood, Ashok K.; Welser, Roger E.; Sood, Adam W.; Puri, Yash R.; Manzur, Tariq; Dhar, Nibir K.; Polla, Dennis L.; Wang, Zhong L.; Wijewarnasuriya, Priyalal S.; Anwar, A. F. M.

2011-06-01

Next Generation EO/IR focal plane arrays using nanostructure materials are being developed for a variety of Defense and Homeland Security Sensor Applications. Several different nanomaterials are being evaluated for these applications. These include ZnO nanowires, GaN Nanowires and II-VI nanowires, which have demonstrated large signal to noise ratio as a wide band gap nanostructure material in the UV band. Similarly, the work is under way using Carbon Nanotubes (CNT) for a high speed detector and focal plane array as two-dimensional array as bolometer for IR bands of interest, which can be implemented for the sensors for homeland security applications. In this paper, we will discuss the sensor design and model predicting performance of an EO/IR focal plane array and Sensor that can cover the UV to IR bands of interest. The model can provide a robust means for comparing performance of the EO/IR FPA's and Sensors that can operate in the UV, Visible-NIR (0.4- 1.8μ), SWIR (2.0-2.5μ), MWIR (3-5μ), and LWIR bands (8-14μ). This model can be used as a tool for predicting performance of nanostructure arrays under development. We will also discuss our results on growth and characterization of ZnO nanowires and CNT's for the next generation sensor applications. We also present several approaches for integrated energy harvesting using nanostructure based solar cells and Nanogenerators that can be used to supplement the energy required for nanostructure based sensors.

Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information.

PubMed

Bao, Xu; Li, Haijian; Qin, Lingqiao; Xu, Dongwei; Ran, Bin; Rong, Jian

2016-10-27

To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads.

Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information

PubMed Central

Bao, Xu; Li, Haijian; Qin, Lingqiao; Xu, Dongwei; Ran, Bin; Rong, Jian

2016-01-01

To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads. PMID:27801794

Model-Scale Experiment of the Seakeeping Performance for R/V Melville, Model 5720

DTIC Science & Technology

2012-07-01

Angle 1 Y None Deg Sensor Bourns Rotary Potentiometer 6574S-1-103 NA 39596 KVH Sin 2 Y None volts Sensor KVH Fluxgate Compass C-100...NA Deg Sensor KVH Calc Heading NA N None DegM Calculated KVH Fluxgate Compass C-100 39449 Bow Tracker Sensor Bottom NA N None...3DM-3XI combined three axis of angular rate gyros, accelerometers, and magnetometers to provide various combinations of gyro stabilized Euler

Driver behavior profiling: An investigation with different smartphone sensors and machine learning

PubMed Central

Ferreira, Jair; Carvalho, Eduardo; Ferreira, Bruno V.; de Souza, Cleidson; Suhara, Yoshihiko; Pentland, Alex

2017-01-01

Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement. PMID:28394925

Trustworthy data collection from implantable medical devices via high-speed security implementation based on IEEE 1363.

PubMed

Hu, Fei; Hao, Qi; Lukowiak, Marcin; Sun, Qingquan; Wilhelm, Kyle; Radziszowski, Stanisław; Wu, Yao

2010-11-01

Implantable medical devices (IMDs) have played an important role in many medical fields. Any failure in IMDs operations could cause serious consequences and it is important to protect the IMDs access from unauthenticated access. This study investigates secure IMD data collection within a telehealthcare [mobile health (m-health)] network. We use medical sensors carried by patients to securely access IMD data and perform secure sensor-to-sensor communications between patients to relay the IMD data to a remote doctor's server. To meet the requirements on low computational complexity, we choose N-th degree truncated polynomial ring (NTRU)-based encryption/decryption to secure IMD-sensor and sensor-sensor communications. An extended matryoshkas model is developed to estimate direct/indirect trust relationship among sensors. An NTRU hardware implementation in very large integrated circuit hardware description language is studied based on industry Standard IEEE 1363 to increase the speed of key generation. The performance analysis results demonstrate the security robustness of the proposed IMD data access trust model.

Enhanced modeling and simulation of EO/IR sensor systems

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; May, Christopher

2015-05-01

The testing and evaluation process developed by the Night Vision and Electronic Sensors Directorate (NVESD) Modeling and Simulation Division (MSD) provides end to end systems evaluation, testing, and training of EO/IR sensors. By combining NV-LabCap, the Night Vision Integrated Performance Model (NV-IPM), One Semi-Automated Forces (OneSAF) input sensor file generation, and the Night Vision Image Generator (NVIG) capabilities, NVESD provides confidence to the M&S community that EO/IR sensor developmental and operational testing and evaluation are accurately represented throughout the lifecycle of an EO/IR system. This new process allows for both theoretical and actual sensor testing. A sensor can be theoretically designed in NV-IPM, modeled in NV-IPM, and then seamlessly input into the wargames for operational analysis. After theoretical design, prototype sensors can be measured by using NV-LabCap, then modeled in NV-IPM and input into wargames for further evaluation. The measurement process to high fidelity modeling and simulation can then be repeated again and again throughout the entire life cycle of an EO/IR sensor as needed, to include LRIP, full rate production, and even after Depot Level Maintenance. This is a prototypical example of how an engineering level model and higher level simulations can share models to mutual benefit.

ATTIRE (analytical tools for thermal infrared engineering): A sensor simulation and modeling package

NASA Astrophysics Data System (ADS)

Jaggi, S.

1993-02-01

The Advanced Sensor Development Laboratory (ASDL) at the Stennis Space Center develops, maintains and calibrates remote sensing instruments for the National Aeronautics & Space Administration (NASA). To perform system design trade-offs, analysis, and establish system parameters, ASDL has developed a software package for analytical simulation of sensor systems. This package called 'Analytical Tools for Thermal InfraRed Engineering' - ATTIRE, simulates the various components of a sensor system. The software allows each subsystem of the sensor to be analyzed independently for its performance. These performance parameters are then integrated to obtain system level information such as Signal-to-Noise Ratio (SNR), Noise Equivalent Radiance (NER), Noise Equivalent Temperature Difference (NETD) etc. This paper describes the uses of the package and the physics that were used to derive the performance parameters.

Third-generation imaging sensor system concepts

NASA Astrophysics Data System (ADS)

Reago, Donald A.; Horn, Stuart B.; Campbell, James, Jr.; Vollmerhausen, Richard H.

1999-07-01

Second generation forward looking infrared sensors, based on either parallel scanning, long wave (8 - 12 um) time delay and integration HgCdTe detectors or mid wave (3 - 5 um), medium format staring (640 X 480 pixels) InSb detectors, are being fielded. The science and technology community is now turning its attention toward the definition of a future third generation of FLIR sensors, based on emerging research and development efforts. Modeled third generation sensor performance demonstrates a significant improvement in performance over second generation, resulting in enhanced lethality and survivability on the future battlefield. In this paper we present the current thinking on what third generation sensors systems will be and the resulting requirements for third generation focal plane array detectors. Three classes of sensors have been identified. The high performance sensor will contain a megapixel or larger array with at least two colors. Higher operating temperatures will also be the goal here so that power and weight can be reduced. A high performance uncooled sensor is also envisioned that will perform somewhere between first and second generation cooled detectors, but at significantly lower cost, weight, and power. The final third generation sensor is a very low cost micro sensor. This sensor can open up a whole new IR market because of its small size, weight, and cost. Future unattended throwaway sensors, micro UAVs, and helmet mounted IR cameras will be the result of this new class.

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

PubMed

Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K

2018-06-01

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Cross Deployment Networking and Systematic Performance Analysis of Underwater Wireless Sensor Networks.

PubMed

Wei, Zhengxian; Song, Min; Yin, Guisheng; Wang, Hongbin; Ma, Xuefei; Song, Houbing

2017-07-12

Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward. Firstly, the normal body-centered cubic lattice to cross body-centered cubic lattice (CBCL) has been improved, and a deployment process and topology generation method are built. Then most importantly, a cross deployment networking method (CDNM) for UWSNs suitable for the underwater environment is proposed. Furthermore, a systematic quar-performance calculation model (SQPCM) is proposed from an integrated perspective, in which the systematic performance of a UWSN includes coverage, connectivity, durability and rapid-reactivity. Besides, measurement models are established based on the relationship between systematic performance and influencing parameters. Finally, the influencing parameters are divided into three types, namely, constraint parameters, device performance and networking parameters. Based on these, a networking parameters adjustment method (NPAM) for optimized systematic performance of UWSNs has been presented. The simulation results demonstrate that the approach proposed in this paper is feasible and efficient in networking and performance calculation of UWSNs.

Cross Deployment Networking and Systematic Performance Analysis of Underwater Wireless Sensor Networks

PubMed Central

Wei, Zhengxian; Song, Min; Yin, Guisheng; Wang, Hongbin; Ma, Xuefei

2017-01-01

Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward. Firstly, the normal body-centered cubic lattice to cross body-centered cubic lattice (CBCL) has been improved, and a deployment process and topology generation method are built. Then most importantly, a cross deployment networking method (CDNM) for UWSNs suitable for the underwater environment is proposed. Furthermore, a systematic quar-performance calculation model (SQPCM) is proposed from an integrated perspective, in which the systematic performance of a UWSN includes coverage, connectivity, durability and rapid-reactivity. Besides, measurement models are established based on the relationship between systematic performance and influencing parameters. Finally, the influencing parameters are divided into three types, namely, constraint parameters, device performance and networking parameters. Based on these, a networking parameters adjustment method (NPAM) for optimized systematic performance of UWSNs has been presented. The simulation results demonstrate that the approach proposed in this paper is feasible and efficient in networking and performance calculation of UWSNs. PMID:28704959

Human Activity Recognition by Combining a Small Number of Classifiers.

PubMed

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Flight model of HISUI hyperspectral sensor onboard ISS (International Space Station)

NASA Astrophysics Data System (ADS)

Tanii, Jun; Kashimura, Osamu; Ito, Yoshiyuki; Iwasaki, Akira

2017-09-01

Hyperspectral Imager Suite (HISUI) is a next-generation Japanese sensor that will be mounted on Japanese Experiment Module (JEM) of ISS (International Space Station) in 2019 as timeframe. HISUI hyperspectral sensor obtains spectral images of 185 bands with the ground sampling distance of 20x31 meter from the visible to shortwave-infrared wavelength region. The sensor is the follow-on mission of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) in the visible to shortwave infrared region. The critical design review of the instrument was accomplished in 2014. Integration and tests of a Flight Model (FM) of HISUI hyperspectral sensor have been completed in the beginning of 2017. Simultaneously, the development of JEMExternal Facility (EF) Payload system for the instrument is being carried out. The system includes the structure, the thermal control sub-system and the electrical sub-system. The tests results of flight model, such as optical performance, optical distortion and radiometric performance are reported.

Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness.

PubMed

Kamphuis, C; Frank, E; Burke, J K; Verkerk, G A; Jago, J G

2013-01-01

The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected between November 2010 and June 2012 on 5 commercial pasture-based dairy farms. Sensor data from weigh scales (liveweight), pedometers (activity), and milk meters (milking order, unadjusted and adjusted milk yield in the first 2 min of milking, total milk yield, and milking duration) were collected at every milking from 4,904 cows. Lameness events were recorded by farmers who were trained in detecting lameness before the study commenced. A total of 318 lameness events affecting 292 cows were available for statistical analyses. For each lameness event, the lame cow's sensor data for a time period of 14 d before observation date were randomly matched by farm and date to 10 healthy cows (i.e., cows that were not lame and had no other health event recorded for the matched time period). Sensor data relating to the 14-d time periods were used for developing univariable (using one source of sensor data) and multivariable (using multiple sources of sensor data) models. Model development involved the use of additive logistic regression by applying the LogitBoost algorithm with a regression tree as base learner. The model's output was a probability estimate for lameness, given the sensor data collected during the 14-d time period. Models were validated using leave-one-farm-out cross-validation and, as a result of this validation, each cow in the data set (318 lame and 3,180 nonlame cows) received a probability estimate for lameness. Based on the area under the curve (AUC), results indicated that univariable models had low predictive potential, with the highest AUC values found for liveweight (AUC=0.66), activity (AUC=0.60), and milking order (AUC=0.65). Combining these 3 sensors improved AUC to 0.74. Detection performance of this combined model varied between farms but it consistently and significantly outperformed univariable models across farms at a fixed specificity of 80%. Still, detection performance was not high enough to be implemented in practice on large, pasture-based dairy farms. Future research may improve performance by developing variables based on sensor data of liveweight, activity, and milking order, but that better describe changes in sensor data patterns when cows go lame. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Evaluation of a fault tolerant system for an integrated avionics sensor configuration with TSRV flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.

1985-01-01

The performance analysis results of a fault inferring nonlinear detection system (FINDS) using sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment is presented. First, a statistical analysis of the flight recorded sensor data was made in order to determine the characteristics of sensor inaccuracies. Next, modifications were made to the detection and decision functions in the FINDS algorithm in order to improve false alarm and failure detection performance under real modelling errors present in the flight data. Finally, the failure detection and false alarm performance of the FINDS algorithm were analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minute flight data. In general, the detection speed, failure level estimation, and false alarm performance showed a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed was faster for filter measurement sensors soon as MLS than for filter input sensors such as flight control accelerometers.

Optimization of Self-Directed Target Coverage in Wireless Multimedia Sensor Network

PubMed Central

Yang, Yang; Wang, Yufei; Pi, Dechang; Wang, Ruchuan

2014-01-01

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor's current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm's performance and the effect of number of targets on the resulting subset. PMID:25136667

Comparison of the performance of intraoral X-ray sensors using objective image quality assessment.

PubMed

Hellén-Halme, Kristina; Johansson, Curt; Nilsson, Mats

2016-05-01

The main aim of this study was to evaluate the performance of 10 individual sensors of the same make, using objective measures of key image quality parameters. A further aim was to compare 8 brands of sensors. Ten new sensors of 8 different models from 6 manufacturers (i.e., 80 sensors) were included in the study. All sensors were exposed in a standardized way using an X-ray tube voltage of 60 kVp and different exposure times. Sensor response, noise, low-contrast resolution, spatial resolution and uniformity were measured. Individual differences between sensors of the same brand were surprisingly large in some cases. There were clear differences in the characteristics of the different brands of sensors. The largest variations were found for individual sensor response for some of the brands studied. Also, noise level and low contrast resolution showed large variations between brands. Sensors, even of the same brand, vary significantly in their quality. It is thus valuable to establish action levels for the acceptance of newly delivered sensors and to use objective image quality control for commissioning purposes and periodic checks to ensure high performance of individual digital sensors. Copyright © 2016 Elsevier Inc. All rights reserved.

Finite element modelling of fibre Bragg grating strain sensors and experimental validation

NASA Astrophysics Data System (ADS)

Malik, Shoaib A.; Mahendran, Ramani S.; Harris, Dee; Paget, Mark; Pandita, Surya D.; Machavaram, Venkata R.; Collins, David; Burns, Jonathan M.; Wang, Liwei; Fernando, Gerard F.

2009-03-01

Fibre Bragg grating (FBG) sensors continue to be used extensively for monitoring strain and temperature in and on engineering materials and structures. Previous researchers have also developed analytical models to predict the loadtransfer characteristics of FBG sensors as a function of applied strain. The general properties of the coating or adhesive that is used to surface-bond the FBG sensor to the substrate has also been modelled using finite element analysis. In this current paper, a technique was developed to surface-mount FBG sensors with a known volume and thickness of adhesive. The substrates used were aluminium dog-bone tensile test specimens. The FBG sensors were tensile tested in a series of ramp-hold sequences until failure. The reflected FBG spectra were recorded using a commercial instrument. Finite element analysis was performed to model the response of the surface-mounted FBG sensors. In the first instance, the effect of the mechanical properties of the adhesive and substrate were modelled. This was followed by modelling the volume of adhesive used to bond the FBG sensor to the substrate. Finally, the predicted values obtained via finite element modelling were correlated to the experimental results. In addition to the FBG sensors, the tensile test specimens were instrumented with surface-mounted electrical resistance strain gauges.

Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme.

PubMed

Wang, Hao; Jiang, Jie; Zhang, Guangjun

2017-04-21

The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV) optical navigation sensor using the well capacity adjusting (WCA) scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters.

Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme

PubMed Central

Wang, Hao; Jiang, Jie; Zhang, Guangjun

2017-01-01

The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV) optical navigation sensor using the well capacity adjusting (WCA) scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters. PMID:28430132

Comparison of wavefront sensor models for simulation of adaptive optics.

PubMed

Wu, Zhiwen; Enmark, Anita; Owner-Petersen, Mette; Andersen, Torben

2009-10-26

The new generation of extremely large telescopes will have adaptive optics. Due to the complexity and cost of such systems, it is important to simulate their performance before construction. Most systems planned will have Shack-Hartmann wavefront sensors. Different mathematical models are available for simulation of such wavefront sensors. The choice of wavefront sensor model strongly influences computation time and simulation accuracy. We have studied the influence of three wavefront sensor models on performance calculations for a generic, adaptive optics (AO) system designed for K-band operation of a 42 m telescope. The performance of this AO system has been investigated both for reduced wavelengths and for reduced r(0) in the K band. The telescope AO system was designed for K-band operation, that is both the subaperture size and the actuator pitch were matched to a fixed value of r(0) in the K-band. We find that under certain conditions, such as investigating limiting guide star magnitude for large Strehl-ratios, a full model based on Fraunhofer propagation to the subimages is significantly more accurate. It does however require long computation times. The shortcomings of simpler models based on either direct use of average wavefront tilt over the subapertures for actuator control, or use of the average tilt to move a precalculated point spread function in the subimages are most pronounced for studies of system limitations to operating parameter variations. In the long run, efficient parallelization techniques may be developed to overcome the problem.

A New Multi-Sensor Track Fusion Architecture for Multi-Sensor Information Integration

DTIC Science & Technology

2004-09-01

NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION ...NAME(S) AND ADDRESS(ES) Lockheed Martin Aeronautical Systems Company,Marietta,GA,3063 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...tracking process and degrades the track accuracy. ARCHITECHTURE OF MULTI-SENSOR TRACK FUSION MODEL The Alpha

A Novel Petri Nets-Based Modeling Method for the Interaction between the Sensor and the Geographic Environment in Emerging Sensor Networks

PubMed Central

Zhang, Feng; Xu, Yuetong; Chou, Jarong

2016-01-01

The service of sensor device in Emerging Sensor Networks (ESNs) is the extension of traditional Web services. Through the sensor network, the service of sensor device can communicate directly with the entity in the geographic environment, and even impact the geographic entity directly. The interaction between the sensor device in ESNs and geographic environment is very complex, and the interaction modeling is a challenging problem. This paper proposed a novel Petri Nets-based modeling method for the interaction between the sensor device and the geographic environment. The feature of the sensor device service in ESNs is more easily affected by the geographic environment than the traditional Web service. Therefore, the response time, the fault-tolerant ability and the resource consumption become important factors in the performance of the whole sensor application system. Thus, this paper classified IoT services as Sensing services and Controlling services according to the interaction between IoT service and geographic entity, and classified GIS services as data services and processing services. Then, this paper designed and analyzed service algebra and Colored Petri Nets model to modeling the geo-feature, IoT service, GIS service and the interaction process between the sensor and the geographic enviroment. At last, the modeling process is discussed by examples. PMID:27681730

Optical detection of chemical warfare agents and toxic industrial chemicals

NASA Astrophysics Data System (ADS)

Webber, Michael E.; Pushkarsky, Michael B.; Patel, C. Kumar N.

2004-12-01

We present an analytical model evaluating the suitability of optical absorption based spectroscopic techniques for detection of chemical warfare agents (CWAs) and toxic industrial chemicals (TICs) in ambient air. The sensor performance is modeled by simulating absorption spectra of a sample containing both the target and multitude of interfering species as well as an appropriate stochastic noise and determining the target concentrations from the simulated spectra via a least square fit (LSF) algorithm. The distribution of the LSF target concentrations determines the sensor sensitivity, probability of false positives (PFP) and probability of false negatives (PFN). The model was applied to CO2 laser based photoacosutic (L-PAS) CWA sensor and predicted single digit ppb sensitivity with very low PFP rates in the presence of significant amount of interferences. This approach will be useful for assessing sensor performance by developers and users alike; it also provides methodology for inter-comparison of different sensing technologies.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

ROBUST ONLINE MONITORING FOR CALIBRATION ASSESSMENT OF TRANSMITTERS AND INSTRUMENTATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Tipireddy, Ramakrishna; Lerchen, Megan E.

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Specifically, the next generation of OLM technology is expected to include newly developed advanced algorithms that improve monitoring of sensor/system performance and enable the use of plant data to derive information that currently cannot be measured. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research beingmore » performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation – fault detection and selection of acceptance criteria • Virtual sensing – signal value prediction and acceptance criteria • Response-time assessment – fault detection and acceptance criteria selection A GP-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for use in sensor-fault detection and virtual sensing. For signal validation, the various components to the OLM residual (which is computed using an AAKR model) were explicitly defined and modeled using a GP. Evaluation was conducted using flow loop data from multiple sources. Results using experimental data from laboratory-scale flow loops indicate that the approach, while capable of detecting sensor drift, may be incapable of discriminating between sensor drift and model inadequacy. This may be due to a simplification applied in the initial modeling, where the sensor degradation is assumed to be stationary. In the case of virtual sensors, the GP model was used in a predictive mode to estimate the correct sensor reading for sensors that may have failed. Results have indicated the viability of using this approach for virtual sensing. However, the GP model has proven to be computationally expensive, and so alternative algorithms for virtual sensing are being evaluated. Finally, automated approaches to performing noise analysis for extracting sensor response time were developed. Evaluation of this technique using laboratory-scale data indicates that it compares well with manual techniques previously used for noise analysis. Moreover, the automated and manual approaches for noise analysis also compare well with the current “gold standard”, hydraulic ramp testing, for response time monitoring. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

A Gaussian Mixture Model-based continuous Boundary Detection for 3D sensor networks.

PubMed

Chen, Jiehui; Salim, Mariam B; Matsumoto, Mitsuji

2010-01-01

This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node's spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains.

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

PubMed

Eide, Ingvar; Westad, Frank

2018-01-01

A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

Numerical modeling and performance analysis of zinc oxide (ZnO) thin-film based gas sensor

NASA Astrophysics Data System (ADS)

Punetha, Deepak; Ranjan, Rashmi; Pandey, Saurabh Kumar

2018-05-01

This manuscript describes the modeling and analysis of Zinc Oxide thin film based gas sensor. The conductance and sensitivity of the sensing layer has been described by change in temperature as well as change in gas concentration. The analysis has been done for reducing and oxidizing agents. Simulation results revealed the change in resistance and sensitivity of the sensor with respect to temperature and different gas concentration. To check the feasibility of the model, all the simulated results have been analyze by different experimental reported work. Wolkenstein theory has been used to model the proposed sensor and the simulation results have been shown by using device simulation software.

COMSOL-Based Modeling and Simulation of SnO2/rGO Gas Sensor for Detection of NO2.

PubMed

Yaghouti Niyat, Farshad; Shahrokh Abadi, M H

2018-02-01

Despite SIESTA and COMSOL being increasingly used for the simulation of the sensing mechanism in the gas sensors, there are no modeling and simulation reports in literature for detection of NO 2 based rGO/SnO 2 sensors. In the present study, we model, simulate, and characterize an NO 2 based rGO/SnO 2 gas sensor using COMSOL by solving the Poisson's equations under associated boundary conditions of mass, heat and electrical transitions. To perform the simulation, we use an exposure model for presenting the required NO 2 , a heat transfer model to obtain a reaction temperature, and an electrical model to characterize the sensor's response in the presence of the gas. We characterize the sensor's response in the presence of different concentrations of NO 2 at different working temperatures and compare the results with the experimental data, reported by Zhang et al. The results from the simulated sensor show a good agreement with the real sensor with some inconsistencies due to differences between the practical conditions in the real chamber and applied conditions to the analytical equations. The results also show that the method can be used to define and predict the behavior of the rGO-based gas sensors before undergoing the fabrication process.

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network.

PubMed

Han, Changcai; Yang, Jinsheng

2017-10-30

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes.

Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network

PubMed Central

Han, Changcai; Yang, Jinsheng

2017-01-01

The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes. PMID:29084155

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

PubMed

Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali

2017-12-01

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

Attitude Estimation for Large Field-of-View Sensors

NASA Technical Reports Server (NTRS)

Cheng, Yang; Crassidis, John L.; Markley, F. Landis

2005-01-01

The QUEST measurement noise model for unit vector observations has been widely used in spacecraft attitude estimation for more than twenty years. It was derived under the approximation that the noise lies in the tangent plane of the respective unit vector and is axially symmetrically distributed about the vector. For large field-of-view sensors, however, this approximation may be poor, especially when the measurement falls near the edge of the field of view. In this paper a new measurement noise model is derived based on a realistic noise distribution in the focal-plane of a large field-of-view sensor, which shows significant differences from the QUEST model for unit vector observations far away from the sensor boresight. An extended Kalman filter for attitude estimation is then designed with the new measurement noise model. Simulation results show that with the new measurement model the extended Kalman filter achieves better estimation performance using large field-of-view sensor observations.

Incorporating signal-dependent noise for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Morman, Christopher J.; Meola, Joseph

2015-05-01

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

Star centroiding error compensation for intensified star sensors.

PubMed

Jiang, Jie; Xiong, Kun; Yu, Wenbo; Yan, Jinyun; Zhang, Guangjun

2016-12-26

A star sensor provides high-precision attitude information by capturing a stellar image; however, the traditional star sensor has poor dynamic performance, which is attributed to its low sensitivity. Regarding the intensified star sensor, the image intensifier is utilized to improve the sensitivity, thereby further improving the dynamic performance of the star sensor. However, the introduction of image intensifier results in star centroiding accuracy decrease, further influencing the attitude measurement precision of the star sensor. A star centroiding error compensation method for intensified star sensors is proposed in this paper to reduce the influences. First, the imaging model of the intensified detector, which includes the deformation parameter of the optical fiber panel, is established based on the orthographic projection through the analysis of errors introduced by the image intensifier. Thereafter, the position errors at the target points based on the model are obtained by using the Levenberg-Marquardt (LM) optimization method. Last, the nearest trigonometric interpolation method is presented to compensate for the arbitrary centroiding error of the image plane. Laboratory calibration result and night sky experiment result show that the compensation method effectively eliminates the error introduced by the image intensifier, thus remarkably improving the precision of the intensified star sensors.

Design of inductive sensors for tongue control system for computers and assistive devices.

PubMed

Lontis, Eugen R; Struijk, Lotte N S A

2010-07-01

The paper introduces a novel design of air-core inductive sensors in printed circuit board (PCB) technology for a tongue control system. The tongue control system provides a quadriplegic person with a keyboard and a joystick type of mouse for interaction with a computer or for control of an assistive device. Activation of inductive sensors was performed with a cylindrical, soft ferromagnetic material (activation unit). Comparative analysis of inductive sensors in PCB technology with existing hand-made inductive sensors was performed with respect to inductance, resistance, and sensitivity to activation when the activation unit was placed in the center of the sensor. Optimisation of the activation unit was performed in a finite element model. PCBs with air-core inductive sensors were manufactured in a 10 layers, 100 microm and 120 microm line width technology. These sensors provided quality signals that could drive the electronics of the hand-made sensors. Furthermore, changing the geometry of the sensors allowed generation of variable signals correlated with the 2D movement of the activation unit at the sensors' surface. PCB technology for inductive sensors allows flexibility in design, automation of production and ease of possible integration with supplying electronics. The basic switch function of the inductive sensor can be extended to two-dimensional movement detection for pointing devices.

A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots.

PubMed

Gutiérrez, Marco A; Manso, Luis J; Pandya, Harit; Núñez, Pedro

2017-02-11

Object detection and classification have countless applications in human-robot interacting systems. It is a necessary skill for autonomous robots that perform tasks in household scenarios. Despite the great advances in deep learning and computer vision, social robots performing non-trivial tasks usually spend most of their time finding and modeling objects. Working in real scenarios means dealing with constant environment changes and relatively low-quality sensor data due to the distance at which objects are often found. Ambient intelligence systems equipped with different sensors can also benefit from the ability to find objects, enabling them to inform humans about their location. For these applications to succeed, systems need to detect the objects that may potentially contain other objects, working with relatively low-resolution sensor data. A passive learning architecture for sensors has been designed in order to take advantage of multimodal information, obtained using an RGB-D camera and trained semantic language models. The main contribution of the architecture lies in the improvement of the performance of the sensor under conditions of low resolution and high light variations using a combination of image labeling and word semantics. The tests performed on each of the stages of the architecture compare this solution with current research labeling techniques for the application of an autonomous social robot working in an apartment. The results obtained demonstrate that the proposed sensor architecture outperforms state-of-the-art approaches.

Object Detection for Agricultural and Construction Environments Using an Ultrasonic Sensor.

PubMed

Dvorak, J S; Stone, M L; Self, K P

2016-04-01

This study tested an ultrasonic sensor's ability to detect several objects commonly encountered in outdoor agricultural or construction environments: a water jug, a sheet of oriented strand board (OSB), a metalfence post, a human model, a wooden fence post, a Dracaena plant, a juniper plant, and a dog model. Tests were performed with each target object at distances from 0.01 to 3 m. Five tests were performed with each object at each location, and the sensor's ability to detect the object during each test was categorized as "undetected," "intermittent," "incorrect distance," or "good." Rigid objects that presented a larger surface area to the sensor, such as the water jug and OSB, were better detected than objects with a softer surface texture, which were occasionally not detected as the distance approached 3 m. Objects with extremely soft surface texture, such as the dog model, could be undetected at almost any distance from the sensor. The results of this testing should help designers offuture systems for outdoor environments, as the target objects tested can be found in nearly any agricultural or construction environment.

Theoretical and experimental investigations of sensor location for optimal aeroelastic system state estimation

NASA Technical Reports Server (NTRS)

Liu, G.

1985-01-01

One of the major concerns in the design of an active control system is obtaining the information needed for effective feedback. This involves the combination of sensing and estimation. A sensor location index is defined as the weighted sum of the mean square estimation errors in which the sensor locations can be regarded as estimator design parameters. The design goal is to choose these locations to minimize the sensor location index. The choice of the number of sensors is a tradeoff between the estimation quality based upon the same performance index and the total costs of installing and maintaining extra sensors. An experimental study for choosing the sensor location was conducted on an aeroelastic system. The system modeling which includes the unsteady aerodynamics model developed by Stephen Rock was improved. Experimental results verify the trend of the theoretical predictions of the sensor location index for different sensor locations at various wind speeds.

Submillimetre wave imaging and security: imaging performance and prediction

NASA Astrophysics Data System (ADS)

Appleby, R.; Ferguson, S.

2016-10-01

Within the European Commission Seventh Framework Programme (FP7), CONSORTIS (Concealed Object Stand-Off Real-Time Imaging for Security) has designed and is fabricating a stand-off system operating at sub-millimetre wave frequencies for the detection of objects concealed on people. This system scans people as they walk by the sensor. This paper presents the top level system design which brings together both passive and active sensors to provide good performance. The passive system operates in two bands between 100 and 600GHz and is based on a cryogen free cooled focal plane array sensor whilst the active system is a solid-state 340GHz radar. A modified version of OpenFX was used for modelling the passive system. This model was recently modified to include realistic location-specific skin temperature and to accept animated characters wearing up to three layers of clothing that move dynamically, such as those typically found in cinematography. Targets under clothing have been modelled and the performance simulated. The strengths and weaknesses of this modelling approach are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreedharan, Priya

The sudden release of toxic contaminants that reach indoor spaces can be hazardousto building occupants. To respond effectively, the contaminant release must be quicklydetected and characterized to determine unobserved parameters, such as release locationand strength. Characterizing the release requires solving an inverse problem. Designinga robust real-time sensor system that solves the inverse problem is challenging becausethe fate and transport of contaminants is complex, sensor information is limited andimperfect, and real-time estimation is computationally constrained.This dissertation uses a system-level approach, based on a Bayes Monte Carloframework, to develop sensor-system design concepts and methods. I describe threeinvestigations that explore complex relationships amongmore » sensors, network architecture,interpretation algorithms, and system performance. The investigations use data obtainedfrom tracer gas experiments conducted in a real building. The influence of individual sensor characteristics on the sensor-system performance for binary-type contaminant sensors is analyzed. Performance tradeoffs among sensor accuracy, threshold level and response time are identified; these attributes could not be inferred without a system-level analysis. For example, more accurate but slower sensors are found to outperform less accurate but faster sensors. Secondly, I investigate how the sensor-system performance can be understood in terms of contaminant transport processes and the model representation that is used to solve the inverse problem. The determination of release location and mass are shown to be related to and constrained by transport and mixing time scales. These time scales explain performance differences among different sensor networks. For example, the effect of longer sensor response times is comparably less for releases with longer mixing time scales. The third investigation explores how information fusion from heterogeneous sensors may improve the sensor-system performance and offset the need for more contaminant sensors. Physics- and algorithm-based frameworks are presented for selecting and fusing information from noncontaminant sensors. The frameworks are demonstrated with door-position sensors, which are found to be more useful in natural airflow conditions, but which cannot compensate for poor placement of contaminant sensors. The concepts and empirical findings have the potential to help in the design of sensor systems for more complex building systems. The research has broader relevance to additional environmental monitoring problems, fault detection and diagnostics, and system design.« less

pyBSM: A Python package for modeling imaging systems

NASA Astrophysics Data System (ADS)

LeMaster, Daniel A.; Eismann, Michael T.

2017-05-01

There are components that are common to all electro-optical and infrared imaging system performance models. The purpose of the Python Based Sensor Model (pyBSM) is to provide open source access to these functions for other researchers to build upon. Specifically, pyBSM implements much of the capability found in the ERIM Image Based Sensor Model (IBSM) V2.0 along with some improvements. The paper also includes two use-case examples. First, performance of an airborne imaging system is modeled using the General Image Quality Equation (GIQE). The results are then decomposed into factors affecting noise and resolution. Second, pyBSM is paired with openCV to evaluate performance of an algorithm used to detect objects in an image.

Infrared horizon sensor modeling for attitude determination and control - Analysis and mission experience

NASA Technical Reports Server (NTRS)

Singhal, S. P.; Phenneger, M. C.; Stengle, T. H.

1986-01-01

This paper summarizes the work of the Flight Dynamics Division of the National Aeronautics and Space Administration/Goddard Space Flight Center in analyzing and evaluating the performance of a variety of infrared horizon sensors on 12 spaceflight missions from 1973 to 1984. Earth infrared radiance modeling, using the LOWTRAN 5 Program, and the Horizon Radiance Modeling Utility are also described. Mission data are presented for Magsat and the Earth Radiation Budget Satellite, with analysis to assess the sensor modeling as well as cloud and sun interference effects. Recommendations are made regarding future directions for the infrared horizon technology.

Health Monitoring for Airframe Structural Characterization

NASA Technical Reports Server (NTRS)

Munns, Thomas E.; Kent, Renee M.; Bartolini, Antony; Gause, Charles B.; Borinski, Jason W.; Dietz, Jason; Elster, Jennifer L.; Boyd, Clark; Vicari, Larry; Ray, Asok;

Detection and recognition of simple spatial forms

NASA Technical Reports Server (NTRS)

Watson, A. B.

1983-01-01

A model of human visual sensitivity to spatial patterns is constructed. The model predicts the visibility and discriminability of arbitrary two-dimensional monochrome images. The image is analyzed by a large array of linear feature sensors, which differ in spatial frequency, phase, orientation, and position in the visual field. All sensors have one octave frequency bandwidths, and increase in size linearly with eccentricity. Sensor responses are processed by an ideal Bayesian classifier, subject to uncertainty. The performance of the model is compared to that of the human observer in detecting and discriminating some simple images.

A Prototype Land Information Sensor Web: Design, Implementation and Implication for the SMAP Mission

NASA Astrophysics Data System (ADS)

Su, H.; Houser, P.; Tian, Y.; Geiger, J. K.; Kumar, S. V.; Gates, L.

2009-12-01

Land Surface Model (LSM) predictions are regular in time and space, but these predictions are influenced by errors in model structure, input variables, parameters and inadequate treatment of sub-grid scale spatial variability. Consequently, LSM predictions are significantly improved through observation constraints made in a data assimilation framework. Several multi-sensor satellites are currently operating which provide multiple global observations of the land surface, and its related near-atmospheric properties. However, these observations are not optimal for addressing current and future land surface environmental problems. To meet future earth system science challenges, NASA will develop constellations of smart satellites in sensor web configurations which provide timely on-demand data and analysis to users, and can be reconfigured based on the changing needs of science and available technology. A sensor web is more than a collection of satellite sensors. That means a sensor web is a system composed of multiple platforms interconnected by a communication network for the purpose of performing specific observations and processing data required to support specific science goals. Sensor webs can eclipse the value of disparate sensor components by reducing response time and increasing scientific value, especially when the two-way interaction between the model and the sensor web is enabled. The study of a prototype Land Information Sensor Web (LISW) is sponsored by NASA, trying to integrate the Land Information System (LIS) in a sensor web framework which allows for optimal 2-way information flow that enhances land surface modeling using sensor web observations, and in turn allows sensor web reconfiguration to minimize overall system uncertainty. This prototype is based on a simulated interactive sensor web, which is then used to exercise and optimize the sensor web modeling interfaces. The Land Information Sensor Web Service-Oriented Architecture (LISW-SOA) has been developed and it is the very first sensor web framework developed especially for the land surface studies. Synthetic experiments based on the LISW-SOA and the virtual sensor web provide a controlled environment in which to examine the end-to-end performance of the prototype, the impact of various sensor web design trade-offs and the eventual value of sensor webs for a particular prediction or decision support. In this paper, the design, implementation of the LISW-SOA and the implication for the Soil Moisture Active and Passive (SMAP) mission is presented. Particular attention is focused on examining the relationship between the economic investment on a sensor web (space and air borne, ground based) and the accuracy of the model predicted soil moisture, which can be achieved by using such sensor observations. The Study of Virtual Land Information Sensor Web (LISW) is expected to provide some necessary a priori knowledge for designing and deploying the next generation Global Earth Observing System of systems (GEOSS).

Evaluation of electrolytic tilt sensors for wind tunnel model angle-of-attack (AOA) measurements

NASA Technical Reports Server (NTRS)

Wong, Douglas T.

1991-01-01

The results of a laboratory evaluation of three types of electrolytic tilt sensors as potential candidates for model attitude or angle of attack (AOA) measurements in wind tunnel tests are presented. Their performance was also compared with that from typical servo accelerometers used for AOA measurements. Model RG-37 electrolytic tilt sensors were found to have the highest overall accuracy among the three types. Compared with the servo accelerometer, their accuracies are about one order of magnitude worse and each of them cost about two-thirds less. Therefore, the sensors are unsuitable for AOA measurements although they are less expensive. However, the potential for other applications exists where the errors resulting from roll interaction, vibration, and response time are less, and sensor temperature can be controlled.

Omega Design and FEA Based Coriolis Mass Flow Sensor (CMFS) Analysis Using Titanium Material

NASA Astrophysics Data System (ADS)

Patil, Pravin P.; Kumar, Ashwani; Ahmad, Faraz

2018-02-01

The main highlight of this research work is evaluation of resonant frequency for titanium omega type coriolis mass flow sensor. Coriolis mass flow sensor is used for measuring direct mass flow in pipe useful for various industrial applications. It works on the principle of Coriolis effect. Finite Element Analysis (FEA) simulation of omega flow sensor was performed using Ansys 14.5 and Solid Edge, Pro-E was used for modelling of omega tube. Titanium was selected as omega tube material. Experimental setup was prepared for omega tube coriolis flow sensor for performing different test. Experimental setup was used for investigation of different parameters effect on CMFS and validation of simulation results.

A comparison between using distance sensors for measuring the pantograph vertically movement

NASA Astrophysics Data System (ADS)

Rob, R.; Panoiu, C.; Rusu-Anghel, S.; Panoiu, M.

2018-01-01

In railway transportation the most important problem to solve consists in assuring the safety traffic of people and freight. In this scope some of the geometrical parameters regarding the contact line must be measured. One of this parameter is the pantograph vertically movement, so it must use distance sensors. Present paper studies the performance of two kinds of distance sensors, an ultrasonic distance sensor and an infrared sensor. The performances are studied from the point of view of error distance measurement and the possibility of using a real time acquisition system. The researches were made on a laboratory model for the pantograph realized at the scale 1:2.

Fallback options for airgap sensor fault of an electromagnetic suspension system

NASA Astrophysics Data System (ADS)

Michail, Konstantinos; Zolotas, Argyrios C.; Goodall, Roger M.

2013-06-01

The paper presents a method to recover the performance of an electromagnetic suspension under faulty airgap sensor. The proposed control scheme is a combination of classical control loops, a Kalman Estimator and analytical redundancy (for the airgap signal). In this way redundant airgap sensors are not essential for reliable operation of this system. When the airgap sensor fails the required signal is recovered using a combination of a Kalman estimator and analytical redundancy. The performance of the suspension is optimised using genetic algorithms and some preliminary robustness issues to load and operating airgap variations are discussed. Simulations on a realistic model of such type of suspension illustrate the efficacy of the proposed sensor tolerant control method.

CAIRSENSE Study: Real-world evaluation of low cost sensors ...

EPA Pesticide Factsheets

Low-cost air pollution sensors are a rapidly developing field in air monitoring. In recent years, numerous sensors have been developed that can provide real-time concentration data for different air pollutants at costs accessible to individuals and non-regulatory groups. Additionally, these sensors have the potential to improve the spatial resolution of monitoring networks and provide a better understanding of neighborhood- and local-scale air quality and sources. However, many new sensors have not been evaluated to determine their long-term performance and capabilities. In this study, nine different low-cost sensor models, including O3, NO2 and particle sensors, were deployed in Denver, CO from September 2015 to February 2016. Three sensors of each type were deployed to evaluate instrument precision and consistency over the time period. Sensors were co-located with reference monitors at the Denver NCore site in order to evaluate sensor accuracy and precision. Denver was chosen as the location for this study to evaluate sensor performance in dry, high altitude, and low winter temperatures. Sensors were evaluated for data completeness, performance over time, and comparison with regulatory monitors. This presentation will also address challenges and approaches to data logging and processing. Preliminary analysis revealed that most sensors had high data completeness when data loggers were operational (e.g., the Aeroqual O3 sensor ranged from 94-100%), and exhibited

An Expert System And Simulation Approach For Sensor Management & Control In A Distributed Surveillance Network

NASA Astrophysics Data System (ADS)

Leon, Barbara D.; Heller, Paul R.

1987-05-01

A surveillance network is a group of multiplatform sensors cooperating to improve network performance. Network control is distributed as a measure to decrease vulnerability to enemy threat. The network may contain diverse sensor types such as radar, ESM (Electronic Support Measures), IRST (Infrared search and track) and E-0 (Electro-Optical). Each platform may contain a single sensor or suite of sensors. In a surveillance network it is desirable to control sensors to make the overall system more effective. This problem has come to be known as sensor management and control (SM&C). Two major facets of network performance are surveillance and survivability. In a netted environment, surveillance can be enhanced if information from all sensors is combined and sensor operating conditions are controlled to provide a synergistic effect. In contrast, when survivability is the main concern for the network, the best operating status for all sensors would be passive or off. Of course, improving survivability tends to degrade surveillance. Hence, the objective of SM&C is to optimize surveillance and survivability of the network. Too voluminous data of various formats and the quick response time are two characteristics of this problem which make it an ideal application for Artificial Intelligence. A solution to the SM&C problem, presented as a computer simulation, will be presented in this paper. The simulation is a hybrid production written in LISP and FORTRAN. It combines the latest conventional computer programming methods with Artificial Intelligence techniques to produce a flexible state-of-the-art tool to evaluate network performance. The event-driven simulation contains environment models coupled with an expert system. These environment models include sensor (track-while-scan and agile beam) and target models, local tracking, and system tracking. These models are used to generate the environment for the sensor management and control expert system. The expert system, driven by a forward chaining inference engine, makes decisions based on the global database. The global database contains current track and sensor information supplied by the simulation. At present, the rule base emphasizes the surveillance features with rules grouped into three main categories: maintenance and enhancing track on prioritized targets; filling coverage holes and countering jamming; and evaluating sensor status. The paper will describe the architecture used for the expert system and the reasons for selecting the chosen methods. The SM&C simulation produces a graphical representation of sensors and their associated tracks such that the benefits of the sensor management and control expert system are evident. Jammer locations are also part of the display. The paper will describe results from several scenarios that best illustrate the sensor management and control concepts.

General Model of Photon-Pair Detection with an Image Sensor

NASA Astrophysics Data System (ADS)

Defienne, Hugo; Reichert, Matthew; Fleischer, Jason W.

2018-05-01

We develop an analytic model that relates intensity correlation measurements performed by an image sensor to the properties of photon pairs illuminating it. Experiments using an effective single-photon counting camera, a linear electron-multiplying charge-coupled device camera, and a standard CCD camera confirm the model. The results open the field of quantum optical sensing using conventional detectors.

A Proposal for Modeling Real Hardware, Weather and Marine Conditions for Underwater Sensor Networks

PubMed Central

Climent, Salvador; Capella, Juan Vicente; Blanc, Sara; Perles, Angel; Serrano, Juan José

2013-01-01

Network simulators are useful for researching protocol performance, appraising new hardware capabilities and evaluating real application scenarios. However, these tasks can only be achieved when using accurate models and real parameters that enable the extraction of trustworthy results and conclusions. This paper presents an underwater wireless sensor network ecosystem for the ns-3 simulator. This ecosystem is composed of a new energy-harvesting model and a low-cost, low-power underwater wake-up modem model that, alongside existing models, enables the performance of accurate simulations by providing real weather and marine conditions from the location where the real application is to be deployed. PMID:23748171

Progress in sensor performance testing, modeling and range prediction using the TOD method: an overview

NASA Astrophysics Data System (ADS)

Bijl, Piet; Hogervorst, Maarten A.; Toet, Alexander

2017-05-01

The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview.

ATR performance modeling concepts

NASA Astrophysics Data System (ADS)

Ross, Timothy D.; Baker, Hyatt B.; Nolan, Adam R.; McGinnis, Ryan E.; Paulson, Christopher R.

2016-05-01

Performance models are needed for automatic target recognition (ATR) development and use. ATRs consume sensor data and produce decisions about the scene observed. ATR performance models (APMs) on the other hand consume operating conditions (OCs) and produce probabilities about what the ATR will produce. APMs are needed for many modeling roles of many kinds of ATRs (each with different sensing modality and exploitation functionality combinations); moreover, there are different approaches to constructing the APMs. Therefore, although many APMs have been developed, there is rarely one that fits a particular need. Clarified APM concepts may allow us to recognize new uses of existing APMs and identify new APM technologies and components that better support coverage of the needed APMs. The concepts begin with thinking of ATRs as mapping OCs of the real scene (including the sensor data) to reports. An APM is then a mapping from explicit quantized OCs (represented with less resolution than the real OCs) and latent OC distributions to report distributions. The roles of APMs can be distinguished by the explicit OCs they consume. APMs used in simulations consume the true state that the ATR is attempting to report. APMs used online with the exploitation consume the sensor signal and derivatives, such as match scores. APMs used in sensor management consume neither of those, but estimate performance from other OCs. This paper will summarize the major building blocks for APMs, including knowledge sources, OC models, look-up tables, analytical and learned mappings, and tools for signal synthesis and exploitation.

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks.

PubMed

Wang, Tian; Wu, Qun; Wen, Sheng; Cai, Yiqiao; Tian, Hui; Chen, Yonghong; Wang, Baowei

2017-01-13

WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator's mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost.

Development and investigation of MOEMS type displacement-pressure sensor for biological information monitoring

NASA Astrophysics Data System (ADS)

Ostasevicius, Vytautas; Malinauskas, Karolis; Janusas, Giedrius; Palevicius, Arvydas; Cekas, Elingas

2016-04-01

The aim of this paper is to develop and investigate MOEMS displacement-pressure sensor for biological information monitoring. Developing computational periodical microstructure models using COMSOL Multiphysics modeling software for modal and shape analysis and implementation of these results for design MOEMS displacement-pressure sensor for biological information monitoring was performed. The micro manufacturing technology of periodical microstructure having good diffraction efficiency was proposed. Experimental setup for characterisation of optical properties of periodical microstructure used for design of displacement-pressure sensor was created. Pulsating human artery dynamic characteristics in this paper were analysed.

A simulation of remote sensor systems and data processing algorithms for spectral feature classification

NASA Technical Reports Server (NTRS)

Arduini, R. F.; Aherron, R. M.; Samms, R. W.

1984-01-01

A computational model of the deterministic and stochastic processes involved in multispectral remote sensing was designed to evaluate the performance of sensor systems and data processing algorithms for spectral feature classification. Accuracy in distinguishing between categories of surfaces or between specific types is developed as a means to compare sensor systems and data processing algorithms. The model allows studies to be made of the effects of variability of the atmosphere and of surface reflectance, as well as the effects of channel selection and sensor noise. Examples of these effects are shown.

An airborne remote sensing system for urban air quality

NASA Technical Reports Server (NTRS)

Duncan, L. J.; Friedman, E. J.; Keitz, E. L.; Ward, E. A.

1974-01-01

Several NASA sponsored remote sensors and possible airborne platforms were evaluated. Outputs of dispersion models for SO2 and CO pollution in the Washington, D.C. area were used with ground station data to establish the expected performance and limitations of the remote sensors. Aircraft/sensor support requirements are discussed. A method of optimum flight plan determination was made. Cost trade offs were performed. Conclusions about the implementation of various instrument packages as parts of a comprehensive air quality monitoring system in Washington are presented.

Development of a Meso-Scale Fiberoptic Rotation Sensor for a Torsion Actuator.

PubMed

Sheng, Jun; Desai, Jaydev P

2018-01-01

This paper presents the development of a meso-scale fiberoptic rotation sensor for a shape memory alloy (SMA) torsion actuator for neurosurgical applications. Within the sensor, a rotary head with a reflecting surface is capable of modulating the light intensity collected by optical fibers when the rotary head is coupled to the torsion actuator. The mechanism of light intensity modulation is modeled, followed by experimental model verification. Meanwhile, working performances for different rotary head designs, optical fibers, and fabrication materials are compared. After the calibration of the fiberoptic rotation sensor, the sensor is capable of precisely measuring rotary motion and controlling the SMA torsion actuator with feedback control.

Hypoxia: Exposure Time Until Significant Performance Effects

DTIC Science & Technology

2016-03-07

arterial oxygen saturation (SpO2) from the temporal artery. Datex-Ohmeda 3900 P Pulse Oximeter . The Datex-Ohmeda 3900 P pulse oximeter measured SpO2 at...flight helmet. Nonin ® model 8000 R Ear Cup Sensor. The Nonin ® model 8000 R in-helmet ear cup reflectance sensor is an oximeter that measures

Multiplatform Mission Planning and Operations Simulation Environment for Adaptive Remote Sensors

NASA Astrophysics Data System (ADS)

Smith, G.; Ball, C.; O'Brien, A.; Johnson, J. T.

2017-12-01

We report on the design and development of mission simulator libraries to support the emerging field of adaptive remote sensors. We will outline the current state of the art in adaptive sensing, provide analysis of how the current approach to performing observing system simulation experiments (OSSEs) must be changed to enable adaptive sensors for remote sensing, and present an architecture to enable their inclusion in future OSSEs.The growing potential of sensors capable of real-time adaptation of their operational parameters calls for a new class of mission planning and simulation tools. Existing simulation tools used in OSSEs assume a fixed set of sensor parameters in terms of observation geometry, frequencies used, resolution, or observation time, which allows simplifications to be made in the simulation and allows sensor observation errors to be characterized a priori. Adaptive sensors may vary these parameters depending on the details of the scene observed, so that sensor performance is not simple to model without conducting OSSE simulations that include sensor adaptation in response to varying observational environment. Adaptive sensors are of significance to resource-constrained, small satellite platforms because they enable the management of power and data volumes while providing methods for multiple sensors to collaborate.The new class of OSSEs required to utilize adaptive sensors located on multiple platforms must answer the question: If the physical act of sensing has a cost, how does the system determine if the science value of a measurement is worth the cost and how should that cost be shared among the collaborating sensors?Here we propose to answer this question using an architecture structured around three modules: ADAPT, MANAGE and COLLABORATE. The ADAPT module is a set of routines to facilitate modeling of adaptive sensors, the MANAGE module will implement a set of routines to facilitate simulations of sensor resource management when power and data volume are constrained, and the COLLABORATE module will support simulations of coordination among multiple platforms with adaptive sensors. When used together these modules will for a simulation OSSEs that can enable both the design of adaptive algorithms to support remote sensing and the prediction of the sensor performance.

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network

PubMed Central

Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

2017-01-01

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links. PMID:28282899

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network.

PubMed

Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

2017-03-08

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.

Evaluation of electrolytic tilt sensors for measuring model angle of attack in wind tunnel tests

NASA Technical Reports Server (NTRS)

Wong, Douglas T.

1992-01-01

The results of a laboratory evaluation of electrolytic tilt sensors as potential candidates for measuring model attitude or angle of attack in wind tunnel tests are presented. The performance of eight electrolytic tilt sensors was compared with that of typical servo accelerometers used for angle-of-attack measurements. The areas evaluated included linearity, hysteresis, repeatability, temperature characteristics, roll-on-pitch interaction, sensitivity to lead-wire resistance, step response time, and rectification. Among the sensors being evaluated, the Spectron model RG-37 electrolytic tilt sensors have the highest overall accuracy in terms of linearity, hysteresis, repeatability, temperature sensitivity, and roll sensitivity. A comparison of the sensors with the servo accelerometers revealed that the accuracy of the RG-37 sensors was on the average about one order of magnitude worse. Even though a comparison indicates that the cost of each tilt sensor is about one-third the cost of each servo accelerometer, the sensors are considered unsuitable for angle-of-attack measurements. However, the potential exists for other applications such as wind tunnel wall-attitude measurements where the errors resulting from roll interaction, vibration, and response time are less and sensor temperature can be controlled.

TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors

PubMed Central

Pashami, Sepideh; Lilienthal, Achim J.; Schaffernicht, Erik; Trincavelli, Marco

2013-01-01

Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time. PMID:23736853

Development of Magneto-Resistive Angular Position Sensors for Space Applications

NASA Astrophysics Data System (ADS)

Hahn, Robert; Langendorf, Sven; Seifart, Klaus; Slatter, Rolf; Olberts, Bastian; Romera, Fernando

2015-09-01

Magnetic microsystems in the form of magneto- resistive (MR) sensors are firmly established in automobiles and industrial applications. They measure path, angle, electrical current, or magnetic fields. MR technology opens up new sensor possibilities in space applications and can be an enabling technology for optimal performance, high robustness and long lifetime at reasonable costs. In a recent assessment study performed by HTS GmbH and Sensitec GmbH under ESA Contract a market survey has confirmed that space industry has a very high interest in novel, contactless position sensors based on MR technology. Now, a detailed development stage is pursued, to advance the sensor design up to Engineering Qualification Model (EQM) level and to perform qualification testing for a representative pilot space application.The paper briefly reviews the basics of magneto- resistive effects and possible sensor applications and describes the key benefits of MR angular sensors with reference to currently operational industrial and space applications. The results of the assessment study are presented and potential applications and uses of contactless magneto-resistive angular sensors for spacecraft are identified. The baseline mechanical and electrical sensor design will be discussed. An outlook on the EQM development and qualification tests is provided.

Accuracy of different sensors for the estimation of pollutant concentrations (total suspended solids, total and dissolved chemical oxygen demand) in wastewater and stormwater.

PubMed

Lepot, Mathieu; Aubin, Jean-Baptiste; Bertrand-Krajewski, Jean-Luc

2013-01-01

Many field investigations have used continuous sensors (turbidimeters and/or ultraviolet (UV)-visible spectrophotometers) to estimate with a short time step pollutant concentrations in sewer systems. Few, if any, publications compare the performance of various sensors for the same set of samples. Different surrogate sensors (turbidity sensors, UV-visible spectrophotometer, pH meter, conductivity meter and microwave sensor) were tested to link concentrations of total suspended solids (TSS), total and dissolved chemical oxygen demand (COD), and sensors' outputs. In the combined sewer at the inlet of a wastewater treatment plant, 94 samples were collected during dry weather, 44 samples were collected during wet weather, and 165 samples were collected under both dry and wet weather conditions. From these samples, triplicate standard laboratory analyses were performed and corresponding sensors outputs were recorded. Two outlier detection methods were developed, based, respectively, on the Mahalanobis and Euclidean distances. Several hundred regression models were tested, and the best ones (according to the root mean square error criterion) are presented in order of decreasing performance. No sensor appears as the best one for all three investigated pollutants.

Using sensors to measure activity in people with stroke.

PubMed

Fulk, George D; Sazonov, Edward

2011-01-01

The purpose of this study was to determine the ability of a novel shoe-based sensor that uses accelerometers, pressure sensors, and pattern recognition with a support vector machine (SVM) to accurately identify sitting, standing, and walking postures in people with stroke. Subjects with stroke wore the shoe-based sensor while randomly assuming 3 main postures: sitting, standing, and walking. A SVM classifier was used to train and validate the data to develop individual and group models, which were tested for accuracy, recall, and precision. Eight subjects participated. Both individual and group models were able to accurately identify the different postures (99.1% to 100% individual models and 76.9% to 100% group models). Recall and precision were also high for both individual (0.99 to 1.00) and group (0.82 to 0.99) models. The unique combination of accelerometer and pressure sensors built into the shoe was able to accurately identify postures. This shoe sensor could be used to provide accurate information on community performance of activities in people with stroke as well as provide behavioral enhancing feedback as part of a telerehabilitation intervention.

Study on the Application of an Ultra-High-Frequency Fractal Antenna to Partial Discharge Detection in Switchgears

PubMed Central

Yao, Chenguo; Chen, Pan; Huang, Congjian; Chen, Yu; Qiao, Panpan

2013-01-01

The ultra-high-frequency (UHF) method is used to analyze the insulation condition of electric equipment by detecting the UHF electromagnetic (EM) waves excited by partial discharge (PD). As part of the UHF detection system, the UHF sensor determines the detection system performance in signal extraction and recognition. In this paper, a UHF antenna sensor with the fractal structure for PD detection in switchgears was designed by means of modeling, simulation and optimization. This sensor, with a flat-plate structure, had two resonance frequencies of 583 MHz and 732 MHz. In the laboratory, four kinds of insulation defect models were positioned in the testing switchgear for typical PD tests. The results show that the sensor could reproduce the electromagnetic waves well. Furthermore, to optimize the installation position of the inner sensor for achieving best detection performance, the precise simulation model of switchgear was developed to study the propagation characteristics of UHF signals in switchgear by finite-difference time-domain (FDTD) method. According to the results of simulation and verification test, the sensor should be positioned at the right side of bottom plate in the front cabinet. This research established the foundation for the further study on the application of UHF technique in switchgear PD online detection. PMID:24351641

Virtual DRI dataset development

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Teaney, Brian P.; May, Christopher; Maurer, Tana; Nelson, Michael B.; Pham, Justin R.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military analysis community for sensor design, trade studies, and field performance prediction. This paper analyzes the results of perception tests performed to compare the results of a field DRI (Detection, Recognition, and Identification Test) performed in 2009 to current Soldier performance viewing the same imagery in a laboratory environment and simulated imagery of the same data set. The purpose of the experiment is to build a robust data set for use in the virtual prototyping of infrared sensors. This data set will provide a strong foundation relating, model predictions, field DRI results and simulated imagery.

AFGL Fiscal Year 1984 Air Force Technical Objectives Document.

DTIC Science & Technology

1982-11-01

the near term, to design the performance characteristics of sensors operating from the Shuttle. In the long term, these sensors will provide the...atmosphere are determined from sensors on rockets and satellites. These data, which are used to develop tailored analytic and predictive models for...toward increasing the flight time of the various vehicles. Future research and test- ing of advanced sensors will require rockets with increased

Modeling and performance assessment in QinetiQ of EO and IR airborne reconnaissance systems

NASA Astrophysics Data System (ADS)

Williams, John W.; Potter, Gary E.

2002-11-01

QinetiQ are the technical authority responsible for specifying the performance requirements for the procurement of airborne reconnaissance systems, on behalf of the UK MoD. They are also responsible for acceptance of delivered systems, overseeing and verifying the installed system performance as predicted and then assessed by the contractor. Measures of functional capability are central to these activities. The conduct of these activities utilises the broad technical insight and wide range of analysis tools and models available within QinetiQ. This paper focuses on the tools, methods and models that are applicable to systems based on EO and IR sensors. The tools, methods and models are described, and representative output for systems that QinetiQ has been responsible for is presented. The principle capability applicable to EO and IR airborne reconnaissance systems is the STAR (Simulation Tools for Airborne Reconnaissance) suite of models. STAR generates predictions of performance measures such as GRD (Ground Resolved Distance) and GIQE (General Image Quality) NIIRS (National Imagery Interpretation Rating Scales). It also generates images representing sensor output, using the scene generation software CAMEO-SIM and the imaging sensor model EMERALD. The simulated image 'quality' is fully correlated with the predicted non-imaging performance measures. STAR also generates image and table data that is compliant with STANAG 7023, which may be used to test ground station functionality.

Impact of CCL2 and CCR2 Chemokine / Receptor Deficiencies on Macrophage Recruitment and Continuous Glucose Monitoring in vivo

PubMed Central

Klueh, Ulrike; Czajkowski, Caroline; Ludzinska, Izabela; Qiao, Yi; Frailey, Jackman; Kreutzer, Donald L.

2016-01-01

The accumulation of macrophages (MΦ) at the sensor-tissue interface is thought to be a major player in controlling tissue reactions and sensor performance in vivo. Nevertheless until recently no direct demonstration of the causal relationship between MΦ aggregation and loss of sensor function existed. Using a Continuous Glucose Monitoring (CGM) murine model we previously demonstrated that genetic deficiencies of MΦ or depletion of MΦ decreased MΦ accumulation at sensor implantation sites, which led to significantly enhanced CGM performance, when compared to normal mice. Additional studies in our laboratories have also demonstrated that MΦ can act as “metabolic sinks” by depleting glucose levels at the implanted sensors in vitro and in vivo. In the present study we extended these observations by demonstrating that MΦ chemokine (CCL2) and receptor (CCR2) knockout mice displayed a decrease in inflammation and MΦ recruitment at sensor implantation sites, when compared to normal mice. This decreased MΦ recruitment significantly enhanced CGM performance when compared to control mice. These studies demonstrated the importance of the CCL2 family of chemokines and related receptors in MΦ recruitment and sensor performance and suggest chemokine targets for enhancing CGM in vivo. PMID:27376197

Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy)

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Cortes Arevalo, Vivian Juliette; Wehn, Uta; Alfonso, Leonardo; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri P.

2018-01-01

To improve hydrological predictions, real-time measurements derived from traditional physical sensors are integrated within mathematic models. Recently, traditional sensors are being complemented with crowdsourced data (social sensors). Although measurements from social sensors can be low cost and more spatially distributed, other factors like spatial variability of citizen involvement, decreasing involvement over time, variable observations accuracy and feasibility for model assimilation play an important role in accurate flood predictions. Only a few studies have investigated the benefit of assimilating uncertain crowdsourced data in hydrological and hydraulic models. In this study, we investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess improvements in the model prediction performance for different spatial-temporal scenarios of citizen involvement levels. To that end, we simulate an extreme flood event that occurred in the Bacchiglione catchment (Italy) in May 2013 using a semi-distributed hydrological model with the station at Ponte degli Angeli (Vicenza) as the prediction-validation point. A conceptual hydrological model is implemented by the Alto Adriatico Water Authority and it is used to estimate runoff from the different sub-catchments, while a hydraulic model is implemented to propagate the flow along the river reach. In both models, a Kalman filter is implemented to assimilate the crowdsourced observations. Synthetic crowdsourced observations are generated for either static social or dynamic social sensors because these measures were not available at the time of the study. We consider two sets of experiments: (i) assuming random probability of receiving crowdsourced observations and (ii) using theoretical scenarios of citizen motivations, and consequent involvement levels, based on population distribution. The results demonstrate the usefulness of integrating crowdsourced observations. First, the assimilation of crowdsourced observations located at upstream points of the Bacchiglione catchment ensure high model performance for high lead-time values, whereas observations at the outlet of the catchments provide good results for short lead times. Second, biased and inaccurate crowdsourced observations can significantly affect model results. Third, the theoretical scenario of citizens motivated by their feeling of belonging to a community of friends has the best effect in the model performance. However, flood prediction only improved when such small communities are located in the upstream portion of the Bacchiglione catchment. Finally, decreasing involvement over time leads to a reduction in model performance and consequently inaccurate flood forecasts.

Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states.

PubMed

Montone, Verona O; Fraisse, Clyde W; Peres, Natalia A; Sentelhas, Paulo C; Gleason, Mark; Ellis, Michael; Schnabel, Guido

2016-11-01

Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH ≥90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.

Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states

NASA Astrophysics Data System (ADS)

Montone, Verona O.; Fraisse, Clyde W.; Peres, Natalia A.; Sentelhas, Paulo C.; Gleason, Mark; Ellis, Michael; Schnabel, Guido

2016-11-01

Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH ≥90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.

Chemiresistive Graphene Sensors for Ammonia Detection.

PubMed

Mackin, Charles; Schroeder, Vera; Zurutuza, Amaia; Su, Cong; Kong, Jing; Swager, Timothy M; Palacios, Tomás

2018-05-09

The primary objective of this work is to demonstrate a novel sensor system as a convenient vehicle for scaled-up repeatability and the kinetic analysis of a pixelated testbed. This work presents a sensor system capable of measuring hundreds of functionalized graphene sensors in a rapid and convenient fashion. The sensor system makes use of a novel array architecture requiring only one sensor per pixel and no selector transistor. The sensor system is employed specifically for the evaluation of Co(tpfpp)ClO 4 functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO 4 treated graphene sensors were found to provide 4-fold increased ammonia sensitivity over pristine graphene sensors. Sensors were also found to exhibit excellent selectivity over interfering compounds such as water and common organic solvents. The ability to monitor a large sensor array with 160 pixels provides insights into performance variations and reproducibility-critical factors in the development of practical sensor systems. All sensors exhibit the same linearly related responses with variations in response exhibiting Gaussian distributions, a key finding for variation modeling and quality engineering purposes. The mean correlation coefficient between sensor responses was found to be 0.999 indicating highly consistent sensor responses and excellent reproducibility of Co(tpfpp)ClO 4 functionalization. A detailed kinetic model is developed to describe sensor response profiles. The model consists of two adsorption mechanisms-one reversible and one irreversible-and is shown capable of fitting experimental data with a mean percent error of 0.01%.

Simulation of the spatial frequency-dependent sensitivities of Acoustic Emission sensors

NASA Astrophysics Data System (ADS)

Boulay, N.; Lhémery, A.; Zhang, F.

2018-05-01

Typical configurations of nondestructive testing by Acoustic Emission (NDT/AE) make use of multiple sensors positioned on the tested structure for detecting evolving flaws and possibly locating them by triangulation. Sensors positions must be optimized for ensuring global coverage sensitivity to AE events and minimizing their number. A simulator of NDT/AE is under development to provide help with designing testing configurations and with interpreting measurements. A global model performs sub-models simulating the various phenomena taking place at different spatial and temporal scales (crack growth, AE source and radiation, wave propagation in the structure, reception by sensors). In this context, accurate modelling of sensors behaviour must be developed. These sensors generally consist of a cylindrical piezoelectric element of radius approximately equal to its thickness, without damping and bonded to its case. Sensors themselves are bonded to the structure being tested. Here, a multiphysics finite element simulation tool is used to study the complex behaviour of AE sensor. The simulated behaviour is shown to accurately reproduce the high-amplitude measured contributions used in the AE practice.

A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

PubMed Central

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-01-01

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

A space weather forecasting system with multiple satellites based on a self-recognizing network.

PubMed

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-05-05

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine.

PubMed

Acuña, Gonzalo; Ramirez, Cristian; Curilem, Millaray

2014-01-01

The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In this work, NARX-ANN, NARMAX-ANN, NARX-SVM and NARMAX-SVM models are compared when acting as software sensors of biomass concentration for a solid substrate cultivation (SSC) process. Results show that NARMAX-SVM outperforms the other models with an SMAPE index under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as they include prediction errors as inputs. In the case of perturbation of initial conditions of the autoregressive variable, NARX models exhibited better convergence capabilities. This work also confirms that a difficult to measure variable, like biomass concentration, can be estimated on-line from easy to measure variables like CO₂ and O₂ using an adequate software sensor based on computational intelligence techniques.

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

PubMed Central

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-01-01

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes. PMID:26712766

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation.

PubMed

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-12-26

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and 24 fuzzy rules for the robot's movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

In Vitro, In Vivo and Post Explantation Testing of Glucose-Detecting Biosensors: Current Methods and Recommendations

PubMed Central

Koschwanez, Heidi E.; Reichert, W. Monty

2007-01-01

To date, there have been a number of cases where glucose sensors have performed well over long periods of implantation; however, it remains difficult to predict whether a given sensor will perform reliably, will exhibit gradual degradation of performance, or will fail outright soon after implantation. Typically, the literature emphasizes the sensor that performed well, while only briefly (if at all) mentioning the failed devices. This leaves open the question of whether current sensor designs are adequate for the hostile in vivo environment, and whether these sensors have been assessed by the proper regimen of testing protocols. This paper reviews the current in vitro and in vivo testing procedures used to evaluate the functionality and biocompatibility of implantable glucose sensors. An overview of the standards and regulatory bodies that govern biomaterials and end-product device testing precedes a discussion of up-to-date invasive and non-invasive technologies for diabetes management. Analysis of current in vitro, in vivo, and then post implantation testing is presented. Given the underlying assumption that the success of the sensor in vivo foreshadows the long-term reliability of the sensor in the human body, the relative merits of these testing methods are evaluated with respect to how representative they are of human models. PMID:17524479

3D printed high performance strain sensors for high temperature applications

NASA Astrophysics Data System (ADS)

Rahman, Md Taibur; Moser, Russell; Zbib, Hussein M.; Ramana, C. V.; Panat, Rahul

2018-01-01

Realization of high temperature physical measurement sensors, which are needed in many of the current and emerging technologies, is challenging due to the degradation of their electrical stability by drift currents, material oxidation, thermal strain, and creep. In this paper, for the first time, we demonstrate that 3D printed sensors show a metamaterial-like behavior, resulting in superior performance such as high sensitivity, low thermal strain, and enhanced thermal stability. The sensors were fabricated using silver (Ag) nanoparticles (NPs), using an advanced Aerosol Jet based additive printing method followed by thermal sintering. The sensors were tested under cyclic strain up to a temperature of 500 °C and showed a gauge factor of 3.15 ± 0.086, which is about 57% higher than that of those available commercially. The sensor thermal strain was also an order of magnitude lower than that of commercial gages for operation up to a temperature of 500 °C. An analytical model was developed to account for the enhanced performance of such printed sensors based on enhanced lateral contraction of the NP films due to the porosity, a behavior akin to cellular metamaterials. The results demonstrate the potential of 3D printing technology as a pathway to realize highly stable and high-performance sensors for high temperature applications.

Performance optimization for space-based sensors: simulation and modelling at Fraunhofer IOSB

NASA Astrophysics Data System (ADS)

Schweitzer, Caroline; Stein, Karin

2014-10-01

The prediction of the effectiveness of a space-based sensor for its designated application in space (e.g. special earth surface observations or missile detection) can help to reduce the expenses, especially during the phases of mission planning and instrumentation. In order to optimize the performance of such systems we simulate and analyse the entire operational scenario, including: - optional waveband - various orbit heights and viewing angles - system design characteristics, e. g. pixel size and filter transmission - atmospheric effects, e. g. different cloud types, climate zones and seasons In the following, an evaluation of the appropriate infrared (IR) waveband for the designated sensor application is given. The simulation environment is also capable of simulating moving objects like aircraft or missiles. Therefore, the spectral signature of the object/missile as well as its track along a flight path is implemented. The resulting video sequence is then analysed by a tracking algorithm and an estimation of the effectiveness of the sensor system can be simulated. This paper summarizes the work carried out at Fraunhofer IOSB in the field of simulation and modelling for the performance optimization of space based sensors. The paper is structured as follows: First, an overview of the applied simulation and modelling software is given. Then, the capability of those tools is illustrated by means of a hypothetical threat scenario for space-based early warning (launch of a long-range ballistic missile (BM)).

On the Design of Attitude-Heading Reference Systems Using the Allan Variance.

PubMed

Hidalgo-Carrió, Javier; Arnold, Sascha; Poulakis, Pantelis

2016-04-01

The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV).

Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

Modeling and Validation of Performance Limitations for the Optimal Design of Interferometric and Intensity-Modulated Fiber Optic Displacement Sensors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moro, Erik A.

Optical fiber sensors offer advantages over traditional electromechanical sensors, making them particularly well-suited for certain measurement applications. Generally speaking, optical fiber sensors respond to a desired measurand through modulation of an optical signal's intensity, phase, or wavelength. Practically, non-contacting fiber optic displacement sensors are limited to intensity-modulated and interferometric (or phase-modulated) methodologies. Intensity-modulated fiber optic displacement sensors relate target displacement to a power measurement. The simplest intensity-modulated sensor architectures are not robust to environmental and hardware fluctuations, since such variability may cause changes in the measured power level that falsely indicate target displacement. Differential intensity-modulated sensors have been implemented, offeringmore » robustness to such intensity fluctuations, and the speed of these sensors is limited only by the combined speed of the photodetection hardware and the data acquisition system (kHz-MHz). The primary disadvantages of intensity-modulated sensing are the relatively low accuracy (?m-mm for low-power sensors) and the lack of robustness, which consequently must be designed, often with great difficulty, into the sensor's architecture. White light interferometric displacement sensors, on the other hand, offer increased accuracy and robustness. Unlike their monochromatic-interferometer counterparts, white light interferometric sensors offer absolute, unambiguous displacement measurements over large displacement ranges (cm for low-power, 5 mW, sources), necessitating no initial calibration, and requiring no environmental or feedback control. The primary disadvantage of white light interferometric displacement sensors is that their utility in dynamic testing scenarios is limited, both by hardware bandwidth and by their inherent high-sensitivity to Doppler-effects. The decision of whether to use either an intensity-modulated interferometric sensor depends on an appropriate performance function (e.g., desired displacement range, accuracy, robustness, etc.). In this dissertation, the performance limitations of a bundled differential intensity-modulated displacement sensor are analyzed, where the bundling configuration has been designed to optimize performance. The performance limitations of a white light Fabry-Perot displacement sensor are also analyzed. Both these sensors are non-contacting, but they have access to different regions of the performance-space. Further, both these sensors have different degrees of sensitivity to experimental uncertainty. Made in conjunction with careful analysis, the decision of which sensor to deploy need not be an uninformed one.« less

Stochastic modeling to identify requirements for centralized monitoring of distributed wastewater treatment.

PubMed

Hug, T; Maurer, M

2012-01-01

Distributed (decentralized) wastewater treatment can, in many situations, be a valuable alternative to a centralized sewer network and wastewater treatment plant. However, it is critical for its acceptance whether the same overall treatment performance can be achieved without on-site staff, and whether its performance can be measured. In this paper we argue and illustrate that the system performance depends not only on the design performance and reliability of the individual treatment units, but also significantly on the monitoring scheme, i.e. on the reliability of the process information. For this purpose, we present a simple model of a fleet of identical treatment units. Thereby, their performance depends on four stochastic variables: the reliability of the treatment unit, the respond time for the repair of failed units, the reliability of on-line sensors, and the frequency of routine inspections. The simulated scenarios show a significant difference between the true performance and the observations by the sensors and inspections. The results also illustrate the trade-off between investing in reactor and sensor technology and in human interventions in order to achieve a certain target performance. Modeling can quantify such effects and thereby support the identification of requirements for the centralized monitoring of distributed treatment units. The model approach is generic and can be extended and applied to various distributed wastewater treatment technologies and contexts.

Slug to churn transition analysis using wire-mesh sensor

NASA Astrophysics Data System (ADS)

H. F. Velasco, P.; Ortiz-Vidal, L. E.; Rocha, D. M.; Rodriguez, O. M. H.

2016-06-01

A comparison between some theoretical slug to churn flow-pattern transition models and experimental data is performed. The flow-pattern database considers vertical upward air-water flow at standard temperature and pressure for 50 mm and 32 mm ID pipes. A briefly description of the models and its phenomenology is presented. In general, the performance of the transition models is poor. We found that new experimental studies describing objectively both stable and unstable slug flow-pattern are required. In this sense, the Wire Mesh Sensor (WMS) can assist to that aim. The potential of the WMS is outlined.

Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing

PubMed Central

Papadopoulou, Maria; Vernay, Didier; Smith, Ian F. C.

2017-01-01

Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain. PMID:29240684

Evaluation of Inter-Mountain Labs infrasound sensors : July 2007.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, Darren M.

2007-10-01

Sandia National Laboratories has tested and evaluated three Inter Mountain Labs infrasound sensors. The test results included in this report were in response to static and tonal-dynamic input signals. Most test methodologies used were based on IEEE Standards 1057 for Digitizing Waveform Recorders and 1241 for Analog to Digital Converters; others were designed by Sandia specifically for infrasound application evaluation and for supplementary criteria not addressed in the IEEE standards. The objective of this work was to evaluate the overall technical performance of the Inter Mountain Labs (IML) infrasound sensor model SS. The results of this evaluation were only comparedmore » to relevant noise models; due to a lack of manufactures documentation notes on the sensors under test prior to testing. The tests selected for this system were chosen to demonstrate different performance aspects of the components under test.« less

Automated Decomposition of Model-based Learning Problems

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Millar, Bill

1996-01-01

A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

Color constancy: enhancing von Kries adaption via sensor transformations

NASA Astrophysics Data System (ADS)

Finlayson, Graham D.; Drew, Mark S.; Funt, Brian V.

1993-09-01

Von Kries adaptation has long been considered a reasonable vehicle for color constancy. Since the color constancy performance attainable via the von Kries rule strongly depends on the spectral response characteristics of the human cones, we consider the possibility of enhancing von Kries performance by constructing new `sensors' as linear combinations of the fixed cone sensitivity functions. We show that if surface reflectances are well-modeled by 3 basis functions and illuminants by 2 basis functions then there exists a set of new sensors for which von Kries adaptation can yield perfect color constancy. These new sensors can (like the cones) be described as long-, medium-, and short-wave sensitive; however, both the new long- and medium-wave sensors have sharpened sensitivities -- their support is more concentrated. The new short-wave sensor remains relatively unchanged. A similar sharpening of cone sensitivities has previously been observed in test and field spectral sensitivities measured for the human eye. We present simulation results demonstrating improved von Kries performance using the new sensors even when the restrictions on the illumination and reflectance are relaxed.

The Impact of Measurement Noise in GPA Diagnostic Analysis of a Gas Turbine Engine

NASA Astrophysics Data System (ADS)

Ntantis, Efstratios L.; Li, Y. G.

2013-12-01

The performance diagnostic analysis of a gas turbine is accomplished by estimating a set of internal engine health parameters from available sensor measurements. No physical measuring instruments however can ever completely eliminate the presence of measurement uncertainties. Sensor measurements are often distorted by noise and bias leading to inaccurate estimation results. This paper explores the impact of measurement noise on Gas Turbine GPA analysis. The analysis is demonstrated with a test case where gas turbine performance simulation and diagnostics code TURBOMATCH is used to build a performance model of a model engine similar to Rolls-Royce Trent 500 turbofan engine, and carry out the diagnostic analysis with the presence of different levels of measurement noise. Conclusively, to improve the reliability of the diagnostic results, a statistical analysis of the data scattering caused by sensor uncertainties is made. The diagnostic tool used to deal with the statistical analysis of measurement noise impact is a model-based method utilizing a non-linear GPA.

Self Diagnostic Adhesive for Bonded Joints in Aircraft Structures

DTIC Science & Technology

2016-10-04

validated under the fatigue/dynamic loading condition. 3) Both SEM (Spectral Element Modeling) and FEM ( Finite Element Modeling) simulation of the...Sensors ..................................................................... 22 Parametric Study of Sensor Performance via Finite Element Simulation...The frequency range that we are interested is around 800 kHz. Conventional linear finite element method (FEM) requires a very fine spatial

Microburst vertical wind estimation from horizontal wind measurements

NASA Technical Reports Server (NTRS)

Vicroy, Dan D.

1994-01-01

The vertical wind or downdraft component of a microburst-generated wind shear can significantly degrade airplane performance. Doppler radar and lidar are two sensor technologies being tested to provide flight crews with early warning of the presence of hazardous wind shear. An inherent limitation of Doppler-based sensors is the inability to measure velocities perpendicular to the line of sight, which results in an underestimate of the total wind shear hazard. One solution to the line-of-sight limitation is to use a vertical wind model to estimate the vertical component from the horizontal wind measurement. The objective of this study was to assess the ability of simple vertical wind models to improve the hazard prediction capability of an airborne Doppler sensor in a realistic microburst environment. Both simulation and flight test measurements were used to test the vertical wind models. The results indicate that in the altitude region of interest (at or below 300 m), the simple vertical wind models improved the hazard estimate. The radar simulation study showed that the magnitude of the performance improvement was altitude dependent. The altitude of maximum performance improvement occurred at about 300 m.

Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring

PubMed Central

Ning, Zhi; Ye, Sheng; Sun, Li; Yang, Fenhuan; Wong, Ka Chun; Westerdahl, Dane; Louie, Peter K. K.

2018-01-01

The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), and oxidants (Ox) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO2 and ozone on a newly introduced oxidant sensor. PMID:29360749

Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring.

PubMed

Wei, Peng; Ning, Zhi; Ye, Sheng; Sun, Li; Yang, Fenhuan; Wong, Ka Chun; Westerdahl, Dane; Louie, Peter K K

2018-01-23

The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), and oxidants (O x ) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO₂ and ozone on a newly introduced oxidant sensor.

Highly Stretchable and Transparent Microfluidic Strain Sensors for Monitoring Human Body Motions.

PubMed

Yoon, Sun Geun; Koo, Hyung-Jun; Chang, Suk Tai

2015-12-16

We report a new class of simple microfluidic strain sensors with high stretchability, transparency, sensitivity, and long-term stability with no considerable hysteresis and a fast response to various deformations by combining the merits of microfluidic techniques and ionic liquids. The high optical transparency of the strain sensors was achieved by introducing refractive-index matched ionic liquids into microfluidic networks or channels embedded in an elastomeric matrix. The microfluidic strain sensors offer the outstanding sensor performance under a variety of deformations induced by stretching, bending, pressing, and twisting of the microfluidic strain sensors. The principle of our microfluidic strain sensor is explained by a theoretical model based on the elastic channel deformation. In order to demonstrate its capability of practical usage, the simple-structured microfluidic strain sensors were performed onto a finger, wrist, and arm. The highly stretchable and transparent microfluidic strain sensors were successfully applied as potential platforms for distinctively monitoring a wide range of human body motions in real time. Our novel microfluidic strain sensors show great promise for making future stretchable electronic devices.

Performance evaluation and modeling of a conformal filter (CF) based real-time standoff hazardous material detection sensor

NASA Astrophysics Data System (ADS)

Nelson, Matthew P.; Tazik, Shawna K.; Bangalore, Arjun S.; Treado, Patrick J.; Klem, Ethan; Temple, Dorota

2017-05-01

Hyperspectral imaging (HSI) systems can provide detection and identification of a variety of targets in the presence of complex backgrounds. However, current generation sensors are typically large, costly to field, do not usually operate in real time and have limited sensitivity and specificity. Despite these shortcomings, HSI-based intelligence has proven to be a valuable tool, thus resulting in increased demand for this type of technology. By moving the next generation of HSI technology into a more adaptive configuration, and a smaller and more cost effective form factor, HSI technologies can help maintain a competitive advantage for the U.S. armed forces as well as local, state and federal law enforcement agencies. Operating near the physical limits of HSI system capability is often necessary and very challenging, but is often enabled by rigorous modeling of detection performance. Specific performance envelopes we consistently strive to improve include: operating under low signal to background conditions; at higher and higher frame rates; and under less than ideal motion control scenarios. An adaptable, low cost, low footprint, standoff sensor architecture we have been maturing includes the use of conformal liquid crystal tunable filters (LCTFs). These Conformal Filters (CFs) are electro-optically tunable, multivariate HSI spectrometers that, when combined with Dual Polarization (DP) optics, produce optimized spectral passbands on demand, which can readily be reconfigured, to discriminate targets from complex backgrounds in real-time. With DARPA support, ChemImage Sensor Systems (CISS™) in collaboration with Research Triangle Institute (RTI) International are developing a novel, real-time, adaptable, compressive sensing short-wave infrared (SWIR) hyperspectral imaging technology called the Reconfigurable Conformal Imaging Sensor (RCIS) based on DP-CF technology. RCIS will address many shortcomings of current generation systems and offer improvements in operational agility and detection performance, while addressing sensor weight, form factor and cost needs. This paper discusses recent test and performance modeling results of a RCIS breadboard apparatus.

Desensitized Optimal Filtering and Sensor Fusion Toolkit

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.

2015-01-01

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

LVQ and backpropagation neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Doniere, Timothy F.; Dhawan, Atam P.

1993-01-01

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

FDTD-based optical simulations methodology for CMOS image sensors pixels architecture and process optimization

NASA Astrophysics Data System (ADS)

Hirigoyen, Flavien; Crocherie, Axel; Vaillant, Jérôme M.; Cazaux, Yvon

2008-02-01

This paper presents a new FDTD-based optical simulation model dedicated to describe the optical performances of CMOS image sensors taking into account diffraction effects. Following market trend and industrialization constraints, CMOS image sensors must be easily embedded into even smaller packages, which are now equipped with auto-focus and short-term coming zoom system. Due to miniaturization, the ray-tracing models used to evaluate pixels optical performances are not accurate anymore to describe the light propagation inside the sensor, because of diffraction effects. Thus we adopt a more fundamental description to take into account these diffraction effects: we chose to use Maxwell-Boltzmann based modeling to compute the propagation of light, and to use a software with an FDTD-based (Finite Difference Time Domain) engine to solve this propagation. We present in this article the complete methodology of this modeling: on one hand incoherent plane waves are propagated to approximate a product-use diffuse-like source, on the other hand we use periodic conditions to limit the size of the simulated model and both memory and computation time. After having presented the correlation of the model with measurements we will illustrate its use in the case of the optimization of a 1.75μm pixel.

Percutaneous window chamber method for chronic intravital microscopy of sensor-tissue interactions.

PubMed

Koschwanez, Heidi E; Klitzman, Bruce; Reichert, W Monty

2008-11-01

A dorsal, two-sided skin-fold window chamber model was employed previously by Gough in glucose sensor research to characterize poorly understood physiological factors affecting sensor performance. We have extended this work by developing a percutaneous one-sided window chamber model for the rodent dorsum that offers both a larger subcutaneous area and a less restrictive tissue space than previous animal models. A surgical procedure for implanting a sensor into the subcutis beneath an acrylic window (15 mm diameter) is presented. Methods to quantify changes in the microvascular network and red blood cell perfusion around the sensors using noninvasive intravital microscopy and laser Doppler flowmetry are described. The feasibility of combining interstitial glucose monitoring from an implanted sensor with intravital fluorescence microscopy was explored using a bolus injection of fluorescein and dextrose to observe real-time mass transport of a small molecule at the sensor-tissue interface. The percutaneous window chamber provides an excellent model for assessing the influence of different sensor modifications, such as surface morphologies, on neovascularization using real-time monitoring of the microvascular network and tissue perfusion. However, the tissue response to an implanted sensor was variable, and some sensors migrated entirely out of the field of view and could not be observed adequately. A percutaneous optical window provides direct, real-time images of the development and dynamics of microvascular networks, microvessel patency, and fibrotic encapsulation at the tissue-sensor interface. Additionally, observing microvessels following combined bolus injections of a fluorescent dye and glucose in the local sensor environment demonstrated a valuable technique to visualize mass transport at the sensor surface.

Crosslinked basement membrane-based coatings enhance glucose sensor function and continuous glucose monitoring in vivo.

PubMed

Klueh, Ulrike; Ludzinska, Izabela; Czajkowski, Caroline; Qiao, Yi; Kreutzer, Donald L

2018-01-01

Overcoming sensor-induced tissue reactions is an essential element of achieving successful continuous glucose monitoring (CGM) in the management of diabetes, particularly when used in closed loop technology. Recently, we demonstrated that basement membrane (BM)-based glucose sensor coatings significantly reduced tissue reactions at sites of device implantation. However, the biocompatible BM-based biohydrogel sensor coating rapidly degraded over a less than a 3-week period, which effectively eliminated the protective sensor coating. In an effort to increase the stability and effectiveness of the BM coating, we evaluated the impact of crosslinking BM utilizing glutaraldehyde as a crosslinking agent, designated as X-Cultrex. Sensor performance (nonrecalibrated) was evaluated for the impact of these X-Cultrex coatings in vitro and in vivo. Sensor performance was assessed over a 28-day time period in a murine CGM model and expressed as mean absolute relative difference (MARD) values. Tissue reactivity of Cultrex-coated, X-Cultrex-coated, and uncoated glucose sensors was evaluated over a 28-day time period in vivo using standard histological techniques. These studies demonstrated that X-Cultrex-based sensor coatings had no effect on glucose sensor function in vitro. In vivo, glucose sensor performance was significantly enhanced following X-Cultrex coating throughout the 28-day study. Histological evaluations of X-Cultrex-treated sensors demonstrated significantly less tissue reactivity when compared to uncoated sensors. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 7-16, 2018. © 2017 Wiley Periodicals, Inc.

Modeling and characterization of supercapacitors for wireless sensor network applications

NASA Astrophysics Data System (ADS)

Zhang, Ying; Yang, Hengzhao

A simple circuit model is developed to describe supercapacitor behavior, which uses two resistor-capacitor branches with different time constants to characterize the charging and redistribution processes, and a variable leakage resistance to characterize the self-discharge process. The parameter values of a supercapacitor can be determined by a charging-redistribution experiment and a self-discharge experiment. The modeling and characterization procedures are illustrated using a 22F supercapacitor. The accuracy of the model is compared with that of other models often used in power electronics applications. The results show that the proposed model has better accuracy in characterizing the self-discharge process while maintaining similar performance as other models during charging and redistribution processes. Additionally, the proposed model is evaluated in a simplified energy storage system for self-powered wireless sensors. The model performance is compared with that of a commonly used energy recursive equation (ERE) model. The results demonstrate that the proposed model can predict the evolution profile of voltage across the supercapacitor more accurately than the ERE model, and therefore provides a better alternative for supporting research on storage system design and power management for wireless sensor networks.

Verify MesoNAM Performance

NASA Technical Reports Server (NTRS)

Bauman, William H., III

2010-01-01

The AMU conducted an objective analysis of the MesoNAM forecasts compared to observed values from sensors at specified KSC/CCAFS wind towers by calculating the following statistics to verify the performance of the model: 1) Bias (mean difference), 2) Standard deviation of Bias, 3) Root Mean Square Error (RMSE), and 4) Hypothesis test for Bias = O. The 45 WS LWOs use the MesoNAM to support launch weather operations. However, the actual performance of the model at KSC and CCAFS had not been measured objectively. The analysis compared the MesoNAM forecast winds, temperature and dew point to the observed values from the sensors on wind towers. The data were stratified by tower sensor, month and onshore/offshore wind direction based on the orientation of the coastline to each tower's location. The model's performance statistics were then calculated for each wind tower based on sensor height and model initialization time. The period of record for the data used in this task was based on the operational start of the current MesoNAM in mid-August 2006 and so the task began with the first full month of data, September 2006, through May 2010. The analysis of model performance indicated: a) The accuracy decreased as the forecast valid time from the model initialization increased, b) There was a diurnal signal in T with a cool bias during the late night and a warm bias during the afternoon, c) There was a diurnal signal in Td with a low bias during the afternoon and a high bias during the late night, and d) The model parameters at each vertical level most closely matched the observed parameters at heights closest to those vertical levels. The AMU developed a GUI that consists of a multi-level drop-down menu written in JavaScript embedded within the HTML code. This tool allows the LWO to easily and efficiently navigate among the charts and spreadsheet files containing the model performance statistics. The objective statistics give the LWOs knowledge of the model's strengths and weaknesses and the GUI allows quick access to the data which will result in improved forecasts for operations.

Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks

PubMed Central

Wang, Tian; Wu, Qun; Wen, Sheng; Cai, Yiqiao; Tian, Hui; Chen, Yonghong; Wang, Baowei

2017-01-01

WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator’s mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost. PMID:28098748

Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements.

PubMed

McLeod, Adam; Bochniewicz, Elaine M; Lum, Peter S; Holley, Rahsaan J; Emmer, Geoff; Dromerick, Alexander W

2016-02-01

To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking. Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users. Rehabilitation hospital training apartment. Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18). Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials. Percent correct classification. With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly. We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

PubMed

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-12-12

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

PubMed Central

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-01-01

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

Modeling and analysis of hybrid pixel detector deficiencies for scientific applications

NASA Astrophysics Data System (ADS)

Fahim, Farah; Deptuch, Grzegorz W.; Hoff, James R.; Mohseni, Hooman

2015-08-01

Semiconductor hybrid pixel detectors often consist of a pixellated sensor layer bump bonded to a matching pixelated readout integrated circuit (ROIC). The sensor can range from high resistivity Si to III-V materials, whereas a Si CMOS process is typically used to manufacture the ROIC. Independent, device physics and electronic design automation (EDA) tools are used to determine sensor characteristics and verify functional performance of ROICs respectively with significantly different solvers. Some physics solvers provide the capability of transferring data to the EDA tool. However, single pixel transient simulations are either not feasible due to convergence difficulties or are prohibitively long. A simplified sensor model, which includes a current pulse in parallel with detector equivalent capacitor, is often used; even then, spice type top-level (entire array) simulations range from days to weeks. In order to analyze detector deficiencies for a particular scientific application, accurately defined transient behavioral models of all the functional blocks are required. Furthermore, various simulations, such as transient, noise, Monte Carlo, inter-pixel effects, etc. of the entire array need to be performed within a reasonable time frame without trading off accuracy. The sensor and the analog front-end can be modeling using a real number modeling language, as complex mathematical functions or detailed data can be saved to text files, for further top-level digital simulations. Parasitically aware digital timing is extracted in a standard delay format (sdf) from the pixel digital back-end layout as well as the periphery of the ROIC. For any given input, detector level worst-case and best-case simulations are performed using a Verilog simulation environment to determine the output. Each top-level transient simulation takes no more than 10-15 minutes. The impact of changing key parameters such as sensor Poissonian shot noise, analog front-end bandwidth, jitter due to clock distribution etc. can be accurately analyzed to determine ROIC architectural viability and bottlenecks. Hence the impact of the detector parameters on the scientific application can be studied.

Data-driven modeling, control and tools for cyber-physical energy systems

NASA Astrophysics Data System (ADS)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building's facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn's campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.

Decoupling Principle Analysis and Development of a Parallel Three-Dimensional Force Sensor

PubMed Central

Zhao, Yanzhi; Jiao, Leihao; Weng, Dacheng; Zhang, Dan; Zheng, Rencheng

2016-01-01

In the development of the multi-dimensional force sensor, dimension coupling is the ubiquitous factor restricting the improvement of the measurement accuracy. To effectively reduce the influence of dimension coupling on the parallel multi-dimensional force sensor, a novel parallel three-dimensional force sensor is proposed using a mechanical decoupling principle, and the influence of the friction on dimension coupling is effectively reduced by making the friction rolling instead of sliding friction. In this paper, the mathematical model is established by combining with the structure model of the parallel three-dimensional force sensor, and the modeling and analysis of mechanical decoupling are carried out. The coupling degree (ε) of the designed sensor is defined and calculated, and the calculation results show that the mechanical decoupling parallel structure of the sensor possesses good decoupling performance. A prototype of the parallel three-dimensional force sensor was developed, and FEM analysis was carried out. The load calibration and data acquisition experiment system are built, and then calibration experiments were done. According to the calibration experiments, the measurement accuracy is less than 2.86% and the coupling accuracy is less than 3.02%. The experimental results show that the sensor system possesses high measuring accuracy, which provides a basis for the applied research of the parallel multi-dimensional force sensor. PMID:27649194

Application of historical mobility testing to sensor-based robotic performance

NASA Astrophysics Data System (ADS)

Willoughby, William E.; Jones, Randolph A.; Mason, George L.; Shoop, Sally A.; Lever, James H.

2006-05-01

The USA Engineer Research and Development Center (ERDC) has conducted on-/off-road experimental field testing with full-sized and scale-model military vehicles for more than fifty years. Some 4000 acres of local terrain are available for tailored field evaluations or verification/validation of future robotic designs in a variety of climatic regimes. Field testing and data collection procedures, as well as techniques for quantifying terrain in engineering terms, have been developed and refined into algorithms and models for predicting vehicle-terrain interactions and resulting forces or speeds of military-sized vehicles. Based on recent experiments with Matilda, Talon, and Pacbot, these predictive capabilities appear to be relevant to most robotic systems currently in development. Utilization of current testing capabilities with sensor-based vehicle drivers, or use of the procedures for terrain quantification from sensor data, would immediately apply some fifty years of historical knowledge to the development, refinement, and implementation of future robotic systems. Additionally, translation of sensor-collected terrain data into engineering terms would allow assessment of robotic performance a priori deployment of the actual system and ensure maximum system performance in the theater of operation.

The Effects of Sensor Performance as Modeled by Signal Detection Theory on the Performance of Reinforcement Learning in a Target Acquisition Task

NASA Astrophysics Data System (ADS)

Quirion, Nate

Unmanned Aerial Systems (UASs) today are fulfilling more roles than ever before. There is a general push to have these systems feature more advanced autonomous capabilities in the near future. To achieve autonomous behavior requires some unique approaches to control and decision making. More advanced versions of these approaches are able to adapt their own behavior and examine their past experiences to increase their future mission performance. To achieve adaptive behavior and decision making capabilities this study used Reinforcement Learning algorithms. In this research the effects of sensor performance, as modeled through Signal Detection Theory (SDT), on the ability of RL algorithms to accomplish a target localization task are examined. Three levels of sensor sensitivity are simulated and compared to the results of the same system using a perfect sensor. To accomplish the target localization task, a hierarchical architecture used two distinct agents. A simulated human operator is assumed to be a perfect decision maker, and is used in the system feedback. An evaluation of the system is performed using multiple metrics, including episodic reward curves and the time taken to locate all targets. Statistical analyses are employed to detect significant differences in the comparison of steady-state behavior of different systems.

Biomimetic Models for An Ecological Approach to Massively-Deployed Sensor Networks

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2005-01-01

Promises of ubiquitous control of the physical environment by massively-deployed wireless sensor networks open avenues for new applications that will redefine the way we live and work. Due to small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors ubiquitous throughout our environment working in concert. Recent research has concentrated on developing techniques for performing relatively simple tasks with minimal energy expense, assuming some form of centralized control. Unfortunately, centralized control is not conducive to parallel activities and does not scale to massive size networks. Execution of simple tasks in sparse networks will not lead to the sophisticated applications predicted. We propose a new way of looking at massively-deployed sensor networks, motivated by lessons learned from the way biological ecosystems are organized. We demonstrate that in such a model, fully distributed data aggregation can be performed in a scalable fashion in massively deployed sensor networks, where motes operate on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects. We show that such architectures may be used to facilitate communication and synchronization in a fault-tolerant manner, while balancing workload and required energy expenditure throughout the network.

Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things

PubMed Central

Bagula, Antoine; Abidoye, Ademola Philip; Zodi, Guy-Alain Lusilao

2015-01-01

Current generation wireless sensor routing algorithms and protocols have been designed based on a myopic routing approach, where the motes are assumed to have the same sensing and communication capabilities. Myopic routing is not a natural fit for the IoT, as it may lead to energy imbalance and subsequent short-lived sensor networks, routing the sensor readings over the most service-intensive sensor nodes, while leaving the least active nodes idle. This paper revisits the issue of energy efficiency in sensor networks to propose a clustering model where sensor devices’ service delivery is mapped into an energy awareness model, used to design a clustering algorithm that finds service-aware clustering (SAC) configurations in IoT settings. The performance evaluation reveals the relative energy efficiency of the proposed SAC algorithm compared to related routing algorithms in terms of energy consumption, the sensor nodes’ life span and its traffic engineering efficiency in terms of throughput and delay. These include the well-known low energy adaptive clustering hierarchy (LEACH) and LEACH-centralized (LEACH-C) algorithms, as well as the most recent algorithms, such as DECSA and MOCRN. PMID:26703619

Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things.

PubMed

Bagula, Antoine; Abidoye, Ademola Philip; Zodi, Guy-Alain Lusilao

2015-12-23

Current generation wireless sensor routing algorithms and protocols have been designed based on a myopic routing approach, where the motes are assumed to have the same sensing and communication capabilities. Myopic routing is not a natural fit for the IoT, as it may lead to energy imbalance and subsequent short-lived sensor networks, routing the sensor readings over the most service-intensive sensor nodes, while leaving the least active nodes idle. This paper revisits the issue of energy efficiency in sensor networks to propose a clustering model where sensor devices' service delivery is mapped into an energy awareness model, used to design a clustering algorithm that finds service-aware clustering (SAC) configurations in IoT settings. The performance evaluation reveals the relative energy efficiency of the proposed SAC algorithm compared to related routing algorithms in terms of energy consumption, the sensor nodes' life span and its traffic engineering efficiency in terms of throughput and delay. These include the well-known low energy adaptive clustering hierarchy (LEACH) and LEACH-centralized (LEACH-C) algorithms, as well as the most recent algorithms, such as DECSA and MOCRN.

Grain shape influence on semiconducting metal oxide based gas sensor performance: modeling versus experiment.

PubMed

Rebholz, Julia; Bonanati, Peter; Weimar, Udo; Barsan, Nicolae

2014-06-01

A model for sensing with semiconducting metal oxide (SMOX)-based gas sensors was developed which takes the effect of the shape of the grains in the sensing layers into account. Its validity is limited to materials in which the grains of the SMOX sensing layer are large enough to have an undepleted bulk region (large grains). This means that in all experimental conditions, the SMOX properties ensure that the influence of surface phenomena is not extended to the whole grain. The model takes the surface chemistry and its impact on the electrical properties of the sensing material into consideration. In this way, it relates the sensor signal--defined as the relative change of the sensor's conductance--directly to the concentration of the target gas and also exhibits meaningful chemical parameters, such as the type of reactive oxygen species, the reaction constants, and the concentration of adsorption sites. The validity of the model is confirmed experimentally by applying it to data gathered by measuring homemade sensors in relevant conditions.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Hydrogen Safety Sensor Performance and Use Gap Analysis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buttner, William J; Burgess, Robert M; Schmidt, Kara

Hydrogen sensors are recognized as an important technology for facilitating the safe implementation of hydrogen as an alternative fuel, and there are numerous reports of a sensor alarm successfully preventing a potentially serious event. However, gaps in sensor metrological specifications, as well as in their performance for some applications, exist.The U.S. Department of Energy (DOE) Fuel Cell Technology Office published a short list of critical gaps in the 2007 and 2012 multiyear project plans; more detailed gap analyses were independently performed by the JRC and NREL. There have been, however, some significant advances in sensor technologies since these assessments, includingmore » the commercial availability of hydrogen sensors with fast response times (t90 less than 1 s, which had been an elusive DOE target since 2007), improved robustness to chemical poisons, improved selectivity, and improved lifetime and stability. These improvements, however, have not been universal and typically pertain to select platforms or models. Moreover, as hydrogen markets grow and new applications are being explored, more demands will be imposed on sensor performance. The hydrogen sensor laboratories at NREL and JRC are currently updating the hydrogen safety sensor gap analysis through direct interaction with international stakeholders in the hydrogen community, especially end-users. NREL and the JRC are currently organizing a series of workshops (in Europe and the U.S.) with sensor developers, end-users, and other stakeholders in 2017 to identify technology gaps and to develop a path forward to address them. One workshop is scheduled for May 10 in Brussels, Belgium at the Headquarters of the Fuel Cell and Hydrogen Joint Undertaking. A second workshop is planned at the National Renewable Energy Laboratory in Golden, CO, USA. This presentation will review improvements in sensor technologies in the past 5 to 10 years, identify gaps in sensor performance and use requirements, and identify potential research strategies to address the gaps. The presentation will also summarize the outcomes of the Hydrogen Sensors Workshops.« less

Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error

NASA Astrophysics Data System (ADS)

El-Diasty, M.; El-Rabbany, A.; Pagiatakis, S.

2007-11-01

We examine the effect of varying the temperature points on MEMS inertial sensors' noise models using Allan variance and least-squares spectral analysis (LSSA). Allan variance is a method of representing root-mean-square random drift error as a function of averaging times. LSSA is an alternative to the classical Fourier methods and has been applied successfully by a number of researchers in the study of the noise characteristics of experimental series. Static data sets are collected at different temperature points using two MEMS-based IMUs, namely MotionPakII and Crossbow AHRS300CC. The performance of the two MEMS inertial sensors is predicted from the Allan variance estimation results at different temperature points and the LSSA is used to study the noise characteristics and define the sensors' stochastic model parameters. It is shown that the stochastic characteristics of MEMS-based inertial sensors can be identified using Allan variance estimation and LSSA and the sensors' stochastic model parameters are temperature dependent. Also, the Kaiser window FIR low-pass filter is used to investigate the effect of de-noising stage on the stochastic model. It is shown that the stochastic model is also dependent on the chosen cut-off frequency.

A hybrid electronically scanned pressure module for cryogenic environments

NASA Technical Reports Server (NTRS)

Chapman, J. J.; Hopson, P., Jr.; Kruse, N.

1995-01-01

Pressure is one of the most important parameters measured when testing models in wind tunnels. For models tested in the cryogenic environment of the National Transonic Facility at NASA Langley Research Center, the technique of utilizing commercially available multichannel pressure modules inside the models is difficult due to the small internal volume of the models and the requirement of keeping the pressure transducer modules within an acceptable temperature range well above the -173 degrees C tunnel temperature. A prototype multichannel pressure transducer module has been designed and fabricated with stable, repeatable sensors and materials optimized for reliable performance in the cryogenic environment. The module has 16 single crystal silicon piezoresistive pressure sensors electrostatically bonded to a metalized Pyrex substrate for sensing the wind tunnel model pressures. An integral temperature sensor mounted on each silicon micromachined pressure sensor senses real-time temperature fluctuations to within 0.1 degrees C to correct for thermally induced non-random sensor drift. The data presented here are from a prototype sensor module tested in the 0.3 M cryogenic tunnel and thermal equilibrium conditions in an environmental chamber which approximates the thermal environment (-173 degrees C to +60 degrees C) of the National Transonic Facility.

Affordable and personalized lighting using inverse modeling and virtual sensors

NASA Astrophysics Data System (ADS)

Basu, Chandrayee; Chen, Benjamin; Richards, Jacob; Dhinakaran, Aparna; Agogino, Alice; Martin, Rodney

2014-03-01

Wireless sensor networks (WSN) have great potential to enable personalized intelligent lighting systems while reducing building energy use by 50%-70%. As a result WSN systems are being increasingly integrated in state-ofart intelligent lighting systems. In the future these systems will enable participation of lighting loads as ancillary services. However, such systems can be expensive to install and lack the plug-and-play quality necessary for user-friendly commissioning. In this paper we present an integrated system of wireless sensor platforms and modeling software to enable affordable and user-friendly intelligent lighting. It requires ⇠ 60% fewer sensor deployments compared to current commercial systems. Reduction in sensor deployments has been achieved by optimally replacing the actual photo-sensors with real-time discrete predictive inverse models. Spatially sparse and clustered sub-hourly photo-sensor data captured by the WSN platforms are used to develop and validate a piece-wise linear regression of indoor light distribution. This deterministic data-driven model accounts for sky conditions and solar position. The optimal placement of photo-sensors is performed iteratively to achieve the best predictability of the light field desired for indoor lighting control. Using two weeks of daylight and artificial light training data acquired at the Sustainability Base at NASA Ames, the model was able to predict the light level at seven monitored workstations with 80%-95% accuracy. We estimate that 10% adoption of this intelligent wireless sensor system in commercial buildings could save 0.2-0.25 quads BTU of energy nationwide.

A sensor simulation framework for the testing and evaluation of external hazard monitors and integrated alerting and notification functions

NASA Astrophysics Data System (ADS)

Uijt de Haag, Maarten; Venable, Kyle; Bezawada, Rajesh; Adami, Tony; Vadlamani, Ananth K.

2009-05-01

This paper discusses a sensor simulator/synthesizer framework that can be used to test and evaluate various sensor integration strategies for the implementation of an External Hazard Monitor (EHM) and Integrated Alerting and Notification (IAN) function as part of NASA's Integrated Intelligent Flight Deck (IIFD) project. The IIFD project under the NASA's Aviation Safety program "pursues technologies related to the flight deck that ensure crew workload and situational awareness are both safely optimized and adapted to the future operational environment as envisioned by NextGen." Within the simulation framework, various inputs to the IIFD and its subsystems, the EHM and IAN, are simulated, synthesized from actual collected data, or played back from actual flight test sensor data. Sensors and avionics included in this framework are TCAS, ADS-B, Forward-Looking Infrared, Vision cameras, GPS, Inertial navigators, EGPWS, Laser Detection and Ranging sensors, altimeters, communication links with ATC, and weather radar. The framework is implemented in Simulink, a modeling language developed by The Mathworks. This modeling language allows for test and evaluation of various sensor and communication link configurations as well as the inclusion of feedback from the pilot on the performance of the aircraft. Specifically, this paper addresses the architecture of the simulator, the sensor model interfaces, the timing and database (environment) aspects of the sensor models, the user interface of the modeling environment, and the various avionics implementations.

IoGET: Internet of Geophysical and Environmental Things

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mudunuru, Maruti Kumar

The objective of this project is to provide novel and fast reduced-order models for onboard computation at sensor nodes for real-time analysis. The approach will require that LANL perform high-fidelity numerical simulations, construct simple reduced-order models (ROMs) using machine learning and signal processing algorithms, and use real-time data analysis for ROMs and compressive sensing at sensor nodes.

Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. PMID:25608211

Modeling of 1.5 μm range gated imaging for small surface vessel identification

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Steinvall, Ove; Elmquist, Magnus; Karlsson, Kjell

2010-10-01

Within the framework of the NATO group (NATO SET-132/RTG-72) on imaging ladars, a test was performed to collect simultaneous multi-mode LADAR signatures of maritime objects entering and leaving San Diego Harbor. Beside ladars, passive sensors were also employed during the test which occurred during April 2009 from Point Loma and the harbor in San Diego. This paper will report on 1.5 μm gated imaging on a number of small civilian surface vessels with the aim to present human perception experimental results and comparisons with sensor performance models developed by US Army RDECOM CERDEC NVESD. We use controlled human perception tests to measure target identification performance and compare the experimental results with model predictions.

How to pass a sensor acceptance test: using the gap between acceptance criteria and operational performance

NASA Astrophysics Data System (ADS)

Bijl, Piet

2016-10-01

When acquiring a new imaging system and operational task performance is a critical factor for success, it is necessary to specify minimum acceptance requirements that need to be met using a sensor performance model and/or performance tests. Currently, there exist a variety of models and test from different origin (defense, security, road safety, optometry) and they all do different predictions. This study reviews a number of frequently used methods and shows the effects that small changes in procedure or threshold criteria can have on the outcome of a test. For example, a system may meet the acceptance requirements but not satisfy the needs for the operational task, or the choice of test may determine the rank order of candidate sensors. The goal of the paper is to make people aware of the pitfalls associated with the acquisition process, by i) illustrating potential tricks to have a system accepted that is actually not suited for the operational task, and ii) providing tips to avoid this unwanted situation.

Consistent Evolution of Software Artifacts and Non-Functional Models

DTIC Science & Technology

2014-11-14

induce bad software performance)? 15. SUBJECT TERMS EOARD, Nano particles, Photo-Acoustic Sensors, Model-Driven Engineering ( MDE ), Software Performance...Università degli Studi dell’Aquila, Via Vetoio, 67100 L’Aquila, Italy Email: vittorio.cortellessa@univaq.it Web : http: // www. di. univaq. it/ cortelle/ Phone...Model-Driven Engineering ( MDE ), Software Performance Engineering (SPE), Change Propagation, Performance Antipatterns. For sake of readability of the

Development of a validation model for the defense meteorological satellite program's special sensor microwave imager

NASA Technical Reports Server (NTRS)

Swift, C. T.; Goodberlet, M. A.; Wilkerson, J. C.

1990-01-01

The Defence Meteorological Space Program's (DMSP) Special Sensor Microwave/Imager (SSM/I), an operational wind speed algorithm was developed. The algorithm is based on the D-matrix approach which seeks a linear relationship between measured SSM/I brightness temperatures and environmental parameters. D-matrix performance was validated by comparing algorithm derived wind speeds with near-simultaneous and co-located measurements made by off-shore ocean buoys. Other topics include error budget modeling, alternate wind speed algorithms, and D-matrix performance with one or more inoperative SSM/I channels.

Range estimation of passive infrared targets through the atmosphere

NASA Astrophysics Data System (ADS)

Cho, Hoonkyung; Chun, Joohwan; Seo, Doochun; Choi, Seokweon

2013-04-01

Target range estimation is traditionally based on radar and active sonar systems in modern combat systems. However, jamming signals tremendously degrade the performance of such active sensor devices. We introduce a simple target range estimation method and the fundamental limits of the proposed method based on the atmosphere propagation model. Since passive infrared (IR) sensors measure IR signals radiating from objects in different wavelengths, this method has robustness against electromagnetic jamming. The measured target radiance of each wavelength at the IR sensor depends on the emissive properties of target material and various attenuation factors (i.e., the distance between sensor and target and atmosphere environment parameters). MODTRAN is a tool that models atmospheric propagation of electromagnetic radiation. Based on the results from MODTRAN and atmosphere propagation-based modeling, the target range can be estimated. To analyze the proposed method's performance statistically, we use maximum likelihood estimation (MLE) and evaluate the Cramer-Rao lower bound (CRLB) via the probability density function of measured radiance. We also compare CRLB and the variance of MLE using Monte-Carlo simulation.

Structure Optimization of a Grain Impact Piezoelectric Sensor and Its Application for Monitoring Separation Losses on Tangential-Axial Combine Harvesters

PubMed Central

Liang, Zhenwei; Li, Yaoming; Zhao, Zhan; Xu, Lizhang

2015-01-01

Grain separation losses is a key parameter to weigh the performance of combine harvesters, and also a dominant factor for automatically adjusting their major working parameters. The traditional separation losses monitoring method mainly rely on manual efforts, which require a high labor intensity. With recent advancements in sensor technology, electronics and computational processing power, this paper presents an indirect method for monitoring grain separation losses in tangential-axial combine harvesters in real-time. Firstly, we developed a mathematical monitoring model based on detailed comparative data analysis of different feeding quantities. Then, we developed a grain impact piezoelectric sensor utilizing a YT-5 piezoelectric ceramic as the sensing element, and a signal process circuit designed according to differences in voltage amplitude and rise time of collision signals. To improve the sensor performance, theoretical analysis was performed from a structural vibration point of view, and the optimal sensor structural has been selected. Grain collide experiments have shown that the sensor performance was greatly improved. Finally, we installed the sensor on a tangential-longitudinal axial combine harvester, and grain separation losses monitoring experiments were carried out in North China, which results have shown that the monitoring method was feasible, and the biggest measurement relative error was 4.63% when harvesting rice. PMID:25594592

Structure optimization of a grain impact piezoelectric sensor and its application for monitoring separation losses on tangential-axial combine harvesters.

PubMed

Liang, Zhenwei; Li, Yaoming; Zhao, Zhan; Xu, Lizhang

2015-01-14

Grain separation losses is a key parameter to weigh the performance of combine harvesters, and also a dominant factor for automatically adjusting their major working parameters. The traditional separation losses monitoring method mainly rely on manual efforts, which require a high labor intensity. With recent advancements in sensor technology, electronics and computational processing power, this paper presents an indirect method for monitoring grain separation losses in tangential-axial combine harvesters in real-time. Firstly, we developed a mathematical monitoring model based on detailed comparative data analysis of different feeding quantities. Then, we developed a grain impact piezoelectric sensor utilizing a YT-5 piezoelectric ceramic as the sensing element, and a signal process circuit designed according to differences in voltage amplitude and rise time of collision signals. To improve the sensor performance, theoretical analysis was performed from a structural vibration point of view, and the optimal sensor structural has been selected. Grain collide experiments have shown that the sensor performance was greatly improved. Finally, we installed the sensor on a tangential-longitudinal axial combine harvester, and grain separation losses monitoring experiments were carried out in North China, which results have shown that the monitoring method was feasible, and the biggest measurement relative error was 4.63% when harvesting rice.

Generic Helicopter-Based Testbed for Surface Terrain Imaging Sensors

NASA Technical Reports Server (NTRS)

Alexander, James; Goldberg, Hannah; Montgomery, James; Spiers, Gary; Liebe, Carl; Johnson, Andrew; Gromov, Konstantin; Konefat, Edward; Lam, Raymond; Meras, Patrick

2008-01-01

To be certain that a candidate sensor system will perform as expected during missions, we have developed a field test system and have executed test flights with a helicopter-mounted sensor platform over desert terrains, which simulate Lunar features. A key advantage to this approach is that different sensors can be tested and characterized in an environment relevant to the flight needs prior to flight. Testing the various sensors required the development of a field test system, including an instrument to validate the truth of the sensor system under test. The field test system was designed to be flexible enough to cover the test needs of many sensors (lidar, radar, cameras) that require an aerial test platform, including helicopters, airplanes, unmanned aerial vehicles (UAV), or balloons. To validate the performance of the sensor under test, the dynamics of the test platform must be known with sufficient accuracy to provide accurate models for input into algorithm development. The test system provides support equipment to measure the dynamics of the field test sensor platform, and allow computation of the truth position, velocity, attitude, and time.

New atmospheric sensor analysis study

NASA Technical Reports Server (NTRS)

Parker, K. G.

1989-01-01

The functional capabilities of the ESAD Research Computing Facility are discussed. The system is used in processing atmospheric measurements which are used in the evaluation of sensor performance, conducting design-concept simulation studies, and also in modeling the physical and dynamical nature of atmospheric processes. The results may then be evaluated to furnish inputs into the final design specifications for new space sensors intended for future Spacelab, Space Station, and free-flying missions. In addition, data gathered from these missions may subsequently be analyzed to provide better understanding of requirements for numerical modeling of atmospheric phenomena.

Multisatellite attitude determination/optical aspect bias determination (MSAD/OABIAS) system description and operating guide. Volume 3: Operating guide

NASA Technical Reports Server (NTRS)

Joseph, M.; Keat, J.; Liu, K. S.; Plett, M. E.; Shear, M. A.; Shinohara, T.; Wertz, J. R.

1983-01-01

The Multisatellite Attitude Determination/Optical Aspect Bias Determination (MSAD/OABIAS) System, designed to determine spin axis orientation and biases in the alignment or performance of optical or infrared horizon sensors and Sun sensors used for spacecraft attitude determination, is described. MSAD/OABIAS uses any combination of eight observation models to process data from a single onboard horizon sensor and Sun sensor to determine simultaneously the two components of the attitude of the spacecraft, the initial phase of the Sun sensor, the spin rate, seven sensor biases, and the orbital in-track error associated with the spacecraft ephemeris information supplied to the system. In addition, the MSAD/OABIAS system provides a data simulator for system and performance testing, an independent deterministic attitude system for preprocessing and independent testing of biases determined, and a multipurpose data prediction and comparison system.

Multisatellite attitude determination/optical aspect bias determination (MSAD/OABIAS) system description and operating guide. Volume 1: Introduction and analysis

NASA Technical Reports Server (NTRS)

Joseph, M.; Ket, J. E.; Liu, K. S.; Plett, M. E.; Shear, M. A.; Shinohara, T.; Wertz, J. R.

1983-01-01

The Multisatellite Attitude Determination/Optical Aspect Bias Determination (MSAD/OABIAS) System, designed to determine spin axis orientation and biases in the alignment or performance of optical or infrared horizon sensors and Sun sensors used for spacecraft attitude determination is described. MSAD/OABIAS uses any combination of eight observation models to process data from a single onboard horizon sensor and Sun sensor to determine simultaneously the two components of the attitude of the spacecraft, the initial phase of the Sun sensor, the spin rate, seven sensor biases, and the orbital in-track error associated with the spacecraft ephemeris information supplied to the system. In addition, the MSAD/OABIAS System provides a data simulator for system and performance testing, an independent deterministic attitude system for preprocessing and independent testing of biases determined, and a multipurpose data prediction and comparison system.

Modeling and analysis of pinhole occulter experiment: Initial study phase

NASA Technical Reports Server (NTRS)

Vandervoort, R. J.

1985-01-01

The feasibility of using a generic simulation, TREETOPS, to simulate the Pinhole/Occulter Facility (P/OF) to be tested on the space shuttle was demonstrated. The baseline control system was used to determine the pointing performance of the P/OF. The task included modeling the structure as a three body problem (shuttle-instrument pointing system- P/OP) including the flexibility of the 32 meter P/OF boom. Modeling of sensors, actuators, and control algorithms was also required. Detailed mathematical models for the structure, sensors, and actuators are presented, as well as the control algorithm and corresponding design procedure. Closed loop performance using this controller and computer listings for the simulator are also given.

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks

PubMed Central

Shi, Binbin; Wei, Wei; Wang, Yihuai; Shu, Wanneng

2016-01-01

In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network. PMID:27754405

A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks.

PubMed

Shi, Binbin; Wei, Wei; Wang, Yihuai; Shu, Wanneng

2016-10-14

In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network.

Space-based infrared scanning sensor LOS determination and calibration using star observation

NASA Astrophysics Data System (ADS)

Chen, Jun; Xu, Zhan; An, Wei; Deng, Xin-Pu; Yang, Jun-Gang

2015-10-01

This paper provides a novel methodology for removing sensor bias from a space based infrared (IR) system (SBIRS) through the use of stars detected in the background field of the sensor. Space based IR system uses the LOS (line of sight) of target for target location. LOS determination and calibration is the key precondition of accurate location and tracking of targets in Space based IR system and the LOS calibration of scanning sensor is one of the difficulties. The subsequent changes of sensor bias are not been taking into account in the conventional LOS determination and calibration process. Based on the analysis of the imaging process of scanning sensor, a theoretical model based on the estimation of bias angles using star observation is proposed. By establishing the process model of the bias angles and the observation model of stars, using an extended Kalman filter (EKF) to estimate the bias angles, and then calibrating the sensor LOS. Time domain simulations results indicate that the proposed method has a high precision and smooth performance for sensor LOS determination and calibration. The timeliness and precision of target tracking process in the space based infrared (IR) tracking system could be met with the proposed algorithm.

Course Keeping Control of an Autonomous Boat using Low Cost Sensors

NASA Astrophysics Data System (ADS)

Yu, Zhenyu; Bao, Xinping; Nonami, Kenzo

This paper discusses the course keeping control problem for a small autonomous boat using low cost sensors. Comparing with full scale ships, a small boat is more sensitive to the environmental disturbances because of its small size and low inertia. The sensors available in the boat are a low cost GPS and a rate gyro while the commonly used compass in ship control is absent. The combined effect from disturbance, poor accuracy and significant delay in GPS measurement makes it a challenging task to achieve good performance. In this paper, we propose a simple dynamic model for the boat's horizontal motion. The model is based on the Nomoto's model and can be seen as an extension to it. The model describes the dynamics between rudder deflection and the boat's velocity vector angle while Nomoto's model reveals that between rudder deflection and the boat's yaw angle. With the proposed model there is no need for a yaw sensor for control if the boat's moving direction can be measured. GPS is a convenient device for that job. Based on the derived model, we apply mixed H2/H∞ control method to design the controller. It can guarantee the robust stability, and as the same time it can optimize the performance in the sense of H2 norm. The experimental data show that the proposed approach is proved to be effective and useful.

Reliable before-fabrication forecasting of normal and touch mode MEMS capacitive pressure sensor: modeling and simulation

NASA Astrophysics Data System (ADS)

Jindal, Sumit Kumar; Mahajan, Ankush; Raghuwanshi, Sanjeev Kumar

2017-10-01

An analytical model and numerical simulation for the performance of MEMS capacitive pressure sensors in both normal and touch modes is required for expected behavior of the sensor prior to their fabrication. Obtaining such information should be based on a complete analysis of performance parameters such as deflection of diaphragm, change of capacitance when the diaphragm deflects, and sensitivity of the sensor. In the literature, limited work has been carried out on the above-stated issue; moreover, due to approximation factors of polynomials, a tolerance error cannot be overseen. Reliable before-fabrication forecasting requires exact mathematical calculation of the parameters involved. A second-order polynomial equation is calculated mathematically for key performance parameters of both modes. This eliminates the approximation factor, and an exact result can be studied, maintaining high accuracy. The elimination of approximation factors and an approach of exact results are based on a new design parameter (δ) that we propose. The design parameter gives an initial hint to the designers on how the sensor will behave once it is fabricated. The complete work is aided by extensive mathematical detailing of all the parameters involved. Next, we verified our claims using MATLAB® simulation. Since MATLAB® effectively provides the simulation theory for the design approach, more complicated finite element method is not used.

Chain-Based Communication in Cylindrical Underwater Wireless Sensor Networks

PubMed Central

Javaid, Nadeem; Jafri, Mohsin Raza; Khan, Zahoor Ali; Alrajeh, Nabil; Imran, Muhammad; Vasilakos, Athanasios

2015-01-01

Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, i.e., rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate. PMID:25658394

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor.

PubMed

Biagi, Lyvia; Ramkissoon, Charrise M; Facchinetti, Andrea; Leal, Yenny; Vehi, Josep

2017-06-12

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Minimum time search in uncertain dynamic domains with complex sensorial platforms.

PubMed

Lanillos, Pablo; Besada-Portas, Eva; Lopez-Orozco, Jose Antonio; de la Cruz, Jesus Manuel

2014-08-04

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models.

Minimum Time Search in Uncertain Dynamic Domains with Complex Sensorial Platforms

PubMed Central

Lanillos, Pablo; Besada-Portas, Eva; Lopez-Orozco, Jose Antonio; de la Cruz, Jesus Manuel

2014-01-01

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models. PMID:25093345

Separation of presampling and postsampling modulation transfer functions in infrared sensor systems

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Olson, Jeffrey T.; O'Shea, Patrick D.; Hodgkin, Van A.; Jacobs, Eddie L.

2006-05-01

New methods of measuring the modulation transfer function (MTF) of electro-optical sensor systems are investigated. These methods are designed to allow the separation and extraction of presampling and postsampling components from the total system MTF. The presampling MTF includes all the effects prior to the sampling stage of the imaging process, such as optical blur and detector shape. The postsampling MTF includes all the effects after sampling, such as interpolation filters and display characteristics. Simulation and laboratory measurements are used to assess the utility of these techniques. Knowledge of these components and inclusion into sensor models, such as the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate's NVThermIP, will allow more accurate modeling and complete characterization of sensor performance.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

PubMed

Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J

2018-01-01

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

Passive wireless sensor systems can recognize activites of daily living.

PubMed

Urwyler, Prabitha; Stucki, Reto; Muri, Rene; Mosimann, Urs P; Nef, Tobias

2015-08-01

The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.

System performance and modeling of a bioaerosol detection lidar sensor utilizing polarization diversity

NASA Astrophysics Data System (ADS)

Glennon, John J.; Nichols, Terry; Gatt, Phillip; Baynard, Tahllee; Marquardt, John H.; Vanderbeek, Richard G.

2009-05-01

The weaponization and dissemination of biological warfare agents (BWA) constitute a high threat to civilians and military personnel. An aerosol release, disseminated from a single point, can directly affect large areas and many people in a short time. Because of this threat real-time standoff detection of BWAs is a key requirement for national and military security. BWAs are a general class of material that can refer to spores, bacteria, toxins, or viruses. These bioaerosols have a tremendous size, shape, and chemical diversity that, at present, are not well characterized [1]. Lockheed Martin Coherent Technologies (LMCT) has developed a standoff lidar sensor with high sensitivity and robust discrimination capabilities with a size and ruggedness that is appropriate for military use. This technology utilizes multiwavelength backscatter polarization diversity to discriminate between biological threats and naturally occurring interferents such as dust, smoke, and pollen. The optical design and hardware selection of the system has been driven by performance modeling leading to an understanding of measured system sensitivity. Here we briefly discuss the challenges of standoff bioaerosol discrimination and the approach used by LMCT to overcome these challenges. We review the radiometric calculations involved in modeling direct-detection of a distributed aerosol target and methods for accurately estimating wavelength dependent plume backscatter coefficients. Key model parameters and their validation are discussed and outlined. Metrics for sensor sensitivity are defined, modeled, and compared directly to data taken at Dugway Proving Ground, UT in 2008. Sensor sensitivity is modeled to predict performance changes between day and night operation and in various challenging environmental conditions.

Additively Manufactured IN718 Components with Wirelessly Powered and Interrogated Embedded Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Attridge, Paul; Bajekal, Sanjay; Klecka, Michael

A methodology is described for embedding commercial-off-the-shelf sensors together with wireless communication and power circuit elements using direct laser metal sintered additively manufactured components. Physics based models of the additive manufacturing processes and sensor/wireless level performance models guided the design and embedment processes. A combination of cold spray deposition and laser engineered net shaping was used to fashion the transmitter/receiving elements and embed the sensors, thereby providing environmental protection and component robustness/survivability for harsh conditions. By design, this complement of analog and digital sensors were wirelessly powered and interrogated using a health and utilization monitoring system; enabling real-time, in situmore » prognostics and diagnostics.« less

Modeling of Current Consumption in 802.15.4/ZigBee Sensor Motes

PubMed Central

Casilari, Eduardo; Cano-García, Jose M.; Campos-Garrido, Gonzalo

2010-01-01

Battery consumption is a key aspect in the performance of wireless sensor networks. One of the most promising technologies for this type of networks is 802.15.4/ZigBee. This paper presents an empirical characterization of battery consumption in commercial 802.15.4/ZigBee motes. This characterization is based on the measurement of the current that is drained from the power source under different 802.15.4 communication operations. The measurements permit the definition of an analytical model to predict the maximum, minimum and mean expected battery lifetime of a sensor networking application as a function of the sensor duty cycle and the size of the sensed data. PMID:22219671

Modeling of current consumption in 802.15.4/ZigBee sensor motes.

PubMed

Casilari, Eduardo; Cano-García, Jose M; Campos-Garrido, Gonzalo

2010-01-01

Battery consumption is a key aspect in the performance of wireless sensor networks. One of the most promising technologies for this type of networks is 802.15.4/ZigBee. This paper presents an empirical characterization of battery consumption in commercial 802.15.4/ZigBee motes. This characterization is based on the measurement of the current that is drained from the power source under different 802.15.4 communication operations. The measurements permit the definition of an analytical model to predict the maximum, minimum and mean expected battery lifetime of a sensor networking application as a function of the sensor duty cycle and the size of the sensed data.

Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: robust virtual sensor design.

PubMed

Halim, Dunant; Cheng, Li; Su, Zhongqing

2011-03-01

The work was aimed to develop a robust virtual sensing design methodology for sensing and active control applications of vibro-acoustic systems. The proposed virtual sensor was designed to estimate a broadband acoustic interior sound pressure using structural sensors, with robustness against certain dynamic uncertainties occurring in an acoustic-structural coupled enclosure. A convex combination of Kalman sub-filters was used during the design, accommodating different sets of perturbed dynamic model of the vibro-acoustic enclosure. A minimax optimization problem was set up to determine an optimal convex combination of Kalman sub-filters, ensuring an optimal worst-case virtual sensing performance. The virtual sensing and active noise control performance was numerically investigated on a rectangular panel-cavity system. It was demonstrated that the proposed virtual sensor could accurately estimate the interior sound pressure, particularly the one dominated by cavity-controlled modes, by using a structural sensor. With such a virtual sensing technique, effective active noise control performance was also obtained even for the worst-case dynamics. © 2011 Acoustical Society of America

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Assessment of sensor performance

NASA Astrophysics Data System (ADS)

Waldmann, C.; Tamburri, M.; Prien, R. D.; Fietzek, P.

2010-02-01

There is an international commitment to develop a comprehensive, coordinated and sustained ocean observation system. However, a foundation for any observing, monitoring or research effort is effective and reliable in situ sensor technologies that accurately measure key environmental parameters. Ultimately, the data used for modelling efforts, management decisions and rapid responses to ocean hazards are only as good as the instruments that collect them. There is also a compelling need to develop and incorporate new or novel technologies to improve all aspects of existing observing systems and meet various emerging challenges. Assessment of Sensor Performance was a cross-cutting issues session at the international OceanSensors08 workshop in Warnemünde, Germany, which also has penetrated some of the papers published as a result of the workshop (Denuault, 2009; Kröger et al., 2009; Zielinski et al., 2009). The discussions were focused on how best to classify and validate the instruments required for effective and reliable ocean observations and research. The following is a summary of the discussions and conclusions drawn from this workshop, which specifically addresses the characterisation of sensor systems, technology readiness levels, verification of sensor performance and quality management of sensor systems.

Case studies for observation planning algorithm of a Japanese spaceborne sensor: Hyperspectral Imager Suite (HISUI)

NASA Astrophysics Data System (ADS)

Ogawa, Kenta; Konno, Yukiko; Yamamoto, Satoru; Matsunaga, Tsuneo; Tachikawa, Tetsushi; Komoda, Mako; Kashimura, Osamu; Rokugawa, Shuichi

2016-10-01

Hyperspectral Imager Suite (HISUI)[1] is a Japanese future spaceborne hyperspectral instrument being developed by Ministry of Economy, Trade, and Industry (METI) and will be delivered to ISS in 2018. In HISUI project, observation strategy is important especially for hyperspectral sensor, and relationship between the limitations of sensor operation and the planned observation scenarios have to be studied. We have developed concept of multiple algorithms approach. The concept is to use two (or more) algorithm models (Long Strip Model and Score Downfall Model) for selecting observing scenes from complex data acquisition requests with satisfactory of sensor constrains. We have tested the algorithm, and found that the performance of two models depends on remaining data acquisition requests, i.e. distribution score along with orbits. We conclude that the multiple algorithms approach will be make better collection plans for HISUI comparing with single fixed approach.

Ares I Scale Model Acoustic Tests Instrumentation for Acoustic and Pressure Measurements

NASA Technical Reports Server (NTRS)

Vargas, Magda B.; Counter, Douglas D.

2011-01-01

The Ares I Scale Model Acoustic Test (ASMAT) was a development test performed at the Marshall Space Flight Center (MSFC) East Test Area (ETA) Test Stand 116. The test article included a 5% scale Ares I vehicle model and tower mounted on the Mobile Launcher. Acoustic and pressure data were measured by approximately 200 instruments located throughout the test article. There were four primary ASMAT instrument suites: ignition overpressure (IOP), lift-off acoustics (LOA), ground acoustics (GA), and spatial correlation (SC). Each instrumentation suite incorporated different sensor models which were selected based upon measurement requirements. These requirements included the type of measurement, exposure to the environment, instrumentation check-outs and data acquisition. The sensors were attached to the test article using different mounts and brackets dependent upon the location of the sensor. This presentation addresses the observed effect of the sensors and mounts on the acoustic and pressure measurements.

Basement Membrane-Based Glucose Sensor Coatings Enhance Continuous Glucose Monitoring in Vivo.

PubMed

Klueh, Ulrike; Qiao, Yi; Czajkowski, Caroline; Ludzinska, Izabela; Antar, Omar; Kreutzer, Donald L

2015-08-25

Implantable glucose sensors demonstrate a rapid decline in function that is likely due to biofouling of the sensor. Previous efforts directed at overcoming this issue has generally focused on the use of synthetic polymer coatings, with little apparent effect in vivo, clearly a novel approach is required. We believe that the key to extending sensor life span in vivo is the development of biocompatible basement membrane (BM) based bio-hydrogels as coatings for glucose sensors. BM based bio-hydrogel sensor coatings were developed using purified BM preparations (ie, Cultrex from Trevigen Inc). Modified Abbott sensors were coated with Cultrex BM extracts. Sensor performance was evaluated for the impact of these coatings in vitro and in vivo in a continuous glucose monitoring (CGM) mouse model. In vivo sensor function was assessed over a 28-day time period expressed as mean absolute relative difference (MARD) values. Tissue reactivity of both Cultrex coated and uncoated glucose sensors was evaluated at 7, 14, 21 and 28 days post-sensor implantation with standard histological techniques. The data demonstrate that Cultrex-based sensor coatings had no effect on glucose sensor function in vitro. In vivo glucose sensor performance was enhanced following BM coating as determined by MARD analysis, particularly in weeks 2 and 3. In vivo studies also demonstrated that Cultrex coatings significantly decreased sensor-induced tissue reactions at the sensor implantation sites. Basement-membrane-based sensor coatings enhance glucose sensor function in vivo, by minimizing or preventing sensor-induced tissues reactions. © 2015 Diabetes Technology Society.

Autonomous Mission Operations for Sensor Webs

NASA Astrophysics Data System (ADS)

Underbrink, A.; Witt, K.; Stanley, J.; Mandl, D.

2008-12-01

We present interim results of a 2005 ROSES AIST project entitled, "Using Intelligent Agents to Form a Sensor Web for Autonomous Mission Operations", or SWAMO. The goal of the SWAMO project is to shift the control of spacecraft missions from a ground-based, centrally controlled architecture to a collaborative, distributed set of intelligent agents. The network of intelligent agents intends to reduce management requirements by utilizing model-based system prediction and autonomic model/agent collaboration. SWAMO agents are distributed throughout the Sensor Web environment, which may include multiple spacecraft, aircraft, ground systems, and ocean systems, as well as manned operations centers. The agents monitor and manage sensor platforms, Earth sensing systems, and Earth sensing models and processes. The SWAMO agents form a Sensor Web of agents via peer-to-peer coordination. Some of the intelligent agents are mobile and able to traverse between on-orbit and ground-based systems. Other agents in the network are responsible for encapsulating system models to perform prediction of future behavior of the modeled subsystems and components to which they are assigned. The software agents use semantic web technologies to enable improved information sharing among the operational entities of the Sensor Web. The semantics include ontological conceptualizations of the Sensor Web environment, plus conceptualizations of the SWAMO agents themselves. By conceptualizations of the agents, we mean knowledge of their state, operational capabilities, current operational capacities, Web Service search and discovery results, agent collaboration rules, etc. The need for ontological conceptualizations over the agents is to enable autonomous and autonomic operations of the Sensor Web. The SWAMO ontology enables automated decision making and responses to the dynamic Sensor Web environment and to end user science requests. The current ontology is compatible with Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) Sensor Model Language (SensorML) concepts and structures. The agents are currently deployed on the U.S. Naval Academy MidSTAR-1 satellite and are actively managing the power subsystem on-orbit without the need for human intervention.

Novel design and sensitivity analysis of displacement measurement system utilizing knife edge diffraction for nanopositioning stages.

PubMed

Lee, ChaBum; Lee, Sun-Kyu; Tarbutton, Joshua A

2014-09-01

This paper presents a novel design and sensitivity analysis of a knife edge-based optical displacement sensor that can be embedded with nanopositioning stages. The measurement system consists of a laser, two knife edge locations, two photodetectors, and axillary optics components in a simple configuration. The knife edge is installed on the stage parallel to its moving direction and two separated laser beams are incident on knife edges. While the stage is in motion, the direct transverse and diffracted light at each knife edge is superposed producing interference at the detector. The interference is measured with two photodetectors in a differential amplification configuration. The performance of the proposed sensor was mathematically modeled, and the effect of the optical and mechanical parameters, wavelength, beam diameter, distances from laser to knife edge to photodetector, and knife edge topography, on sensor outputs was investigated to obtain a novel analytical method to predict linearity and sensitivity. From the model, all parameters except for the beam diameter have a significant influence on measurement range and sensitivity of the proposed sensing system. To validate the model, two types of knife edges with different edge topography were used for the experiment. By utilizing a shorter wavelength, smaller sensor distance and higher edge quality increased measurement sensitivity can be obtained. The model was experimentally validated and the results showed a good agreement with the theoretically estimated results. This sensor is expected to be easily implemented into nanopositioning stage applications at a low cost and mathematical model introduced here can be used for design and performance estimation of the knife edge-based sensor as a tool.

Quantification of LiDAR measurement uncertainty through propagation of errors due to sensor sub-systems and terrain morphology

NASA Astrophysics Data System (ADS)

Goulden, T.; Hopkinson, C.

2013-12-01

The quantification of LiDAR sensor measurement uncertainty is important for evaluating the quality of derived DEM products, compiling risk assessment of management decisions based from LiDAR information, and enhancing LiDAR mission planning capabilities. Current quality assurance estimates of LiDAR measurement uncertainty are limited to post-survey empirical assessments or vendor estimates from commercial literature. Empirical evidence can provide valuable information for the performance of the sensor in validated areas; however, it cannot characterize the spatial distribution of measurement uncertainty throughout the extensive coverage of typical LiDAR surveys. Vendor advertised error estimates are often restricted to strict and optimal survey conditions, resulting in idealized values. Numerical modeling of individual pulse uncertainty provides an alternative method for estimating LiDAR measurement uncertainty. LiDAR measurement uncertainty is theoretically assumed to fall into three distinct categories, 1) sensor sub-system errors, 2) terrain influences, and 3) vegetative influences. This research details the procedures for numerical modeling of measurement uncertainty from the sensor sub-system (GPS, IMU, laser scanner, laser ranger) and terrain influences. Results show that errors tend to increase as the laser scan angle, altitude or laser beam incidence angle increase. An experimental survey over a flat and paved runway site, performed with an Optech ALTM 3100 sensor, showed an increase in modeled vertical errors of 5 cm, at a nadir scan orientation, to 8 cm at scan edges; for an aircraft altitude of 1200 m and half scan angle of 15°. In a survey with the same sensor, at a highly sloped glacial basin site absent of vegetation, modeled vertical errors reached over 2 m. Validation of error models within the glacial environment, over three separate flight lines, respectively showed 100%, 85%, and 75% of elevation residuals fell below error predictions. Future work in LiDAR sensor measurement uncertainty must focus on the development of vegetative error models to create more robust error prediction algorithms. To achieve this objective, comprehensive empirical exploratory analysis is recommended to relate vegetative parameters to observed errors.

Fiber Optic Bragg Grating Sensors for Thermographic Detection of Subsurface Anomalies

NASA Technical Reports Server (NTRS)

Allison, Sidney G.; Winfree, William P.; Wu, Meng-Chou

2009-01-01

Conventional thermography with an infrared imager has been shown to be an extremely viable technique for nondestructively detecting subsurface anomalies such as thickness variations due to corrosion. A recently developed technique using fiber optic sensors to measure temperature holds potential for performing similar inspections without requiring an infrared imager. The structure is heated using a heat source such as a quartz lamp with fiber Bragg grating (FBG) sensors at the surface of the structure to detect temperature. Investigated structures include a stainless steel plate with thickness variations simulated by small platelets attached to the back side using thermal grease. A relationship is shown between the FBG sensor thermal response and variations in material thickness. For comparison, finite element modeling was performed and found to agree closely with the fiber optic thermography results. This technique shows potential for applications where FBG sensors are already bonded to structures for Integrated Vehicle Health Monitoring (IVHM) strain measurements and can serve dual-use by also performing thermographic detection of subsurface anomalies.

Loss-compensation technique using a split-spectrum approach for optical fiber air-gap intensity-based sensors

NASA Astrophysics Data System (ADS)

Wang, Anbo; Miller, Mark S.; Gunther, Michael F.; Murphy, Kent A.; Claus, Richard O.

1993-03-01

A self-referencing technique compensating for fiber losses and source fluctuations in air-gap intensity-based optical fiber sensors is described and demonstrated. A resolution of 0.007 micron has been obtained over a measurement range of 0-250 microns for an intensity-based displacement sensor using this referencing technique. The sensor is shown to have minimal sensitivity to fiber bending losses and variations in the LED input power. A theoretical model for evaluation of step-index multimode optical fiber splice is proposed. The performance of the sensor as a displacement sensor agrees well with the theoretical analysis.

Using Neural Networks for Sensor Validation

NASA Technical Reports Server (NTRS)

Mattern, Duane L.; Jaw, Link C.; Guo, Ten-Huei; Graham, Ronald; McCoy, William

1998-01-01

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.

Target acquisition modeling over the exact optical path: extending the EOSTAR TDA with the TOD sensor performance model

NASA Astrophysics Data System (ADS)

Dijk, J.; Bijl, P.; Oppeneer, M.; ten Hove, R. J. M.; van Iersel, M.

2017-10-01

The Electro-Optical Signal Transmission and Ranging (EOSTAR) model is an image-based Tactical Decision Aid (TDA) for thermal imaging systems (MWIR/LWIR) developed for a sea environment with an extensive atmosphere model. The Triangle Orientation Discrimination (TOD) Target Acquisition model calculates the sensor and signal processing effects on a set of input triangle test pattern images, judges their orientation using humans or a Human Visual System (HVS) model and derives the system image quality and operational field performance from the correctness of the responses. Combination of the TOD model and EOSTAR, basically provides the possibility to model Target Acquisition (TA) performance over the exact path from scene to observer. In this method ship representative TOD test patterns are placed at the position of the real target, subsequently the combined effects of the environment (atmosphere, background, etc.), sensor and signal processing on the image are calculated using EOSTAR and finally the results are judged by humans. The thresholds are converted into Detection-Recognition-Identification (DRI) ranges of the real target. In experiments is shown that combination of the TOD model and the EOSTAR model is indeed possible. The resulting images look natural and provide insight in the possibilities of combining the two models. The TOD observation task can be done well by humans, and the measured TOD is consistent with analytical TOD predictions for the same camera that was modeled in the ECOMOS project.

In-flight measurement of the National Oceanic and Atmospheric Administration (NOAA)-10 static Earth sensor error

NASA Technical Reports Server (NTRS)

Harvie, E.; Filla, O.; Baker, D.

1993-01-01

Analysis performed in the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) measures error in the static Earth sensor onboard the National Oceanic and Atmospheric Administration (NOAA)-10 spacecraft using flight data. Errors are computed as the difference between Earth sensor pitch and roll angle telemetry and reference pitch and roll attitude histories propagated by gyros. The flight data error determination illustrates the effect on horizon sensing of systemic variation in the Earth infrared (IR) horizon radiance with latitude and season, as well as the effect of anomalies in the global IR radiance. Results of the analysis provide a comparison between static Earth sensor flight performance and that of scanning Earth sensors studied previously in the GSFC/FDD. The results also provide a baseline for evaluating various models of the static Earth sensor. Representative days from the NOAA-10 mission indicate the extent of uniformity and consistency over time of the global IR horizon. A unique aspect of the NOAA-10 analysis is the correlation of flight data errors with independent radiometric measurements of stratospheric temperature. The determination of the NOAA-10 static Earth sensor error contributes to realistic performance expectations for missions to be equipped with similar sensors.

Modeling and analysis of pinhole occulter experiment

NASA Technical Reports Server (NTRS)

Ring, J. R.

1986-01-01

The objectives were to improve pointing control system implementation by converting the dynamic compensator from a continuous domain representation to a discrete one; to determine pointing stability sensitivites to sensor and actuator errors by adding sensor and actuator error models to treetops and by developing an error budget for meeting pointing stability requirements; and to determine pointing performance for alternate mounting bases (space station for example).

Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis.

PubMed

Jensen, Dan B; Hogeveen, Henk; De Vries, Albert

2016-09-01

Rapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes' theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of clinical mastitis, and time windows make comparisons across studies difficult. We found the DLM/NBC method to be a flexible method for combining multiple sensor and nonsensor data sources to predict clinical mastitis and accommodate missing observations. Further research is needed before practical implementation is possible. In particular, the performance of our method needs to be improved in the first 2 wk of lactation. The DLM method produces forecasts that are based on continuously estimated multivariate normal distributions, which makes forecasts and forecast errors easy to interpret, and new sensors can easily be added. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Optical flows method for lightweight agile remote sensor design and instrumentation

NASA Astrophysics Data System (ADS)

Wang, Chong; Xing, Fei; Wang, Hongjian; You, Zheng

2013-08-01

Lightweight agile remote sensors have become one type of the most important payloads and were widely utilized in space reconnaissance and resource survey. These imaging sensors are designed to obtain the high spatial, temporary and spectral resolution imageries. Key techniques in instrumentation include flexible maneuvering, advanced imaging control algorithms and integrative measuring techniques, which are closely correlative or even acting as the bottle-necks for each other. Therefore, mutual restrictive problems must be solved and optimized. Optical flow is the critical model which to be fully represented in the information transferring as well as radiation energy flowing in dynamic imaging. For agile sensors, especially with wide-field-of view, imaging optical flows may distort and deviate seriously when they perform large angle attitude maneuvering imaging. The phenomena are mainly attributed to the geometrical characteristics of the three-dimensional earth surface as well as the coupled effects due to the complicated relative motion between the sensor and scene. Under this circumstance, velocity fields distribute nonlinearly, the imageries may badly be smeared or probably the geometrical structures are changed since the image velocity matching errors are not having been eliminated perfectly. In this paper, precise imaging optical flow model is established for agile remote sensors, for which optical flows evolving is factorized by two forms, which respectively due to translational movement and image shape changing. Moreover, base on that, agile remote sensors instrumentation was investigated. The main techniques which concern optical flow modeling include integrative design with lightweight star sensors along with micro inertial measurement units and corresponding data fusion, the assemblies of focal plane layout and control, imageries post processing for agile remote sensors etc. Some experiments show that the optical analyzing method is effective to eliminate the limitations for the performance indexes, and succeeded to be applied for integrative system design. Finally, a principle prototype of agile remote sensor designed by the method is discussed.

Influence of time and length size feature selections for human activity sequences recognition.

PubMed

Fang, Hongqing; Chen, Long; Srinivasan, Raghavendiran

2014-01-01

In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances. © 2013 ISA Published by ISA All rights reserved.

Improved tactile resonance sensor for robotic assisted surgery

NASA Astrophysics Data System (ADS)

Oliva Uribe, David; Schoukens, Johan; Stroop, Ralf

2018-01-01

This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. The results of the variance analysis are discussed showing that ability of the proposed tactile sensor to perform high quality measurements.

Design optimization of an ironless inductive position sensor for the LHC collimators

NASA Astrophysics Data System (ADS)

Danisi, A.; Masi, A.; Losito, R.; Perriard, Y.

2013-09-01

The Ironless Inductive Position Sensor (I2PS) is an air-cored displacement sensor which has been conceived to be totally immune to external DC/slowly-varying magnetic fields. It can thus be used as a valid alternative to Linear Variable Differential Transformers (LVDTs), which can show a position error in magnetic environments. In addition, since it retains the excellent properties of LVDTs, the I2PS can be used in harsh environments, such as nuclear plants, plasma control and particle accelerators. This paper focuses on the design optimization of the sensor, considering the CERN LHC Collimators as application. In particular, the optimization comes after a complete review of the electromagnetic and thermal modeling of the sensor, as well as the proper choice of the reading technique. The design optimization stage is firmly based on these preliminary steps. Therefore, the paper summarises the sensor's complete development, from its modeling to its actual implementation. A set of experimental measurements demonstrates the sensor's performances to be those expected in the design phase.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fahim, Farah; Deptuch, Grzegorz W.; Hoff, James R.

Semiconductor hybrid pixel detectors often consist of a pixellated sensor layer bump bonded to a matching pixelated readout integrated circuit (ROIC). The sensor can range from high resistivity Si to III-V materials, whereas a Si CMOS process is typically used to manufacture the ROIC. Independent, device physics and electronic design automation (EDA) tools are used to determine sensor characteristics and verify functional performance of ROICs respectively with significantly different solvers. Some physics solvers provide the capability of transferring data to the EDA tool. However, single pixel transient simulations are either not feasible due to convergence difficulties or are prohibitively long.more » A simplified sensor model, which includes a current pulse in parallel with detector equivalent capacitor, is often used; even then, spice type top-level (entire array) simulations range from days to weeks. In order to analyze detector deficiencies for a particular scientific application, accurately defined transient behavioral models of all the functional blocks are required. Furthermore, various simulations, such as transient, noise, Monte Carlo, inter-pixel effects, etc. of the entire array need to be performed within a reasonable time frame without trading off accuracy. The sensor and the analog front-end can be modeling using a real number modeling language, as complex mathematical functions or detailed data can be saved to text files, for further top-level digital simulations. Parasitically aware digital timing is extracted in a standard delay format (sdf) from the pixel digital back-end layout as well as the periphery of the ROIC. For any given input, detector level worst-case and best-case simulations are performed using a Verilog simulation environment to determine the output. Each top-level transient simulation takes no more than 10-15 minutes. The impact of changing key parameters such as sensor Poissonian shot noise, analog front-end bandwidth, jitter due to clock distribution etc. can be accurately analyzed to determine ROIC architectural viability and bottlenecks. Hence the impact of the detector parameters on the scientific application can be studied.« less

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-01-01

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm. PMID:28587084

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors.

PubMed

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-05-25

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ -connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

Fibre optic sensors for temperature and pressure monitoring in laser ablation: experiments on ex-vivo animal model

NASA Astrophysics Data System (ADS)

Tosi, Daniele; Saccomandi, Paola; Schena, Emiliano; Duraibabu, Dinesh B.; Poeggel, Sven; Adilzhan, Abzal; Aliakhmet, Kamilla; Silvestri, Sergio; Leen, Gabriel; Lewis, Elfed

2016-05-01

Optical fibre sensors have been applied to perform biophysical measurement in ex-vivo laser ablation (LA), on pancreas animal phantom. Experiments have been performed using Fibre Bragg Grating (FBG) arrays for spatially resolved temperature detection, and an all-glass Extrinsic Fabry-Perot Interferometer (EFPI) for pressure measurement. Results using a Nd:YAG laser source as ablation device, are presented and discussed.

Joint Sensing/Sampling Optimization for Surface Drifting Mine Detection with High-Resolution Drift Model

DTIC Science & Technology

2012-09-01

as potential tools for large area detection coverage while being moderately inexpensive (Wettergren, Performance of Search via Track - Before - Detect for...via Track - Before - Detect for Distribute 34 Sensor Networks, 2008). These statements highlight three specific needs to further sensor network research...Bay hydrography. Journal of Marine Systems, 12, 221–236. Wettergren, T. A. (2008). Performance of search via track - before - detect for distributed

Soft sensor modeling based on variable partition ensemble method for nonlinear batch processes

NASA Astrophysics Data System (ADS)

Wang, Li; Chen, Xiangguang; Yang, Kai; Jin, Huaiping

2017-01-01

Batch processes are always characterized by nonlinear and system uncertain properties, therefore, the conventional single model may be ill-suited. A local learning strategy soft sensor based on variable partition ensemble method is developed for the quality prediction of nonlinear and non-Gaussian batch processes. A set of input variable sets are obtained by bootstrapping and PMI criterion. Then, multiple local GPR models are developed based on each local input variable set. When a new test data is coming, the posterior probability of each best performance local model is estimated based on Bayesian inference and used to combine these local GPR models to get the final prediction result. The proposed soft sensor is demonstrated by applying to an industrial fed-batch chlortetracycline fermentation process.

Effective application of optical sensing technology for sustainable liquid level sensing and rainfall measurement

NASA Astrophysics Data System (ADS)

Afzal, Muhammad Hassan Bin

2015-05-01

Rainfall measurement is performed on regular basis to facilitate effectively the weather stations and local inhabitants. Different types of rain gauges are available with different measuring principle for rainfall measurement. In this research work, a novel optical rain sensor is designed, which precisely calculate the rainfall level according to rainfall intensity. This proposed optical rain sensor model introduced in this paper, which is basically designed for remote sensing of rainfall and it designated as R-ORMS (Remote Optical Rainfall Measurement sensor). This sensor is combination of some improved method of tipping bucket rain gauge and most of the optical hydreon rain sensor's principle. This optical sensor can detect the starting time and ending time of rain, rain intensity and rainfall level. An infrared beam from Light Emitting Diode (LED) through powerful convex lens can accurately determines the diameter of each rain drops by total internal reflection principle. Calculations of these accumulative results determine the rain intensity and rainfall level. Accurate rainfall level is determined by internal optical LED based sensor which is embedded in bucket wall. This internal sensor is also following the total internal reflection (TIR) principle and the Fresnel's law. This is an entirely novel design of optical sensing principle based rain sensor and also suitable for remote sensing rainfall level. The performance of this proposed sensor has been comprehensively compared with other sensors with similar attributes and it showed better and sustainable result. Future related works have been proposed at the end of this paper, to provide improved and enhanced performance of proposed novel rain sensor.

Phase aided 3D imaging and modeling: dedicated systems and case studies

NASA Astrophysics Data System (ADS)

Yin, Yongkai; He, Dong; Liu, Zeyi; Liu, Xiaoli; Peng, Xiang

2014-05-01

Dedicated prototype systems for 3D imaging and modeling (3DIM) are presented. The 3D imaging systems are based on the principle of phase-aided active stereo, which have been developed in our laboratory over the past few years. The reported 3D imaging prototypes range from single 3D sensor to a kind of optical measurement network composed of multiple node 3D-sensors. To enable these 3D imaging systems, we briefly discuss the corresponding calibration techniques for both single sensor and multi-sensor optical measurement network, allowing good performance of the 3DIM prototype systems in terms of measurement accuracy and repeatability. Furthermore, two case studies including the generation of high quality color model of movable cultural heritage and photo booth from body scanning are presented to demonstrate our approach.

Response mechanism for surface acoustic wave gas sensors based on surface-adsorption.

PubMed

Liu, Jiansheng; Lu, Yanyan

2014-04-16

A theoretical model is established to describe the response mechanism of surface acoustic wave (SAW) gas sensors based on physical adsorption on the detector surface. Wohljent's method is utilized to describe the relationship of sensor output (frequency shift of SAW oscillator) and the mass loaded on the detector surface. The Brunauer-Emmett-Teller (BET) formula and its improved form are introduced to depict the adsorption behavior of gas on the detector surface. By combining the two methods, we obtain a theoretical model for the response mechanism of SAW gas sensors. By using a commercial SAW gas chromatography (GC) analyzer, an experiment is performed to measure the frequency shifts caused by different concentration of dimethyl methylphosphonate (DMMP). The parameters in the model are given by fitting the experimental results and the theoretical curve agrees well with the experimental data.

Study of detecting mechanism of carbon nanotubes gas sensor based on multi-stable stochastic resonance model.

PubMed

Jingyi, Zhu

2015-01-01

The detecting mechanism of carbon nanotubes gas sensor based on multi-stable stochastic resonance (MSR) model was studied in this paper. A numerically stimulating model based on MSR was established. And gas-ionizing experiment by adding electronic white noise to induce 1.65 MHz periodic component in the carbon nanotubes gas sensor was performed. It was found that the signal-to-noise ratio (SNR) spectrum displayed 2 maximal values, which accorded to the change of the broken-line potential function. The experimental results of gas-ionizing experiment demonstrated that periodic component of 1.65 MHz had multiple MSR phenomena, which was in accordance with the numerical stimulation results. In this way, the numerical stimulation method provides an innovative method for the detecting mechanism research of carbon nanotubes gas sensor.

Package analysis of 3D-printed piezoresistive strain gauge sensors

NASA Astrophysics Data System (ADS)

Das, Sumit Kumar; Baptist, Joshua R.; Sahasrabuddhe, Ritvij; Lee, Woo H.; Popa, Dan O.

2016-05-01

Poly(3,4-ethyle- nedioxythiophene)-poly(styrenesulfonate) or PEDOT:PSS is a flexible polymer which exhibits piezo-resistive properties when subjected to structural deformation. PEDOT:PSS has a high conductivity and thermal stability which makes it an ideal candidate for use as a pressure sensor. Applications of this technology includes whole body robot skin that can increase the safety and physical collaboration of robots in close proximity to humans. In this paper, we present a finite element model of strain gauge touch sensors which have been 3D-printed onto Kapton and silicone substrates using Electro-Hydro-Dynamic ink-jetting. Simulations of the piezoresistive and structural model for the entire packaged sensor was carried out using COMSOLR , and compared with experimental results for validation. The model will be useful in designing future robot skin with predictable performances.

Battlefield decision aid for acoustical ground sensors with interface to meteorological data sources

NASA Astrophysics Data System (ADS)

Wilson, D. Keith; Noble, John M.; VanAartsen, Bruce H.; Szeto, Gregory L.

2001-08-01

The performance of acoustical ground sensors depends heavily on the local atmospheric and terrain conditions. This paper describes a prototype physics-based decision aid, called the Acoustic Battlefield Aid (ABFA), for predicting these environ-mental effects. ABFA integrates advanced models for acoustic propagation, atmospheric structure, and array signal process-ing into a convenient graphical user interface. The propagation calculations are performed in the frequency domain on user-definable target spectra. The solution method involves a parabolic approximation to the wave equation combined with a ter-rain diffraction model. Sensor performance is characterized with Cramer-Rao lower bounds (CRLBs). The CRLB calcula-tions include randomization of signal energy and wavefront orientation resulting from atmospheric turbulence. Available performance characterizations include signal-to-noise ratio, probability of detection, direction-finding accuracy for isolated receiving arrays, and location-finding accuracy for networked receiving arrays. A suite of integrated tools allows users to create new target descriptions from standard digitized audio files and to design new sensor array layouts. These tools option-ally interface with the ARL Database/Automatic Target Recognition (ATR) Laboratory, providing access to an extensive library of target signatures. ABFA also includes a Java-based capability for network access of near real-time data from sur-face weather stations or forecasts from the Army's Integrated Meteorological System. As an example, the detection footprint of an acoustical sensor, as it evolves over a 13-hour period, is calculated.

Preliminary design of land displacement-optical fiber sensor and analysis of observation during laboratory and field test

NASA Astrophysics Data System (ADS)

Bayuwati, Dwi; Waluyo, Tomi B.; Widiyatmoko, Bambang

2015-01-01

An optical fiber optic sensor for detecting land displacement is discussed in this paper. The sensor system consists of a laser at wavelength 1.3 um, optical fiber coupler, optical fiber as sensor and light transmitting media, PIN photodiodedetector system, data logger and personal computer. Sensor was made from a curved optical fiber with diameter 35 mm, which will be changed into a heart-shape fiber if it is pulled. The heart-shape fiber sensor is the modification of the earlier displacement fiber sensor model which was in an ellipse form. Light to and from the optical fiber sensor was transmitted into a length of a multi core, single mode optical fiber cable. The scheme of the optical displacement sensor system has been described here. Characterization in the laboratory has been done by applying a series of pulling mechanism, on the heart-shape fiber sensor; which represents the land displacement process. Characterization in the field was carried out by mounting the sensor system on a scaled-down model of a land slope and artificially reproducing the landslide process using a steady-flow of artificial rainfall as the trigger. The voltage sensor output was recorded during the artificial landslide process. The displacement occurence can be indicated from the declining of the sensor signal received by the detector while the reference signal is steady. Characterization in the laboratory resulted in the performance of the optical fiber land displacement, namely, sensitivity 0.027(mV/mV)/mm, resolution 0.37 mm and measurement range 30 mm; compared with earlier optical fiber sensor performance with similar sensitivity and resolution which works only in 8 mm displacement range. Based on the experiment of landslides simulation in the field, we can define a critical condition in the real situation before landslides occurence to take any measures to prevent more casualties and losses.

Spatial frequency dependence of target signature for infrared performance modeling

NASA Astrophysics Data System (ADS)

Du Bosq, Todd; Olson, Jeffrey

2011-05-01

The standard model used to describe the performance of infrared imagers is the U.S. Army imaging system target acquisition model, based on the targeting task performance metric. The model is characterized by the resolution and sensitivity of the sensor as well as the contrast and task difficulty of the target set. The contrast of the target is defined as a spatial average contrast. The model treats the contrast of the target set as spatially white, or constant, over the bandlimit of the sensor. Previous experiments have shown that this assumption is valid under normal conditions and typical target sets. However, outside of these conditions, the treatment of target signature can become the limiting factor affecting model performance accuracy. This paper examines target signature more carefully. The spatial frequency dependence of the standard U.S. Army RDECOM CERDEC Night Vision 12 and 8 tracked vehicle target sets is described. The results of human perception experiments are modeled and evaluated using both frequency dependent and independent target signature definitions. Finally the function of task difficulty and its relationship to a target set is discussed.

Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model.

PubMed

Rose, Michael; Curtze, Carolin; O'Sullivan, Joseph; El-Gohary, Mahmoud; Crawford, Dennis; Friess, Darin; Brady, Jacqueline M

2017-12-01

To develop a model using wearable inertial sensors to assess the performance of orthopaedic residents while performing a diagnostic knee arthroscopy. Fourteen subjects performed a diagnostic arthroscopy on a cadaveric right knee. Participants were divided into novices (5 postgraduate year 3 residents), intermediates (5 postgraduate year 4 residents), and experts (4 faculty) based on experience. Arm movement data were collected by inertial measurement units (Opal sensors) by securing 2 sensors to each upper extremity (dorsal forearm and lateral arm) and 2 sensors to the trunk (sternum and lumbar spine). Kinematics of the elbow and shoulder joints were calculated from the inertial data by biomechanical modeling based on a sequence of links connected by joints. Range of motion required to complete the procedure was calculated for each group. Histograms were used to compare the distribution of joint positions for an expert, intermediate, and novice. For both the right and left upper extremities, skill level corresponded well with shoulder abduction-adduction and elbow prono-supination. Novices required on average 17.2° more motion in the right shoulder abduction-adduction plane than experts to complete the diagnostic arthroscopy (P = .03). For right elbow prono-supination (probe hand), novices required on average 23.7° more motion than experts to complete the procedure (P = .03). Histogram data showed novices had markedly more variability in shoulder abduction-adduction and elbow prono-supination compared with the other groups. Our data show wearable inertial sensors can measure joint kinematics during diagnostic knee arthroscopy. Range-of-motion data in the shoulder and elbow correlated inversely with arthroscopic experience. Motion pattern-based analysis shows promise as a metric of resident skill acquisition and development in arthroscopy. Wearable inertial sensors show promise as metrics of arthroscopic skill acquisition among residents. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Enhanced performance of microfluidic soft pressure sensors with embedded solid microspheres

NASA Astrophysics Data System (ADS)

Shin, Hee-Sup; Ryu, Jaiyoung; Majidi, Carmel; Park, Yong-Lae

2016-02-01

The cross-sectional geometry of an embedded microchannel influences the electromechanical response of a soft microfluidic sensor to applied surface pressure. When a pressure is exerted on the surface of the sensor deforming the soft structure, the cross-sectional area of the embedded channel filled with a conductive fluid decreases, increasing the channel’s electrical resistance. This electromechanical coupling can be tuned by adding solid microspheres into the channel. In order to determine the influence of microspheres, we use both analytic and computational methods to predict the pressure responses of soft microfluidic sensors with two different channel cross-sections: a square and an equilateral triangular. The analytical models were derived from contact mechanics in which microspheres were regarded as spherical indenters, and finite element analysis (FEA) was used for simulation. For experimental validation, sensor samples with the two different channel cross-sections were prepared and tested. For comparison, the sensor samples were tested both with and without microspheres. All three results from the analytical models, the FEA simulations, and the experiments showed reasonable agreement confirming that the multi-material soft structure significantly improved its pressure response in terms of both linearity and sensitivity. The embedded solid particles enhanced the performance of soft sensors while maintaining their flexible and stretchable mechanical characteristic. We also provide analytical and experimental analyses of hysteresis of microfluidic soft sensors considering a resistive force to the shape recovery of the polymer structure by the embedded viscous fluid.

Modeling and experimental characterization of a new piezoelectric sensor for low-amplitude vibration measurement

NASA Astrophysics Data System (ADS)

Hou, X. Y.; Koh, C. G.; Kuang, K. S. C.; Lee, W. H.

2017-07-01

This paper investigates the capability of a novel piezoelectric sensor for low-frequency and low-amplitude vibration measurement. The proposed design effectively amplifies the input acceleration via two amplifying mechanisms and thus eliminates the use of the external charge amplifier or conditioning amplifier typically employed for measurement system. The sensor is also self-powered, i.e. no external power unit is required. Consequently, wiring and electrical insulation for on-site measurement are considerably simpler. In addition, the design also greatly reduces the interference from rotational motion which often accompanies the translational acceleration to be measured. An analytical model is developed based on a set of piezoelectric constitutive equations and beam theory. Closed-form expression is derived to correlate sensor geometry and material properties with its dynamic performance. Experimental calibration is then carried out to validate the analytical model. After calibration, experiments are carried out to check the feasibility of the new sensor in structural vibration detection. From experimental results, it is concluded that the proposed sensor is suitable for measuring low-frequency and low-amplitude vibrations.

Performance Analysis for Lateral-Line-Inspired Sensor Arrays

DTIC Science & Technology

2011-06-01

found to affect numerous aspects of behavior including maneuvering in complex fluid environments, schooling, prey tracking, and environment mapping...190 5-29 Maps of the cost function for a reflected vortex model with an increasing array length but constant sensor spacing . The x at...length but constant sensor spacing . The x in each image denotes the true location of the vortex. The black lines correspond to level sets generated by the

Real-time sensor validation and fusion for distributed autonomous sensors

NASA Astrophysics Data System (ADS)

Yuan, Xiaojing; Li, Xiangshang; Buckles, Bill P.

2004-04-01

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Distributed Peer-to-Peer Target Tracking in Wireless Sensor Networks

PubMed Central

Wang, Xue; Wang, Sheng; Bi, Dao-Wei; Ma, Jun-Jie

2007-01-01

Target tracking is usually a challenging application for wireless sensor networks (WSNs) because it is always computation-intensive and requires real-time processing. This paper proposes a practical target tracking system based on the auto regressive moving average (ARMA) model in a distributed peer-to-peer (P2P) signal processing framework. In the proposed framework, wireless sensor nodes act as peers that perform target detection, feature extraction, classification and tracking, whereas target localization requires the collaboration between wireless sensor nodes for improving the accuracy and robustness. For carrying out target tracking under the constraints imposed by the limited capabilities of the wireless sensor nodes, some practically feasible algorithms, such as the ARMA model and the 2-D integer lifting wavelet transform, are adopted in single wireless sensor nodes due to their outstanding performance and light computational burden. Furthermore, a progressive multi-view localization algorithm is proposed in distributed P2P signal processing framework considering the tradeoff between the accuracy and energy consumption. Finally, a real world target tracking experiment is illustrated. Results from experimental implementations have demonstrated that the proposed target tracking system based on a distributed P2P signal processing framework can make efficient use of scarce energy and communication resources and achieve target tracking successfully.

A performance study of unmanned aerial vehicle-based sensor networks under cyber attack

NASA Astrophysics Data System (ADS)

Puchaty, Ethan M.

In UAV-based sensor networks, an emerging area of interest is the performance of these networks under cyber attack. This study seeks to evaluate the performance trade-offs from a System-of-Systems (SoS) perspective between various UAV communications architecture options in the context two missions: tracking ballistic missiles and tracking insurgents. An agent-based discrete event simulation is used to model a sensor communication network consisting of UAVs, military communications satellites, ground relay stations, and a mission control center. Network susceptibility to cyber attack is modeled with probabilistic failures and induced data variability, with performance metrics focusing on information availability, latency, and trustworthiness. Results demonstrated that using UAVs as routers increased network availability with a minimal latency penalty and communications satellite networks were best for long distance operations. Redundancy in the number of links between communication nodes helped mitigate cyber-caused link failures and add robustness in cases of induced data variability by an adversary. However, when failures were not independent, redundancy and UAV routing were detrimental in some cases to network performance. Sensitivity studies indicated that long cyber-caused downtimes and increasing failure dependencies resulted in build-ups of failures and caused significant degradations in network performance.

Game theoretic sensor management for target tracking

NASA Astrophysics Data System (ADS)

Shen, Dan; Chen, Genshe; Blasch, Erik; Pham, Khanh; Douville, Philip; Yang, Chun; Kadar, Ivan

2010-04-01

This paper develops and evaluates a game-theoretic approach to distributed sensor-network management for target tracking via sensor-based negotiation. We present a distributed sensor-based negotiation game model for sensor management for multi-sensor multi-target tacking situations. In our negotiation framework, each negotiation agent represents a sensor and each sensor maximizes their utility using a game approach. The greediness of each sensor is limited by the fact that the sensor-to-target assignment efficiency will decrease if too many sensor resources are assigned to a same target. It is similar to the market concept in real world, such as agreements between buyers and sellers in an auction market. Sensors are willing to switch targets so that they can obtain their highest utility and the most efficient way of applying their resources. Our sub-game perfect equilibrium-based negotiation strategies dynamically and distributedly assign sensors to targets. Numerical simulations are performed to demonstrate our sensor-based negotiation approach for distributed sensor management.

On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

PubMed Central

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered. PMID:22969386

In-fiber torsion sensor based on dual polarized Mach-Zehnder interference.

PubMed

Chen, Lei; Zhang, Wei-Gang; Wang, Li; Zhang, Hao; Sieg, Jonathan; Zhou, Quan; Zhang, Li-Yu; Wang, Biao; Yan, Tie-Yi

2014-12-29

This paper presents a novel optical fiber torsion sensor based on dual polarized Mach-Zehnder interference (DPMZI). Unlike the conventional fiber sensor, the proposed sensor is composed of a sensor part and a demodulator. The demodulator is made by a bared single mode fiber (SMF) loop, and the sensor part is a segment of a coated SMF placed before the loop. A mathematical model is proposed based on DPMZI mechanism and from the model when the sensor part is twisted, the E-field rotational angle will bring a quasi-linear impact on the resonance dip wavelength in their matched detecting range. A proof-of-concept experiment was performed to verify the theoretical prediction. From the experimental data, a sensitivity of -0.3703, -1.00962, and -0.59881 nm•m/rad is achieved with the determining range of 12.0936, 7.6959, and 10.4444 rad/m respectively. The sensor which is composed only of the SMF has the advantages of low insertion loss (~-2dB), healthy structure, low manufacture cost, and easy assembly and application.

On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition.

PubMed

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

Finite element model for MOI applications using A-V formulation

NASA Astrophysics Data System (ADS)

Xuan, L.; Shanker, B.; Udpa, L.; Shih, W.; Fitzpatrick, G.

2001-04-01

Magneto-optic imaging (MOI) is a relatively new sensor application of an extension of bubble memory technology to NDT and produce easy-to-interpret, real time analog images. MOI systems use a magneto-optic (MO) sensor to produce analog images of magnetic flux leakage from surface and subsurface defects. The instrument's capability in detecting the relatively weak magnetic fields associated with subsurface defects depends on the sensitivity of the magneto-optic sensor. The availability of a theoretical model that can simulate the MOI system performance is extremely important for optimization of the MOI sensor and hardware system. A nodal finite element model based on magnetic vector potential formulation has been developed for simulating MOI phenomenon. This model has been used for predicting the magnetic fields in simple test geometry with corrosion dome defects. In the case of test samples with multiple discontinuities, a more robust model using the magnetic vector potential Ā and electrical scalar potential V is required. In this paper, a finite element model based on A-V formulation is developed to model complex circumferential crack under aluminum rivets in dimpled countersink.

State estimation of spatio-temporal phenomena

NASA Astrophysics Data System (ADS)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm. The proposed algorithm outperforms the augmented two-stage Kalman Filter (ASKF) and the unbiased minimum-variance (UMV) algorithm are shown in several examples. Finally, we propose a framework to place multiple mobile sensors to optimize the long-term performance of KF in the estimation of the state of a PDE. The major challenges are that placing multiple sensors is an NP-hard problem, and the optimization problem is non-convex in general. In this dissertation, first, we construct an ROM using RPOD* algorithm, and then reduce the feasible sensor locations into a subset using the ROM. The Information Space Receding Horizon Control (I-RHC) approach and a modified Monte Carlo Tree Search (MCTS) approach are applied to solve the sensor scheduling problem using the subset. Various applications have been provided to demonstrate the performance of the proposed approach.

Generic Sensor Modeling Using Pulse Method

NASA Technical Reports Server (NTRS)

Helder, Dennis L.; Choi, Taeyoung

2005-01-01

Recent development of high spatial resolution satellites such as IKONOS, Quickbird and Orbview enable observation of the Earth's surface with sub-meter resolution. Compared to the 30 meter resolution of Landsat 5 TM, the amount of information in the output image was dramatically increased. In this era of high spatial resolution, the estimation of spatial quality of images is gaining attention. Historically, the Modulation Transfer Function (MTF) concept has been used to estimate an imaging system's spatial quality. Sometimes classified by target shapes, various methods were developed in laboratory environment utilizing sinusoidal inputs, periodic bar patterns and narrow slits. On-orbit sensor MTF estimation was performed on 30-meter GSD Landsat4 Thematic Mapper (TM) data from the bridge pulse target as a pulse input . Because of a high resolution sensor s small Ground Sampling Distance (GSD), reasonably sized man-made edge, pulse, and impulse targets can be deployed on a uniform grassy area with accurate control of ground targets using tarps and convex mirrors. All the previous work cited calculated MTF without testing the MTF estimator's performance. In previous report, a numerical generic sensor model had been developed to simulate and improve the performance of on-orbit MTF estimating techniques. Results from the previous sensor modeling report that have been incorporated into standard MTF estimation work include Fermi edge detection and the newly developed 4th order modified Savitzky-Golay (MSG) interpolation technique. Noise sensitivity had been studied by performing simulations on known noise sources and a sensor model. Extensive investigation was done to characterize multi-resolution ground noise. Finally, angle simulation was tested by using synthetic pulse targets with angles from 2 to 15 degrees, several brightness levels, and different noise levels from both ground targets and imaging system. As a continuing research activity using the developed sensor model, this report was dedicated to MTF estimation via pulse input method characterization using the Fermi edge detection and 4th order MSG interpolation method. The relationship between pulse width and MTF value at Nyquist was studied including error detection and correction schemes. Pulse target angle sensitivity was studied by using synthetic targets angled from 2 to 12 degrees. In this report, from the ground and system noise simulation, a minimum SNR value was suggested for a stable MTF value at Nyquist for the pulse method. Target width error detection and adjustment technique based on a smooth transition of MTF profile is presented, which is specifically applicable only to the pulse method with 3 pixel wide targets.

Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-Efficient Wireless Sensor Networks.

PubMed

Al-Mekhlafi, Zeyad Ghaleb; Hanapi, Zurina Mohd; Othman, Mohamed; Zukarnain, Zuriati Ahmad

2017-01-01

Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, this paper proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25%. The results showed that the performance improved by 13% in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs.

Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-Efficient Wireless Sensor Networks

PubMed Central

Hanapi, Zurina Mohd; Othman, Mohamed; Zukarnain, Zuriati Ahmad

2017-01-01

Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, this paper proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25%. The results showed that the performance improved by 13% in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs. PMID:28056020

The optimal location of piezoelectric actuators and sensors for vibration control of plates

NASA Astrophysics Data System (ADS)

Kumar, K. Ramesh; Narayanan, S.

2007-12-01

This paper considers the optimal placement of collocated piezoelectric actuator-sensor pairs on a thin plate using a model-based linear quadratic regulator (LQR) controller. LQR performance is taken as objective for finding the optimal location of sensor-actuator pairs. The problem is formulated using the finite element method (FEM) as multi-input-multi-output (MIMO) model control. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem. A genetic algorithm (GA) is used to solve the zero-one optimization problem. Different classical control strategies like direct proportional feedback, constant-gain negative velocity feedback and the LQR optimal control scheme are applied to study the control effectiveness.

Basement Membrane-Based Glucose Sensor Coatings Enhance Continuous Glucose Monitoring in Vivo

PubMed Central

Klueh, Ulrike; Qiao, Yi; Czajkowski, Caroline; Ludzinska, Izabela; Antar, Omar; Kreutzer, Donald L.

2015-01-01

Background: Implantable glucose sensors demonstrate a rapid decline in function that is likely due to biofouling of the sensor. Previous efforts directed at overcoming this issue has generally focused on the use of synthetic polymer coatings, with little apparent effect in vivo, clearly a novel approach is required. We believe that the key to extending sensor life span in vivo is the development of biocompatible basement membrane (BM) based bio-hydrogels as coatings for glucose sensors. Method: BM based bio-hydrogel sensor coatings were developed using purified BM preparations (ie, Cultrex from Trevigen Inc). Modified Abbott sensors were coated with Cultrex BM extracts. Sensor performance was evaluated for the impact of these coatings in vitro and in vivo in a continuous glucose monitoring (CGM) mouse model. In vivo sensor function was assessed over a 28-day time period expressed as mean absolute relative difference (MARD) values. Tissue reactivity of both Cultrex coated and uncoated glucose sensors was evaluated at 7, 14, 21 and 28 days post–sensor implantation with standard histological techniques. Results: The data demonstrate that Cultrex-based sensor coatings had no effect on glucose sensor function in vitro. In vivo glucose sensor performance was enhanced following BM coating as determined by MARD analysis, particularly in weeks 2 and 3. In vivo studies also demonstrated that Cultrex coatings significantly decreased sensor-induced tissue reactions at the sensor implantation sites. Conclusion: Basement-membrane-based sensor coatings enhance glucose sensor function in vivo, by minimizing or preventing sensor-induced tissues reactions. PMID:26306494

Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis

PubMed Central

Goršič, Maja; Kamnik, Roman; Ambrožič, Luka; Vitiello, Nicola; Lefeber, Dirk; Pasquini, Guido; Munih, Marko

2014-01-01

This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training. PMID:24521944

A Soft Sensor-Based Three-Dimensional (3-D) Finger Motion Measurement System

PubMed Central

Park, Wookeun; Ro, Kyongkwan; Kim, Suin; Bae, Joonbum

2017-01-01

In this study, a soft sensor-based three-dimensional (3-D) finger motion measurement system is proposed. The sensors, made of the soft material Ecoflex, comprise embedded microchannels filled with a conductive liquid metal (EGaln). The superior elasticity, light weight, and sensitivity of soft sensors allows them to be embedded in environments in which conventional sensors cannot. Complicated finger joints, such as the carpometacarpal (CMC) joint of the thumb are modeled to specify the location of the sensors. Algorithms to decouple the signals from soft sensors are proposed to extract the pure flexion, extension, abduction, and adduction joint angles. The performance of the proposed system and algorithms are verified by comparison with a camera-based motion capture system. PMID:28241414

Modeling of low-finesse, extrinsic fiber optic Fabry-Perot white light interferometers

NASA Astrophysics Data System (ADS)

Ma, Cheng; Tian, Zhipeng; Wang, Anbo

2012-06-01

This article introduces an approach for modeling the fiber optic low-finesse extrinsic Fabry-Pérot Interferometers (EFPI), aiming to address signal processing problems in EFPI demodulation algorithms based on white light interferometry. The main goal is to seek physical interpretations to correlate the sensor spectrum with the interferometer geometry (most importantly, the optical path difference). Because the signal demodulation quality and reliability hinge heavily on the understanding of such relationships, the model sheds light on optimizing the sensor performance.

Evaluation of hazard and integrity monitor functions for integrated alerting and notification using a sensor simulation framework

NASA Astrophysics Data System (ADS)

Bezawada, Rajesh; Uijt de Haag, Maarten

2010-04-01

This paper discusses the results of an initial evaluation study of hazard and integrity monitor functions for use with integrated alerting and notification. The Hazard and Integrity Monitor (HIM) (i) allocates information sources within the Integrated Intelligent Flight Deck (IIFD) to required functionality (like conflict detection and avoidance) and determines required performance of these information sources as part of that function; (ii) monitors or evaluates the required performance of the individual information sources and performs consistency checks among various information sources; (iii) integrates the information to establish tracks of potential hazards that can be used for the conflict probes or conflict prediction for various time horizons including the 10, 5, 3, and <3 minutes used in our scenario; (iv) detects and assesses the class of the hazard and provide possible resolutions. The HIM monitors the operation-dependent performance parameters related to the potential hazards in a manner similar to the Required Navigation Performance (RNP). Various HIM concepts have been implemented and evaluated using a previously developed sensor simulator/synthesizer. Within the simulation framework, various inputs to the IIFD and its subsystems are simulated, synthesized from actual collected data, or played back from actual flight test sensor data. The framework and HIM functions are implemented in SimulinkR, a modeling language developed by The MathworksTM. This modeling language allows for test and evaluation of various sensor and communication link configurations as well as the inclusion of feedback from the pilot on the performance of the aircraft.

High-sensitivity high-selectivity detection of CWAs and TICs using tunable laser photoacoustic spectroscopy

NASA Astrophysics Data System (ADS)

Pushkarsky, Michael; Webber, Michael; Patel, C. Kumar N.

2005-03-01

We provide a general technique for evaluating the performance of an optical sensor for the detection of chemical warfare agents (CWAs) in realistic environments and present data from a simulation model based on a field deployed discretely tunable 13CO2 laser photoacoustic spectrometer (L-PAS). Results of our calculations show the sensor performance in terms of usable sensor sensitivity as a function of probability of false positives (PFP). The false positives arise from the presence of many other gases in the ambient air that could be interferents. Using the L-PAS as it exists today, we can achieve a detection threshold of about 4 ppb for the CWAs while maintaining a PFP of less than 1:106. Our simulation permits us to vary a number of parameters in the model to provide guidance for performance improvement. We find that by using a larger density of laser lines (such as those obtained through the use of tunable semiconductor lasers), improving the detector noise and maintaining the accuracy of laser frequency determination, optical detection schemes can make possible CWA sensors having sub-ppb detection capability with <1:108 PFP. We also describe the results of a preliminary experiment that verifies the results of the simulation model. Finally, we discuss the use of continuously tunable quantum cascade lasers in L-PAS for CWA and TIC detection.

Track classification within wireless sensor network

NASA Astrophysics Data System (ADS)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Oxidative stress detection by MEMS cantilever sensor array based electronic nose

NASA Astrophysics Data System (ADS)

Gupta, Anurag; Singh, T. Sonamani; Singh, Priyanka; Yadava, R. D. S.

2018-05-01

This paper is concerned with analyzing the role of polymer swelling induced surface stress in MEMS chemical sensors. The objective is to determine the impact of surface stress on the chemical discrimination ability of MEMS resonator sensors. We considered a case study of hypoxia detection by MEMS sensor array and performed several types of simulation experiments for detection of oxidative stress volatile organic markers in human breath. Both types of sensor response models that account for the surface stress effect and that did not were considered for the analyses in comparison. It is found that the surface stress (hence the polymer swelling) provides better chemical discrimination ability to polymer coated MEMS sensors.

Perspectives on MEMS in bioengineering: a novel capacitive position microsensor.

PubMed

Pedrocchi, A; Hoen, S; Ferrigno, G; Pedotti, A

2000-01-01

We describe a novel capacitive position sensor using micromachining to achieve high sensitivity and large range of motion. These sensors require a new theoretical framework to describe and optimize their performance. Employing a complete description of the electrical fields, the sensor should deviate from the standard geometries used for capacitive sensors. By this optimization, the sensor gains a twofold increase in sensitivity. Results on a PC board 10x model imply that the micromachined sensor should achieve a sensitivity of less than 10 nm over 500-micron range of travel. Some bioengineering applications are addressed, including positioning of micromirrors for laser surgery and dose control for implantable drug delivery systems.

Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes.

PubMed

Ahmed, Faisal; Tamberg, Gert; Le Moullec, Yannick; Annus, Paul

2018-04-05

In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90-94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models.

Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes

PubMed Central

Ahmed, Faisal

2018-01-01

In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90–94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models. PMID:29621169

Optimal Sensor Selection for Health Monitoring Systems

NASA Technical Reports Server (NTRS)

Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert B.

2005-01-01

Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.

A comparative study of sensor fault diagnosis methods based on observer for ECAS system

NASA Astrophysics Data System (ADS)

Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

2017-03-01

The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

NASA Technical Reports Server (NTRS)

Sun, Joy Z.; Josh, Suresh M.

2009-01-01

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.

The role of shoe design on the prediction of free torque at the shoe-surface interface using pressure insole technology.

PubMed

Weaver, Brian Thomas; Fitzsimons, Kathleen; Braman, Jerrod; Haut, Roger

2016-09-01

The goal of the current study was to expand on previous work to validate the use of pressure insole technology in conjunction with linear regression models to predict the free torque at the shoe-surface interface that is generated while wearing different athletic shoes. Three distinctly different shoe designs were utilised. The stiffness of each shoe was determined with a material's testing machine. Six participants wore each shoe that was fitted with an insole pressure measurement device and performed rotation trials on an embedded force plate. A pressure sensor mask was constructed from those sensors having a high linear correlation with free torque values. Linear regression models were developed to predict free torques from these pressure sensor data. The models were able to accurately predict their own free torque well (RMS error 3.72 ± 0.74 Nm), but not that of the other shoes (RMS error 10.43 ± 3.79 Nm). Models performing self-prediction were also able to measure differences in shoe stiffness. The results of the current study showed the need for participant-shoe specific linear regression models to insure high prediction accuracy of free torques from pressure sensor data during isolated internal and external rotations of the body with respect to a planted foot.

Effect of citizen engagement levels in flood forecasting by assimilating crowdsourced observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Cortes Arevalo, Juliette; Alfonso, Leonardo; Wehn, Uta; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri

2017-04-01

In the past years, a number of methods have been proposed to reduce uncertainty in flood prediction by means of model updating techniques. Traditional physical observations are usually integrated into hydrological and hydraulic models to improve model performances and consequent flood predictions. Nowadays, low-cost sensors can be used for crowdsourced observations. Different type of social sensors can measure, in a more distributed way, physical variables such as precipitation and water level. However, these crowdsourced observations are not integrated into a real-time fashion into water-system models due to their varying accuracy and random spatial-temporal coverage. We assess the effect in model performance due to the assimilation of crowdsourced observations of water level. Our method consists in (1) implementing a Kalman filter into a cascade of hydrological and hydraulic models. (2) defining observation errors depending on the type of sensor either physical or social. Randomly distributed errors are based on accuracy ranges that slightly improve according to the citizens' expertise level. (3) Using a simplified social model to realistically represent citizen engagement levels based on population density and citizens' motivation scenarios. To test our method, we synthetically derive crowdsourced observations for different citizen engagement levels from a distributed network of physical and social sensors. The observations are assimilated during a particular flood event occurred in the Bacchiglione catchment, Italy. The results of this study demonstrate that sharing crowdsourced water level observations (often motivated by a feeling of belonging to a community of friends) can help in improving flood prediction. On the other hand, a growing participation of individual citizens or weather enthusiasts sharing hydrological observations in cities can help to improve model performance. This study is a first step to assess the effects of crowdsourced observations in flood model predictions. Effective communication and feedback about the quality of observations from water authorities to engaged citizens are further required to minimize their intrinsic low-variable accuracy.

Hybrid graphene-copper UWB array sensor for brain tumor detection via scattering parameters in microwave detection system

NASA Astrophysics Data System (ADS)

Jamlos, Mohd Aminudin; Ismail, Abdul Hafiizh; Jamlos, Mohd Faizal; Narbudowicz, Adam

2017-01-01

Hybrid graphene-copper ultra-wideband array sensor applied to microwave imaging technique is successfully used in detecting and visualizing tumor inside human brain. The sensor made of graphene coated film for the patch while copper for both the transmission line and parasitic element. The hybrid sensor performance is better than fully copper sensor. Hybrid sensor recorded wider bandwidth of 2.0-10.1 GHz compared with fully copper sensor operated from 2.5 to 10.1 GHz. Higher gain of 3.8-8.5 dB is presented by hybrid sensor, while fully copper sensor stated lower gain ranging from 2.6 to 6.7 dB. Both sensors recorded excellent total efficiency averaged at 97 and 94%, respectively. The sensor used for both transmits equivalent signal and receives backscattering signal from stratified human head model in detecting tumor. Difference in the data of the scattering parameters recorded from the head model with presence and absence of tumor is used as the main data to be further processed in confocal microwave imaging algorithm in generating image. MATLAB software is utilized to analyze S-parameter signals obtained from measurement. Tumor presence is indicated by lower S-parameter values compared to higher values recorded by tumor absence.

Satellite passive remote sensing of off-shore pollutants, volume 2

NASA Technical Reports Server (NTRS)

1979-01-01

Satellite detection and monitoring of off-shore dumped pollutants, other than oil, are discussed. Summaries of satellite sensor performance in three spectral bands (visible, infrared, and microwave) are presented. The bulk of the report gives all the calculations, trade-offs and limitations of the three sensor systems. It is asserted that the problem of pollution monitoring is not a sensor problem but a problem of mathematical modeling and data processing.

Review of Methods and Algorithms for Dynamic Management of CBRNE Collection Assets

DTIC Science & Technology

2013-07-01

where they should be looking. An example sensor is a satellite with a limited energy budget, which may have power to operate, say, only 10 percent of...calculations by incorporating sensor data with initial dispersion estimates.1 DTRA and the Joint Science and Technology Office for Chem- Bio Defense (JSTO-CBD...detection performance through remote processing and fusion of sensor data and modeling of the operational environment. DTRA is actively developing

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

PubMed Central

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases. PMID:22163766

A virtual sensor for online fault detection of multitooth-tools.

PubMed

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

Virtual Sensors for Advanced Controllers in Rehabilitation Robotics.

PubMed

Mancisidor, Aitziber; Zubizarreta, Asier; Cabanes, Itziar; Portillo, Eva; Jung, Je Hyung

2018-03-05

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.

Response Mechanism for Surface Acoustic Wave Gas Sensors Based on Surface-Adsorption

PubMed Central

Liu, Jiansheng; Lu, Yanyan

2014-01-01

A theoretical model is established to describe the response mechanism of surface acoustic wave (SAW) gas sensors based on physical adsorption on the detector surface. Wohljent's method is utilized to describe the relationship of sensor output (frequency shift of SAW oscillator) and the mass loaded on the detector surface. The Brunauer-Emmett-Teller (BET) formula and its improved form are introduced to depict the adsorption behavior of gas on the detector surface. By combining the two methods, we obtain a theoretical model for the response mechanism of SAW gas sensors. By using a commercial SAW gas chromatography (GC) analyzer, an experiment is performed to measure the frequency shifts caused by different concentration of dimethyl methylphosphonate (DMMP). The parameters in the model are given by fitting the experimental results and the theoretical curve agrees well with the experimental data. PMID:24743157

Numerical Study on the Particle Trajectory Tracking in a Micro-UV Bio-Fluorescence Sensor.

PubMed

Byeon, Sun-Seok; Cho, Moon-Young; Lee, Jong-Chul; Kim, Youn-Jea

2015-03-01

A micro-UV bio-fluorescence sensor was developed to detect primary biological aerosols including bacteria, bacterial spores, fungal spores, pollens, viruses, algae, etc. In order to effectively detect the bio-particles in a micro-UV bio-fluorescence sensor, numerical calculations were performed to adjust for appropriate flow conditions of the sensor by regulating the sample aerosols and sheath flow. In particular, a CFD-based model of hydrodynamic processes was developed by computing the trajectory of particles using commercially available ANSYS CFX-14 software and the Lagrangian tracking model. The established model was evaluated with regard to the variation of sheath flow rate and particle size. Results showed that the sheath flow was changed rapidly at the end of nozzle tip, but the sample particles moved near the center of aerosol jet for aerodynamic focusing with little deviation from the axis.

Midwave infrared and visible sensor performance modeling: small craft identification discrimination criteria for maritime security

NASA Astrophysics Data System (ADS)

Krapels, Keith; Driggers, Ronald G.; Deaver, Dawne; Moker, Steven K.; Palmer, John

2007-10-01

The new emphasis on Anti-Terrorism and Force Protection (AT/FP), for both shore and sea platform protection, has resulted in a need for infrared imager design and evaluation tools that demonstrate field performance against U.S. Navy AT/FP requirements. In the design of infrared imaging systems for target acquisition, a discrimination criterion is required for successful sensor realization. It characterizes the difficulty of the task being performed by the observer and varies for different target sets. This criterion is used in both assessment of existing infrared sensor and in the design of new conceptual sensors. We collected 12 small craft signatures (military and civilian) in the visible band during the day and the long-wave and midwave infrared spectra in both the day and the night environments. These signatures were processed to determine the targets' characteristic dimension and contrast. They were also processed to band limit the signature's spatial information content (simulating longer range), and a perception experiment was performed to determine the task difficulty (N50 and V50). The results are presented and can be used for Navy and Coast Guard imaging infrared sensor design and evaluation.

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Fast analytical model of MZI micro-opto-mechanical pressure sensor

NASA Astrophysics Data System (ADS)

Rochus, V.; Jansen, R.; Goyvaerts, J.; Neutens, P.; O’Callaghan, J.; Rottenberg, X.

2018-06-01

This paper presents a fast analytical procedure in order to design a micro-opto-mechanical pressure sensor (MOMPS) taking into account the mechanical nonlinearity and the optical losses. A realistic model of the photonic MZI is proposed, strongly coupled to a nonlinear mechanical model of the membrane. Based on the membrane dimensions, the residual stress, the position of the waveguide, the optical wavelength and the phase variation due to the opto-mechanical coupling, we derive an analytical model which allows us to predict the response of the total system. The effect of the nonlinearity and the losses on the total performance are carefully studied and measurements on fabricated devices are used to validate the model. Finally, a design procedure is proposed in order to realize fast design of this new type of pressure sensor.

High-Performance Sensors Based on Resistance Fluctuations of Single-Layer-Graphene Transistors.

PubMed

Amin, Kazi Rafsanjani; Bid, Aveek

2015-09-09

One of the most interesting predicted applications of graphene-monolayer-based devices is as high-quality sensors. In this article, we show, through systematic experiments, a chemical vapor sensor based on the measurement of low-frequency resistance fluctuations of single-layer-graphene field-effect-transistor devices. The sensor has extremely high sensitivity, very high specificity, high fidelity, and fast response times. The performance of the device using this scheme of measurement (which uses resistance fluctuations as the detection parameter) is more than 2 orders of magnitude better than a detection scheme in which changes in the average value of the resistance is monitored. We propose a number-density-fluctuation-based model to explain the superior characteristics of a noise-measurement-based detection scheme presented in this article.

Objectively Optimized Observation Direction System Providing Situational Awareness for a Sensor Web

NASA Astrophysics Data System (ADS)

Aulov, O.; Lary, D. J.

2010-12-01

There is great utility in having a flexible and automated objective observation direction system for the decadal survey missions and beyond. Such a system allows us to optimize the observations made by suite of sensors to address specific goals from long term monitoring to rapid response. We have developed such a prototype using a network of communicating software elements to control a heterogeneous network of sensor systems, which can have multiple modes and flexible viewing geometries. Our system makes sensor systems intelligent and situationally aware. Together they form a sensor web of multiple sensors working together and capable of automated target selection, i.e. the sensors “know” where they are, what they are able to observe, what targets and with what priorities they should observe. This system is implemented in three components. The first component is a Sensor Web simulator. The Sensor Web simulator describes the capabilities and locations of each sensor as a function of time, whether they are orbital, sub-orbital, or ground based. The simulator has been implemented using AGIs Satellite Tool Kit (STK). STK makes it easy to analyze and visualize optimal solutions for complex space scenarios, and perform complex analysis of land, sea, air, space assets, and shares results in one integrated solution. The second component is target scheduler that was implemented with STK Scheduler. STK Scheduler is powered by a scheduling engine that finds better solutions in a shorter amount of time than traditional heuristic algorithms. The global search algorithm within this engine is based on neural network technology that is capable of finding solutions to larger and more complex problems and maximizing the value of limited resources. The third component is a modeling and data assimilation system. It provides situational awareness by supplying the time evolution of uncertainty and information content metrics that are used to tell us what we need to observe and the priority we should give to the observations. A prototype of this component was implemented with AutoChem. AutoChem is NASA release software constituting an automatic code generation, symbolic differentiator, analysis, documentation, and web site creation tool for atmospheric chemical modeling and data assimilation. Its model is explicit and uses an adaptive time-step, error monitoring time integration scheme for stiff systems of equations. AutoChem was the first model to ever have the facility to perform 4D-Var data assimilation and Kalman filter. The project developed a control system with three main accomplishments. First, fully multivariate observational and theoretical information with associated uncertainties was combined using a full Kalman filter data assimilation system. Second, an optimal distribution of the computations and of data queries was achieved by utilizing high performance computers/load balancing and a set of automatically mirrored databases. Third, inter-instrument bias correction was performed using machine learning. The PI for this project was Dr. David Lary of the UMBC Joint Center for Earth Systems Technology at NASA/Goddard Space Flight Center.

The Effect of Sensor Performance on Safe Minefield Transit

DTIC Science & Technology

2002-12-01

the results of the simpler model are not good approximations of the results obtained with the more complex model, suggesting that even greater complexity in maneuver modeling may be desirable for some purposes.

Space-based sensor management and geostationary satellites tracking

NASA Astrophysics Data System (ADS)

El-Fallah, A.; Zatezalo, A.; Mahler, R.; Mehra, R. K.; Donatelli, D.

2007-04-01

Sensor management for space situational awareness presents a daunting theoretical and practical challenge as it requires the use of multiple types of sensors on a variety of platforms to ensure that the space environment is continuously monitored. We demonstrate a new approach utilizing the Posterior Expected Number of Targets (PENT) as the sensor management objective function, an observation model for a space-based EO/IR sensor platform, and a Probability Hypothesis Density Particle Filter (PHD-PF) tracker. Simulation and results using actual Geostationary Satellites are presented. We also demonstrate enhanced performance by applying the ProgressiveWeighting Correction (PWC) method for regularization in the implementation of the PHD-PF tracker.

Digital imaging and remote sensing image generator (DIRSIG) as applied to NVESD sensor performance modeling

NASA Astrophysics Data System (ADS)

Kolb, Kimberly E.; Choi, Hee-sue S.; Kaur, Balvinder; Olson, Jeffrey T.; Hill, Clayton F.; Hutchinson, James A.

2016-05-01

The US Army's Communications Electronics Research, Development and Engineering Center (CERDEC) Night Vision and Electronic Sensors Directorate (referred to as NVESD) is developing a virtual detection, recognition, and identification (DRI) testing methodology using simulated imagery as a means of augmenting the field testing component of sensor performance evaluation, which is expensive, resource intensive, time consuming, and limited to the available target(s) and existing atmospheric visibility and environmental conditions at the time of testing. Existing simulation capabilities such as the Digital Imaging Remote Sensing Image Generator (DIRSIG) and NVESD's Integrated Performance Model Image Generator (NVIPM-IG) can be combined with existing detection algorithms to reduce cost/time, minimize testing risk, and allow virtual/simulated testing using full spectral and thermal object signatures, as well as those collected in the field. NVESD has developed an end-to-end capability to demonstrate the feasibility of this approach. Simple detection algorithms have been used on the degraded images generated by NVIPM-IG to determine the relative performance of the algorithms on both DIRSIG-simulated and collected images. Evaluating the degree to which the algorithm performance agrees between simulated versus field collected imagery is the first step in validating the simulated imagery procedure.

Development of Electrical Capacitance Sensors for Accident Tolerant Fuel (ATF) Testing at the Transient Reactor Test (TREAT) Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Maolong; Ryals, Matthew; Ali, Amir

2016-08-01

A variety of instruments are being developed and qualified to support the Accident Tolerant Fuels (ATF) program and future transient irradiations at the Transient Reactor Test (TREAT) facility at Idaho National Laboratory (INL). The University of New Mexico (UNM) is working with INL to develop capacitance-based void sensors for determining the timing of critical boiling phenomena in static capsule fuel testing and the volume-averaged void fraction in flow-boiling in-pile water loop fuel testing. The static capsule sensor developed at INL is a plate-type configuration, while UNM is utilizing a ring-type capacitance sensor. Each sensor design has been theoretically and experimentallymore » investigated at INL and UNM. Experiments are being performed at INL in an autoclave to investigate the performance of these sensors under representative Pressurized Water Reactor (PWR) conditions in a static capsule. Experiments have been performed at UNM using air-water two-phase flow to determine the sensitivity and time response of the capacitance sensor under a flow boiling configuration. Initial measurements from the capacitance sensor have demonstrated the validity of the concept to enable real-time measurement of void fraction. The next steps include designing the cabling interface with the flow loop at UNM for Reactivity Initiated Accident (RIA) ATF testing at TREAT and further characterization of the measurement response for each sensor under varying conditions by experiments and modeling.« less

Correction of contaminated yaw rate signal and estimation of sensor bias for an electric vehicle under normal driving conditions

NASA Astrophysics Data System (ADS)

Zhang, Guoguang; Yu, Zitian; Wang, Junmin

2017-03-01

Yaw rate is a crucial signal for the motion control systems of ground vehicles. Yet it may be contaminated by sensor bias. In order to correct the contaminated yaw rate signal and estimate the sensor bias, a robust gain-scheduling observer is proposed in this paper. First of all, a two-degree-of-freedom (2DOF) vehicle lateral and yaw dynamic model is presented, and then a Luenberger-like observer is proposed. To make the observer more applicable to real vehicle driving operations, a 2DOF vehicle model with uncertainties on the coefficients of tire cornering stiffness is employed. Further, a gain-scheduling approach and a robustness enhancement are introduced, leading to a robust gain-scheduling observer. Sensor bias detection mechanism is also designed. Case studies are conducted using an electric ground vehicle to assess the performance of signal correction and sensor bias estimation under difference scenarios.

Multi-Sensor Testing for Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Lab

NASA Technical Reports Server (NTRS)

Brewster, Linda L.; Howard, Richard T.; Johnston, A. S.; Carrington, Connie; Mitchell, Jennifer D.; Cryan, Scott P.

2008-01-01

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success ofthe Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor-proposed relative navigation sensor suite will meet the requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-Ioop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of "pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL) using the FRL's 6-DOF gantry system, called the Dynamic Overhead Target System (DOTS). The target vehicle for "docking" in the laboratory was a mockup that was representative of the proposed CEV docking system, with added retroreflectors for the AVGS.' The multi-sensor test configuration used 35 open-loop test trajectories covering three major objectives: (l) sensor characterization trajectories designed to test a wide range of performance parameters; (2) CEV-specific trajectories designed to test performance during CEV-like approach and departure profiles; and (3) sensor characterization tests designed for evaluating sensor performance under more extreme conditions as might be induced during a spacecraft failure or during contingency situations. This paper describes the test development, test facility, test preparations, test execution, and test results of the multisensor series oftrajectories

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

NASA Technical Reports Server (NTRS)

2004-01-01

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Development of an In-Situ Decommissioning Sensor Network Test Bed for Structural Condition Monitoring - 12156

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeigler, Kristine E.; Ferguson, Blythe A.

2012-07-01

The Savannah River National Laboratory (SRNL) has established an In Situ Decommissioning (ISD) Sensor Network Test Bed, a unique, small scale, configurable environment, for the assessment of prospective sensors on actual ISD system material, at minimal cost. The Department of Energy (DOE) is presently implementing permanent entombment of contaminated, large nuclear structures via ISD. The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. Validation of ISD system performance models and verification of actual system conditions can be achieved through the development a system of sensors to monitor the materials andmore » condition of the structure. The ISD Sensor Network Test Bed has been designed and deployed to addresses the DOE-Environmental Management Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building at the Savannah River Site. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of groutable thermistors for temperature and moisture monitoring, strain gauges for crack growth monitoring, tilt-meters for settlement monitoring, and a communication system for data collection. Baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment. The Sensor Network Test Bed at SRNL uses COTS sensors on concrete blocks from the outer wall of the P Reactor Building to measure conditions expected to occur in ISD structures. Knowledge and lessons learned gained from installation, testing, and monitoring of the equipment will be applied to sensor installation in a meso-scale test bed at FIU and in future ISD structures. The initial data collected from the sensors installed on the P Reactor Building blocks define the baseline materials condition of the P Reactor ISD external concrete structure. Continued monitoring of the blocks will enable evaluation of the effects of aging on the P Reactor ISD structure. The collected data will support validation of the material degradation model and assessment of the condition of the ISD structure over time. The following are recommendations for continued development of the ISD Sensor Network Test Bed: - Establish a long-term monitoring program using the concrete blocks with existing sensor and/or additional sensors for trending the concrete materials and structural condition; - Continue development of a stand-alone test bed sensor system that is self-powered and provides wireless transmission of data to a user-accessible dashboard; - Develop and implement periodic NDE/DE characterization of the concrete blocks to provide verification and validation for the measurements obtained through the sensor system and concrete degradation model(s). (authors)« less

Unobtrusive measurement of indoor energy expenditure using an infrared sensor-based activity monitoring system.

PubMed

Hwang, Bosun; Han, Jonghee; Choi, Jong Min; Park, Kwang Suk

2008-11-01

The purpose of this study was to develop an unobtrusive energy expenditure (EE) measurement system using an infrared (IR) sensor-based activity monitoring system to measure indoor activities and to estimate individual quantitative EE. IR-sensor activation counts were measured with a Bluetooth-based monitoring system and the standard EE was calculated using an established regression equation. Ten male subjects participated in the experiment and three different EE measurement systems (gas analyzer, accelerometer, IR sensor) were used simultaneously in order to determine the regression equation and evaluate the performance. As a standard measurement, oxygen consumption was simultaneously measured by a portable metabolic system (Metamax 3X, Cortex, Germany). A single room experiment was performed to develop a regression model of the standard EE measurement from the proposed IR sensor-based measurement system. In addition, correlation and regression analyses were done to compare the performance of the IR system with that of the Actigraph system. We determined that our proposed IR-based EE measurement system shows a similar correlation to the Actigraph system with the standard measurement system.

Methodology for the design, production, and test of plastic optical displacement sensors

NASA Astrophysics Data System (ADS)

Rahlves, Maik; Kelb, Christian; Reithmeier, Eduard; Roth, Bernhard

2016-08-01

Optical displacement sensors made entirely from plastic materials offer various advantages such as biocompatibility and high flexibility compared to their commonly used electrical and glass-based counterparts. In addition, various low-cost and large-scale fabrication techniques can potentially be utilized for their fabrication. In this work we present a toolkit for the design, production, and test of such sensors. Using the introduced methods, we demonstrate the development of a simple all-optical displacement sensor based on multimode plastic waveguides. The system consists of polymethylmethacrylate and cyclic olefin polymer which serve as cladding and core materials, respectively. We discuss several numerical models which are useful for the design and simulation of the displacement sensors as well as two manufacturing methods capable of mass-producing such devices. Prior to fabrication, the sensor layout and performance are evaluated by means of a self-implemented ray-optical simulation which can be extended to various other types of sensor concepts. Furthermore, we discuss optical and mechanical test procedures as well as a high-precision tensile testing machine especially suited for the characterization of the opto-mechanical performance of such plastic optical displacement sensors.

Development of a Magneto-Resistive Angular Position Sensor for Space Mechanisms

NASA Technical Reports Server (NTRS)

Hahn, Robert; Schmidt, Tilo; Seifart, Klaus; Olberts, Bastian; Romera, Fernando

2016-01-01

Magnetic microsystems in the form of magneto-resistive (MR) sensors are firmly established in automobiles and industrial applications. They are used to measure travel, angle, electrical current, or magnetic fields. MR technology opens up new sensor possibilities in space applications and can be an enabling technology for optimal performance, high robustness and long lifetime at reasonable costs. In some science missions, the technology is already applied, however, the designs are proprietary and case specific, for instance in case of the angular sensors used for JPL/NASA's Mars rover Curiosity [1]. Since 2013 HTS GmbH and Sensitec GmbH have teamed up to develop and qualify a standardized yet flexible to use MR angular sensor for space mechanisms. Starting with a first assessment study and market survey performed under ESA contract, a very strong industry interest in novel, contactless position measurement means was found. Currently a detailed and comprehensive development program is being performed by HTS and Sensitec. The objective of this program is to advance the sensor design up to Engineering Qualification Model level and to perform qualification testing for a representative space application. The paper briefly reviews the basics of magneto-resistive effects and possible sensor applications and describes the key benefits of MR angular sensors with reference to currently operational industrial and space applications. The key applications and specification are presented and the preliminary baseline mechanical and electrical design will be discussed. An outlook on the upcoming development and test stages as well as the qualification program will be provided.

Mastication noise reduction method for fully implantable hearing aid using piezo-electric sensor.

PubMed

Na, Sung Dae; Lee, Gihyoun; Wei, Qun; Seong, Ki Woong; Cho, Jin Ho; Kim, Myoung Nam

2017-07-20

Fully implantable hearing devices (FIHDs) can be affected by generated biomechanical noise such as mastication noise. To reduce the mastication noise using a piezo-electric sensor, the mastication noise is measured with the piezo-electric sensor, and noise reduction is practiced by the energy difference. For the experiment on mastication noise, a skull model was designed using artificial skull model and a piezo-electric sensor that can measure the vibration signals better than other sensors. A 1 kHz pure-tone sound through a standard speaker was applied to the model while the lower jawbone of the model was moved in a masticatory fashion. The correlation coefficients and signal-to-noise ratio (SNR) before and after application of the proposed method were compared. It was found that the signal-to-noise ratio and correlation coefficients increased by 4.48 dB and 0.45, respectively. The mastication noise is measured by piezo-electric sensor as the mastication noise that occurred during vibration. In addition, the noise was reduced by using the proposed method in conjunction with MATLAB. In order to confirm the performance of the proposed method, the correlation coefficients and signal-to-noise ratio before and after signal processing were calculated. In the future, an implantable microphone for real-time processing will be developed.

Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels.

PubMed

Ebara, Takeshi; Azuma, Ryohei; Shoji, Naoto; Matsukawa, Tsuyoshi; Yamada, Yasuyuki; Akiyama, Tomohiro; Kurihara, Takahiro; Yamada, Shota

2017-11-25

Objective measurements using built-in smartphone sensors that can measure physical activity/inactivity in daily working life have the potential to provide a new approach to assessing workers' health effects. The aim of this study was to elucidate the characteristics and reliability of built-in step counting sensors on smartphones for development of an easy-to-use objective measurement tool that can be applied in ergonomics or epidemiological research. To evaluate the reliability of step counting sensors embedded in seven major smartphone models, the 6-minute walk test was conducted and the following analyses of sensor precision and accuracy were performed: 1) relationship between actual step count and step count detected by sensors, 2) reliability between smartphones of the same model, and 3) false detection rates when sitting during office work, while riding the subway, and driving. On five of the seven models, the inter-class correlations coefficient (ICC (3,1) ) showed high reliability with a range of 0.956-0.993. The other two models, however, had ranges of 0.443-0.504 and the relative error ratios of the sensor-detected step count to the actual step count were ±48.7%-49.4%. The level of agreement between the same models was ICC (3,1) : 0.992-0.998. The false detection rates differed between the sitting conditions. These results suggest the need for appropriate regulation of step counts measured by sensors, through means such as correction or calibration with a predictive model formula, in order to obtain the highly reliable measurement results that are sought in scientific investigation.

Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

2017-04-01

To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

Efficient and anonymous two-factor user authentication in wireless sensor networks: achieving user anonymity with lightweight sensor computation.

PubMed

Nam, Junghyun; Choo, Kim-Kwang Raymond; Han, Sangchul; Kim, Moonseong; Paik, Juryon; Won, Dongho

2015-01-01

A smart-card-based user authentication scheme for wireless sensor networks (hereafter referred to as a SCA-WSN scheme) is designed to ensure that only users who possess both a smart card and the corresponding password are allowed to gain access to sensor data and their transmissions. Despite many research efforts in recent years, it remains a challenging task to design an efficient SCA-WSN scheme that achieves user anonymity. The majority of published SCA-WSN schemes use only lightweight cryptographic techniques (rather than public-key cryptographic techniques) for the sake of efficiency, and have been demonstrated to suffer from the inability to provide user anonymity. Some schemes employ elliptic curve cryptography for better security but require sensors with strict resource constraints to perform computationally expensive scalar-point multiplications; despite the increased computational requirements, these schemes do not provide user anonymity. In this paper, we present a new SCA-WSN scheme that not only achieves user anonymity but also is efficient in terms of the computation loads for sensors. Our scheme employs elliptic curve cryptography but restricts its use only to anonymous user-to-gateway authentication, thereby allowing sensors to perform only lightweight cryptographic operations. Our scheme also enjoys provable security in a formal model extended from the widely accepted Bellare-Pointcheval-Rogaway (2000) model to capture the user anonymity property and various SCA-WSN specific attacks (e.g., stolen smart card attacks, node capture attacks, privileged insider attacks, and stolen verifier attacks).

Efficient and Anonymous Two-Factor User Authentication in Wireless Sensor Networks: Achieving User Anonymity with Lightweight Sensor Computation

PubMed Central

Nam, Junghyun; Choo, Kim-Kwang Raymond; Han, Sangchul; Kim, Moonseong; Paik, Juryon; Won, Dongho

2015-01-01

A smart-card-based user authentication scheme for wireless sensor networks (hereafter referred to as a SCA-WSN scheme) is designed to ensure that only users who possess both a smart card and the corresponding password are allowed to gain access to sensor data and their transmissions. Despite many research efforts in recent years, it remains a challenging task to design an efficient SCA-WSN scheme that achieves user anonymity. The majority of published SCA-WSN schemes use only lightweight cryptographic techniques (rather than public-key cryptographic techniques) for the sake of efficiency, and have been demonstrated to suffer from the inability to provide user anonymity. Some schemes employ elliptic curve cryptography for better security but require sensors with strict resource constraints to perform computationally expensive scalar-point multiplications; despite the increased computational requirements, these schemes do not provide user anonymity. In this paper, we present a new SCA-WSN scheme that not only achieves user anonymity but also is efficient in terms of the computation loads for sensors. Our scheme employs elliptic curve cryptography but restricts its use only to anonymous user-to-gateway authentication, thereby allowing sensors to perform only lightweight cryptographic operations. Our scheme also enjoys provable security in a formal model extended from the widely accepted Bellare-Pointcheval-Rogaway (2000) model to capture the user anonymity property and various SCA-WSN specific attacks (e.g., stolen smart card attacks, node capture attacks, privileged insider attacks, and stolen verifier attacks). PMID:25849359

Multisensor fusion with non-optimal decision rules: the challenges of open world sensing

NASA Astrophysics Data System (ADS)

Minor, Christian; Johnson, Kevin

2014-05-01

In this work, simple, generic models of chemical sensing are used to simulate sensor array data and to illustrate the impact on overall system performance that specific design choices impart. The ability of multisensor systems to perform multianalyte detection (i.e., distinguish multiple targets) is explored by examining the distinction between fundamental design-related limitations stemming from mismatching of mixture composition to fused sensor measurement spaces, and limitations that arise from measurement uncertainty. Insight on the limits and potential of sensor fusion to robustly address detection tasks in realistic field conditions can be gained through an examination of a) the underlying geometry of both the composition space of sources one hopes to elucidate and the measurement space a fused sensor system is capable of generating, and b) the informational impact of uncertainty on both of these spaces. For instance, what is the potential impact on sensor fusion in an open world scenario where unknown interferants may contaminate target signals? Under complex and dynamic backgrounds, decision rules may implicitly become non-optimal and adding sensors may increase the amount of conflicting information observed. This suggests that the manner in which a decision rule handles sensor conflict can be critical in leveraging sensor fusion for effective open world sensing, and becomes exponentially more important as more sensors are added. Results and design considerations for handling conflicting evidence in Bayes and Dempster-Shafer fusion frameworks are presented. Bayesian decision theory is used to provide an upper limit on detector performance of simulated sensor systems.

New intravascular flow sensor using fiber optics

NASA Astrophysics Data System (ADS)

Stenow, Erik N. D.

1994-12-01

A new sensor using fiber optics is suggested for blood flow measurements in small vessels. The sensor principle and a first evaluation on a flow model are presented. The new sensor uses small CO2 gas bubbles as flow markers for optical detection. When the bubbles pass an optical window, light emitted from one fiber is reflected and scattered into another fiber. The sensor has been proven to work in a 3 mm flow model using two 110 micrometers optical fibers and a 100 micrometers steel capillary inserted into a 1 mm guide wire. The evaluation of a sensor archetype shows that the new sensor provides a promising method for intravascular blood flow measurement in small vessels. The linearity for steady state flow is studied in the flow interval 30 - 130 ml/min. comparison with ultrasound Doppler flowmetry was performed for pulsatile flow in the interval 25 - 125 ml/min. with a pulse length between 0.5 and 2 s. The use of intravascular administered CO2 in small volumes is harmless because the gas is rapidly dissolved in whole blood.

Home and Clinical Cardiovascular Care Center (H4C): a Framework for Integrating Body Sensor Networks and QTRU Cryptography System.

PubMed

Zakerolhosseini, Ali; Sokouti, Massoud; Pezeshkian, Massoud

2013-01-01

Quick responds to heart attack patients before arriving to hospital is a very important factor. In this paper, a combined model of Body Sensor Network and Personal Digital Access using QTRU cipher algorithm in Wifi networks is presented to efficiently overcome these life threatening attacks. The algorithm for optimizing the routing paths between sensor nodes and an algorithm for reducing the power consumption are also applied for achieving the best performance by this model. This system is consumes low power and has encrypting and decrypting processes. It also has an efficient routing path in a fast manner.

Home and Clinical Cardiovascular Care Center (H4C): a Framework for Integrating Body Sensor Networks and QTRU Cryptography System

PubMed Central

Zakerolhosseini, Ali; Sokouti, Massoud; Pezeshkian, Massoud

2013-01-01

Quick responds to heart attack patients before arriving to hospital is a very important factor. In this paper, a combined model of Body Sensor Network and Personal Digital Access using QTRU cipher algorithm in Wifi networks is presented to efficiently overcome these life threatening attacks. The algorithm for optimizing the routing paths between sensor nodes and an algorithm for reducing the power consumption are also applied for achieving the best performance by this model. This system is consumes low power and has encrypting and decrypting processes. It also has an efficient routing path in a fast manner. PMID:24252988

MORTICIA, a statistical analysis software package for determining optical surveillance system effectiveness.

NASA Astrophysics Data System (ADS)

Ramkilowan, A.; Griffith, D. J.

2017-10-01

Surveillance modelling in terms of the standard Detect, Recognise and Identify (DRI) thresholds remains a key requirement for determining the effectiveness of surveillance sensors. With readily available computational resources it has become feasible to perform statistically representative evaluations of the effectiveness of these sensors. A new capability for performing this Monte-Carlo type analysis is demonstrated in the MORTICIA (Monte- Carlo Optical Rendering for Theatre Investigations of Capability under the Influence of the Atmosphere) software package developed at the Council for Scientific and Industrial Research (CSIR). This first generation, python-based open-source integrated software package, currently in the alpha stage of development aims to provide all the functionality required to perform statistical investigations of the effectiveness of optical surveillance systems in specific or generic deployment theatres. This includes modelling of the mathematical and physical processes that govern amongst other components of a surveillance system; a sensor's detector and optical components, a target and its background as well as the intervening atmospheric influences. In this paper we discuss integral aspects of the bespoke framework that are critical to the longevity of all subsequent modelling efforts. Additionally, some preliminary results are presented.

An agenda-based routing protocol in delay tolerant mobile sensor networks.

PubMed

Wang, Xiao-Min; Zhu, Jin-Qi; Liu, Ming; Gong, Hai-Gang

2010-01-01

Routing in delay tolerant mobile sensor networks (DTMSNs) is challenging due to the networks' intermittent connectivity. Most existing routing protocols for DTMSNs use simplistic random mobility models for algorithm design and performance evaluation. In the real world, however, due to the unique characteristics of human mobility, currently existing random mobility models may not work well in environments where mobile sensor units are carried (such as DTMSNs). Taking a person's social activities into consideration, in this paper, we seek to improve DTMSN routing in terms of social structure and propose an agenda based routing protocol (ARP). In ARP, humans are classified based on their agendas and data transmission is made according to sensor nodes' transmission rankings. The effectiveness of ARP is demonstrated through comprehensive simulation studies.

Robust design of an inkjet-printed capacitive sensor for position tracking of a MOEMS-mirror in a Michelson interferometer setup

NASA Astrophysics Data System (ADS)

Faller, Lisa-Marie; Zangl, Hubert

2017-05-01

To guarantee high performance of Micro Optical Electro Mechanical Systems (MOEMS), precise position feedback is crucial. To overcome drawbacks of widely used optical feedback, we propose an inkjet-printed capacitive position sensor as smart packaging solution. Printing processes suffer from tolerances in excess of those from standard processes. Thus, FEM simulations covering assumed tolerances of the system are adopted. These simulations are structured following a Design Of Computer Experiments (DOCE) and are then employed to determine a optimal sensor design. Based on the simulation results, statistical models are adopted for the dynamic system. These models are to be used together with specifically designed hardware, considered to cope with challenging requirements of ≍50nm position accuracy at 10MS/s with 1000μm measurement range. Noise analysis is performed considering the influence of uncertainties to assess resolution and bandwidth capabilities.

Intelligent Control and Health Monitoring. Chapter 3

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Kumar, Aditya; Mathews, H. Kirk; Rosenfeld, Taylor; Rybarik, Pavol; Viassolo, Daniel E.

2009-01-01

Advanced model-based control architecture overcomes the limitations state-of-the-art engine control and provides the potential of virtual sensors, for example for thrust and stall margin. "Tracking filters" are used to adapt the control parameters to actual conditions and to individual engines. For health monitoring standalone monitoring units will be used for on-board analysis to determine the general engine health and detect and isolate sudden faults. Adaptive models open up the possibility of adapting the control logic to maintain desired performance in the presence of engine degradation or to accommodate any faults. Improved and new sensors are required to allow sensing at stations within the engine gas path that are currently not instrumented due in part to the harsh conditions including high operating temperatures and to allow additional monitoring of vibration, mass flows and energy properties, exhaust gas composition, and gas path debris. The environmental and performance requirements for these sensors are summarized.

International Space Station Future Correlation Analysis Improvements

NASA Technical Reports Server (NTRS)

Laible, Michael R.; Pinnamaneni, Murthy; Sugavanam, Sujatha; Grygier, Michael

2018-01-01

Ongoing modal analyses and model correlation are performed on different configurations of the International Space Station (ISS). These analyses utilize on-orbit dynamic measurements collected using four main ISS instrumentation systems: External Wireless Instrumentation System (EWIS), Internal Wireless Instrumentation System (IWIS), Space Acceleration Measurement System (SAMS), and Structural Dynamic Measurement System (SDMS). Remote Sensor Units (RSUs) are network relay stations that acquire flight data from sensors. Measured data is stored in the Remote Sensor Unit (RSU) until it receives a command to download data via RF to the Network Control Unit (NCU). Since each RSU has its own clock, it is necessary to synchronize measurements before analysis. Imprecise synchronization impacts analysis results. A study was performed to evaluate three different synchronization techniques: (i) measurements visually aligned to analytical time-response data using model comparison, (ii) Frequency Domain Decomposition (FDD), and (iii) lag from cross-correlation to align measurements. This paper presents the results of this study.

Querying and Extracting Timeline Information from Road Traffic Sensor Data

PubMed Central

Imawan, Ardi; Indikawati, Fitri Indra; Kwon, Joonho; Rao, Praveen

2016-01-01

The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system—a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset. PMID:27563900

Analyzing Responses of Chemical Sensor Arrays

NASA Technical Reports Server (NTRS)

Zhou, Hanying

2007-01-01

NASA is developing a third-generation electronic nose (ENose) capable of continuous monitoring of the International Space Station s cabin atmosphere for specific, harmful airborne contaminants. Previous generations of the ENose have been described in prior NASA Tech Briefs issues. Sensor selection is critical in both (prefabrication) sensor material selection and (post-fabrication) data analysis of the ENose, which detects several analytes that are difficult to detect, or that are at very low concentration ranges. Existing sensor selection approaches usually include limited statistical measures, where selectivity is more important but reliability and sensitivity are not of concern. When reliability and sensitivity can be major limiting factors in detecting target compounds reliably, the existing approach is not able to provide meaningful selection that will actually improve data analysis results. The approach and software reported here consider more statistical measures (factors) than existing approaches for a similar purpose. The result is a more balanced and robust sensor selection from a less than ideal sensor array. The software offers quick, flexible, optimal sensor selection and weighting for a variety of purposes without a time-consuming, iterative search by performing sensor calibrations to a known linear or nonlinear model, evaluating the individual sensor s statistics, scoring the individual sensor s overall performance, finding the best sensor array size to maximize class separation, finding optimal weights for the remaining sensor array, estimating limits of detection for the target compounds, evaluating fingerprint distance between group pairs, and finding the best event-detecting sensors.

76 FR 8278 - Special Conditions: Gulfstream Model GVI Airplane; Enhanced Flight Vision System

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-14

... detected by infrared sensors can be much different from that detected by natural pilot vision. On a dark... by many imaging infrared systems. On the other hand, contrasting colors in visual wavelengths may be... of the EFVS image and the level of EFVS infrared sensor performance could depend significantly on...

Luminance compensation for AMOLED displays using integrated MIS sensors

NASA Astrophysics Data System (ADS)

Vygranenko, Yuri; Fernandes, Miguel; Louro, Paula; Vieira, Manuela

2017-05-01

Active-matrix organic light-emitting diodes (AMOLEDs) are ideal for future TV applications due to their ability to faithfully reproduce real images. However, pixel luminance can be affected by instability of driver TFTs and aging effect in OLEDs. This paper reports on a pixel driver utilizing a metal-insulator-semiconductor (MIS) sensor for luminance control of the OLED element. In the proposed pixel architecture for bottom-emission AMOLEDs, the embedded MIS sensor shares the same layer stack with back-channel etched a Si:H TFTs to maintain the fabrication simplicity. The pixel design for a large-area HD display is presented. The external electronics performs image processing to modify incoming video using correction parameters for each pixel in the backplane, and also sensor data processing to update the correction parameters. The luminance adjusting algorithm is based on realistic models for pixel circuit elements to predict the relation between the programming voltage and OLED luminance. SPICE modeling of the sensing part of the backplane is performed to demonstrate its feasibility. Details on the pixel circuit functionality including the sensing and programming operations are also discussed.

Sensor Modelling for the ’Cyclops’ Focal Plane Detector Array Based Technology Demonstrator

DTIC Science & Technology

1992-12-01

Detector Array IFOV Instantaneous field of view IRFPDA Infrared Focal Plane Detector Array LWIR Long-Wave Infrared 0 MCT Mercury Cadmium Telluride MTF...scale focal plane detector array (FPDA). The sensor system operates in the long-wave infrared ( LWIR ) spectral region. The detector array consists of...charge transfer inefficiencies in the readout circuitry. The performance of the HgCdTe FPDA based sensor is limited by the nonuniformity of the

Automatic Docking System Sensor Design, Test, and Mission Performance

NASA Technical Reports Server (NTRS)

Jackson, John L.; Howard, Richard T.; Cole, Helen J.

1998-01-01

The Video Guidance Sensor is a key element of an automatic rendezvous and docking program administered by NASA that was flown on STS-87 in November of 1997. The system used laser illumination of a passive target in the field of view of an on-board camera and processed the video image to determine the relative position and attitude between the target and the sensor. Comparisons of mission results with theoretical models and laboratory measurements will be discussed.

COBE attitude as seen from the FDF

NASA Technical Reports Server (NTRS)

Sedlak, J.; Chu, D.; Scheidker, E.

1990-01-01

The goal of the Flight Dynamics Facility (FDF) attitude support is twofold: to determine spacecraft attitude and to explain deviations from nominal attitude behavior. Attitude determination often requires resolving contradictions in the sensor observations. This may be accomplished by applying calibration corrections or by revising the observation models. After accounting for all known sources of error, solution accuracy should be limited only by observation and propagation noise. The second half of the goal is to explain why the attitude may not be as originally intended. Reasons for such deviations include sensor or actuator misalignments and control system performance. In these cases, the ability to explain the behavior should, in principle, be limited only by knowledge of the sensor and actuator data and external torques. Documented here are some results obtained to date in support of the Cosmic Background Explorer (COBE). Advantages and shortcomings of the integrated attitude determination/sensor calibration software are discussed. Some preliminary attitude solutions using data from the Diffuse Infrared Background Experiment (DIRBE) instrument are presented and compared to solutions using Sun and Earth sensors. A dynamical model is constructed to illustrate the relative importance of the various sensor imprefections. This model also shows the connection between the high- and low-frequency attitude oscillations.

Study of the motion artefacts of skin-mounted inertial sensors under different attachment conditions.

PubMed

Forner-Cordero, A; Mateu-Arce, M; Forner-Cordero, I; Alcántara, E; Moreno, J C; Pons, J L

2008-04-01

A common problem shared by accelerometers, inertial sensors and any motion measurement method based on skin-mounted sensors is the movement of the soft tissues covering the bones. The aim of this work is to propose a method for the validation of the attachment of skin-mounted sensors. A second-order (mass-spring-damper) model was proposed to characterize the behaviour of the soft tissue between the bone and the sensor. Three sets of experiments were performed. In the first one, different procedures to excite the system were evaluated to select an adequate excitation stimulus. In the second one, the selected stimulus was applied under varying attachment conditions while the third experiment was used to test the model. The heel drop was chosen as the excitation method because it showed lower variability and could discriminate between different attachment conditions. There was, in agreement with the model, a trend to increase the natural frequency of the system with decreasing accelerometer mass. An important result is the development of a standard procedure to test the bandwidth of skin-mounted inertial sensors, such as accelerometers mounted on the skin or markers heavier than a few grams.

Humidity detection using chitosan film based sensor

NASA Astrophysics Data System (ADS)

Nasution, T. I.; Nainggolan, I.; Dalimunthe, D.; Balyan, M.; Cuana, R.; Khanifah, S.

2018-02-01

A humidity sensor made of the natural polymer chitosan has been successfully fabricated in the film form by a solution casting method. Humidity testing was performed by placing a chitosan film sensor in a cooling machine room, model KT-2000 Ahu. The testing results showed that the output voltage values of chitosan film sensor increased with the increase in humidity percentage. For the increase in humidity percentage from 30 to 90% showed that the output voltage of chitosan film sensor increased from 32.19 to 138.75 mV. It was also found that the sensor evidenced good repeatability and stability during the testing. Therefore, chitosan has a great potential to be used as new sensing material for the humidity detection of which was cheaper and environmentally friendly.

Guidelines for spaceborne microwave remote sensors

NASA Technical Reports Server (NTRS)

Litman, V.; Nicholas, J.

1982-01-01

A handbook was developed to provide information and support to the spaceborne remote sensing and frequency management communities: to guide sensor developers in the choice of frequencies; to advise regulators on sensor technology needs and sharing potential; to present sharing analysis models and, through example, methods for determining sensor sharing feasibility; to introduce developers to the regulatory process; to create awareness of proper assignment procedures; to present sensor allocations; and to provide guidelines on the use and limitations of allocated bands. Controlling physical factors and user requirements and the regulatory environment are discussed. Sensor frequency allocation achievable performance and usefulness are reviewed. Procedures for national and international registration, the use of non-allocated bands and steps for obtaining new frequency allocations, and procedures for reporting interference are also discussed.

Research on MEMS sensor in hydraulic system flow detection

NASA Astrophysics Data System (ADS)

Zhang, Hongpeng; Zhang, Yindong; Liu, Dong; Ji, Yulong; Jiang, Jihai; Sun, Yuqing

2011-05-01

With the development of mechatronics technology and fault diagnosis theory, people regard flow information much more than before. Cheap, fast and accurate flow sensors are urgently needed by hydraulic industry. So MEMS sensor, which is small, low cost, well performed and easy to integrate, will surely play an important role in this field. Based on the new method of flow measurement which was put forward by our research group, this paper completed the measurement of flow rate in hydraulic system by setting up the mathematical model, using numerical simulation method and doing physical experiment. Based on viscous fluid flow equations we deduced differential pressure-velocity model of this new sensor and did optimization on parameters. Then, we designed and manufactured the throttle and studied the velocity and pressure field inside the sensor by FLUENT. Also in simulation we get the differential pressure-velocity curve .The model machine was simulated too to direct experiment. In the static experiments we calibrated the MEMS sensing element and built some sample sensors. Then in a hydraulic testing system we compared the sensor signal with a turbine meter. It presented good linearity and could meet general hydraulic system use. Based on the CFD curves, we analyzed the error reasons and made some suggestion to improve. In the dynamic test, we confirmed this sensor can realize high frequency flow detection by a 7 piston-pump.

Research on MEMS sensor in hydraulic system flow detection

NASA Astrophysics Data System (ADS)

Zhang, Hongpeng; Zhang, Yindong; Liu, Dong; Ji, Yulong; Jiang, Jihai; Sun, Yuqing

2010-12-01

With the development of mechatronics technology and fault diagnosis theory, people regard flow information much more than before. Cheap, fast and accurate flow sensors are urgently needed by hydraulic industry. So MEMS sensor, which is small, low cost, well performed and easy to integrate, will surely play an important role in this field. Based on the new method of flow measurement which was put forward by our research group, this paper completed the measurement of flow rate in hydraulic system by setting up the mathematical model, using numerical simulation method and doing physical experiment. Based on viscous fluid flow equations we deduced differential pressure-velocity model of this new sensor and did optimization on parameters. Then, we designed and manufactured the throttle and studied the velocity and pressure field inside the sensor by FLUENT. Also in simulation we get the differential pressure-velocity curve .The model machine was simulated too to direct experiment. In the static experiments we calibrated the MEMS sensing element and built some sample sensors. Then in a hydraulic testing system we compared the sensor signal with a turbine meter. It presented good linearity and could meet general hydraulic system use. Based on the CFD curves, we analyzed the error reasons and made some suggestion to improve. In the dynamic test, we confirmed this sensor can realize high frequency flow detection by a 7 piston-pump.

Parylene MEMS patency sensor for assessment of hydrocephalus shunt obstruction.

PubMed

Kim, Brian J; Jin, Willa; Baldwin, Alexander; Yu, Lawrence; Christian, Eisha; Krieger, Mark D; McComb, J Gordon; Meng, Ellis

2016-10-01

Neurosurgical ventricular shunts inserted to treat hydrocephalus experience a cumulative failure rate of 80 % over 12 years; obstruction is responsible for most failures with a majority occurring at the proximal catheter. Current diagnosis of shunt malfunction is imprecise and involves neuroimaging studies and shunt tapping, an invasive measurement of intracranial pressure and shunt patency. These patients often present emergently and a delay in care has dire consequences. A microelectromechanical systems (MEMS) patency sensor was developed to enable direct and quantitative tracking of shunt patency in order to detect proximal shunt occlusion prior to the development of clinical symptoms thereby avoiding delays in treatment. The sensor was fabricated on a flexible polymer substrate to eventually allow integration into a shunt. In this study, the sensor was packaged for use with external ventricular drainage systems for clinical validation. Insights into the transduction mechanism of the sensor were obtained. The impact of electrode size, clinically relevant temperatures and flows, and hydrogen peroxide (H2O2) plasma sterilization on sensor function were evaluated. Sensor performance in the presence of static and dynamic obstruction was demonstrated using 3 different models of obstruction. Electrode size was found to have a minimal effect on sensor performance and increased temperature and flow resulted in a slight decrease in the baseline impedance due to an increase in ionic mobility. However, sensor response did not vary within clinically relevant temperature and flow ranges. H2O2 plasma sterilization also had no effect on sensor performance. This low power and simple format sensor was developed with the intention of future integration into shunts for wireless monitoring of shunt state and more importantly, a more accurate and timely diagnosis of shunt failure.

Strain field reconstruction on composite spars based on the identification of equivalent load conditions

NASA Astrophysics Data System (ADS)

Airoldi, A.; Marelli, L.; Bettini, P.; Sala, G.; Apicella, A.

2017-04-01

Technologies based on optical fibers provide the possibility of installing relatively dense networks of sensors that can perform effective strain sensing functions during the operational life of structures. A contemporary trend is the increasing adoption of composite materials in aerospace constructions, which leads to structural architectures made of large monolithic elements. The paper is aimed at showing the feasibility of a detailed reconstruction of the strain field in a composite spar, which is based on the development of reference finite element models and the identification of load modes, consisting of a parameterized set of forces. The procedure is described and assessed in ideal conditions. Thereafter, a surrogate model is used to obtain realistic representation of the data acquired by the strain sensing system, so that the developed procedure is evaluated considering local effects due to the introduction of loads, significant modelling discrepancy in the development of the reference model and the presence of measurement noise. Results show that the method can obtain a robust and quite detailed reconstruction of strain fields, even at the level of local distributions, of the internal forces in the spars and of the displacements, by identifying an equivalent set of load parameters. Finally, the trade-off between the number of sensor and the accuracy, and the optimal position of the sensors for a given maximum number of sensors is evaluated by performing a multi-objective optimization, thus showing that even a relative dense network of externally applied sensors can be used to achieve good quality results.

A data-driven approach to modeling physical fatigue in the workplace using wearable sensors.

PubMed

Sedighi Maman, Zahra; Alamdar Yazdi, Mohammad Ali; Cavuoto, Lora A; Megahed, Fadel M

2017-11-01

Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tilt to horizontal global solar irradiance conversion: application to PV systems data

NASA Astrophysics Data System (ADS)

Housmans, Caroline; Leloux, Jonathan; Bertrand, Cédric

2017-04-01

Many transposition models have been proposed in the literature to convert solar irradiance on the horizontal plane to that on a tilted plane requiring that at least two of the three solar components (i.e. global, direct and diffuse) are known. When only global irradiance measurements are available, the conversion from horizontal to tilted planes is still possible but in this case transposition models have to be coupled with decomposition models (i.e. models that predict the direct and diffuse components from the global one). Here, two different approaches have been considered to solve the reverse process, i.e. the conversion from tilted to horizontal: (i) one-sensor approach and (ii) multi-sensors approach. Because only one tilted plane is involved in the one-sensor approach, a decomposition model need to be coupled with a transposition model to solve the problem. By contrast, at least two tilted planes being considered in the multi-sensors approach, only a transposition model is required to perform the conversion. First, global solar irradiance measurements recorded on the roof of the Royal Meteorological Institute of Belgium's radiation tower in Uccle were used to evaluate the performance of both approaches. Four pyranometers (one mounted in the horizontal plane and three on inclined surfaces with different tilts and orientations) were involved in the validation exercise. Second, the inverse transposition was applied to tilted global solar irradiance values retrieved from the energy production registered at residential PV systems located in the vicinity of Belgian radiometric stations operated by RMI (for validation purposes).

Comparison between a model-based and a conventional pyramid sensor reconstructor.

PubMed

Korkiakoski, Visa; Vérinaud, Christophe; Le Louarn, Miska; Conan, Rodolphe

2007-08-20

A model of a non-modulated pyramid wavefront sensor (P-WFS) based on Fourier optics has been presented. Linearizations of the model represented as Jacobian matrices are used to improve the P-WFS phase estimates. It has been shown in simulations that a linear approximation of the P-WFS is sufficient in closed-loop adaptive optics. Also a method to compute model-based synthetic P-WFS command matrices is shown, and its performance is compared to the conventional calibration. It was observed that in poor visibility the new calibration is better than the conventional.

Frequency Domain Decomposition performed on the strain data obtained from the aluminium model of an offshore support structure

NASA Astrophysics Data System (ADS)

Mieloszyk, M.; Opoka, S.; Ostachowicz, W.

2015-07-01

This paper presents an application of Fibre Bragg Grating (FBG) sensors for Structural Health Monitoring (SHM) of offshore wind energy support structure model. The analysed structure is a tripod equipped with 16 FBG sensors. From a wide variety of Operational Modal Analysis (OMA) methods Frequency Domain Decomposition (FDD) technique is used in this paper under assumption that the input loading is similar to a white noise excitation. The FDD method can be applied using different sets of sensors, i.e. the one which contains all FBG sensors and the other set of sensors localised only on a particular tripod's leg. The cases considered during investigation were as follows: damaged and undamaged scenarios, different support conditions. The damage was simulated as an dismantled flange on an upper brace in one of the tripod legs. First the model was fixed to an antishaker table and investigated in the air under impulse excitations. Next the tripod was submerged into water basin in order to check the quality of the measurement set-up in different environmental condition. In this case the model was excited by regular waves.

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

PubMed Central

Li, Chao; Zhang, Zhenjiang; Chao, Han-Chieh

2017-01-01

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. PMID:29280950

Cross-layer protocol design for QoS optimization in real-time wireless sensor networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

2010-04-01

The metrics of quality of service (QoS) for each sensor type in a wireless sensor network can be associated with metrics for multimedia that describe the quality of fused information, e.g., throughput, delay, jitter, packet error rate, information correlation, etc. These QoS metrics are typically set at the highest, or application, layer of the protocol stack to ensure that performance requirements for each type of sensor data are satisfied. Application-layer metrics, in turn, depend on the support of the lower protocol layers: session, transport, network, data link (MAC), and physical. The dependencies of the QoS metrics on the performance of the higher layers of the Open System Interconnection (OSI) reference model of the WSN protocol, together with that of the lower three layers, are the basis for a comprehensive approach to QoS optimization for multiple sensor types in a general WSN model. The cross-layer design accounts for the distributed power consumption along energy-constrained routes and their constituent nodes. Following the author's previous work, the cross-layer interactions in the WSN protocol are represented by a set of concatenated protocol parameters and enabling resource levels. The "best" cross-layer designs to achieve optimal QoS are established by applying the general theory of martingale representations to the parameterized multivariate point processes (MVPPs) for discrete random events occurring in the WSN. Adaptive control of network behavior through the cross-layer design is realized through the parametric factorization of the stochastic conditional rates of the MVPPs. The cross-layer protocol parameters for optimal QoS are determined in terms of solutions to stochastic dynamic programming conditions derived from models of transient flows for heterogeneous sensor data and aggregate information over a finite time horizon. Markov state processes, embedded within the complex combinatorial history of WSN events, are more computationally tractable and lead to simplifications for any simulated or analytical performance evaluations of the cross-layer designs.

Modeling and evaluating the performance of Brillouin distributed optical fiber sensors.

PubMed

Soto, Marcelo A; Thévenaz, Luc

2013-12-16

A thorough analysis of the key factors impacting on the performance of Brillouin distributed optical fiber sensors is presented. An analytical expression is derived to estimate the error on the determination of the Brillouin peak gain frequency, based for the first time on real experimental conditions. This expression is experimentally validated, and describes how this frequency uncertainty depends on measurement parameters, such as Brillouin gain linewidth, frequency scanning step and signal-to-noise ratio. Based on the model leading to this expression and considering the limitations imposed by nonlinear effects and pump depletion, a figure-of-merit is proposed to fairly compare the performance of Brillouin distributed sensing systems. This figure-of-merit offers to the research community and to potential users the possibility to evaluate with an objective metric the real performance gain resulting from any proposed configuration.

Analytical Models of Cross-Layer Protocol Optimization in Real-Time Wireless Sensor Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

The real-time interactions among the nodes of a wireless sensor network (WSN) to cooperatively process data from multiple sensors are modeled. Quality-of-service (QoS) metrics are associated with the quality of fused information: throughput, delay, packet error rate, etc. Multivariate point process (MVPP) models of discrete random events in WSNs establish stochastic characteristics of optimal cross-layer protocols. Discrete-event, cross-layer interactions in mobile ad hoc network (MANET) protocols have been modeled using a set of concatenated design parameters and associated resource levels by the MVPPs. Characterization of the "best" cross-layer designs for a MANET is formulated by applying the general theory of martingale representations to controlled MVPPs. Performance is described in terms of concatenated protocol parameters and controlled through conditional rates of the MVPPs. Modeling limitations to determination of closed-form solutions versus explicit iterative solutions for ad hoc WSN controls are examined.

Cost Modeling for low-cost planetary missions

NASA Technical Reports Server (NTRS)

Kwan, Eric; Habib-Agahi, Hamid; Rosenberg, Leigh

2005-01-01

This presentation will provide an overview of the JPL parametric cost models used to estimate flight science spacecrafts and instruments. This material will emphasize the cost model approaches to estimate low-cost flight hardware, sensors, and instrumentation, and to perform cost-risk assessments. This presentation will also discuss JPL approaches to perform cost modeling and the methodologies and analyses used to capture low-cost vs. key cost drivers.

Geological terrain models

NASA Technical Reports Server (NTRS)

Kaupp, V. H.; Macdonald, H. C.; Waite, W. P.

1981-01-01

The initial phase of a program to determine the best interpretation strategy and sensor configuration for a radar remote sensing system for geologic applications is discussed. In this phase, terrain modeling and radar image simulation were used to perform parametric sensitivity studies. A relatively simple computer-generated terrain model is presented, and the data base, backscatter file, and transfer function for digital image simulation are described. Sets of images are presented that simulate the results obtained with an X-band radar from an altitude of 800 km and at three different terrain-illumination angles. The simulations include power maps, slant-range images, ground-range images, and ground-range images with statistical noise incorporated. It is concluded that digital image simulation and computer modeling provide cost-effective methods for evaluating terrain variations and sensor parameter changes, for predicting results, and for defining optimum sensor parameters.

An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors

PubMed Central

Li, Jian; Wei, Xinguo; Zhang, Guangjun

2017-01-01

Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method. PMID:28825684

An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors.

PubMed

Li, Jian; Wei, Xinguo; Zhang, Guangjun

2017-08-21

Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method.

TPS In-Flight Health Monitoring Project Progress Report

NASA Technical Reports Server (NTRS)

Kostyk, Chris; Richards, Lance; Hudston, Larry; Prosser, William

2007-01-01

Progress in the development of new thermal protection systems (TPS) is reported. New approaches use embedded lightweight, sensitive, fiber optic strain and temperature sensors within the TPS. Goals of the program are to develop and demonstrate a prototype TPS health monitoring system, develop a thermal-based damage detection algorithm, characterize limits of sensor/system performance, and develop ea methodology transferable to new designs of TPS health monitoring systems. Tasks completed during the project helped establish confidence in understanding of both test setup and the model and validated system/sensor performance in a simple TPS structure. Other progress included complete initial system testing, commencement of the algorithm development effort, generation of a damaged thermal response characteristics database, initial development of a test plan for integration testing of proven FBG sensors in simple TPS structure, and development of partnerships to apply the technology.

Physical Human Activity Recognition Using Wearable Sensors.

PubMed

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-12-11

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System.

PubMed

Mouapi, Alex; Hakem, Nadir

2018-01-05

Energy Harvesting techniques are increasingly seen as the solution for freeing the wireless sensor nodes from their battery dependency. However, it remains evident that network performance features, such as network size, packet length, and duty cycle, are influenced by the sum of recovered energy. This paper proposes a new approach to defining the specifications of a stand-alone wireless node based on a Radio-frequency Energy Harvesting System (REHS). To achieve adequate performance regarding the range of the Wireless Sensor Network (WSN), techniques for minimizing the energy consumed by the sensor node are combined with methods for optimizing the performance of the REHS. For more rigor in the design of the autonomous node, a comprehensive energy model of the node in a wireless network is established. For an equitable distribution of network charges between the different nodes that compose it, the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for this purpose. The model considers five energy-consumption sources, most of which are ignored in recently used models. By using the hardware parameters of commercial off-the-shelf components (Mica2 Motes and CC2520 of Texas Instruments), the energy requirement of a sensor node is quantified. A miniature REHS based on a judicious choice of rectifying diodes is then designed and developed to achieve optimal performance in the Industrial Scientific and Medical (ISM) band centralized at 2.45 GHz . Due to the mismatch between the REHS and the antenna, a band pass filter is designed to reduce reflection losses. A gradient method search is used to optimize the output characteristics of the adapted REHS. At 1 mW of input RF power, the REHS provides an output DC power of 0.57 mW and a comparison with the energy requirement of the node allows the Base Station (BS) to be located at 310 m from the wireless nodes when the Wireless Sensor Network (WSN) has 100 nodes evenly spread over an area of 300 × 300 m 2 and when each round lasts 10 min . The result shows that the range of the autonomous WSN increases when the controlled physical phenomenon varies very slowly. Having taken into account all the dissipation sources coexisting in a sensor node and using actual measurements of an REHS, this work provides the guidelines for the design of autonomous nodes based on REHS.

Field Performance of Photovoltaic Systems in the Tucson Desert

NASA Astrophysics Data System (ADS)

Orsburn, Sean; Brooks, Adria; Cormode, Daniel; Greenberg, James; Hardesty, Garrett; Lonij, Vincent; Salhab, Anas; St. Germaine, Tyler; Torres, Gabe; Cronin, Alexander

2011-10-01

At the Tucson Electric Power (TEP) solar test yard, over 20 different grid-connected photovoltaic (PV) systems are being tested. The goal at the TEP solar test yard is to measure and model real-world performance of PV systems and to benchmark new technologies such as holographic concentrators. By studying voltage and current produced by the PV systems as a function of incident irradiance, and module temperature, we can compare our measurements of field-performance (in a harsh desert environment) to manufacturer specifications (determined under laboratory conditions). In order to measure high-voltage and high-current signals, we designed and built reliable, accurate sensors that can handle extreme desert temperatures. We will present several benchmarks of sensors in a controlled environment, including shunt resistors and Hall-effect current sensors, to determine temperature drift and accuracy. Finally we will present preliminary field measurements of PV performance for several different PV technologies.

Multi-Sensor Testing for Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

NASA Technical Reports Server (NTRS)

Brewster, L.; Johnston, A.; Howard, R.; Mitchell, J.; Cryan, S.

2007-01-01

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor proposed relative navigation sensor suite will meet the requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-loop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of"pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL) using the FRL's 6-DOF gantry system, called the Dynamic Overhead Target System (DOTS). The target vehicle for "docking" in the laboratory was a mockup that was representative of the proposed CEV docking system, with added retroreflectors for the AVGS. The multi-sensor test configuration used 35 open-loop test trajectories covering three major objectives: (1) sensor characterization trajectories designed to test a wide range of performance parameters; (2) CEV-specific trajectories designed to test performance during CEV-like approach and departure profiles; and (3) sensor characterization tests designed for evaluating sensor performance under more extreme conditions as might be induced during a spacecraft failure or during contingency situations. This paper describes the test development, test facility, test preparations, test execution, and test results of the multi-sensor series of trajectories.

INTELLIGENT MONITORING SYSTEM WITH HIGH TEMPERATURE DISTRIBUTED FIBEROPTIC SENSOR FOR POWER PLANT COMBUSTION PROCESSES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kwang Y. Lee; Stuart S. Yin; Andre Boheman

2004-12-26

The objective of the proposed work is to develop an intelligent distributed fiber optical sensor system for real-time monitoring of high temperature in a boiler furnace in power plants. Of particular interest is the estimation of spatial and temporal distributions of high temperatures within a boiler furnace, which will be essential in assessing and controlling the mechanisms that form and remove pollutants at the source, such as NOx. The basic approach in developing the proposed sensor system is three fold: (1) development of high temperature distributed fiber optical sensor capable of measuring temperatures greater than 2000 C degree with spatialmore » resolution of less than 1 cm; (2) development of distributed parameter system (DPS) models to map the three-dimensional (3D) temperature distribution for the furnace; and (3) development of an intelligent monitoring system for real-time monitoring of the 3D boiler temperature distribution. Under Task 1, improvement was made on the performance of in-fiber grating fabricated in single crystal sapphire fibers, test was performed on the grating performance of single crystal sapphire fiber with new fabrication methods, and the fabricated grating was applied to high temperature sensor. Under Task 2, models obtained from 3-D modeling of the Demonstration Boiler were used to study relationships between temperature and NOx, as the multi-dimensionality of such systems are most comparable with real-life boiler systems. Studies show that in boiler systems with no swirl, the distributed temperature sensor may provide information sufficient to predict trends of NOx at the boiler exit. Under Task 3, we investigate a mathematical approach to extrapolation of the temperature distribution within a power plant boiler facility, using a combination of a modified neural network architecture and semigroup theory. The 3D temperature data is furnished by the Penn State Energy Institute using FLUENT. Given a set of empirical data with no analytic expression, we first develop an analytic description and then extend that model along a single axis.« less

A High Fidelity Approach to Data Simulation for Space Situational Awareness Missions

NASA Astrophysics Data System (ADS)

Hagerty, S.; Ellis, H., Jr.

2016-09-01

Space Situational Awareness (SSA) is vital to maintaining our Space Superiority. A high fidelity, time-based simulation tool, PROXOR™ (Proximity Operations and Rendering), supports SSA by generating realistic mission scenarios including sensor frame data with corresponding truth. This is a unique and critical tool for supporting mission architecture studies, new capability (algorithm) development, current/future capability performance analysis, and mission performance prediction. PROXOR™ provides a flexible architecture for sensor and resident space object (RSO) orbital motion and attitude control that simulates SSA, rendezvous and proximity operations scenarios. The major elements of interest are based on the ability to accurately simulate all aspects of the RSO model, viewing geometry, imaging optics, sensor detector, and environmental conditions. These capabilities enhance the realism of mission scenario models and generated mission image data. As an input, PROXOR™ uses a library of 3-D satellite models containing 10+ satellites, including low-earth orbit (e.g., DMSP) and geostationary (e.g., Intelsat) spacecraft, where the spacecraft surface properties are those of actual materials and include Phong and Maxwell-Beard bidirectional reflectance distribution function (BRDF) coefficients for accurate radiometric modeling. We calculate the inertial attitude, the changing solar and Earth illumination angles of the satellite, and the viewing angles from the sensor as we propagate the RSO in its orbit. The synthetic satellite image is rendered at high resolution and aggregated to the focal plane resolution resulting in accurate radiometry even when the RSO is a point source. The sensor model includes optical effects from the imaging system [point spread function (PSF) includes aberrations, obscurations, support structures, defocus], detector effects (CCD blooming, left/right bias, fixed pattern noise, image persistence, shot noise, read noise, and quantization noise), and environmental effects (radiation hits with selectable angular distributions and 4-layer atmospheric turbulence model for ground based sensors). We have developed an accurate flash Light Detection and Ranging (LIDAR) model that supports reconstruction of 3-dimensional information on the RSO. PROXOR™ contains many important imaging effects such as intra-frame smear, realized by oversampling the image in time and capturing target motion and jitter during the integration time.

TAMDAR Sensor Validation in 2003 AIRS II

NASA Technical Reports Server (NTRS)

Daniels, Taumi S.; Murray, John J.; Anderson, Mark V.; Mulally, Daniel J.; Jensen, Kristopher R.; Grainger, Cedric A.; Delene, David J.

2005-01-01

This study entails an assessment of TAMDAR in situ temperature, relative humidity and winds sensor data from seven flights of the UND Citation II. These data are undergoing rigorous assessment to determine their viability to significantly augment domestic Meteorological Data Communications Reporting System (MDCRS) and the international Aircraft Meteorological Data Reporting (AMDAR) system observational databases to improve the performance of regional and global numerical weather prediction models. NASA Langley Research Center participated in the Second Alliance Icing Research Study from November 17 to December 17, 2003. TAMDAR data taken during this period is compared with validation data from the UND Citation. The data indicate acceptable performance of the TAMDAR sensor when compared to measurements from the UND Citation research instruments.

Aircraft Aerodynamic Parameter Detection Using Micro Hot-Film Flow Sensor Array and BP Neural Network Identification

PubMed Central

Que, Ruiyi; Zhu, Rong

2012-01-01

Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed. PMID:23112638

Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and BP neural network identification.

PubMed

Que, Ruiyi; Zhu, Rong

2012-01-01

Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring.

PubMed

Sim, Jai Kyoung; Yoon, Sunghyun; Cho, Young-Ho

2018-01-19

We propose watch-type sweat rate sensors capable of automatic natural ventilation by integrating miniaturized thermo-pneumatic actuators, and experimentally verify their performances and applicability. Previous sensors using natural ventilation require manual ventilation process or high-power bulky thermo-pneumatic actuators to lift sweat rate detection chambers above skin for continuous measurement. The proposed watch-type sweat rate sensors reduce operation power by minimizing expansion fluid volume to 0.4 ml through heat circuit modeling. The proposed sensors reduce operation power to 12.8% and weight to 47.6% compared to previous portable sensors, operating for 4 hours at 6 V batteries. Human experiment for thermal comfort monitoring is performed by using the proposed sensors having sensitivity of 0.039 (pF/s)/(g/m 2 h) and linearity of 97.9% in human sweat rate range. Average sweat rate difference for each thermal status measured in three subjects shows (32.06 ± 27.19) g/m 2 h in thermal statuses including 'comfortable', 'slightly warm', 'warm', and 'hot'. The proposed sensors thereby can discriminate and compare four stages of thermal status. Sweat rate measurement error of the proposed sensors is less than 10% under air velocity of 1.5 m/s corresponding to human walking speed. The proposed sensors are applicable for wearable and portable use, having potentials for daily thermal comfort monitoring applications.

Assessing the Performance of a Network of Low Cost Particulate Matter Sensors Deployed in Sacramento, California

NASA Astrophysics Data System (ADS)

Mukherjee, A. D.; Brown, S. G.; McCarthy, M. C.

2017-12-01

A new generation of low cost air quality sensors have the potential to provide valuable information on the spatial-temporal variability of air pollution - if the measurements have sufficient quality. This study examined the performance of a particulate matter sensor model, the AirBeam (HabitatMap Inc., Brooklyn, NY), over a three month period in the urban environment of Sacramento, California. Nineteen AirBeam sensors were deployed at a regulatory air monitoring site collocated with meteorology measurements and as a local network over an 80 km2 domain in Sacramento, CA. This study presents the methodology to evaluate the precision, accuracy, and reliability of the sensors over a range of meteorological and aerosol conditions. The sensors demonstrated a robust degree of precision during collocated measurement periods (R2 = 0.98 - 0.99) and a moderate degree of correlation against a Beta Attenuation Monitor PM2.5 monitor (R2 0.6). A normalization correction is applied during the study period so that each AirBeam sensor in the network reports a comparable value. The role of the meteorological environment on the accuracy of the sensor measurements is investigated, along with the possibility of improving the measurements through a meteorology weighted correction. The data quality of the network of sensors is examined, and the spatial variability of particulate matter through the study domain derived from the sensor network is presented.

The effects of pressure sensor acoustics on airdata derived from a High-angle-of-attack Flush Airdata Sensing (HI-FADS) system

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.

1991-01-01

The accuracy of a nonintrusive high angle-of-attack flush airdata sensing (HI-FADS) system was verified for quasi-steady flight conditions up to 55 deg angle of attack during the F-18 High Alpha Research Vehicle (HARV) Program. The system is a matrix of nine pressure ports arranged in annular rings on the aircraft nose. The complete airdata set is estimated using nonlinear regression. Satisfactory frequency response was verified to the system Nyquist frequency (12.5 Hz). The effects of acoustical distortions within the individual pressure sensors of the nonintrusive pressure matrix on overall system performance are addressed. To quantify these effects, a frequency-response model describing the dynamics of acoustical distortion is developed and simple design criteria are derived. The model adjusts measured HI-FADS pressure data for the acoustical distortion and quantifies the effects of internal sensor geometries on system performance. Analysis results indicate that sensor frequency response characteristics very greatly with altitude, thus it is difficult to select satisfactory sensor geometry for all altitudes. The solution used presample filtering to eliminate resonance effects, and short pneumatic tubing sections to reduce lag effects. Without presample signal conditioning the system designer must use the pneumatic transmission line to attenuate the resonances and accept the resulting altitude variability.

Radioactive threat detection using scintillant-based detectors

NASA Astrophysics Data System (ADS)

Chalmers, Alex

2004-09-01

An update to the performance of AS&E's Radioactive Threat Detection sensor technology. A model is presented detailing the components of the scintillant-based RTD system employed in AS&E products aimed at detecting radiological WMD. An overview of recent improvements in the sensors, electrical subsystems and software algorithms are presented. The resulting improvements in performance are described and sample results shown from existing systems. Advanced and future capabilities are described with an assessment of their feasibility and their application to Homeland Defense.

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry.

PubMed

Nait Aicha, Ahmed; Englebienne, Gwenn; van Schooten, Kimberley S; Pijnappels, Mirjam; Kröse, Ben

2018-05-22

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

PubMed Central

Englebienne, Gwenn; Pijnappels, Mirjam

2018-01-01

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. PMID:29786659

Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors.

PubMed

Uriarte Itzazelaia, Mikel; Astorga, Jasone; Jacob, Eduardo; Huarte, Maider; Romaña, Pedro

2018-02-13

Upcoming smart scenarios enabled by the Internet of Things (IoT) envision smart objects that provide services that can adapt to user behavior or be managed to achieve greater productivity. In such environments, smart things are inexpensive and, therefore, constrained devices. However, they are also critical components because of the importance of the information that they provide. Given this, strong security is a requirement, but not all security mechanisms in general and access control models in particular are feasible. In this paper, we present the feasibility assessment of an access control model that utilizes a hybrid architecture and a policy language that provides dynamic fine-grained policy enforcement in the sensors, which requires an efficient message exchange protocol called Hidra. This experimental performance assessment includes a prototype implementation, a performance evaluation model, the measurements and related discussions, which demonstrate the feasibility and adequacy of the analyzed access control model.

Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors

PubMed Central

Huarte, Maider; Romaña, Pedro

2018-01-01

Upcoming smart scenarios enabled by the Internet of Things (IoT) envision smart objects that provide services that can adapt to user behavior or be managed to achieve greater productivity. In such environments, smart things are inexpensive and, therefore, constrained devices. However, they are also critical components because of the importance of the information that they provide. Given this, strong security is a requirement, but not all security mechanisms in general and access control models in particular are feasible. In this paper, we present the feasibility assessment of an access control model that utilizes a hybrid architecture and a policy language that provides dynamic fine-grained policy enforcement in the sensors, which requires an efficient message exchange protocol called Hidra. This experimental performance assessment includes a prototype implementation, a performance evaluation model, the measurements and related discussions, which demonstrate the feasibility and adequacy of the analyzed access control model. PMID:29438338

Optimized star sensors laboratory calibration method using a regularization neural network.

PubMed

Zhang, Chengfen; Niu, Yanxiong; Zhang, Hao; Lu, Jiazhen

2018-02-10

High-precision ground calibration is essential to ensure the performance of star sensors. However, the complex distortion and multi-error coupling have brought great difficulties to traditional calibration methods, especially for large field of view (FOV) star sensors. Although increasing the complexity of models is an effective way to improve the calibration accuracy, it significantly increases the demand for calibration data. In order to achieve high-precision calibration of star sensors with large FOV, a novel laboratory calibration method based on a regularization neural network is proposed. A multi-layer structure neural network is designed to represent the mapping of the star vector and the corresponding star point coordinate directly. To ensure the generalization performance of the network, regularization strategies are incorporated into the net structure and the training algorithm. Simulation and experiment results demonstrate that the proposed method can achieve high precision with less calibration data and without any other priori information. Compared with traditional methods, the calibration error of the star sensor decreased by about 30%. The proposed method can satisfy the precision requirement for large FOV star sensors.

Curved sensors for compact high-resolution wide-field designs: prototype demonstration and optical characterization

NASA Astrophysics Data System (ADS)

Chambion, Bertrand; Gaschet, Christophe; Behaghel, Thibault; Vandeneynde, Aurélie; Caplet, Stéphane; Gétin, Stéphane; Henry, David; Hugot, Emmanuel; Jahn, Wilfried; Lombardo, Simona; Ferrari, Marc

2018-02-01

Over the recent years, a huge interest has grown for curved electronics, particularly for opto-electronics systems. Curved sensors help the correction of off-axis aberrations, such as Petzval Field Curvature, astigmatism, and bring significant optical and size benefits for imaging systems. In this paper, we first describe advantages of curved sensor and associated packaging process applied on a 1/1.8'' format 1.3Mpx global shutter CMOS sensor (Teledyne EV76C560) into its standard ceramic package with a spherical radius of curvature Rc=65mm and 55mm. The mechanical limits of the die are discussed (Finite Element Modelling and experimental), and electro-optical performances are investigated. Then, based on the monocentric optical architecture, we proposed a new design, compact and with a high resolution, developed specifically for a curved image sensor including optical optimization, tolerances, assembly and optical tests. Finally, a functional prototype is presented through a benchmark approach and compared to an existing standard optical system with same performances and a x2.5 reduction of length. The finality of this work was a functional prototype demonstration on the CEA-LETI during Photonics West 2018 conference. All these experiments and optical results demonstrate the feasibility and high performances of systems with curved sensors.

Crack Monitoring Method for an FRP-Strengthened Steel Structure Based on an Antenna Sensor.

PubMed

Liu, Zhiping; Chen, Kai; Li, Zongchen; Jiang, Xiaoli

2017-10-20

Fiber-reinforced polymer (FRP) has been increasingly applied to steel structures for structural strengthening or crack repair, given its high strength-to-weight ratio and high stiffness-to-weight ratio. Cracks in steel structures are the dominant hidden threats to structural safety. However, it is difficult to monitor structural cracks under FRP coverage and there is little related research. In this paper, a crack monitoring method for an FRP-strengthened steel structure deploying a microstrip antenna sensor is presented. A theoretical model of the dual-substrate antenna sensor with FRP is established and the sensitivity of crack monitoring is studied. The effects of the weak conductivity of carbon fiber reinforced polymers (CFRPs) on the performance of crack monitoring are analyzed via contrast experiments. The effects of FRP thickness on the performance of the antenna sensor are studied. The influence of structural strain on crack detection coupling is studied through strain-crack coupling experiments. The results indicate that the antenna sensor can detect cracks in steel structures covered by FRP (including CFRP). FRP thickness affects the antenna sensor's performance significantly, while the effects of strain can be ignored. The results provide a new approach for crack monitoring of FRP-strengthened steel structures with extensive application prospects.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Porous TiO₂-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis.

PubMed

Galstyan, Vardan

2017-12-19

Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO₂, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO₂-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO₂ may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material.

Porous TiO2-Based Gas Sensors for Cyber Chemical Systems to Provide Security and Medical Diagnosis

PubMed Central

2017-01-01

Gas sensors play an important role in our life, providing control and security of technical processes, environment, transportation and healthcare. Consequently, the development of high performance gas sensor devices is the subject of intense research. TiO2, with its excellent physical and chemical properties, is a very attractive material for the fabrication of chemical sensors. Meanwhile, the emerging technologies are focused on the fabrication of more flexible and smart systems for precise monitoring and diagnosis in real-time. The proposed cyber chemical systems in this paper are based on the integration of cyber elements with the chemical sensor devices. These systems may have a crucial effect on the environmental and industrial safety, control of carriage of dangerous goods and medicine. This review highlights the recent developments on fabrication of porous TiO2-based chemical gas sensors for their application in cyber chemical system showing the convenience and feasibility of such a model to provide the security and to perform the diagnostics. The most of reports have demonstrated that the fabrication of doped, mixed and composite structures based on porous TiO2 may drastically improve its sensing performance. In addition, each component has its unique effect on the sensing properties of material. PMID:29257076

Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

NASA Astrophysics Data System (ADS)

Johnson, Nicholas E.; Bonczak, Bartosz; Kontokosta, Constantine E.

2018-07-01

The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36-0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68-0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors.

Towards real-time assimilation of crowdsourced observations in hydrological modeling

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Verlaan, Martin; Alfonso, Leonardo; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri

2016-04-01

The continued technological advances have stimulated the spread of low-cost sensors that can be used by citizens to provide crowdsourced observations (CO) of different hydrological variables. An example of such low-cost sensors is a staff gauge connected to a QR code on which people can read the water level indication and send the measurement via a mobile phone application. The goal of this study is to assess the combined effect of the assimilation of CO coming from a distributed network of low-cost sensors, and the existing streamflow observations from physical sensors, on the performance of a semi-distributed hydrological model. The methodology is applied to the Bacchiglione catchment, North East of Italy, where an early warning system is used by the Alto Adriatico Water Authority to issue forecasted water level along the river network which cross important cities such as Vicenza and Padua. In this study, forecasted precipitation values are used as input in the hydrological model to estimate the simulated streamflow hydrograph used as boundary condition for the hydraulic model. Observed precipitation values are used to generate realistic synthetic streamflow values with various characteristics of arrival frequency and accuracy, to simulate CO coming at irregular time steps. These observations are assimilated into the semi-distributed model using a Kalman filter based method. The results of this study show that CO, asynchronous in time and with variable accuracy, can still improve flood prediction when integrated in hydrological models. When both physical and low-cost sensors are located at the same places, the assimilation of CO gives the same model improvement than the assimilation of physical observations only for high number of non-intermittent sensors. However, the integration of observations from low-cost sensors and single physical sensors can improve the flood prediction even when small a number of intermittent CO are available. This study is part of the FP7 European Project WeSenseIt Citizen Water Observatory (www.http://wesenseit.eu/).

Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis

NASA Astrophysics Data System (ADS)

Monekosso, Dorothy; Remagnino, Paolo

This paper describes a data generator that produces synthetic data to simulate observations from an array of environment monitoring sensors. The overall goal of our work is to monitor the well-being of one occupant in a home. Sensors are embedded in a smart home to unobtrusively record environmental parameters. Based on the sensor observations, behavior analysis and modeling are performed. However behavior analysis and modeling require large data sets to be collected over long periods of time to achieve the level of accuracy expected. A data generator - was developed based on initial data i.e. data collected over periods lasting weeks to facilitate concurrent data collection and development of algorithms. The data generator is based on statistical inference techniques. Variation is introduced into the data using perturbation models.

Autonomous Sun-Direction Estimation Using Partially Underdetermined Coarse Sun Sensor Configurations

NASA Astrophysics Data System (ADS)

O'Keefe, Stephen A.

In recent years there has been a significant increase in interest in smaller satellites as lower cost alternatives to traditional satellites, particularly with the rise in popularity of the CubeSat. Due to stringent mass, size, and often budget constraints, these small satellites rely on making the most of inexpensive hardware components and sensors, such as coarse sun sensors (CSS) and magnetometers. More expensive high-accuracy sun sensors often combine multiple measurements, and use specialized electronics, to deterministically solve for the direction of the Sun. Alternatively, cosine-type CSS output a voltage relative to the input light and are attractive due to their very low cost, simplicity to manufacture, small size, and minimal power consumption. This research investigates using coarse sun sensors for performing robust attitude estimation in order to point a spacecraft at the Sun after deployment from a launch vehicle, or following a system fault. As an alternative to using a large number of sensors, this thesis explores sun-direction estimation techniques with low computational costs that function well with underdetermined sets of CSS. Single-point estimators are coupled with simultaneous nonlinear control to achieve sun-pointing within a small percentage of a single orbit despite the partially underdetermined nature of the sensor suite. Leveraging an extensive analysis of the sensor models involved, sequential filtering techniques are shown to be capable of estimating the sun-direction to within a few degrees, with no a priori attitude information and using only CSS, despite the significant noise and biases present in the system. Detailed numerical simulations are used to compare and contrast the performance of the five different estimation techniques, with and without rate gyro measurements, their sensitivity to rate gyro accuracy, and their computation time. One of the key concerns with reducing the number of CSS is sensor degradation and failure. In this thesis, a Modified Rodrigues Parameter based CSS calibration filter suitable for autonomous on-board operation is developed. The sensitivity of this method's accuracy to the available Earth albedo data is evaluated and compared to the required computational effort. The calibration filter is expanded to perform sensor fault detection, and promising results are shown for reduced resolution albedo models. All of the methods discussed provide alternative attitude, determination, and control system algorithms for small satellite missions looking to use inexpensive, small sensors due to size, power, or budget limitations.

Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels

PubMed Central

Ebara, Takeshi; Azuma, Ryohei; Shoji, Naoto; Matsukawa, Tsuyoshi; Yamada, Yasuyuki; Akiyama, Tomohiro; Kurihara, Takahiro; Yamada, Shota

2017-01-01

Objectives: Objective measurements using built-in smartphone sensors that can measure physical activity/inactivity in daily working life have the potential to provide a new approach to assessing workers' health effects. The aim of this study was to elucidate the characteristics and reliability of built-in step counting sensors on smartphones for development of an easy-to-use objective measurement tool that can be applied in ergonomics or epidemiological research. Methods: To evaluate the reliability of step counting sensors embedded in seven major smartphone models, the 6-minute walk test was conducted and the following analyses of sensor precision and accuracy were performed: 1) relationship between actual step count and step count detected by sensors, 2) reliability between smartphones of the same model, and 3) false detection rates when sitting during office work, while riding the subway, and driving. Results: On five of the seven models, the inter-class correlations coefficient (ICC (3,1)) showed high reliability with a range of 0.956-0.993. The other two models, however, had ranges of 0.443-0.504 and the relative error ratios of the sensor-detected step count to the actual step count were ±48.7%-49.4%. The level of agreement between the same models was ICC (3,1): 0.992-0.998. The false detection rates differed between the sitting conditions. Conclusions: These results suggest the need for appropriate regulation of step counts measured by sensors, through means such as correction or calibration with a predictive model formula, in order to obtain the highly reliable measurement results that are sought in scientific investigation. PMID:28835575

Augmented switching linear dynamical system model for gas concentration estimation with MOX sensors in an open sampling system.

PubMed

Di Lello, Enrico; Trincavelli, Marco; Bruyninckx, Herman; De Laet, Tinne

2014-07-11

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector.

Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System

PubMed Central

Di Lello, Enrico; Trincavelli, Marco; Bruyninckx, Herman; De Laet, Tinne

2014-01-01

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector. PMID:25019637

CHIMERA II - A real-time multiprocessing environment for sensor-based robot control

NASA Technical Reports Server (NTRS)

Stewart, David B.; Schmitz, Donald E.; Khosla, Pradeep K.

1989-01-01

A multiprocessing environment for a wide variety of sensor-based robot system, providing the flexibility, performance, and UNIX-compatible interface needed for fast development of real-time code is addressed. The requirements imposed on the design of a programming environment for sensor-based robotic control is outlined. The details of the current hardware configuration are presented, along with the details of the CHIMERA II software. Emphasis is placed on the kernel, low-level interboard communication, user interface, extended file system, user-definable and dynamically selectable real-time schedulers, remote process synchronization, and generalized interprocess communication. A possible implementation of a hierarchical control model, the NASA/NBS standard reference model for telerobot control system is demonstrated.

Development of a Data Acquisition System for Unmanned Aerial Vehicle (UAV) System Identification

NASA Astrophysics Data System (ADS)

Lear, Donald Joseph

Aircraft system identification techniques are developed for fixed wing Unmanned Aerial Vehicles (UAV). The use of a designed flight experiment with measured system inputs/outputs can be used to derive aircraft stability derivatives. This project set out to develop a methodology to support an experiment to model pitch damping in the longitudinal short-period mode of a UAV. A Central Composite Response Surface Design was formed using angle of attack and power levels as factors to test for the pitching moment coefficient response induced by a multistep pitching maneuver. Selecting a high-quality data acquisition platform was critical to the success of the project. This system was designed to support fixed wing research through the addition of a custom air data vane capable of measuring angle of attack and sideslip, as well as an airspeed sensor. A Pixhawk autopilot system serves as the core and modification of the device firmware allowed for the integration of custom sensors and custom RC channels dedicated to performing system identification maneuvers. Tests were performed on all existing Pixhawk sensors to validate stated uncertainty values. The air data system was calibrated in a low speed wind tunnel and dynamic performance was verified. The assembled system was then installed in a commercially available UAV known as an Air Titan FPV in order to test the Pixhawk's automated flight maneuvers and determine the final performance of each sensor. Flight testing showed all the critical sensors produced acceptable data for further research. The Air Titan FPV airframe was found to be very flexible and did not lend itself well to accurate measurement of inertial properties. This realization prohibited the construction of the required math models for longitudinal dynamics. It is recommended that future projects using the developed methods choose an aircraft with a more rigid airframe.

Optimization of geometric characteristics to improve sensing performance of MEMS piezoresistive strain sensors

NASA Astrophysics Data System (ADS)

Mohammed, Ahmed A. S.; Moussa, Walied A.; Lou, Edmond

2010-01-01

In this paper, the design of MEMS piezoresistive strain sensor is described. ANSYS®, finite element analysis (FEA) software, was used as a tool to model the performance of the silicon-based sensor. The incorporation of stress concentration regions (SCRs), to localize stresses, was explored in detail. This methodology employs the structural design of the sensor silicon carrier. Therefore, the induced strain in the sensing chip yielded stress concentration in the vicinity of the SCRs. Hence, this concept was proved to enhance the sensor sensitivity. Another advantage of the SCRs is to reduce the sensor transverse gauge factor, which offered a great opportunity to develop a MEMS sensor with minimal cross sensitivity. Two basic SCR designs were studied. The depth of the SCRs was also investigated. Moreover, FEA simulation is utilized to investigate the effect of the sensing element depth on the sensor sensitivity. Simulation results showed that the sensor sensitivity is independent of the piezoresistors' depth. The microfabrication process flow was introduced to prototype the different sensor designs. The experiments covered operating temperature range from -50 °C to +50 °C. Finally, packaging scheme and bonding adhesive selection were discussed. The experimental results showed good agreement with the FEA simulation results. The findings of this study confirmed the feasibility of introducing SCRs in the sensor silicon carrier to improve the sensor sensitivity while using relatively high doping levels (5 × 1019 atoms cm-3). The fabricated sensors have a gauge factor about three to four times higher compared to conventional thin-foil strain gauges.

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

PubMed Central

Jiang, Hao; Zhao, Dehua; Cai, Ying; An, Shuqing

2012-01-01

In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models.

System model the processing of heterogeneous sensory information in robotized complex

NASA Astrophysics Data System (ADS)

Nikolaev, V.; Titov, V.; Syryamkin, V.

2018-05-01

Analyzed the scope and the types of robotic systems consisting of subsystems of the form "a heterogeneous sensors data processing subsystem". On the basis of the Queuing theory model is developed taking into account the unevenness of the intensity of information flow from the sensors to the subsystem of information processing. Analytical solution to assess the relationship of subsystem performance and uneven flows. The research of the obtained solution in the range of parameter values of practical interest.

Horizon sensor errors calculated by computer models compared with errors measured in orbit

NASA Technical Reports Server (NTRS)

Ward, K. A.; Hogan, R.; Andary, J.

1982-01-01

Using a computer program to model the earth's horizon and to duplicate the signal processing procedure employed by the ESA (Earth Sensor Assembly), errors due to radiance variation have been computed for a particular time of the year. Errors actually occurring in flight at the same time of year are inferred from integrated rate gyro data for a satellite of the TIROS series of NASA weather satellites (NOAA-A). The predicted performance is compared with actual flight history.

Search and detection modeling of military imaging systems

NASA Astrophysics Data System (ADS)

Maurer, Tana; Wilson, David L.; Driggers, Ronald G.

2013-04-01

For more than 50 years, the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) has been studying the science behind the human processes of searching and detecting, and using that knowledge to develop and refine its models for military imaging systems. Modeling how human observers perform military tasks while using imaging systems in the field and linking that model with the physics of the systems has resulted in the comprehensive sensor models we have today. These models are used by the government, military, industry, and academia for sensor development, sensor system acquisition, military tactics development, and war-gaming. From the original hypothesis put forth by John Johnson in 1958, to modeling time-limited search, to modeling the impact of motion on target detection, to modeling target acquisition performance in different spectral bands, the concept of search has a wide-ranging history. Our purpose is to present a snapshot of that history; as such, it will begin with a description of the search-modeling task, followed by a summary of highlights from the early years, and concluding with a discussion of search and detection modeling today and the changing battlefield. Some of the topics to be discussed will be classic search, clutter, computational vision models and the ACQUIRE model with its variants. We do not claim to present a complete history here, but rather a look at some of the work that has been done, and this is meant to be an introduction to an extensive amount of work on a complex topic. That said, it is hoped that this overview of the history of search and detection modeling of military imaging systems pursued by NVESD directly, or in association with other government agencies or contractors, will provide both the novice and experienced search modeler with a useful historical summary and an introduction to current issues and future challenges.

Passive wireless antenna sensor for strain and crack sensing—electromagnetic modeling, simulation, and testing

NASA Astrophysics Data System (ADS)

Yi, Xiaohua; Cho, Chunhee; Cooper, James; Wang, Yang; Tentzeris, Manos M.; Leon, Roberto T.

2013-08-01

This research investigates a passive wireless antenna sensor designed for strain and crack sensing. When the antenna experiences deformation, the antenna shape changes, causing a shift in the electromagnetic resonance frequency of the antenna. A radio frequency identification (RFID) chip is adopted for antenna signal modulation, so that a wireless reader can easily distinguish the backscattered sensor signal from unwanted environmental reflections. The RFID chip captures its operating power from an interrogation electromagnetic wave emitted by the reader, which allows the antenna sensor to be passive (battery-free). This paper first reports the latest simulation results on radiation patterns, surface current density, and electromagnetic field distribution. The simulation results are followed with experimental results on the strain and crack sensing performance of the antenna sensor. Tensile tests show that the wireless antenna sensor can detect small strain changes lower than 20 με, and can perform well at large strains higher than 10 000 με. With a high-gain reader antenna, the wireless interrogation distance can be increased up to 2.1 m. Furthermore, an array of antenna sensors is capable of measuring the strain distribution in close proximity. During emulated crack and fatigue crack tests, the antenna sensor is able to detect the growth of a small crack.

Performance Monitoring of Diabetic Patient Systems

DTIC Science & Technology

2001-10-25

a process delay that is due to the dynamics of the glucose sensor. A. Bergman Model The Bergman and AIDA models both utilize a \\minimal model...approxima- tion of the process must be made to achieve reasonable performance. A rst order approximation, ~g(s), of both the Bergman and AIDA models is...Within the IMC framework, both the Bergman and AIDA models can be controlled within acceptable toler- ances. The simulated faults are stochastic

Design of a miniature wind turbine for powering wireless sensors

NASA Astrophysics Data System (ADS)

Xu, F. J.; Yuan, F. G.; Hu, J. Z.; Qiu, Y. P.

2010-04-01

In this paper, a miniature wind turbine (MWT) system composed of commercially available off-the-shelf components was designed and tested for harvesting energy from ambient airflow to power wireless sensors. To make MWT operate at very low air flow rates, a 7.6 cm thorgren plastic Propeller blade was adopted as the wind turbine blade. A sub watt brushless DC motor was used as generator. To predict the performance of the MWT, an equivalent circuit model was employed for analyzing the output power and the net efficiency of the MWT system. In theory, the maximum net efficiency 14.8% of the MWT system was predicted. Experimental output power of the MWT versus resistive loads ranging from 5 ohms to 500 ohms under wind speeds from 3 m/s to 4.5 m/s correlates well with those from the predicted model, which means that the equivalent circuit model provides a guideline for optimizing the performance of the MWT and can be used for fulfilling the design requirements by selecting specific components for powering wireless sensors.

SNDR Limits of Oscillator-Based Sensor Readout Circuits.

PubMed

Cardes, Fernando; Quintero, Andres; Gutierrez, Eric; Buffa, Cesare; Wiesbauer, Andreas; Hernandez, Luis

2018-02-03

This paper analyzes the influence of phase noise and distortion on the performance of oscillator-based sensor data acquisition systems. Circuit noise inherent to the oscillator circuit manifests as phase noise and limits the SNR. Moreover, oscillator nonlinearity generates distortion for large input signals. Phase noise analysis of oscillators is well known in the literature, but the relationship between phase noise and the SNR of an oscillator-based sensor is not straightforward. This paper proposes a model to estimate the influence of phase noise in the performance of an oscillator-based system by reflecting the phase noise to the oscillator input. The proposed model is based on periodic steady-state analysis tools to predict the SNR of the oscillator. The accuracy of this model has been validated by both simulation and experiment in a 130 nm CMOS prototype. We also propose a method to estimate the SNDR and the dynamic range of an oscillator-based readout circuit that improves by more than one order of magnitude the simulation time compared to standard time domain simulations. This speed up enables the optimization and verification of this kind of systems with iterative algorithms.

Numerical performance analysis of quartz tuning fork-based force sensors

NASA Astrophysics Data System (ADS)

Dagdeviren, Omur E.; Schwarz, Udo D.

2017-01-01

Quartz tuning fork-based force sensors where one prong is immobilized onto a holder while the other one is allowed to oscillate freely (‘qPlus’ configuration) are in widespread use for high-resolution scanning probe microscopy applications. Due to the small size of the tuning forks (≈3 mm) and the complexity of the sensor assemblies, the reliable and repeatable manufacturing of the sensors has been challenging. In this paper, we investigate the contribution of the amount and location of the epoxy glue used to attach the tuning fork to its holder on the sensor’s performance. Towards this end, we use finite element analysis to model the entire sensor assembly and to perform static and dynamic numerical simulations. Our analysis reveals that increasing the thickness of the epoxy layer between prong and holder results in a decrease of the sensor’s spring constant, eigenfrequency, and quality factor while showing an increasing deviation from oscillation in its primary modal shape. Adding epoxy at the sides of the tuning fork also leads to a degradation of the quality factor even though in this case, spring constant and eigenfrequency rise in tandem with a lessening of the deviation from its ideal modal shape.

Study of the Integration of LIDAR and Photogrammetric Datasets by in Situ Camera Calibration and Integrated Sensor Orientation

NASA Astrophysics Data System (ADS)

Mitishita, E.; Costa, F.; Martins, M.

2017-05-01

Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO) approach was performed using Interior Orientation Parameter (IOP) values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

Learning and diagnosing faults using neural networks

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

1990-01-01

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

PubMed

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

Performance bounds for modal analysis using sparse linear arrays

NASA Astrophysics Data System (ADS)

Li, Yuanxin; Pezeshki, Ali; Scharf, Louis L.; Chi, Yuejie

2017-05-01

We study the performance of modal analysis using sparse linear arrays (SLAs) such as nested and co-prime arrays, in both first-order and second-order measurement models. We treat SLAs as constructed from a subset of sensors in a dense uniform linear array (ULA), and characterize the performance loss of SLAs with respect to the ULA due to using much fewer sensors. In particular, we claim that, provided the same aperture, in order to achieve comparable performance in terms of Cramér-Rao bound (CRB) for modal analysis, SLAs require more snapshots, of which the number is about the number of snapshots used by ULA times the compression ratio in the number of sensors. This is shown analytically for the case with one undamped mode, as well as empirically via extensive numerical experiments for more complex scenarios. Moreover, the misspecified CRB proposed by Richmond and Horowitz is also studied, where SLAs suffer more performance loss than their ULA counterpart.

Non-destructive evaluation of laminated composite plates using dielectrometry sensors

NASA Astrophysics Data System (ADS)

Nassr, Amr A.; El-Dakhakhni, Wael W.

2009-05-01

The use of composite materials in marine, aerospace and automotive applications is increasing; however, several kinds of damages of composite materials may influence its durability and future applications. In this paper, a methodology was presented for damage detection of laminated composite plates using dielectrometry sensors. The presence of damage in the laminated composite plate leads to changes in its dielectric characteristics, causing variation in the measured capacitance by the sensors. An analytical model was used to analyse the influence of different sensor parameters on the output signals and to optimize sensor design. Two-dimensional finite element (FE) simulations were performed to assess the validity of the analytical results and to evaluate other sensor design-related parameters. To experimentally verify the model, the dielectric permittivity of the composite plate was measured. In addition, a glass fibre reinforced polymer (GFRP) laminated plate containing pre-fabricated slots through its thickness to simulate delamination and water intrusion defects was inspected in a laboratory setting. Excellent agreements were found between the experimental capacitance response signals and those predicated from the FE simulations. This cost-effective technique can be used for rapid damage screening, regular scheduled inspection, or as a permanent sensor network within the composite system.

Input/output models for general aviation piston-prop aircraft fuel economy

NASA Technical Reports Server (NTRS)

Sweet, L. M.

1982-01-01

A fuel efficient cruise performance model for general aviation piston engine airplane was tested. The following equations were made: (1) for the standard atmosphere; (2) airframe-propeller-atmosphere cruise performance; and (3) naturally aspirated engine cruise performance. Adjustments are made to the compact cruise performance model as follows: corrected quantities, corrected performance plots, algebraic equations, maximize R with or without constraints, and appears suitable for airborne microprocessor implementation. The following hardwares are recommended: ignition timing regulator, fuel-air mass ration controller, microprocessor, sensors and displays.

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Wearable sensors for patient-specific boundary shape estimation to improve the forward model for electrical impedance tomography (EIT) of neonatal lung function.

PubMed

Khor, Joo Moy; Tizzard, Andrew; Demosthenous, Andreas; Bayford, Richard

2014-06-01

Electrical impedance tomography (EIT) could be significantly advantageous to continuous monitoring of lung development in newborn and, in particular, preterm infants as it is non-invasive and safe to use within the intensive care unit. It has been demonstrated that accurate boundary form of the forward model is important to minimize artefacts in reconstructed electrical impedance images. This paper presents the outcomes of initial investigations for acquiring patient-specific thorax boundary information using a network of flexible sensors that imposes no restrictions on the patient's normal breathing and movements. The investigations include: (1) description of the basis of the reconstruction algorithms, (2) tests to determine a minimum number of bend sensors, (3) validation of two approaches to reconstruction and (4) an example of a commercially available bend sensor and its performance. Simulation results using ideal sensors show that, in the worst case, a total shape error of less than 6% with respect to its total perimeter can be achieved.

Adaptive Sampling of Time Series During Remote Exploration

NASA Technical Reports Server (NTRS)

Thompson, David R.

2012-01-01

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Atmospheric Modeling And Sensor Simulation (AMASS) study

NASA Technical Reports Server (NTRS)

Parker, K. G.

1984-01-01

The capabilities of the atmospheric modeling and sensor simulation (AMASS) system were studied in order to enhance them. This system is used in processing atmospheric measurements which are utilized in the evaluation of sensor performance, conducting design-concept simulation studies, and also in the modeling of the physical and dynamical nature of atmospheric processes. The study tasks proposed in order to both enhance the AMASS system utilization and to integrate the AMASS system with other existing equipment to facilitate the analysis of data for modeling and image processing are enumerated. The following array processors were evaluated for anticipated effectiveness and/or improvements in throughput by attachment of the device to the P-e: (1) Floating Point Systems AP-120B; (2) Floating Point Systems 5000; (3) CSP, Inc. MAP-400; (4) Analogic AP500; (5) Numerix MARS-432; and (6) Star Technologies, Inc. ST-100.

Hidden Markov model analysis of force/torque information in telemanipulation

NASA Technical Reports Server (NTRS)

Hannaford, Blake; Lee, Paul

1991-01-01

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

Theoretical analysis of the performance of glucose sensors with layer-by-layer assembled outer membranes.

PubMed

Croce, Robert A; Vaddiraju, Santhisagar; Papadimitrakopoulos, Fotios; Jain, Faquir C

2012-10-01

The performance of implantable electrochemical glucose sensors is highly dependent on the flux-limiting (glucose, H(2)O(2), O(2)) properties of their outer membranes. A careful understanding of the diffusion profiles of the participating species throughout the sensor architecture (enzyme and membrane layer) plays a crucial role in designing a robust sensor for both in vitro and in vivo operation. This paper reports the results from the mathematical modeling of Clark's first generation amperometric glucose sensor coated with layer-by-layer assembled outer membranes in order to obtain and compare the diffusion profiles of various participating species and their effect on sensor performance. Devices coated with highly glucose permeable (HAs/Fe(3+)) membranes were compared with devices coated with PSS/PDDA membranes, which have an order of magnitude lower permeability. The simulation showed that the low glucose permeable membrane (PSS/PDDA) sensors exhibited a 27% higher amperometric response than the high glucose permeable (HAs/Fe(3+)) sensors. Upon closer inspection of H(2)O(2)diffusion profiles, this non-typical higher response from PSS/PDDA is not due to either a larger glucose flux or comparatively larger O(2) concentrations within the sensor geometry, but rather is attributed to a 48% higher H(2)O(2) concentration in the glucose oxidase enzyme layer of PSS/PDDA coated sensors as compared to HAs/Fe(3+) coated ones. These simulated results corroborate our experimental findings reported previously. The high concentration of H(2)O(2) in the PSS/PDDA coated sensors is due to the low permeability of H(2)O(2) through the PSS/PDDA membrane, which also led to an undesired increase in sensor response time. Additionally, it was found that this phenomenon occurs for all enzyme thicknesses investigated (15, 20 and 25 nm), signifying the need for a holistic approach in designing outer membranes for amperometric biosensors.

Mars 2020 Entry, Descent, and Landing Instrumentation 2 (MEDLI2) Sensor Suite

NASA Technical Reports Server (NTRS)

Hwang, Helen; Wright, Henry; Kuhl, Chris; Schoenenberger, Mark; White, Todd; Karlgaard, Chris; Mahzari, Milad; Oishi, Tomo; Pennington, Steve; Trombetta, Nick;

Accurate prediction of energy expenditure using a shoe-based activity monitor.

PubMed

Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

2011-07-01

The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model

PubMed Central

Bagula, Antoine; Castelli, Lorenzo; Zennaro, Marco

2015-01-01

Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called “anchor” nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network. PMID:26134104

On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model.

PubMed

Bagula, Antoine; Castelli, Lorenzo; Zennaro, Marco

2015-06-30

Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called "anchor" nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network.

Analytical modeling and sensor monitoring for optimal processing of advanced textile structural composites by resin transfer molding

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Macrae, John D.; Hammond, Vincent H.; Kranbuehl, David E.; Hart, Sean M.; Hasko, Gregory H.; Markus, Alan M.

1993-01-01

A two-dimensional model of the resin transfer molding (RTM) process was developed which can be used to simulate the infiltration of resin into an anisotropic fibrous preform. Frequency dependent electromagnetic sensing (FDEMS) has been developed for in situ monitoring of the RTM process. Flow visualization tests were performed to obtain data which can be used to verify the sensor measurements and the model predictions. Results of the tests showed that FDEMS can accurately detect the position of the resin flow-front during mold filling, and that the model predicted flow-front patterns agreed well with the measured flow-front patterns.

Bayesian model for matching the radiometric measurements of aerospace and field ocean color sensors.

PubMed

Salama, Mhd Suhyb; Su, Zhongbo

2010-01-01

A Bayesian model is developed to match aerospace ocean color observation to field measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R(2) > 0.88 and relative error < 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors.

Stellar figure sensor

NASA Technical Reports Server (NTRS)

Peters, W. N.

1973-01-01

A compilation of analytical and experimental data is presented concerning the stellar figure sensor. The sensor is an interferometric device which is located in the focal plane of an orbiting large space telescope (LST). The device was designed to perform interferometry on the optical wavefront of a single star after it has propagated through the LST. An analytical model of the device was developed and its accuracy was verified by an operating laboratory breadboard. A series of linear independent control equations were derived which define the operations required for utilizing a focal plane figure sensor in the control loop for the secondary mirror position and for active control of the primary mirror.

Discrete piezoelectric sensors and actuators for active control of two-dimensional spacecraft components

NASA Technical Reports Server (NTRS)

Bayer, Janice I.; Varadan, V. V.; Varadan, V. K.

1991-01-01

This paper describes research into the use of discrete piezoelectric sensors and actuators for active modal control of flexible two-dimensional structures such as might be used as components for spacecraft. A dynamic coupling term is defined between the sensor/actuator and the structure in terms of structural model shapes, location and piezoelectric behavior. The relative size of the coupling term determines sensor/actuator placement. Results are shown for a clamped square plate and for a large antenna. An experiment was performed on a thin foot-square plate clamped on all sides. Sizable vibration control was achieved for first, second/third (degenerate) and fourth modes.

Thermal luminescence spectroscopy chemical imaging sensor.

PubMed

Carrieri, Arthur H; Buican, Tudor N; Roese, Erik S; Sutter, James; Samuels, Alan C

2012-10-01

The authors present a pseudo-active chemical imaging sensor model embodying irradiative transient heating, temperature nonequilibrium thermal luminescence spectroscopy, differential hyperspectral imaging, and artificial neural network technologies integrated together. We elaborate on various optimizations, simulations, and animations of the integrated sensor design and apply it to the terrestrial chemical contamination problem, where the interstitial contaminant compounds of detection interest (analytes) comprise liquid chemical warfare agents, their various derivative condensed phase compounds, and other material of a life-threatening nature. The sensor must measure and process a dynamic pattern of absorptive-emissive middle infrared molecular signature spectra of subject analytes to perform its chemical imaging and standoff detection functions successfully.

Experimental Robot Position Sensor Fault Tolerance Using Accelerometers and Joint Torque Sensors

NASA Technical Reports Server (NTRS)

Aldridge, Hal A.; Juang, Jer-Nan

1997-01-01

Robot systems in critical applications, such as those in space and nuclear environments, must be able to operate during component failure to complete important tasks. One failure mode that has received little attention is the failure of joint position sensors. Current fault tolerant designs require the addition of directly redundant position sensors which can affect joint design. The proposed method uses joint torque sensors found in most existing advanced robot designs along with easily locatable, lightweight accelerometers to provide a joint position sensor fault recovery mode. This mode uses the torque sensors along with a virtual passive control law for stability and accelerometers for joint position information. Two methods for conversion from Cartesian acceleration to joint position based on robot kinematics, not integration, are presented. The fault tolerant control method was tested on several joints of a laboratory robot. The controllers performed well with noisy, biased data and a model with uncertain parameters.

Natural User Interface Sensors for Human Body Measurement

NASA Astrophysics Data System (ADS)

Boehm, J.

2012-08-01

The recent push for natural user interfaces (NUI) in the entertainment and gaming industry has ushered in a new era of low cost three-dimensional sensors. While the basic idea of using a three-dimensional sensor for human gesture recognition dates some years back it is not until recently that such sensors became available on the mass market. The current market leader is PrimeSense who provide their technology for the Microsoft Xbox Kinect. Since these sensors are developed to detect and observe human users they should be ideally suited to measure the human body. We describe the technology of a line of NUI sensors and assess their performance in terms of repeatability and accuracy. We demonstrate the implementation of a prototype scanner integrating several NUI sensors to achieve full body coverage. We present the results of the obtained surface model of a human body.

A three-axis force sensor for dual finger haptic interfaces.

PubMed

Fontana, Marco; Marcheschi, Simone; Salsedo, Fabio; Bergamasco, Massimo

2012-10-10

In this work we present the design process, the characterization and testing of a novel three-axis mechanical force sensor. This sensor is optimized for use in closed-loop force control of haptic devices with three degrees of freedom. In particular the sensor has been conceived for integration with a dual finger haptic interface that aims at simulating forces that occur during grasping and surface exploration. The sensing spring structure has been purposely designed in order to match force and layout specifications for the application. In this paper the design of the sensor is presented, starting from an analytic model that describes the characteristic matrix of the sensor. A procedure for designing an optimal overload protection mechanism is proposed. In the last part of the paper the authors describe the experimental characterization and the integrated test on a haptic hand exoskeleton showing the improvements in the controller performances provided by the inclusion of the force sensor.

The impact of missing sensor information on surgical workflow management.

PubMed

Liebmann, Philipp; Meixensberger, Jürgen; Wiedemann, Peter; Neumuth, Thomas

2013-09-01

Sensor systems in the operating room may encounter intermittent data losses that reduce the performance of surgical workflow management systems (SWFMS). Sensor data loss could impact SWFMS-based decision support, device parameterization, and information presentation. The purpose of this study was to understand the robustness of surgical process models when sensor information is partially missing. SWFMS changes caused by wrong or no data from the sensor system which tracks the progress of a surgical intervention were tested. The individual surgical process models (iSPMs) from 100 different cataract procedures of 3 ophthalmologic surgeons were used to select a randomized subset and create a generalized surgical process model (gSPM). A disjoint subset was selected from the iSPMs and used to simulate the surgical process against the gSPM. The loss of sensor data was simulated by removing some information from one task in the iSPM. The effect of missing sensor data was measured using several metrics: (a) successful relocation of the path in the gSPM, (b) the number of steps to find the converging point, and (c) the perspective with the highest occurrence of unsuccessful path findings. A gSPM built using 30% of the iSPMs successfully found the correct path in 90% of the cases. The most critical sensor data were the information regarding the instrument used by the surgeon. We found that use of a gSPM to provide input data for a SWFMS is robust and can be accurate despite missing sensor data. A surgical workflow management system can provide the surgeon with workflow guidance in the OR for most cases. Sensor systems for surgical process tracking can be evaluated based on the stability and accuracy of functional and spatial operative results.

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Laser absorption of nitric oxide for thermometry in high-enthalpy air

NASA Astrophysics Data System (ADS)

Spearrin, R. M.; Schultz, I. A.; Jeffries, J. B.; Hanson, R. K.

2014-12-01

The design and demonstration of a laser absorption sensor for thermometry in high-enthalpy air is presented. The sensor exploits the highly temperature-sensitive and largely pressure-independent concentration of nitric oxide in air at chemical equilibrium. Temperature is thus inferred from an in situ measurement of nascent nitric oxide. The strategy is developed by utilizing a quantum cascade laser source for access to the strong fundamental absorption band in the mid-infrared spectrum of nitric oxide. Room temperature measurements in a high-pressure static cell validate the suitability of the Voigt lineshape model to the nitric oxide spectra at high gas densities. Shock-tube experiments enable calibration of a collision-broadening model for temperatures between 1200-3000 K. Finally, sensor performance is demonstrated in a high-pressure shock tube by measuring temperature behind reflected shock waves for both fixed-chemistry experiments where nitric oxide is seeded, and for experiments involving nitric oxide formation in shock-heated mixtures of N2 and O2. Results show excellent performance of the sensor across a wide range of operating conditions from 1100-2950 K and at pressures up to 140 atm.

The influence of the in situ camera calibration for direct georeferencing of aerial imagery

NASA Astrophysics Data System (ADS)

Mitishita, E.; Barrios, R.; Centeno, J.

2014-11-01

The direct determination of exterior orientation parameters (EOPs) of aerial images via GNSS/INS technologies is an essential prerequisite in photogrammetric mapping nowadays. Although direct sensor orientation technologies provide a high degree of automation in the process due to the GNSS/INS technologies, the accuracies of the obtained results depend on the quality of a group of parameters that models accurately the conditions of the system at the moment the job is performed. One sub-group of parameters (lever arm offsets and boresight misalignments) models the position and orientation of the sensors with respect to the IMU body frame due to the impossibility of having all sensors on the same position and orientation in the airborne platform. Another sub-group of parameters models the internal characteristics of the sensor (IOP). A system calibration procedure has been recommended by worldwide studies to obtain accurate parameters (mounting and sensor characteristics) for applications of the direct sensor orientation. Commonly, mounting and sensor characteristics are not stable; they can vary in different flight conditions. The system calibration requires a geometric arrangement of the flight and/or control points to decouple correlated parameters, which are not available in the conventional photogrammetric flight. Considering this difficulty, this study investigates the feasibility of the in situ camera calibration to improve the accuracy of the direct georeferencing of aerial images. The camera calibration uses a minimum image block, extracted from the conventional photogrammetric flight, and control point arrangement. A digital Vexcel UltraCam XP camera connected to POS AV TM system was used to get two photogrammetric image blocks. The blocks have different flight directions and opposite flight line. In situ calibration procedures to compute different sets of IOPs are performed and their results are analyzed and used in photogrammetric experiments. The IOPs from the in situ camera calibration improve significantly the accuracies of the direct georeferencing. The obtained results from the experiments are shown and discussed.

Threat assessment and sensor management in a modular architecture

NASA Astrophysics Data System (ADS)

Page, S. F.; Oldfield, J. P.; Islip, S.; Benfold, B.; Brandon, R.; Thomas, P. A.; Stubbins, D. J.

2016-10-01

Many existing asset/area protection systems, for example those deployed to protect critical national infrastructure, are comprised of multiple sensors such as EO/IR, radar, and Perimeter Intrusion Detection Systems (PIDS), loosely integrated with a central Command and Control (C2) system. Whilst some sensors provide automatic event detection and C2 systems commonly provide rudimentary multi-sensor rule based alerting, the performance of such systems is limited by the lack of deep integration and autonomy. As a result, these systems have a high degree of operator burden. To address these challenges, an architectural concept termed "SAPIENT" was conceived. SAPIENT is based on multiple Autonomous Sensor Modules (ASMs) connected to a High-Level Decision Making Module (HLDMM) that provides data fusion, situational awareness, alerting, and sensor management capability. The aim of the SAPIENT concept is to allow for the creation of a surveillance system, in a modular plug-and-play manner, that provides high levels of autonomy, threat detection performance, and reduced operator burden. This paper considers the challenges associated with developing an HLDMM aligned with the SAPIENT concept, through the discussion of the design of a realised HLDMM. Particular focus is drawn to how high levels of system level performance can be achieved whilst retaining modularity and flexibility. A number of key aspects of our HLDMM are presented, including an integrated threat assessment and sensor management framework, threat sequence matching, and ASM trust modelling. The results of real-world testing of the HLDMM, in conjunction with multiple Laser, Radar, and EO/IR sensors, in representative semi-urban environments, are discussed.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Evaluating sustainable energy harvesting systems for human implantable sensors

NASA Astrophysics Data System (ADS)

AL-Oqla, Faris M.; Omar, Amjad A.; Fares, Osama

2018-03-01

Achieving most appropriate energy-harvesting technique for human implantable sensors is still challenging for the industry where keen decisions have to be performed. Moreover, the available polymeric-based composite materials are offering plentiful renewable applications that can help sustainable development as being useful for the energy-harvesting systems such as photovoltaic, piezoelectric, thermoelectric devices as well as other energy storage systems. This work presents an expert-based model capable of better evaluating and examining various available renewable energy-harvesting techniques in urban surroundings subject to various technical and economic, often conflicting, criteria. Wide evaluation criteria have been adopted in the proposed model after examining their suitability as well as ensuring the expediency and reliability of the model by worldwide experts' feedback. The model includes establishing an analytic hierarchy structure with simultaneous 12 conflicting factors to establish a systematic road map for designers to better assess such techniques for human implantable medical sensors. The energy-harvesting techniques considered were limited to Wireless, Thermoelectric, Infrared Radiator, Piezoelectric, Magnetic Induction and Electrostatic Energy Harvesters. Results have demonstrated that the best decision was in favour of wireless-harvesting technology for the medical sensors as it is preferable by most of the considered evaluation criteria in the model.

Continuous glucose monitoring--a study of the Enlite sensor during hypo- and hyperbaric conditions.

PubMed

Adolfsson, Peter; Örnhagen, Hans; Eriksson, Bengt M; Cooper, Ken; Jendle, Johan

2012-06-01

The performance and accuracy of the Enlite(™) (Medtronic, Inc., Northridge, CA) sensor may be affected by microbubble formation at the electrode surface during hypo- and hyperbaric conditions. The effects of acute pressure changes and of prewetting of sensors were investigated. On Day 1, 24 sensors were inserted on the right side of the abdomen and back in one healthy individual; 12 were prewetted with saline solution, and 12 were inserted dry. On Day 2, this procedure was repeated on the left side. All sensors were attached to an iPro continuous glucose monitoring (CGM) recorder. Hypobaric and hyperbaric tests were conducted in a pressure chamber, with each test lasting 105 min. Plasma glucose values were obtained at 5-min intervals with a HemoCue(®) (Ängelholm, Sweden) model 201 glucose analyzer for comparison with sensor glucose values. Ninety percent of the CGM systems operated during the tests. The mean absolute relative difference was lower during hyperbaric than hypobaric conditions (6.7% vs. 14.9%, P<0.001). Sensor sensitivity was slightly decreased (P<0.05) during hypobaric but not during hyperbaric conditions. Clarke Error Grid Analysis showed that 100% of the values were found in the A+B region. No differences were found between prewetted and dry sensors. The Enlite sensor performed adequately during acute pressure changes and was more accurate during hyperbaric than hypobaric conditions. Prewetting the sensors did not improve accuracy. Further studies on type 1 diabetes subjects are needed under various pressure conditions.

From conventional sensors to fibre optic sensors for strain and force measurements in biomechanics applications: a review.

PubMed

Roriz, Paulo; Carvalho, Lídia; Frazão, Orlando; Santos, José Luís; Simões, José António

2014-04-11

In vivo measurement, not only in animals but also in humans, is a demanding task and is the ultimate goal in experimental biomechanics. For that purpose, measurements in vivo must be performed, under physiological conditions, to obtain a database and contribute for the development of analytical models, used to describe human biomechanics. The knowledge and control of the mechanisms involved in biomechanics will allow the optimization of the performance in different topics like in clinical procedures and rehabilitation, medical devices and sports, among others. Strain gages were first applied to bone in a live animal in 40's and in 80's for the first time were applied fibre optic sensors to perform in vivo measurements of Achilles tendon forces in man. Fibre optic sensors proven to have advantages compare to conventional sensors and a great potential for biomechanical and biomedical applications. Compared to them, they are smaller, easier to implement, minimally invasive, with lower risk of infection, highly accurate, well correlated, inexpensive and multiplexable. The aim of this review article is to give an overview about the evolution of the experimental techniques applied in biomechanics, from conventional to fibre optic sensors. In the next sections the most relevant contributions of these sensors, for strain and force in biomechanical applications, will be presented. Emphasis was given to report of in vivo experiments and clinical applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

Detecting Signatures of GRACE Sensor Errors in Range-Rate Residuals

NASA Astrophysics Data System (ADS)

Goswami, S.; Flury, J.

2016-12-01

In order to reach the accuracy of the GRACE baseline, predicted earlier from the design simulations, efforts are ongoing since a decade. GRACE error budget is highly dominated by noise from sensors, dealiasing models and modeling errors. GRACE range-rate residuals contain these errors. Thus, their analysis provides an insight to understand the individual contribution to the error budget. Hence, we analyze the range-rate residuals with focus on contribution of sensor errors due to mis-pointing and bad ranging performance in GRACE solutions. For the analysis of pointing errors, we consider two different reprocessed attitude datasets with differences in pointing performance. Then range-rate residuals are computed from these two datasetsrespectively and analysed. We further compare the system noise of four K-and Ka- band frequencies of the two spacecrafts, with range-rate residuals. Strong signatures of mis-pointing errors can be seen in the range-rate residuals. Also, correlation between range frequency noise and range-rate residuals are seen.

Real-time contaminant sensing and control in civil infrastructure systems

NASA Astrophysics Data System (ADS)

Rimer, Sara; Katopodes, Nikolaos

2014-11-01

A laboratory-scale prototype has been designed and implemented to test the feasibility of real-time contaminant sensing and control in civil infrastructure systems. A blower wind tunnel is the basis of the prototype design, with propylene glycol smoke as the ``contaminant.'' A camera sensor and compressed-air vacuum nozzle system is set up at the test section portion of the prototype to visually sense and then control the contaminant; a real-time controller is programmed to read in data from the camera sensor and administer pressure to regulators controlling the compressed air operating the vacuum nozzles. A computational fluid dynamics model is being integrated in with this prototype to inform the correct pressure to supply to the regulators in order to optimally control the contaminant's removal from the prototype. The performance of the prototype has been evaluated against the computational fluid dynamics model and is discussed in this presentation. Furthermore, the initial performance of the sensor-control system implemented in the test section of the prototype is discussed. NSF-CMMI 0856438.

Geometric investigation of a gaming active device

NASA Astrophysics Data System (ADS)

Menna, Fabio; Remondino, Fabio; Battisti, Roberto; Nocerino, Erica

2011-07-01

3D imaging systems are widely available and used for surveying, modeling and entertainment applications, but clear statements regarding their characteristics, performances and limitations are still missing. The VDI/VDE and the ASTME57 committees are trying to set some standards but the commercial market is not reacting properly. Since many new users are approaching these 3D recording methodologies, clear statements and information clarifying if a package or system satisfies certain requirements before investing are fundamental for those users who are not really familiar with these technologies. Recently small and portable consumer-grade active sensors came on the market, like TOF rangeimaging cameras or low-cost triangulation-based range sensor. A quite interesting active system was produced by PrimeSense and launched on the market thanks to the Microsoft Xbox project with the name of Kinect. The article reports the geometric investigation of the Kinect active sensors, considering its measurement performances, the accuracy of the retrieved range data and the possibility to use it for 3D modeling application.

A Sub-Millimetric 3-DOF Force Sensing Instrument with Integrated Fiber Bragg Grating for Retinal Microsurgery

PubMed Central

He, Xingchi; Handa, James; Gehlbach, Peter; Taylor, Russell; Iordachita, Iulian

2013-01-01

Vitreoretinal surgery requires very fine motor control to perform precise manipulation of the delicate tissue in the interior of the eye. Besides physiological hand tremor, fatigue, poor kinesthetic feedback, and patient movement, the absence of force sensing is one of the main technical challenges. Previous two degrees of freedom (DOF) force sensing instruments have demonstrated robust force measuring performance. The main design challenge is to incorporate high sensitivity axial force sensing. This paper reports the development of a sub-millimetric 3-DOF force sensing pick instrument based on fiber Bragg grating (FBG) sensors. The configuration of the four FBG sensors is arranged to maximize the decoupling between axial and transverse force sensing. A super-elastic nitinol flexure is designed to achieve high axial force sensitivity. An automated calibration system was developed for repeatability testing, calibration, and validation. Experimental results demonstrate a FBG sensor repeatability of 1.3 pm. The linear model for calculating the transverse forces provides an accurate global estimate. While the linear model for axial force is only locally accurate within a conical region with a 30° vertex angle, a second-order polynomial model can provide a useful global estimate for axial force. Combining the linear model for transverse forces and nonlinear model for axial force, the 3-DOF force sensing instrument can provide sub-millinewton resolution for axial force and a quarter millinewton for transverse forces. Validation with random samples show the force sensor can provide consistent and accurate measurement of three dimensional forces. PMID:24108455

Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

PubMed Central

Cho, Taehwan; Lee, Changho; Choi, Sangbang

2013-01-01

The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter. PMID:23535715

Multi-sensor fusion with interacting multiple model filter for improved aircraft position accuracy.

PubMed

Cho, Taehwan; Lee, Changho; Choi, Sangbang

2013-03-27

The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.

MEMS based Doppler velocity measurement system

NASA Astrophysics Data System (ADS)

Shin, Minchul

The design, fabrication, modeling and characterization of a capacitive micromachined ultrasonic transducer (cMUT) based in-air Doppler velocity measurement system using a 1 cm2 planar array are described. Continuous wave operation in a narrowband was chosen in order to maximize range, as it allows for better rejection of broadband noise. The sensor array has a 160-185 kHz resonant frequency to achieve a 10 degree beamwidth. A model for the cMUT and the acoustic system which includes electrical, mechanical, and acoustic components is provided. Furthermore, characterization of the cMUT sensor with a variety of testing procedures is provided. Laser Doppler vibrometry (LDV), beampattern, reflection, and velocity testing characterize the performance of the sensors. The sensor is capable of measuring the velocity of a moving specular reflector with a resolution of 5 cm/s, an update rate of 0.016 second, and a range of 1.5 m.

Development and modification of a device for three-dimensional measurement of orthodontic force system: The V-bend system re-visited.

PubMed

Lai, WeiJen; Midorikawa, Yoshiyuki; Kanno, Zuisei; Takemura, Hiroshi; Suga, Kazuhiro; Soga, Kohei; Ono, Takashi; Uo, Motohiro

2016-12-01

We developed a device to evaluate the orthodontic force applied by systems requiring high operability. A life-sized, two-tooth model was designed, and the measurements were performed using a custom-made jointed attachment, referred to as an "action stick", to allow clearance for the oversized six-axis sensors. This tooth-sensor apparatus was accurately calibrated, and the error was limited. Vector analysis and rotating coordinate transformation were required to derive the force and moment at the tooth from the sensor readings. The device was then used to obtain measurements of the force and moment generated by the V-bend system. Our device was effective, providing results that were consistent with those of previous studies. This measurement device can be manufactured with force sensors of any size, and it can also be expanded to models with any number of teeth.

Investigation of the feasibility of non-invasive optical sensors for the quantitative assessment of dehydration.

PubMed

Visser, Cobus; Kieser, Eduard; Dellimore, Kiran; van den Heever, Dawie; Smith, Johan

2017-10-01

This study explores the feasibility of prospectively assessing infant dehydration using four non-invasive, optical sensors based on the quantitative and objective measurement of various clinical markers of dehydration. The sensors were investigated to objectively and unobtrusively assess the hydration state of an infant based on the quantification of capillary refill time (CRT), skin recoil time (SRT), skin temperature profile (STP) and skin tissue hydration by means of infrared spectrometry (ISP). To evaluate the performance of the sensors a clinical study was conducted on a cohort of 10 infants (aged 6-36 months) with acute gastroenteritis. High sensitivity and specificity were exhibited by the sensors, in particular the STP and SRT sensors, when combined into a fusion regression model (sensitivity: 0.90, specificity: 0.78). The SRT and STP sensors and the fusion model all outperformed the commonly used "gold standard" clinical dehydration scales including the Gorelick scale (sensitivity: 0.56, specificity: 0.56), CDS scale (sensitivity: 1.0, specificity: 0.2) and WHO scale (sensitivity: 0.13, specificity: 0.79). These results suggest that objective and quantitative assessment of infant dehydration may be possible using the sensors investigated. However, further evaluation of the sensors on a larger sample population is needed before deploying them in a clinical setting. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

POD-based constrained sensor placement and field reconstruction from noisy wind measurements: A perturbation study

DOE PAGES

Zhang, Zhongqiang; Yang, Xiu; Lin, Guang

2016-04-14

Sensor placement at the extrema of Proper Orthogonal Decomposition (POD) is efficient and leads to accurate reconstruction of the wind field from a limited number of measure- ments. In this paper we extend this approach of sensor placement and take into account measurement errors and detect possible malfunctioning sensors. We use the 48 hourly spa- tial wind field simulation data sets simulated using the Weather Research an Forecasting (WRF) model applied to the Maine Bay to evaluate the performances of our methods. Specifically, we use an exclusion disk strategy to distribute sensors when the extrema of POD modes are close.more » It turns out that this strategy can also reduce the error of recon- struction from noise measurements. Also, by a cross-validation technique, we successfully locate the malfunctioning sensors.« less

A Decentralized Wireless Solution to Monitor and Diagnose PV Solar Module Performance Based on Symmetrized-Shifted Gompertz Functions

PubMed Central

Molina-García, Angel; Campelo, José Carlos; Blanc, Sara; Serrano, Juan José; García-Sánchez, Tania; Bueso, María C.

2015-01-01

This paper proposes and assesses an integrated solution to monitor and diagnose photovoltaic (PV) solar modules based on a decentralized wireless sensor acquisition system. Both DC electrical variables and environmental data are collected at PV module level using low-cost and high-energy efficiency node sensors. Data is real-time processed locally and compared with expected PV module performances obtained by a PV module model based on symmetrized-shifted Gompertz functions (as previously developed and assessed by the authors). Sensor nodes send data to a centralized sink-computing module using a multi-hop wireless sensor network architecture. Such integration thus provides extensive analysis of PV installations, and avoids off-line tests or post-processing processes. In comparison with previous approaches, this solution is enhanced with a low-cost system and non-critical performance constraints, and it is suitable for extensive deployment in PV power plants. Moreover, it is easily implemented in existing PV installations, since no additional wiring is required. The system has been implemented and assessed in a Spanish PV power plant connected to the grid. Results and estimations of PV module performances are also included in the paper. PMID:26230694

A Decentralized Wireless Solution to Monitor and Diagnose PV Solar Module Performance Based on Symmetrized-Shifted Gompertz Functions.

PubMed

Molina-García, Angel; Campelo, José Carlos; Blanc, Sara; Serrano, Juan José; García-Sánchez, Tania; Bueso, María C

2015-07-29

This paper proposes and assesses an integrated solution to monitor and diagnose photovoltaic (PV) solar modules based on a decentralized wireless sensor acquisition system. Both DC electrical variables and environmental data are collected at PV module level using low-cost and high-energy efficiency node sensors. Data is real-time processed locally and compared with expected PV module performances obtained by a PV module model based on symmetrized-shifted Gompertz functions (as previously developed and assessed by the authors). Sensor nodes send data to a centralized sink-computing module using a multi-hop wireless sensor network architecture. Such integration thus provides extensive analysis of PV installations, and avoids off-line tests or post-processing processes. In comparison with previous approaches, this solution is enhanced with a low-cost system and non-critical performance constraints, and it is suitable for extensive deployment in PV power plants. Moreover, it is easily implemented in existing PV installations, since no additional wiring is required. The system has been implemented and assessed in a Spanish PV power plant connected to the grid. Results and estimations of PV module performances are also included in the paper.

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

NASA Technical Reports Server (NTRS)

Sowers, T. Shane; Kopasakis, George; Simon, Donald L.

2008-01-01

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight, and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

NASA Technical Reports Server (NTRS)

Sowers, T. Shane; Kopasakis, George; Simon, Donald L.

2008-01-01

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Investigation, Modeling and Validation of Digital Bridge for a New Generation Hot-Wire Anemometer

NASA Astrophysics Data System (ADS)

Joshi, Karthik Kamalakar

The Digital Bridge Thermal Anemometer (DBTA) is a new generation anemometer that uses advanced electronics and a modified half-Wheatstone bridge configuration, specifically a sensor and a shunt resistor in series. This allows the miniaturization of the anemometer and the communication between host computer and anemometer is carried out using serial or ethernet which eliminates the noise due to the use of long cables in conventional anemometer and the digital data sent to host computer is immune to electrical noise. In the new configuration the potential drop across a shunt resistor is used to control the bridge. This thesis is confined to the anemometer used in constant temperature (CT) mode. The heat transfer relations are studied and new expressions are developed based on the new configuration of the bridge using perturbation analysis. The theoretical plant model of a commercially available sensor and a custom built sensor are derived and quantified. The plant model is used to design a controller to control the plant in closed-loop using feedback. To test the performance of the modified sensor used with a "generation-I" bridge and DAQ, an experiment was conducted. The controller was implemented in a user interface in LabVIEW. The test is to compare the results between a conventional TSI sensor with an IFA 300 anemometer and the setup describe above, in the wake behind a circular cylinder. Performance of the DBTA is satisfactory at low frequencies. A user interface capable of communicating with the anemometer to control the operation and collect data generated by anemometer is developed in LabVIEW.

Analysis, testing, and operation of the MAGI thermal control system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yi, Sonny; Hall, Jeffrey L.; Kasper, Brian P.

2014-01-29

The Aerospace Corporation has completed the development of the Mineral and Gas Identifier (MAGI) sensor - an airborne multi-spectral infrared instrument that is designed to discriminate surface composition and to detect gas emissions from the environment. Sensor performance was demonstrated in a series of flights aboard a Twin Otter aircraft in December 2011 as a stepping stone to a future satellite sensor design. To meet sensor performance requirements the thermal control system was designed to operate the HgCdTe focal plane array (FPA) at 50 K with a 1.79 W heat rejection load to a 44.7 K sink and the opticalmore » assembly at 100 K with a 7.5 W heat load to a 82.3 K sink. Two commercial off-theshelf (COTS) Sunpower Stirling cryocoolers were used to meet the instrument’s cooling requirements. A thermal model constructed in Thermal Desktop was used to run parametric studies that guided the mechanical design and sized the two cryocoolers. This paper discusses the development, validation, and operation of the MAGI thermal control system. Detailed energy balances and temperature predictions are presented for various test cases to demonstrate the utility and accuracy of the thermal model. Model inputs included measured values of heat lift as a function of input power and cold tip temperature for the two cryocoolers. These measurements were also used to make predictions of the cool-down behavior from ambient conditions. Advanced heater software was developed to meet unique requirements for both sensor cool-down rate and stability at the set point temperatures.« less

Finite Element Modelling and Analysis of Damage Detection Methodology in Piezo Electric Sensor and Actuator Integrated Sandwich Cantilever Beam

NASA Astrophysics Data System (ADS)

Pradeep, K. R.; Thomas, A. M.; Basker, V. T.

2018-03-01

Structural health monitoring (SHM) is an essential component of futuristic civil, mechanical and aerospace structures. It detects the damages in system or give warning about the degradation of structure by evaluating performance parameters. This is achieved by the integration of sensors and actuators into the structure. Study of damage detection process in piezoelectric sensor and actuator integrated sandwich cantilever beam is carried out in this paper. Possible skin-core debond at the root of the cantilever beam is simulated and compared with undamaged case. The beam is actuated using piezoelectric actuators and performance differences are evaluated using Polyvinylidene fluoride (PVDF) sensors. The methodology utilized is the voltage/strain response of the damaged versus undamaged beam against transient actuation. Finite element model of piezo-beam is simulated in ANSYSTM using 8 noded coupled field element, with nodal degrees of freedoms are translations in the x, y directions and voltage. An aluminium sandwich beam with a length of 800mm, thickness of core 22.86mm and thickness of skin 0.3mm is considered. Skin-core debond is simulated in the model as unmerged nodes. Reduction in the fundamental frequency of the damaged beam is found to be negligible. But the voltage response of the PVDF sensor under transient excitation shows significantly visible change indicating the debond. Piezo electric based damage detection system is an effective tool for the damage detection of aerospace and civil structural system having inaccessible/critical locations and enables online monitoring possibilities as the power requirement is minimal.

Specialty fibers for fiber optic sensor application

NASA Astrophysics Data System (ADS)

Bennett, K.; Koh, J.; Coon, J.; Chien, C. K.; Artuso, A.; Chen, X.; Nolan, D.; Li, M.-J.

2007-09-01

Over the last several years, Fiber Optic Sensor (FOS) applications have seen an increased acceptance in many areas including oil & gas production monitoring, gyroscopes, current sensors, structural sensing and monitoring, and aerospace applications. High level optical and mechanical reliability of optical fiber is necessary to guarantee reliable performance of FOS. In this paper, we review recent research and development activities on new specialty fibers. We discuss fiber design concepts and present both modeling and experimental results. The main approaches to enhancing fiber attributes include new index profile design and fiber coating modification.

Data Centric Sensor Stream Reduction for Real-Time Applications in Wireless Sensor Networks

PubMed Central

Aquino, Andre Luiz Lins; Nakamura, Eduardo Freire

2009-01-01

This work presents a data-centric strategy to meet deadlines in soft real-time applications in wireless sensor networks. This strategy considers three main aspects: (i) The design of real-time application to obtain the minimum deadlines; (ii) An analytic model to estimate the ideal sample size used by data-reduction algorithms; and (iii) Two data-centric stream-based sampling algorithms to perform data reduction whenever necessary. Simulation results show that our data-centric strategies meet deadlines without loosing data representativeness. PMID:22303145

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Coble, Jamie B.; Shumaker, Brent

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or moremore » sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

Enhancing the accuracy of subcutaneous glucose sensors: a real-time deconvolution-based approach.

PubMed

Guerra, Stefania; Facchinetti, Andrea; Sparacino, Giovanni; Nicolao, Giuseppe De; Cobelli, Claudio

2012-06-01

Minimally invasive continuous glucose monitoring (CGM) sensors can greatly help diabetes management. Most of these sensors consist of a needle electrode, placed in the subcutaneous tissue, which measures an electrical current exploiting the glucose-oxidase principle. This current is then transformed to glucose levels after calibrating the sensor on the basis of one, or more, self-monitoring blood glucose (SMBG) samples. In this study, we design and test a real-time signal-enhancement module that, cascaded to the CGM device, improves the quality of its output by a proper postprocessing of the CGM signal. In fact, CGM sensors measure glucose in the interstitium rather than in the blood compartment. We show that this distortion can be compensated by means of a regularized deconvolution procedure relying on a linear regression model that can be updated whenever a pair of suitably sampled SMBG references is collected. Tests performed both on simulated and real data demonstrate a significant accuracy improvement of the CGM signal. Simulation studies also demonstrate the robustness of the method against departures from nominal conditions, such as temporal misplacement of the SMBG samples and uncertainty in the blood-to-interstitium glucose kinetic model. Thanks to its online capabilities, the proposed signal-enhancement algorithm can be used to improve the performance of CGM-based real-time systems such as the hypo/hyper glycemic alert generators or the artificial pancreas.

Image interpolation and denoising for division of focal plane sensors using Gaussian processes.

PubMed

Gilboa, Elad; Cunningham, John P; Nehorai, Arye; Gruev, Viktor

2014-06-16

Image interpolation and denoising are important techniques in image processing. These methods are inherent to digital image acquisition as most digital cameras are composed of a 2D grid of heterogeneous imaging sensors. Current polarization imaging employ four different pixelated polarization filters, commonly referred to as division of focal plane polarization sensors. The sensors capture only partial information of the true scene, leading to a loss of spatial resolution as well as inaccuracy of the captured polarization information. Interpolation is a standard technique to recover the missing information and increase the accuracy of the captured polarization information. Here we focus specifically on Gaussian process regression as a way to perform a statistical image interpolation, where estimates of sensor noise are used to improve the accuracy of the estimated pixel information. We further exploit the inherent grid structure of this data to create a fast exact algorithm that operates in ����(N(3/2)) (vs. the naive ���� (N³)), thus making the Gaussian process method computationally tractable for image data. This modeling advance and the enabling computational advance combine to produce significant improvements over previously published interpolation methods for polarimeters, which is most pronounced in cases of low signal-to-noise ratio (SNR). We provide the comprehensive mathematical model as well as experimental results of the GP interpolation performance for division of focal plane polarimeter.

Noise analysis in air-coupled PVDF ultrasonic sensors.

PubMed

Fiorillo, A S

2000-01-01

In this paper we analyze the noise generated in a piezo-polymer based sensor for low frequency ultrasound in air. The sensor includes two curved PVDF transducers for medium and short range applications. A lumped RLC equivalent circuit was derived from the measurement of the transducer's electrical admittance, in air, by taking into account both mechanical and dielectric losses, which we suppose are the major sources of noise in similar devices. The electrical model was used to study and optimize the noise performance of a 61 kHz transducer and to simulate the electrical behavior of the complete transmitter-receiver system. The validity of the overall electrical model with low noise was confirmed after verifying, with Pspice, agreement of the practical and theoretical results.

State machine analysis of sensor data from dynamic processes

DOEpatents

Cook, William R.; Brabson, John M.; Deland, Sharon M.

2003-12-23

A state machine model analyzes sensor data from dynamic processes at a facility to identify the actual processes that were performed at the facility during a period of interest for the purpose of remote facility inspection. An inspector can further input the expected operations into the state machine model and compare the expected, or declared, processes to the actual processes to identify undeclared processes at the facility. The state machine analysis enables the generation of knowledge about the state of the facility at all levels, from location of physical objects to complex operational concepts. Therefore, the state machine method and apparatus may benefit any agency or business with sensored facilities that stores or manipulates expensive, dangerous, or controlled materials or information.

An epidemic model for biological data fusion in ad hoc sensor networks

NASA Astrophysics Data System (ADS)

Chang, K. C.; Kotari, Vikas

2009-05-01

Bio terrorism can be a very refined and a catastrophic approach of attacking a nation. This requires the development of a complete architecture dedicatedly designed for this purpose which includes but is not limited to Sensing/Detection, Tracking and Fusion, Communication, and others. In this paper we focus on one such architecture and evaluate its performance. Various sensors for this specific purpose have been studied. The accent has been on use of Distributed systems such as ad-hoc networks and on application of epidemic data fusion algorithms to better manage the bio threat data. The emphasis has been on understanding the performance characteristics of these algorithms under diversified real time scenarios which are implemented through extensive JAVA based simulations. Through comparative studies on communication and fusion the performance of channel filter algorithm for the purpose of biological sensor data fusion are validated.

Measurements of the reverse current of highly irradiated silicon sensors to determine the effective energy and current related damage rate

NASA Astrophysics Data System (ADS)

Wiehe, Moritz; Wonsak, S.; Kuehn, S.; Parzefall, U.; Casse, G.

2018-01-01

The reverse current of irradiated silicon sensors leads to self heating of the sensor and degrades the signal to noise ratio of a detector. Precise knowledge of the expected reverse current during detector operation is crucial for planning and running experiments in High Energy Physics. The dependence of the reverse current on sensor temperature and irradiation fluence is parametrized by the effective energy and the current related damage rate, respectively. In this study 18 n-in-p mini silicon strip sensors from companies Hamamatsu Photonics and Micron Semiconductor Ltd. were deployed. Measurements of the reverse current for different bias voltages were performed at temperatures of -32 ° C, -27 ° C and -23 ° C. The sensors were irradiated with reactor neutrons in Ljubljana to fluences ranging from 2 × 1014neq /cm2 to 2 × 1016neq /cm2. The measurements were performed directly after irradiation and after 10 and 30 days of room temperature annealing. The aim of the study presented in this paper is to investigate the reverse current of silicon sensors for high fluences of up to 2 × 1016neq /cm2 and compare the measurements to the parametrization models.

Development of chipless, wireless current sensor system based on giant magnetoimpedance magnetic sensor and surface acoustic wave transponder.

PubMed

Kondalkar, Vijay V; Li, Xiang; Park, Ikmo; Yang, Sang Sik; Lee, Keekeun

2018-02-05

A chipless, wireless current sensor system was developed using a giant magnetoimpedance (GMI) magnetic sensor and one-port surface acoustic wave (SAW) reflective delay line for real-time power monitoring in a current-carrying conductor. The GMI sensor has a high-quality crystalline structure in each layer, which contributes to a high sensitivity and good linearity in a magnetic field of 3-16 Oe. A 400 MHz RF energy generated from the interdigital transducer (IDT)-type reflector on the one-port SAW delay line was used as an activation source for the GMI magnetic sensor. The one-port SAW delay line replaces the presently existing transceiver system, which is composed of thousands of transistors, thus enabling chipless and wireless operation. We confirmed a large variation in the amplitude of the SAW reflection peak with a change in the impedance of the GMI sensor caused by the current flow through the conductor. Good linearity and sensitivity of ~0.691 dB/A were observed for currents in the range 1-12 A. Coupling of Mode (COM) modeling and impedance matching analysis were also performed to predict the device performance in advance and these were compared with the experimental results.

Studying the Effect of Deposition Conditions on the Performance and Reliability of MEMS Gas Sensors

PubMed Central

Sadek, Khaled; Moussa, Walied

2007-01-01

In this paper, the reliability of a micro-electro-mechanical system (MEMS)-based gas sensor has been investigated using Three Dimensional (3D) coupled multiphysics Finite Element (FE) analysis. The coupled field analysis involved a two-way sequential electrothermal fields coupling and a one-way sequential thermal-structural fields coupling. An automated substructuring code was developed to reduce the computational cost involved in simulating this complicated coupled multiphysics FE analysis by up to 76 percent. The substructured multiphysics model was then used to conduct a parametric study of the MEMS-based gas sensor performance in response to the variations expected in the thermal and mechanical characteristics of thin films layers composing the sensing MEMS device generated at various stages of the microfabrication process. Whenever possible, the appropriate deposition variables were correlated in the current work to the design parameters, with good accuracy, for optimum operation conditions of the gas sensor. This is used to establish a set of design rules, using linear and nonlinear empirical relations, which can be utilized in real-time at the design and development decision-making stages of similar gas sensors to enable the microfabrication of these sensors with reliable operation.

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers

PubMed Central

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-01-01

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios. PMID:28398255

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers.

PubMed

Chang, Xiaodong; Huang, Jinquan; Lu, Feng

2017-04-11

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios.

Non-Orthogonal Multiple Access for Ubiquitous Wireless Sensor Networks.

PubMed

Anwar, Asim; Seet, Boon-Chong; Ding, Zhiguo

2018-02-08

Ubiquitous wireless sensor networks (UWSNs) have become a critical technology for enabling smart cities and other ubiquitous monitoring applications. Their deployment, however, can be seriously hampered by the spectrum available to the sheer number of sensors for communication. To support the communication needs of UWSNs without requiring more spectrum resources, the power-domain non-orthogonal multiple access (NOMA) technique originally proposed for 5th Generation (5G) cellular networks is investigated for UWSNs for the first time in this paper. However, unlike 5G networks that operate in the licensed spectrum, UWSNs mostly operate in unlicensed spectrum where sensors also experience cross-technology interferences from other devices sharing the same spectrum. In this paper, we model the interferences from various sources at the sensors using stochastic geometry framework. To evaluate the performance, we derive a theorem and present new closed form expression for the outage probability of the sensors in a downlink scenario under interference limited environment. In addition, diversity analysis for the ordered NOMA users is performed. Based on the derived outage probability, we evaluate the average link throughput and energy consumption efficiency of NOMA against conventional orthogonal multiple access (OMA) technique in UWSNs. Further, the required computational complexity for the NOMA users is presented.

The effect of time synchronization of wireless sensors on the modal analysis of structures

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.; Fowler, K.; Sazonov, E.

2008-10-01

Driven by the need to reduce the installation cost and maintenance cost of structural health monitoring (SHM) systems, wireless sensor networks (WSNs) are becoming increasingly popular. Perfect time synchronization amongst the wireless sensors is a key factor enabling the use of low-cost, low-power WSNs for structural health monitoring applications based on output-only modal analysis of structures. In this paper we present a theoretical framework for analysis of the impact created by time delays in the measured system response on the reconstruction of mode shapes using the popular frequency domain decomposition (FDD) technique. This methodology directly estimates the change in mode shape values based on sensor synchronicity. We confirm the proposed theoretical model by experimental validation in modal identification experiments performed on an aluminum beam. The experimental validation was performed using a wireless intelligent sensor and actuator network (WISAN) which allows for close time synchronization between sensors (0.6-10 µs in the tested configuration) and guarantees lossless data delivery under normal conditions. The experimental results closely match theoretical predictions and show that even very small delays in output response impact the mode shapes.

Effects of electrostatic discharge on three cryogenic temperature sensor models

NASA Astrophysics Data System (ADS)

Courts, S. Scott; Mott, Thomas B.

2014-01-01

Cryogenic temperature sensors are not usually thought of as electrostatic discharge (ESD) sensitive devices. However, the most common cryogenic thermometers in use today are thermally sensitive diodes or resistors - both electronic devices in their base form. As such, they are sensitive to ESD at some level above which either catastrophic or latent damage can occur. Instituting an ESD program for safe handling and installation of the sensor is costly and it is desirable to balance the risk of ESD damage against this cost. However, this risk cannot be evaluated without specific knowledge of the ESD vulnerability of the devices in question. This work examines three types of cryogenic temperature sensors for ESD sensitivity - silicon diodes, Cernox{trade mark, serif} resistors, and wire wound platinum resistors, all manufactured by Lake Shore Cryotronics, Inc. Testing was performed per TIA/EIA FOTP129 (Human Body Model). Damage was found to occur in the silicon diode sensors at discharge levels of 1,500 V. For Cernox{trade mark, serif} temperature sensors, damage was observed at 3,500 V. The platinum temperature sensors were not damaged by ESD exposure levels of 9,900 V. At the lower damage limit, both the silicon diode and the Cernox{trade mark, serif} temperature sensors showed relatively small calibration shifts of 1 to 3 K at room temperature. The diode sensors were stable with time and thermal cycling, but the long term stability of the Cernox{trade mark, serif} sensors was degraded. Catastrophic failure occurred at higher levels of ESD exposure.

Workflow-Oriented Cyberinfrastructure for Sensor Data Analytics

NASA Astrophysics Data System (ADS)

Orcutt, J. A.; Rajasekar, A.; Moore, R. W.; Vernon, F.

2015-12-01

Sensor streams comprise an increasingly large part of Earth Science data. Analytics based on sensor data require an easy way to perform operations such as acquisition, conversion to physical units, metadata linking, sensor fusion, analysis and visualization on distributed sensor streams. Furthermore, embedding real-time sensor data into scientific workflows is of growing interest. We have implemented a scalable networked architecture that can be used to dynamically access packets of data in a stream from multiple sensors, and perform synthesis and analysis across a distributed network. Our system is based on the integrated Rule Oriented Data System (irods.org), which accesses sensor data from the Antelope Real Time Data System (brtt.com), and provides virtualized access to collections of data streams. We integrate real-time data streaming from different sources, collected for different purposes, on different time and spatial scales, and sensed by different methods. iRODS, noted for its policy-oriented data management, brings to sensor processing features and facilities such as single sign-on, third party access control lists ( ACLs), location transparency, logical resource naming, and server-side modeling capabilities while reducing the burden on sensor network operators. Rich integrated metadata support also makes it straightforward to discover data streams of interest and maintain data provenance. The workflow support in iRODS readily integrates sensor processing into any analytical pipeline. The system is developed as part of the NSF-funded Datanet Federation Consortium (datafed.org). APIs for selecting, opening, reaping and closing sensor streams are provided, along with other helper functions to associate metadata and convert sensor packets into NetCDF and JSON formats. Near real-time sensor data including seismic sensors, environmental sensors, LIDAR and video streams are available through this interface. A system for archiving sensor data and metadata in NetCDF format has been implemented and will be demonstrated at AGU.

Inductive passive sensor for intraparenchymal and intraventricular monitoring of intracranial pressure.

PubMed

Behfar, Mohammad H; Abada, Emily; Sydanheimo, Lauri; Goldman, Ken; Fleischman, Aaron J; Gupta, Nalin; Ukkonen, Leena; Roy, Shuvo

2016-08-01

Accurate measurement of intracranial hypertension is crucial for the management of elevated intracranial pressure (ICP). Catheter-based intraventricular ICP measurement is regarded as the gold standard for accurate ICP monitoring. However, this method is invasive, time-limited, and associated with complications. In this paper, we propose an implantable passive sensor that could be used for continuous intraparenchymal and intraventricular ICP monitoring. Moreover, the sensor can be placed simultaneously along with a cerebrospinal fluid shunt system in order to monitor its function. The sensor consists of a flexible coil which is connected to a miniature pressure sensor via an 8-cm long, ultra-thin coaxial cable. An external orthogonal-coil RF probe communicates with the sensor to detect pressure variation. The performance of the sensor was evaluated in an in vitro model for intraparenchymal and intraventricular ICP monitoring. The findings from this study demonstrate proof-of-concept of intraparenchymal and intraventricular ICP measurement using inductive passive pressure sensors.

Statistical modeling of natural backgrounds in hyperspectral LWIR data

NASA Astrophysics Data System (ADS)

Truslow, Eric; Manolakis, Dimitris; Cooley, Thomas; Meola, Joseph

2016-09-01

Hyperspectral sensors operating in the long wave infrared (LWIR) have a wealth of applications including remote material identification and rare target detection. While statistical models for modeling surface reflectance in visible and near-infrared regimes have been well studied, models for the temperature and emissivity in the LWIR have not been rigorously investigated. In this paper, we investigate modeling hyperspectral LWIR data using a statistical mixture model for the emissivity and surface temperature. Statistical models for the surface parameters can be used to simulate surface radiances and at-sensor radiance which drives the variability of measured radiance and ultimately the performance of signal processing algorithms. Thus, having models that adequately capture data variation is extremely important for studying performance trades. The purpose of this paper is twofold. First, we study the validity of this model using real hyperspectral data, and compare the relative variability of hyperspectral data in the LWIR and visible and near-infrared (VNIR) regimes. Second, we illustrate how materials that are easily distinguished in the VNIR, may be difficult to separate when imaged in the LWIR.

A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System

PubMed Central

Hakem, Nadir

2018-01-01

Energy Harvesting techniques are increasingly seen as the solution for freeing the wireless sensor nodes from their battery dependency. However, it remains evident that network performance features, such as network size, packet length, and duty cycle, are influenced by the sum of recovered energy. This paper proposes a new approach to defining the specifications of a stand-alone wireless node based on a Radio-frequency Energy Harvesting System (REHS). To achieve adequate performance regarding the range of the Wireless Sensor Network (WSN), techniques for minimizing the energy consumed by the sensor node are combined with methods for optimizing the performance of the REHS. For more rigor in the design of the autonomous node, a comprehensive energy model of the node in a wireless network is established. For an equitable distribution of network charges between the different nodes that compose it, the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for this purpose. The model considers five energy-consumption sources, most of which are ignored in recently used models. By using the hardware parameters of commercial off-the-shelf components (Mica2 Motes and CC2520 of Texas Instruments), the energy requirement of a sensor node is quantified. A miniature REHS based on a judicious choice of rectifying diodes is then designed and developed to achieve optimal performance in the Industrial Scientific and Medical (ISM) band centralized at 2.45 GHz. Due to the mismatch between the REHS and the antenna, a band pass filter is designed to reduce reflection losses. A gradient method search is used to optimize the output characteristics of the adapted REHS. At 1 mW of input RF power, the REHS provides an output DC power of 0.57 mW and a comparison with the energy requirement of the node allows the Base Station (BS) to be located at 310 m from the wireless nodes when the Wireless Sensor Network (WSN) has 100 nodes evenly spread over an area of 300 × 300 m2 and when each round lasts 10 min. The result shows that the range of the autonomous WSN increases when the controlled physical phenomenon varies very slowly. Having taken into account all the dissipation sources coexisting in a sensor node and using actual measurements of an REHS, this work provides the guidelines for the design of autonomous nodes based on REHS. PMID:29304002

Performance Assessment and Geometric Calibration of RESOURCESAT-2

NASA Astrophysics Data System (ADS)

Radhadevi, P. V.; Solanki, S. S.; Akilan, A.; Jyothi, M. V.; Nagasubramanian, V.

2016-06-01

Resourcesat-2 (RS-2) has successfully completed five years of operations in its orbit. This satellite has multi-resolution and multi-spectral capabilities in a single platform. A continuous and autonomous co-registration, geo-location and radiometric calibration of image data from different sensors with widely varying view angles and resolution was one of the challenges of RS-2 data processing. On-orbit geometric performance of RS-2 sensors has been widely assessed and calibrated during the initial phase operations. Since then, as an ongoing activity, various geometric performance data are being generated periodically. This is performed with sites of dense ground control points (GCPs). These parameters are correlated to the direct geo-location accuracy of the RS-2 sensors and are monitored and validated to maintain the performance. This paper brings out the geometric accuracy assessment, calibration and validation done for about 500 datasets of RS-2. The objectives of this study are to ensure the best absolute and relative location accuracy of different cameras, location performance with payload steering and co-registration of multiple bands. This is done using a viewing geometry model, given ephemeris and attitude data, precise camera geometry and datum transformation. In the model, the forward and reverse transformations between the coordinate systems associated with the focal plane, payload, body, orbit and ground are rigorously and explicitly defined. System level tests using comparisons to ground check points have validated the operational geo-location accuracy performance and the stability of the calibration parameters.

The Rogowski Coil Sensor in High Current Application: A Review

NASA Astrophysics Data System (ADS)

Nazmy Nanyan, Ayob; Isa, Muzamir; Hamid, Haziah Abdul; Nur Khairul Hafizi Rohani, Mohamad; Ismail, Baharuddin

2018-03-01

Rogowski coil is used for measuring the alternating current (AC) and high-speed current pulses. However, the technology makes the Rogowski coil (RC) come out with more improvement, modification and until today it’s still being studied for the new application. The Rogowski coil has a few advantages compared to the high frequency current transformer (HFCT). A brief review on the basic theory and the application of Rogowski coil as a current sensor measurement that been done by previous researchers are presented and discussed in this paper. Additionally, the review also focused on the capability of Rogowski coil for high current sensor measurement and their application for fault detection, over voltage current sensor, lightning current sensor and high impulse current detection. The experimental set up, techniques and measurement parameters in models also been discussed. Finally, a brief review on the performance analysis of current sensor measurement of Rogowski coil likes sensitivity, the maximum and current detection which could be used as a guideline to another researcher in order to develop an advanced RC as high current sensor in future is presented. This review reveal that the RC has a very good performance in high current sensor detection in term of sensitivity which is up to a few nanosecond, higher bandwidth, excellent in detection of high fault and also could measuring lightning current up to 400kA and has many advantages compare to conventional current transformer(CT).

Dielectric elastomer for stretchable sensors: influence of the design and material properties

NASA Astrophysics Data System (ADS)

Jean-Mistral, C.; Iglesias, S.; Pruvost, S.; Duchet-Rumeau, J.; Chesné, S.

2016-04-01

Dielectric elastomers exhibit extended capabilities as flexible sensors for the detection of load distributions, pressure or huge deformations. Tracking the human movements of the fingers or the arms could be useful for the reconstruction of sporting gesture, or to control a human-like robot. Proposing new measurements methods are addressed in a number of publications leading to improving the sensitivity and accuracy of the sensing method. Generally, the associated modelling remains simple (RC or RC transmission line). The material parameters are considered constant or having a negligible effect which can lead to serious reduction of accuracy. Comparisons between measurements and modelling require care and skill, and could be tricky. Thus, we propose here a comprehensive modelling, taking into account the influence of the material properties on the performances of the dielectric elastomer sensor (DES). Various parameters influencing the characteristics of the sensors have been identified: dielectric constant, hyper-elasticity. The variations of these parameters as a function of the strain impact the linearity and sensitivity of the sensor of few percent. The sensitivity of the DES is also evaluated changing geometrical parameters (initial thickness) and its design (rectangular and dog-bone shapes). We discuss the impact of the shape regarding stress. Finally, DES including a silicone elastomer sandwiched between two high conductive stretchable electrodes, were manufactured and investigated. Classic and reliable LCR measurements are detailed. Experimental results validate our numerical model of large strain sensor (>50%).

A study of model deflection measurement techniques applicable within the national transonic facility

NASA Technical Reports Server (NTRS)

Hildebrand, B. P.; Doty, J. L.

1982-01-01

Moire contouring, scanning interferometry, and holographic contouring were examined to determine their practicality and potential to meet performance requirements for a model deflection sensor. The system envisioned is to be nonintrusive, and is to be capable of mapping or contouring the surface of a 1-meter by 1-meter model with a resolution of 50 to 100 points. The available literature was surveyed, and computations and analyses were performed to establish specific performance requirements, as well as the capabilities and limitations of such a sensor within the geometry of the NTF section test section. Of the three systems examined, holographic contouring offers the most promise. Unlike Moire, it is not hampered by limited contour spacing and extraneous fringes. Its transverse resolution can far exceed the limited point sampling resolution of scanning heterodyne interferometry. The availability of the ruby laser as a high power, pulsed, multiple wavelength source makes such a system feasible within the NTF.

Two-Phase flow instrumentation for nuclear accidents simulation

NASA Astrophysics Data System (ADS)

Monni, G.; De Salve, M.; Panella, B.

2014-11-01

The paper presents the research work performed at the Energy Department of the Politecnico di Torino, concerning the development of two-phase flow instrumentation and of models, based on the analysis of experimental data, that are able to interpret the measurement signals. The study has been performed with particular reference to the design of power plants, such as nuclear water reactors, where the two-phase flow thermal fluid dynamics must be accurately modeled and predicted. In two-phase flow typically a set of different measurement instruments (Spool Piece - SP) must be installed in order to evaluate the mass flow rate of the phases in a large range of flow conditions (flow patterns, pressures and temperatures); moreover, an interpretative model of the SP need to be developed and experimentally verified. The investigated meters are: Turbine, Venturi, Impedance Probes, Concave sensors, Wire mesh sensor, Electrical Capacitance Probe. Different instrument combinations have been tested, and the performance of each one has been analyzed.