Multisensor Arrays for Greater Reliability and Accuracy

NASA Technical Reports Server (NTRS)

Immer, Christopher; Eckhoff, Anthony; Lane, John; Perotti, Jose; Randazzo, John; Blalock, Norman; Ree, Jeff

2004-01-01

Arrays of multiple, nominally identical sensors with sensor-output-processing electronic hardware and software are being developed in order to obtain accuracy, reliability, and lifetime greater than those of single sensors. The conceptual basis of this development lies in the statistical behavior of multiple sensors and a multisensor-array (MSA) algorithm that exploits that behavior. In addition, advances in microelectromechanical systems (MEMS) and integrated circuits are exploited. A typical sensor unit according to this concept includes multiple MEMS sensors and sensor-readout circuitry fabricated together on a single chip and packaged compactly with a microprocessor that performs several functions, including execution of the MSA algorithm. In the MSA algorithm, the readings from all the sensors in an array at a given instant of time are compared and the reliability of each sensor is quantified. This comparison of readings and quantification of reliabilities involves the calculation of the ratio between every sensor reading and every other sensor reading, plus calculation of the sum of all such ratios. Then one output reading for the given instant of time is computed as a weighted average of the readings of all the sensors. In this computation, the weight for each sensor is the aforementioned value used to quantify its reliability. In an optional variant of the MSA algorithm that can be implemented easily, a running sum of the reliability value for each sensor at previous time steps as well as at the present time step is used as the weight of the sensor in calculating the weighted average at the present time step. In this variant, the weight of a sensor that continually fails gradually decreases, so that eventually, its influence over the output reading becomes minimal: In effect, the sensor system "learns" which sensors to trust and which not to trust. The MSA algorithm incorporates a criterion for deciding whether there remain enough sensor readings that approximate each other sufficiently closely to constitute a majority for the purpose of quantifying reliability. This criterion is, simply, that if there do not exist at least three sensors having weights greater than a prescribed minimum acceptable value, then the array as a whole is deemed to have failed.

Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

PubMed

Mueller, Amy V; Hemond, Harold F

2016-05-18

Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of statistical representativeness to natural water samples. The accuracy of ANN results improves monotonically with the statistical representativeness of the training set (error decreases by ∼5×), while the expanded neural network architecture contributes a further factor of 2-3.5 decrease in error when trained with the most representative sample set. Results using the most statistically accurate set of training samples (which retain environmentally relevant ion concentrations but avoid the potential interference of humic acids) demonstrated accurate, unbiased quantification of nitrate and ammonium at natural environmental levels (±20% down to <10 μM), as well as the major ions Na(+), K(+), Ca(2+), Mg(2+), Cl(-), and SO4(2-), in unprocessed samples. These results show promise for the development of new in situ instrumentation for the support of scientific field work.

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, Brian; Manipon, Gerald; Hua, Hook; Fetzer, Eric

2014-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map-reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in a hybrid Cloud (private eucalyptus & public Amazon). Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept and prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed "near" the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be perform

A smart multisensor approach to assist blind people in specific urban navigation tasks.

PubMed

Ando, B

2008-12-01

Visually impaired people are often discouraged in using electronic aids due to complexity of operation, large amount of training, nonoptimized degree of information provided to the user, and high cost. In this paper, a new multisensor architecture is discussed, which would help blind people to perform urban mobility tasks. The device is based on a multisensor strategy and adopts smart signal processing.

Overseas testing of a multisensor landmine detection system: results and lessons learned

NASA Astrophysics Data System (ADS)

Keranen, Joe G.; Topolosky, Zeke

2009-05-01

The Nemesis detection system has been developed to provide an efficient and reliable unmanned, multi-sensor, groundbased platform to detect and mark landmines. The detection system consists of two detection sensor arrays: a Ground Penetrating Synthetic Aperture Radar (GPSAR) developed by Planning Systems, Inc. (PSI) and an electromagnetic induction (EMI) sensor array developed by Minelab Electronics, PTY. Limited. Under direction of the Night Vision and Electronic Sensors Directorate (NVESD), overseas testing was performed at Kampong Chhnang Test Center (KCTC), Cambodia, from May 12-30, 2008. Test objectives included: evaluation of detection performance, demonstration of real-time visualization and alarm generation, and evaluation of system operational efficiency. Testing was performed on five sensor test lanes, each consisting of a unique soil mixture and three off-road lanes which include curves, overgrowth, potholes, and non-uniform lane geometry. In this paper, we outline the test objectives, procedures, results, and lessons learned from overseas testing. We also describe the current state of the system, and plans for future enhancements and modifications including clutter rejection and feature-level fusion.

CMOS Imaging of Pin-Printed Xerogel-Based Luminescent Sensor Microarrays.

PubMed

Yao, Lei; Yung, Ka Yi; Khan, Rifat; Chodavarapu, Vamsy P; Bright, Frank V

2010-12-01

We present the design and implementation of a luminescence-based miniaturized multisensor system using pin-printed xerogel materials which act as host media for chemical recognition elements. We developed a CMOS imager integrated circuit (IC) to image the luminescence response of the xerogel-based sensor array. The imager IC uses a 26 × 20 (520 elements) array of active pixel sensors and each active pixel includes a high-gain phototransistor to convert the detected optical signals into electrical currents. The imager includes a correlated double sampling circuit and pixel address/digital control circuit; the image data is read-out as coded serial signal. The sensor system uses a light-emitting diode (LED) to excite the target analyte responsive luminophores doped within discrete xerogel-based sensor elements. As a prototype, we developed a 4 × 4 (16 elements) array of oxygen (O 2 ) sensors. Each group of 4 sensor elements in the array (arranged in a row) is designed to provide a different and specific sensitivity to the target gaseous O 2 concentration. This property of multiple sensitivities is achieved by using a strategic mix of two oxygen sensitive luminophores ([Ru(dpp) 3 ] 2+ and ([Ru(bpy) 3 ] 2+ ) in each pin-printed xerogel sensor element. The CMOS imager consumes an average power of 8 mW operating at 1 kHz sampling frequency driven at 5 V. The developed prototype system demonstrates a low cost and miniaturized luminescence multisensor system.

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

Germanium detectors in homeland security at PNNL

DOE PAGES

Stave, S.

2015-05-01

Neutron and gamma-ray detection is used for non-proliferation and national security applications. While lower energy resolution detectors such as NaI(Tl) have their place, high purity germanium (HPGe) also has a role to play. A detection with HPGe is often a characterization due to the very high energy resolution. However, HPGe crystals remain small and expensive leaving arrays of smaller crystals as an excellent solution. PNNL has developed two similar HPGe arrays for two very different applications. One array, the Multisensor Aerial Radiation Survey (MARS) detector is a fieldable array that has been tested on trucks, boats, and helicopters. The CASCADESmore » HPGe array is an array designed to assay samples in a low background environment. The history of HPGe arrays at PNNL and the development of MARS and CASCADES will be detailed in this paper along with some of the other applications of HPGe at PNNL.« less

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

Stave, Sean C.

Neutron and gamma-ray detection is used for non-proliferation and national security applications. While lower energy resolution detectors such as NaI(Tl) have their place, high purity germanium (HPGe) also has a role to play. A detection with HPGe is often a characterization due to the very high energy resolution. However, HPGe crystals remain small and expensive leaving arrays of smaller crystals as an excellent solution. PNNL has developed two similar HPGe arrays for two very different applications. One array, the Multisensor Aerial Radiation Survey (MARS) detector is a fieldable array that has been tested on trucks, boats, and helicopters. The CASCADESmore » HPGe array is an array designed to assay samples in a low background environment. The history of HPGe arrays at PNNL and the development of MARS and CASCADES will be detailed in this paper along with some of the other applications of HPGe at PNNL.« less

Performance of A Compact Multi-crystal High-purity Germanium Detector Array for Measuring Coincident Gamma-ray Emissions

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

Howard, Chris; Daigle, Stephen; Buckner, Matt

2015-02-18

The Multi-sensor Airborne Radiation Survey (MARS) detector is a 14-crystal array of high-purity germanium (HPGe) detectors housed in a single cryostat. The array was used to measure the astrophysical S-factor for the 14N(p,γ) 15O* reaction for several transition energies at an effective center of mass energy of 163 keV. Owing to the segmented nature of the MARS detector, the effect of gamma-ray summing was greatly reduced in comparison to past experiments which utilized large, single-crystal detectors. The new S-factor values agree within the uncertainties with the past measurements. Details of the analysis and detector performance will be presented.

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E.

2013-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept/prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed "near" the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be performed in an efficient way, with the researcher paying only his own Cloud compute bill.; Figure 1 -- Architecture.

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2013-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved 'clock time' speedups in fusing datasets on our own compute nodes and in the public Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept/prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed 'near' the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be performed in an efficient way, with the researcher paying only his own Cloud compute bill. SciReduce Architecture

Multisensor configurations for early sniper detection

NASA Astrophysics Data System (ADS)

Lindgren, D.; Bank, D.; Carlsson, L.; Dulski, R.; Duval, Y.; Fournier, G.; Grasser, R.; Habberstad, H.; Jacquelard, C.; Kastek, M.; Otterlei, R.; Piau, G.-P.; Pierre, F.; Renhorn, I.; Sjöqvist, L.; Steinvall, O.; Trzaskawka, P.

2011-11-01

This contribution reports some of the fusion results from the EDA SNIPOD project, where different multisensor configurations for sniper detection and localization have been studied. A project aim has been to cover the whole time line from sniper transport and establishment to shot. To do so, different optical sensors with and without laser illumination have been tested, as well as acoustic arrays and solid state projectile radar. A sensor fusion node collects detections and background statistics from all sensors and employs hypothesis testing and multisensor estimation programs to produce unified and reliable sniper alarms and accurate sniper localizations. Operator interfaces that connect to the fusion node should be able to support both sniper countermeasures and the guidance of personnel to safety. Although the integrated platform has not been actually built, sensors have been evaluated at common field trials with military ammunitions in the caliber range 5.56 to 12.7 mm, and at sniper distances up to 900 m. It is concluded that integrating complementary sensors for pre- and postshot sniper detection in a common system with automatic detection and fusion will give superior performance, compared to stand alone sensors. A practical system is most likely designed with a cost effective subset of available complementary sensors.

Multi-sensor electrometer

NASA Technical Reports Server (NTRS)

Gompf, Raymond (Inventor); Buehler, Martin C. (Inventor)

2003-01-01

An array of triboelectric sensors is used for testing the electrostatic properties of a remote environment. The sensors may be mounted in the heel of a robot arm scoop. To determine the triboelectric properties of a planet surface, the robot arm scoop may be rubbed on the soil of the planet and the triboelectrically developed charge measured. By having an array of sensors, different insulating materials may be measured simultaneously. The insulating materials may be selected so their triboelectric properties cover a desired range. By mounting the sensor on a robot arm scoop, the measurements can be obtained during an unmanned mission.

An Electrochemical Quartz Crystal Microbalance Multisensor System Based on Phthalocyanine Nanostructured Films: Discrimination of Musts

PubMed Central

Garcia-Hernandez, Celia; Medina-Plaza, Cristina; Garcia-Cabezon, Cristina; Martin-Pedrosa, Fernando; del Valle, Isabel; de Saja, Jose Antonio; Rodríguez-Méndez, Maria Luz

2015-01-01

An array of electrochemical quartz crystal electrodes (EQCM) modified with nanostructured films based on phthalocyanines was developed and used to discriminate musts prepared from different varieties of grapes. Nanostructured films of iron, nickel and copper phthalocyanines were deposited on Pt/quartz crystals through the Layer by Layer technique by alternating layers of the corresponding phthalocyanine and poly-allylamine hydrochloride. Simultaneous electrochemical and mass measurements were used to study the mass changes accompanying the oxidation of electroactive species present in must samples obtained from six Spanish varieties of grapes (Juan García, Prieto Picudo, Mencía Regadío, Cabernet Sauvignon, Garnacha and Tempranillo). The mass and voltammetric outputs were processed using three-way models. Parallel Factor Analysis (PARAFAC) was successfully used to discriminate the must samples according to their variety. Multi-way partial least squares (N-PLS) evidenced the correlations existing between the voltammetric data and the polyphenolic content measured by chemical methods. Similarly, N-PLS showed a correlation between mass outputs and parameters related to the sugar content. These results demonstrated that electronic tongues based on arrays of EQCM sensors can offer advantages over arrays of mass or voltammetric sensors used separately. PMID:26610494

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety

PubMed Central

Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

2016-01-01

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. PMID:27294931

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety.

PubMed

Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

2016-06-09

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association

PubMed Central

Liu, Jun; Li, Gang; Qi, Lin; Li, Yaowen; He, You

2017-01-01

This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets’ state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems. PMID:29113085

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association.

PubMed

Liu, Yu; Liu, Jun; Li, Gang; Qi, Lin; Li, Yaowen; He, You

2017-11-05

This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets' state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems.

Towards operational multisensor registration

NASA Technical Reports Server (NTRS)

Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.

1991-01-01

To use data from a number of different remote sensors in a synergistic manner, a multidimensional analysis of the data is necessary. However, prior to this analysis, processing to correct for the systematic geometric distortion characteristic of each sensor is required. Furthermore, the registration process must be fully automated to handle a large volume of data and high data rates. A conceptual approach towards an operational multisensor registration algorithm is presented. The performance requirements of the algorithm are first formulated given the spatially, temporally, and spectrally varying factors that influence the image characteristics and the science requirements of various applications. Several registration techniques that fit within the structure of this algorithm are also presented. Their performance was evaluated using a multisensor test data set assembled from LANDSAT TM, SEASAT, SIR-B, Thermal Infrared Multispectral Scanner (TIMS), and SPOT sensors.

Multi-Sensor Scene Synthesis and Analysis

DTIC Science & Technology

1981-09-01

Quad Trees for Image Representation and Processing ...... ... 126 2.6.2 Databases ..... ..... ... ..... ... ..... ..... 138 2.6.2.1 Definitions and...Basic Concepts ....... 138 2.6.3 Use of Databases in Hierarchical Scene Analysis ...... ... ..................... 147 2.6.4 Use of Relational Tables...Multisensor Image Database Systems (MIDAS) . 161 2.7.2 Relational Database System for Pictures .... ..... 168 2.7.3 Relational Pictorial Database

Multisensor fusion for 3-D defect characterization using wavelet basis function neural networks

NASA Astrophysics Data System (ADS)

Lim, Jaein; Udpa, Satish S.; Udpa, Lalita; Afzal, Muhammad

2001-04-01

The primary objective of multi-sensor data fusion, which offers both quantitative and qualitative benefits, has the ability to draw inferences that may not be feasible with data from a single sensor alone. In this paper, data from two sets of sensors are fused to estimate the defect profile from magnetic flux leakage (MFL) inspection data. The two sensors measure the axial and circumferential components of the MFL. Data is fused at the signal level. If the flux is oriented axially, the samples of the axial signal are measured along a direction parallel to the flaw, while the circumferential signal is measured in a direction that is perpendicular to the flaw. The two signals are combined as the real and imaginary components of a complex valued signal. Signals from an array of sensors are arranged in contiguous rows to obtain a complex valued image. A boundary extraction algorithm is used to extract the defect areas in the image. Signals from the defect regions are then processed to minimize noise and the effects of lift-off. Finally, a wavelet basis function (WBF) neural network is employed to map the complex valued image appropriately to obtain the geometrical profile of the defect. The feasibility of the approach was evaluated using the data obtained from the MFL inspection of natural gas transmission pipelines. Results show the effectiveness of the approach.

A Reconfigurable Readout Integrated Circuit for Heterogeneous Display-Based Multi-Sensor Systems

PubMed Central

Park, Kyeonghwan; Kim, Seung Mok; Eom, Won-Jin; Kim, Jae Joon

2017-01-01

This paper presents a reconfigurable multi-sensor interface and its readout integrated circuit (ROIC) for display-based multi-sensor systems, which builds up multi-sensor functions by utilizing touch screen panels. In addition to inherent touch detection, physiological and environmental sensor interfaces are incorporated. The reconfigurable feature is effectively implemented by proposing two basis readout topologies of amplifier-based and oscillator-based circuits. For noise-immune design against various noises from inherent human-touch operations, an alternate-sampling error-correction scheme is proposed and integrated inside the ROIC, achieving a 12-bit resolution of successive approximation register (SAR) of analog-to-digital conversion without additional calibrations. A ROIC prototype that includes the whole proposed functions and data converters was fabricated in a 0.18 μm complementary metal oxide semiconductor (CMOS) process, and its feasibility was experimentally verified to support multiple heterogeneous sensing functions of touch, electrocardiogram, body impedance, and environmental sensors. PMID:28368355

A Reconfigurable Readout Integrated Circuit for Heterogeneous Display-Based Multi-Sensor Systems.

PubMed

Park, Kyeonghwan; Kim, Seung Mok; Eom, Won-Jin; Kim, Jae Joon

2017-04-03

This paper presents a reconfigurable multi-sensor interface and its readout integrated circuit (ROIC) for display-based multi-sensor systems, which builds up multi-sensor functions by utilizing touch screen panels. In addition to inherent touch detection, physiological and environmental sensor interfaces are incorporated. The reconfigurable feature is effectively implemented by proposing two basis readout topologies of amplifier-based and oscillator-based circuits. For noise-immune design against various noises from inherent human-touch operations, an alternate-sampling error-correction scheme is proposed and integrated inside the ROIC, achieving a 12-bit resolution of successive approximation register (SAR) of analog-to-digital conversion without additional calibrations. A ROIC prototype that includes the whole proposed functions and data converters was fabricated in a 0.18 μm complementary metal oxide semiconductor (CMOS) process, and its feasibility was experimentally verified to support multiple heterogeneous sensing functions of touch, electrocardiogram, body impedance, and environmental sensors.

Demonstration of Helicopter Multi-Sensor Towed Array Detection System (MTADS) Magnetometry Technology at Victorville Precision Bombing Range, California

DTIC Science & Technology

2008-09-12

measurement Fluxgate magnetometer 10 RS232- ASCII SerialDevice.fluxgate Provides redundant aircraft attitude measurement Acoustic altimeters 10 Analog...primarily by terrain, vegetation, and structural inhibitions to safe low-altitude flight. The magnetometer data can be analyzed to extract either...to validate the results of the magnetometer survey. ESTCP Victorville PBR WAA Final Report December 2008 Sky Research, Inc. 2 1.2. Objectives of

Modeling change from large-scale high-dimensional spatio-temporal array data

NASA Astrophysics Data System (ADS)

Lu, Meng; Pebesma, Edzer

2014-05-01

The massive data that come from Earth observation satellite and other sensors provide significant information for modeling global change. At the same time, the high dimensionality of the data has brought challenges in data acquisition, management, effective querying and processing. In addition, the output of earth system modeling tends to be data intensive and needs methodologies for storing, validation, analyzing and visualization, e.g. as maps. An important proportion of earth system observations and simulated data can be represented as multi-dimensional array data, which has received increasingly attention in big data management and spatial-temporal analysis. Study cases will be developed in natural science such as climate change, hydrological modeling, sediment dynamics, from which the addressing of big data problems is necessary. Multi-dimensional array-based database management and analytics system such as Rasdaman, SciDB, and R will be applied to these cases. From these studies will hope to learn the strengths and weaknesses of these systems, how they might work together or how semantics of array operations differ, through addressing the problems associated with big data. Research questions include: • How can we reduce dimensions spatially and temporally, or thematically? • How can we extend existing GIS functions to work on multidimensional arrays? • How can we combine data sets of different dimensionality or different resolutions? • Can map algebra be extended to an intelligible array algebra? • What are effective semantics for array programming of dynamic data driven applications? • In which sense are space and time special, as dimensions, compared to other properties? • How can we make the analysis of multi-spectral, multi-temporal and multi-sensor earth observation data easy?

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2015-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be executed "near" the multi-sensor data. Decade-long, multi-sensor studies can be performed without pre-staging data, with the researcher paying only his own Cloud compute bill.

A Passive Wireless Multi-Sensor SAW Technology Device and System Perspectives

PubMed Central

Malocha, Donald C.; Gallagher, Mark; Fisher, Brian; Humphries, James; Gallagher, Daniel; Kozlovski, Nikolai

2013-01-01

This paper will discuss a SAW passive, wireless multi-sensor system under development by our group for the past several years. The device focus is on orthogonal frequency coded (OFC) SAW sensors, which use both frequency diversity and pulse position reflectors to encode the device ID and will be briefly contrasted to other embodiments. A synchronous correlator transceiver is used for the hardware and post processing and correlation techniques of the received signal to extract the sensor information will be presented. Critical device and system parameters addressed include encoding, operational range, SAW device parameters, post-processing, and antenna-SAW device integration. A fully developed 915 MHz OFC SAW multi-sensor system is used to show experimental results. The system is based on a software radio approach that provides great flexibility for future enhancements and diverse sensor applications. Several different sensor types using the OFC SAW platform are shown. PMID:23666124

Joint FACET: the Canada-Netherlands initiative to study multisensor data fusion systems

NASA Astrophysics Data System (ADS)

Bosse, Eloi; Theil, Arne; Roy, Jean; Huizing, Albert G.; van Aartsen, Simon

1998-09-01

This paper presents the progress of a collaborative effort between Canada and The Netherlands in analyzing multi-sensor data fusion systems, e.g. for potential application to their respective frigates. In view of the overlapping interest in studying and comparing applicability and performance and advanced state-of-the-art Multi-Sensor Data FUsion (MSDF) techniques, the two research establishments involved have decided to join their efforts in the development of MSDF testbeds. This resulted in the so-called Joint-FACET, a highly modular and flexible series of applications that is capable of processing both real and synthetic input data. Joint-FACET allows the user to create and edit test scenarios with multiple ships, sensor and targets, generate realistic sensor outputs, and to process these outputs with a variety of MSDF algorithms. These MSDF algorithms can also be tested using typical experimental data collected during live military exercises.

Wireless Sensors and Networks for Advanced Energy Management

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

Hardy, J.E.

Numerous national studies and working groups have identified low-cost, very low-power wireless sensors and networks as a critical enabling technology for increasing energy efficiency, reducing waste, and optimizing processes. Research areas for developing such sensor and network platforms include microsensor arrays, ultra-low power electronics and signal conditioning, data/control transceivers, and robust wireless networks. A review of some of the research in the following areas will be discussed: (1) Low-cost, flexible multi-sensor array platforms (CO{sub 2}, NO{sub x}, CO, humidity, NH{sub 3}, O{sub 2}, occupancy, etc.) that enable energy and emission reductions in applications such as buildings and manufacturing; (2) Modelingmore » investments (energy usage and savings to drive capital investment decisions) and estimated uptime improvements through pervasive gathering of equipment and process health data and its effects on energy; (3) Robust, self-configuring wireless sensor networks for energy management; and (4) Quality-of-service for secure and reliable data transmission from widely distributed sensors. Wireless communications is poised to support technical innovations in the industrial community, with widespread use of wireless sensors forecasted to improve manufacturing production and energy efficiency and reduce emissions. Progress being made in wireless system components, as described in this paper, is helping bring these projected improvements to reality.« less

Development of subminiature multi-sensor hot-wire probes

NASA Technical Reports Server (NTRS)

Westphal, Russell V.; Ligrani, Phillip M.; Lemos, Fred R.

1988-01-01

Limitations on the spatial resolution of multisensor hot wire probes have precluded accurate measurements of Reynolds stresses very near solid surfaces in wind tunnels and in many practical aerodynamic flows. The fabrication, calibration and qualification testing of very small single horizontal and X-array hot-wire probes which are intended to be used near solid boundaries in turbulent flows where length scales are particularly small, is described. Details of the sensor fabrication procedure are reported, along with information needed to successfully operate the probes. As compared with conventional probes, manufacture of the subminiature probes is more complex, requiring special equipment and careful handling. The subminiature probes tested were more fragile and shorter lived than conventional probes; they obeyed the same calibration laws but with slightly larger experimental uncertainty. In spite of these disadvantages, measurements of mean statistical quantities and spectra demonstrate the ability of the subminiature sensors to provide the measurements in the near wall region of turbulent boundary layers that are more accurate than conventional sized probes.

Distributed multi-sensor particle filter for bearings-only tracking

NASA Astrophysics Data System (ADS)

Zhang, Jungen; Ji, Hongbing

2012-02-01

In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer-Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images

PubMed Central

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-01-01

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures. PMID:26007744

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

PubMed

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-05-22

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Intelligent multi-sensor integrations

NASA Technical Reports Server (NTRS)

Volz, Richard A.; Jain, Ramesh; Weymouth, Terry

1989-01-01

Growth in the intelligence of space systems requires the use and integration of data from multiple sensors. Generic tools are being developed for extracting and integrating information obtained from multiple sources. The full spectrum is addressed for issues ranging from data acquisition, to characterization of sensor data, to adaptive systems for utilizing the data. In particular, there are three major aspects to the project, multisensor processing, an adaptive approach to object recognition, and distributed sensor system integration.

Reducing multi-sensor data to a single time course that reveals experimental effects

PubMed Central

2013-01-01

Background Multi-sensor technologies such as EEG, MEG, and ECoG result in high-dimensional data sets. Given the high temporal resolution of such techniques, scientific questions very often focus on the time-course of an experimental effect. In many studies, researchers focus on a single sensor or the average over a subset of sensors covering a “region of interest” (ROI). However, single-sensor or ROI analyses ignore the fact that the spatial focus of activity is constantly changing, and fail to make full use of the information distributed over the sensor array. Methods We describe a technique that exploits the optimality and simplicity of matched spatial filters in order to reduce experimental effects in multivariate time series data to a single time course. Each (multi-sensor) time sample of each trial is replaced with its projection onto a spatial filter that is matched to an observed experimental effect, estimated from the remaining trials (Effect-Matched Spatial filtering, or EMS filtering). The resulting set of time courses (one per trial) can be used to reveal the temporal evolution of an experimental effect, which distinguishes this approach from techniques that reveal the temporal evolution of an anatomical source or region of interest. Results We illustrate the technique with data from a dual-task experiment and use it to track the temporal evolution of brain activity during the psychological refractory period. We demonstrate its effectiveness in separating the means of two experimental conditions, and in significantly improving the signal-to-noise ratio at the single-trial level. It is fast to compute and results in readily-interpretable time courses and topographies. The technique can be applied to any data-analysis question that can be posed independently at each sensor, and we provide one example, using linear regression, that highlights the versatility of the technique. Conclusion The approach described here combines established techniques in a way that strikes a balance between power, simplicity, speed of processing, and interpretability. We have used it to provide a direct view of parallel and serial processes in the human brain that previously could only be measured indirectly. An implementation of the technique in MatLab is freely available via the internet. PMID:24125590

Multi-sensor analysis of urban ecosystems

USGS Publications Warehouse

Gallo, Kevin P.; Ji, Lei

2004-01-01

This study examines the synthesis of multiple space-based sensors to characterize the urban environment Single scene data (e.g., ASTER visible and near-IR surface reflectance, and land surface temperature data), multi-temporal data (e.g., one year of 16-day MODIS and AVHRR vegetation index data), and DMSP-OLS nighttime light data acquired in the early 1990s and 2000 were evaluated for urban ecosystem analysis. The advantages of a multi-sensor approach for the analysis of urban ecosystem processes are discussed.

A composite hydrogels-based photonic crystal multi-sensor

NASA Astrophysics Data System (ADS)

Chen, Cheng; Zhu, Zhigang; Zhu, Xiangrong; Yu, Wei; Liu, Mingju; Ge, Qiaoqiao; Shih, Wei-Heng

2015-04-01

A facile route to prepare stimuli-sensitive poly(vinyl alcohol)/poly(acrylic acid) (PVA/PAA) gelated crystalline colloidal array photonic crystal material was developed. PVA was physically gelated by utilizing an ethanol-assisted method, the resulting hydrogel/crystal composite film was then functionalized with PAA to form an interpenetrating hydrogel film. This sensor film is able to efficiently diffract the visible light and rapidly respond to various environmental stimuli such as solvent, pH and strain, and the accompanying structural color shift can be repeatedly changed and easily distinguished by naked eye.

Real Time Assessment of Potable Water Quality in Distribution Network based on Low Cost Multi-Sensor Array

NASA Astrophysics Data System (ADS)

Bhardwaj, Jyotirmoy; Gupta, Karunesh K.; Khatri, Punit

2018-03-01

New concepts and techniques are replacing traditional methods of water quality parameters measurement systems. This paper proposed a new way of potable water quality assessment in distribution network using Multi Sensor Array (MSA). Extensive research suggests that following parameters i.e. pH, Dissolved Oxygen (D.O.), Conductivity, Oxygen Reduction Potential (ORP), Temperature and Salinity are most suitable to detect overall quality of potable water. Commonly MSA is not an integrated sensor array on some substrate, but rather comprises a set of individual sensors measuring simultaneously different water parameters all together. Based on research, a MSA has been developed followed by signal conditioning unit and finally, an algorithm for easy user interfacing. A dedicated part of this paper also discusses the platform design and significant results. The Objective of this proposed research is to provide simple, efficient, cost effective and socially acceptable means to detect and analyse water bodies regularly and automatically.

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems.

PubMed

Gao, Lei; Bourke, A K; Nelson, John

2014-06-01

Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with "light-weight" signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high recognition accuracies on a multi-sensor system. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Status report on the land processes aircraft science management operations working group

NASA Technical Reports Server (NTRS)

Lawless, James G.; Mann, Lisa J.

1991-01-01

Since its inception three years ago, the Land Processes Aircraft Science Management Operations Working Group (MOWG) provided recommendations on the optimal use of the Agency's aircraft in support of the Land Processes Science Program. Recommendations covered topics such as aircraft and sensor usage, development of long-range plans, Multisensor Airborne Campaigns (MAC), program balance, aircraft sensor databases, new technology and sensor development, and increased University scientist participation in the program. Impacts of these recommendations improved the efficiency of various procedures including the flight request process, tracking of flight hours, and aircraft usage. The group also created a bibliography focused on publications produced by Land Processes scientists from the use of the aircraft program, surveyed NASA funded PI's on their participation in the aircraft program, and developed a planning template for multi-sensor airborne campaigns. Benefits from these activities are summarized.

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017.

PubMed

Jin, Xue-Bo; Sun, Shuli; Wei, Hong; Yang, Feng-Bao

2018-04-11

The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation.

PubMed

Li, Yuecheng; Jia, Wenyan; Yu, Tianjian; Luan, Bo; Mao, Zhi-Hong; Zhang, Hong; Sun, Mingui

2015-04-01

In this paper, the design of a low power heterogeneous wearable multi-sensor system, built with Zynq System-on-Chip (SoC), for human activity evaluation is presented. The powerful data processing capability and flexibility of this SoC represent significant improvements over our previous ARM based system designs. The new system captures and compresses multiple color images and sensor data simultaneously. Several strategies are adopted to minimize power consumption. Our wearable system provides a new tool for the evaluation of human activity, including diet, physical activity and lifestyle.

ARC - A source of multisensor satellite data for polar science

NASA Technical Reports Server (NTRS)

Van Woert, Michael L.; Whritner, Robert H.; Waliser, Duane E.; Bromwich, David H.; Comiso, J. C.

1992-01-01

The NSF's Antarctic Research Center (ARC) has been established to furnish real-time polar-orbiting satellite data in support of Antarctic field studies, as well as to maintain a multisensor satellite data (MSD) archive for retrospective data analysis. An account is presently given of the ways in which the complementary nature of an MSD set can deepen understanding of Antarctic physical processes. An active microwave SAR with 30-m resolution and a radar altimeter will be added to the ARC resources later in this decade, as will the Earth Observing System.

Hybrid Arrays for Chemical Sensing

NASA Astrophysics Data System (ADS)

Kramer, Kirsten E.; Rose-Pehrsson, Susan L.; Johnson, Kevin J.; Minor, Christian P.

In recent years, multisensory approaches to environment monitoring for chemical detection as well as other forms of situational awareness have become increasingly popular. A hybrid sensor is a multimodal system that incorporates several sensing elements and thus produces data that are multivariate in nature and may be significantly increased in complexity compared to data provided by single-sensor systems. Though a hybrid sensor is itself an array, hybrid sensors are often organized into more complex sensing systems through an assortment of network topologies. Part of the reason for the shift to hybrid sensors is due to advancements in sensor technology and computational power available for processing larger amounts of data. There is also ample evidence to support the claim that a multivariate analytical approach is generally superior to univariate measurements because it provides additional redundant and complementary information (Hall, D. L.; Linas, J., Eds., Handbook of Multisensor Data Fusion, CRC, Boca Raton, FL, 2001). However, the benefits of a multisensory approach are not automatically achieved. Interpretation of data from hybrid arrays of sensors requires the analyst to develop an application-specific methodology to optimally fuse the disparate sources of data generated by the hybrid array into useful information characterizing the sample or environment being observed. Consequently, multivariate data analysis techniques such as those employed in the field of chemometrics have become more important in analyzing sensor array data. Depending on the nature of the acquired data, a number of chemometric algorithms may prove useful in the analysis and interpretation of data from hybrid sensor arrays. It is important to note, however, that the challenges posed by the analysis of hybrid sensor array data are not unique to the field of chemical sensing. Applications in electrical and process engineering, remote sensing, medicine, and of course, artificial intelligence and robotics, all share the same essential data fusion challenges. The design of a hybrid sensor array should draw on this extended body of knowledge. In this chapter, various techniques for data preprocessing, feature extraction, feature selection, and modeling of sensor data will be introduced and illustrated with data fusion approaches that have been implemented in applications involving data from hybrid arrays. The example systems discussed in this chapter involve the development of prototype sensor networks for damage control event detection aboard US Navy vessels and the development of analysis algorithms to combine multiple sensing techniques for enhanced remote detection of unexploded ordnance (UXO) in both ground surveys and wide area assessments.

Design and implementation of a hot-wire probe for simultaneous velocity and vorticity vector measurements in boundary layers

NASA Astrophysics Data System (ADS)

Zimmerman, S.; Morrill-Winter, C.; Klewicki, J.

2017-10-01

A multi-sensor hot-wire probe for simultaneously measuring all three components of velocity and vorticity in boundary layers has been designed, fabricated and implemented in experiments up to large Reynolds numbers. The probe consists of eight hot-wires, compactly arranged in two pairs of orthogonal ×-wire arrays. The ×-wire sub-arrays are symmetrically configured such that the full velocity and vorticity vectors are resolved about a single central location. During its design phase, the capacity of this sensor to accurately measure each component of velocity and vorticity was first evaluated via a synthetic experiment in a set of well-resolved DNS fields. The synthetic experiments clarified probe geometry effects, allowed assessment of various processing schemes, and predicted the effects of finite wire length and wire separation on turbulence statistics. The probe was subsequently fabricated and employed in large Reynolds number experiments in the Flow Physics Facility wind tunnel at the University of New Hampshire. Comparisons of statistics from the actual probe with those from the simulated sensor exhibit very good agreement in trend, but with some differences in magnitude. These comparisons also reveal that the use of gradient information in processing the probe data can significantly improve the accuracy of the spanwise velocity measurement near the wall. To the authors' knowledge, the present are the largest Reynolds number laboratory-based measurements of all three vorticity components in boundary layers.

An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications.

PubMed

Mansoor, Mohtashim; Haneef, Ibraheem; Akhtar, Suhail; Rafiq, Muhammad Aftab; De Luca, Andrea; Ali, Syed Zeeshan; Udrea, Florin

2016-11-04

An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA-0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C-1.79 mV/°C in the range 20-300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(V excit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min) -0.1 in the tested range of 0-4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries.

An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications †

PubMed Central

Mansoor, Mohtashim; Haneef, Ibraheem; Akhtar, Suhail; Rafiq, Muhammad Aftab; De Luca, Andrea; Ali, Syed Zeeshan; Udrea, Florin

2016-01-01

An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA–0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C–1.79 mV/°C in the range 20–300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(Vexcit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min)−0.1 in the tested range of 0–4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries. PMID:27827904

Classification of white wine aromas with an electronic nose.

PubMed

Lozano, J; Santos, J P; Horrillo, M C

2005-09-15

This paper reports the use of a tin dioxide multisensor array based electronic nose for recognition of 29 typical aromas in white wine. Headspace technique has been used to extract aroma of the wine. Multivariate analysis, including principal component analysis (PCA) as well as probabilistic neural networks (PNNs), has been used to identify the main aroma added to the wine. The results showed that in spite of the strong influence of ethanol and other majority compounds of wine, the system could discriminate correctly the aromatic compounds added to the wine with a minimum accuracy of 97.2%.

Performance evaluation of an asynchronous multisensor track fusion filter

NASA Astrophysics Data System (ADS)

Alouani, Ali T.; Gray, John E.; McCabe, D. H.

2003-08-01

Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.

Solid-contact potentiometric sensors and multisensors based on polyaniline and thiacalixarene receptors for the analysis of some beverages and alcoholic drinks

NASA Astrophysics Data System (ADS)

Sorvin, Michail; Belyakova, Svetlana; Stoikov, Ivan; Shamagsumova, Rezeda; Evtugyn, Gennady

2018-04-01

Electronic tongue is a sensor array that aims to discriminate and analyze complex media like food and beverages on the base of chemometrics approaches for data mining and pattern recognition. In this review, the concept of electronic tongue comprising of solid-contact potentiometric sensors with polyaniline and thacalix[4]arene derivatives is described. The electrochemical reactions of polyaniline as a background of solid-contact sensors and the characteristics of thiacalixarenes and pillararenes as neutral ionophores are briefly considered. The electronic tongue systems described were successfully applied for assessment of fruit juices, green tea, beer and alcoholic drinks They were classified in accordance with the origination, brands and styles. Variation of the sensor response resulted from the reactions between Fe(III) ions added and sample components, i.e., antioxidants and complexing agents. The use of principal component analysis and discriminant analysis is shown for multisensor signal treatment and visualization. The discrimination conditions can be optimized by variation of the ionophores, Fe(III) concentration and sample dilution. The results obtained were compared with other electronic tongue systems reported for the same subjects.

Solid-Contact Potentiometric Sensors and Multisensors Based on Polyaniline and Thiacalixarene Receptors for the Analysis of Some Beverages and Alcoholic Drinks.

PubMed

Sorvin, Michail; Belyakova, Svetlana; Stoikov, Ivan; Shamagsumova, Rezeda; Evtugyn, Gennady

2018-01-01

Electronic tongue is a sensor array that aims to discriminate and analyze complex media like food and beverages on the base of chemometrics approaches for data mining and pattern recognition. In this review, the concept of electronic tongue comprising of solid-contact potentiometric sensors with polyaniline and thacalix[4]arene derivatives is described. The electrochemical reactions of polyaniline as a background of solid-contact sensors and the characteristics of thiacalixarenes and pillararenes as neutral ionophores are briefly considered. The electronic tongue systems described were successfully applied for assessment of fruit juices, green tea, beer, and alcoholic drinks They were classified in accordance with the origination, brands and styles. Variation of the sensor response resulted from the reactions between Fe(III) ions added and sample components, i.e., antioxidants and complexing agents. The use of principal component analysis and discriminant analysis is shown for multisensor signal treatment and visualization. The discrimination conditions can be optimized by variation of the ionophores, Fe(III) concentration, and sample dilution. The results obtained were compared with other electronic tongue systems reported for the same subjects.

Solid-Contact Potentiometric Sensors and Multisensors Based on Polyaniline and Thiacalixarene Receptors for the Analysis of Some Beverages and Alcoholic Drinks

PubMed Central

Sorvin, Michail; Belyakova, Svetlana; Stoikov, Ivan; Shamagsumova, Rezeda; Evtugyn, Gennady

2018-01-01

Electronic tongue is a sensor array that aims to discriminate and analyze complex media like food and beverages on the base of chemometrics approaches for data mining and pattern recognition. In this review, the concept of electronic tongue comprising of solid-contact potentiometric sensors with polyaniline and thacalix[4]arene derivatives is described. The electrochemical reactions of polyaniline as a background of solid-contact sensors and the characteristics of thiacalixarenes and pillararenes as neutral ionophores are briefly considered. The electronic tongue systems described were successfully applied for assessment of fruit juices, green tea, beer, and alcoholic drinks They were classified in accordance with the origination, brands and styles. Variation of the sensor response resulted from the reactions between Fe(III) ions added and sample components, i.e., antioxidants and complexing agents. The use of principal component analysis and discriminant analysis is shown for multisensor signal treatment and visualization. The discrimination conditions can be optimized by variation of the ionophores, Fe(III) concentration, and sample dilution. The results obtained were compared with other electronic tongue systems reported for the same subjects. PMID:29740577

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

Ocean Drifters Get the Facts

NASA Technical Reports Server (NTRS)

2001-01-01

With the help of Small Business Innovation Research (SBIR) funding from NASA's Goddard Space Flight Center, of Greenbelt, Maryland, Clearwater Instrumentation, of Watertown, Massachusetts, created the ClearSat-Autonomous Drifting Ocean Station (ADOS). The multi-sensor array ocean drifting station was developed to support observations of Earth by NASA satellites. It is a low-cost device for gathering an assortment of data necessary to the integration of present and future satellite measurements of biological and physical processes. Clearwater Instrumentation developed its ADOS technology based on Goddard's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) project, but on a scale that is practical for commercial use. ADOS is used for the in situ measuring of ocean surface layer properties such as ocean color, surface thermal structure, and surface winds. Thus far, multiple ADOS units have been sold to The Scripps Institution of Oceanography, where they are being applied in the field of academic science research. Fisheries can also benefit, because ADOS can locate prime cultivation conditions for this fast-growing industry.

Characterizing land processes in the biosphere

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Tuyahov, A. J.

1984-01-01

NASA long-term planning for the satellite remote sensing of land areas is discussed from the perspective of a holistic interdisciplinary approach to the study of the biosphere. The earth is characterized as a biogeochemical system; the impact of human activity on this system is considered; and the primary scientific goals for their study are defined. Remote-sensing programs are seen as essential in gaining an improved understanding of energy budgets, the hydrological cycle, other biogeological cycles, and the coupling between these cycles, with the construction of a global data base and eventually the development of predictive simulation models which can be used to assess the impact of planned human activities. Current sensor development at NASA includes a multilinear array for the visible and IR and the L-band Shuttle Imaging Radar B, both to be flown on Shuttle missions in the near future; for the 1990s, a large essentially permanent man-tended interdisciplinary multisensor platform connected to an advanced data network is being planned.

Multiple sensor multifrequency eddy current monitor for solidification and growth

NASA Technical Reports Server (NTRS)

Wallace, John

1990-01-01

A compact cylindrical multisensor eddy current measuring system with integral furnace was develop to monitor II-VI crystal growth to provide interfacial information, solutal segregation, and conductivities of the growth materials. The use of an array of sensors surrounding the furnace element allows one to monitor the volume of interest. Coupling these data with inverse multifrequency analysis allows radial conductivity profiles to be generated at each sensor position. These outputs were incorporated to control the processes within the melt volume. The standard eddy current system functions with materials whose electric conductivities are as low as 2E2 Mhos/m. A need was seen to extend the measurement range to poorly conducting media so the unit was modified to allow measurement of materials conductivities 4 order of magnitude lower and bulk dielectric properties. Typically these included submicron thick films and semiinsulating GaAs. This system was used to monitor complex heat transfer in grey bodies as well as semiconductor and metallic solidification.

Multisensor Image Analysis System

DTIC Science & Technology

1993-04-15

AD-A263 679 II Uli! 91 Multisensor Image Analysis System Final Report Authors. Dr. G. M. Flachs Dr. Michael Giles Dr. Jay Jordan Dr. Eric...or decision, unless so designated by other documentation. 93-09739 *>ft s n~. now illlllM3lMVf Multisensor Image Analysis System Final...Multisensor Image Analysis System 3. REPORT TYPE AND DATES COVERED FINAL: LQj&tt-Z JZOfVL 5. FUNDING NUMBERS 93 > 6. AUTHOR(S) Drs. Gerald

SBIR Phase II Final Report: Low cost Autonomous NMR and Multi-sensor Soil Monitoring Instrument

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

Walsh, David O.

In this 32-month SBIR Phase 2 program, Vista Clara designed, assembled and successfully tested four new NMR instruments for soil moisture measurement and monitoring: An enhanced performance man-portable Dart NMR logging probe and control unit for rapid, mobile measurement in core holes and 2” PVC access wells; A prototype 4-level Dart NMR monitoring probe and prototype multi-sensor soil monitoring control unit for long-term unattended monitoring of soil moisture and other measurements in-situ; A non-invasive 1m x 1m Discus NMR soil moisture sensor with surface based magnet/coil array for rapid measurement of soil moisture in the top 50 cm of themore » subsurface; A non-invasive, ultra-lightweight Earth’s field surface NMR instrument for non-invasive measurement and mapping of soil moisture in the top 3 meters of the subsurface. The Phase 2 research and development achieved most, but not all of our technical objectives. The single-coil Dart in-situ sensor and control unit were fully developed, demonstrated and successfully commercialized within the Phase 2 period of performance. The multi-level version of the Dart probe was designed, assembled and demonstrated in Phase 2, but its final assembly and testing were delayed until close to the end of the Phase 2 performance period, which limited our opportunities for demonstration in field settings. Likewise, the multi-sensor version of the Dart control unit was designed and assembled, but not in time for it to be deployed for any long-term monitoring demonstrations. The prototype ultra-lightweight surface NMR instrument was developed and demonstrated, and this result will be carried forward into the development of a new flexible surface NMR instrument and commercial product in 2018.« less

Other remote sensing systems: Retrospect and outlook

NASA Technical Reports Server (NTRS)

1982-01-01

The history of remote sensing is reviewed and the scope and versatility of the several remote sensing systems already in orbit are discussed, especially those with sensors operating in other EM spectral modes. The multisensor approach is examined by interrelating LANDSAT observations with data from other satellite systems. The basic principles and practices underlying the use of thermal infrared and radar sensors are explored and the types of observations and interpretations emanating from the Nimbus, Heat Capacity Mapping Mission, and SEASAT programs are examined. Approved or proposed Earth resources oriented missions for the 1980's previewed include LANDSAT D, Stereosat, Gravsat, the French satellite SPOT-1, and multimission modular spacecraft launched from space shuttle. The pushbroom imager, the linear array pushbroom radiometer, the multispectral linear array, and the operational LANDSAT observing system, to be designed the LANDSAT-E series are also envisioned for this decade.

Modular Analytical Multicomponent Analysis in Gas Sensor Aarrays

PubMed Central

Chaiyboun, Ali; Traute, Rüdiger; Kiesewetter, Olaf; Ahlers, Simon; Müller, Gerhard; Doll, Theodor

2006-01-01

A multi-sensor system is a chemical sensor system which quantitatively and qualitatively records gases with a combination of cross-sensitive gas sensor arrays and pattern recognition software. This paper addresses the issue of data analysis for identification of gases in a gas sensor array. We introduce a software tool for gas sensor array configuration and simulation. It concerns thereby about a modular software package for the acquisition of data of different sensors. A signal evaluation algorithm referred to as matrix method was used specifically for the software tool. This matrix method computes the gas concentrations from the signals of a sensor array. The software tool was used for the simulation of an array of five sensors to determine gas concentration of CH4, NH3, H2, CO and C2H5OH. The results of the present simulated sensor array indicate that the software tool is capable of the following: (a) identify a gas independently of its concentration; (b) estimate the concentration of the gas, even if the system was not previously exposed to this concentration; (c) tell when a gas concentration exceeds a certain value. A gas sensor data base was build for the configuration of the software. With the data base one can create, generate and manage scenarios and source files for the simulation. With the gas sensor data base and the simulation software an on-line Web-based version was developed, with which the user can configure and simulate sensor arrays on-line.

Multisensor multiresolution data fusion for improvement in classification

NASA Astrophysics Data System (ADS)

Rubeena, V.; Tiwari, K. C.

2016-04-01

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

Kinetic studies of BTEX vapour adsorption onto surfaces of calix-4-resorcinarene films

NASA Astrophysics Data System (ADS)

Hassan, A. K.; Ray, A. K.; Nabok, A. V.; Wilkop, T.

2001-10-01

The exposure of spun films of an amphiphilic calix-4-resorcinarene (C-4-RA) derivative to vapours of benzene, toluene, ethylbenzene, and m-xylene (BTEX) has produced a graded response, promising for the development of multisensor arrays. Fast and reversible adsorption of ethylbenzene was associated with changing the refractive index of the sensing layer and is believed to be due to the host-guest interaction between the cavitand C-4-RA molecules and the vapour molecules. Prolonged irradiation of the films with a focused laser beam has resulted in an initial increase of film sensitivity to the different organic vapours.

Improved close-in detection for the mine hunter/killer system

NASA Astrophysics Data System (ADS)

Bishop, Steven S.; Campana, Stephen B.; Duston, Brian M.; Lang, David A.; Wiggins, Carl M.

2001-10-01

The Close-In Detector (CID) is the vehicle-mounted multi-sensor anti-tank landmine detection technology for the Army CECOM Night Vision Electronic Sensors Directorate (NVESD) Mine Hunter-Killer (MH/K) Program. The CID includes two down-looking sensor arrays: a 20-antenna ground-penetrating radar (GPR) and a 16-coil metal detector (MD). These arrays span 3-meters in front of a high mobility, multipurpose wheeled vehicle (HMMWV). The CID also includes a roof-mounted, forward looking infrared (FLIR) camera that images a trapezoidal area of the road ahead of the vehicle. Signals from each of the three sensors are processed separately to detect and localize objects of interest. Features of candidate objects are integrated in a processor that uses them to discriminates between anti-tank (AT) mines and clutter and produces a list of suspected mine locations which are passed to the neutralization subsystem of MH/K. This paper reviews the current design and performance of the CID based on field test results on dirt and gravel mine test lanes. Improvements in CID performance for probability of detection, false alarm rate, target positional accuracy and system rate of advance over the past year and a half that meet most of the program goals are described. Sensor performances are compared, and the effectiveness of six different sensor fusion approaches are measured and compared.

Chemometric analysis of multisensor hyperspectral images of precipitated atmospheric particulate matter.

PubMed

Ofner, Johannes; Kamilli, Katharina A; Eitenberger, Elisabeth; Friedbacher, Gernot; Lendl, Bernhard; Held, Andreas; Lohninger, Hans

2015-09-15

The chemometric analysis of multisensor hyperspectral data allows a comprehensive image-based analysis of precipitated atmospheric particles. Atmospheric particulate matter was precipitated on aluminum foils and analyzed by Raman microspectroscopy and subsequently by electron microscopy and energy dispersive X-ray spectroscopy. All obtained images were of the same spot of an area of 100 × 100 μm(2). The two hyperspectral data sets and the high-resolution scanning electron microscope images were fused into a combined multisensor hyperspectral data set. This multisensor data cube was analyzed using principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex component analysis. The detailed chemometric analysis of the multisensor data allowed an extensive chemical interpretation of the precipitated particles, and their structure and composition led to a comprehensive understanding of atmospheric particulate matter.

Active Multimodal Sensor System for Target Recognition and Tracking

PubMed Central

Zhang, Guirong; Zou, Zhaofan; Liu, Ziyue; Mao, Jiansen

2017-01-01

High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system. PMID:28657609

A parallel implementation of a multisensor feature-based range-estimation method

NASA Technical Reports Server (NTRS)

Suorsa, Raymond E.; Sridhar, Banavar

1993-01-01

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer.

Regional Mapping of Plantation Extent Using Multisensor Imagery

NASA Astrophysics Data System (ADS)

Torbick, N.; Ledoux, L.; Hagen, S.; Salas, W.

2016-12-01

Industrial forest plantations are expanding rapidly across the tropics and monitoring extent is critical for understanding environmental and socioeconomic impacts. In this study, new, multisensor imagery were evaluated and integrated to extract the strengths of each sensor for mapping plantation extent at regional scales. Three distinctly different landscapes with multiple plantation types were chosen to consider scalability and transferability. These were Tanintharyi, Myanmar, West Kalimantan, Indonesia, and southern Ghana. Landsat-8 Operational Land Imager (OLI), Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2), and Sentinel-1A images were fused within a Classification and Regression Tree (CART) framework using random forest and high-resolution surveys. Multi-criteria evaluations showed both L-and C-band gamma nought γ° backscatter decibel (dB), Landsat reflectance ρλ, and texture indices were useful for distinguishing oil palm and rubber plantations from other land types. The classification approach identified 750,822 ha or 23% of the Taninathryi, Myanmar, and 216,086 ha or 25% of western West Kalimantan as plantation with very high cross validation accuracy. The mapping approach was scalable and transferred well across the different geographies and plantation types. As archives for Sentinel-1, Landsat-8, and PALSAR-2 continue to grow, mapping plantation extent and dynamics at moderate resolution over large regions should be feasible.

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph

PubMed Central

Zeng, Qinghua; Chen, Weina; Liu, Jianye; Wang, Huizhe

2017-01-01

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method. PMID:28335570

A Novel Energy-Efficient Multi-Sensor Fusion Wake-Up Control Strategy Based on a Biomimetic Infectious-Immune Mechanism for Target Tracking.

PubMed

Zhou, Jie; Liang, Yan; Shen, Qiang; Feng, Xiaoxue; Pan, Quan

2018-04-18

A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph.

PubMed

Zeng, Qinghua; Chen, Weina; Liu, Jianye; Wang, Huizhe

2017-03-21

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method.

Transitioning mine warfare to network-centric sensor analysis: future PMA technologies & capabilities

NASA Astrophysics Data System (ADS)

Stack, J. R.; Guthrie, R. S.; Cramer, M. A.

2009-05-01

The purpose of this paper is to outline the requisite technologies and enabling capabilities for network-centric sensor data analysis within the mine warfare community. The focus includes both automated processing and the traditional humancentric post-mission analysis (PMA) of tactical and environmental sensor data. This is motivated by first examining the high-level network-centric guidance and noting the breakdown in the process of distilling actionable requirements from this guidance. Examples are provided that illustrate the intuitive and substantial capability improvement resulting from processing sensor data jointly in a network-centric fashion. Several candidate technologies are introduced including the ability to fully process multi-sensor data given only partial overlap in sensor coverage and the ability to incorporate target identification information in stride. Finally the critical enabling capabilities are outlined including open architecture, open business, and a concept of operations. This ability to process multi-sensor data in a network-centric fashion is a core enabler of the Navy's vision and will become a necessity with the increasing number of manned and unmanned sensor systems and the requirement for their simultaneous use.

A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

PubMed Central

Liu, Bailing; Zhang, Fumin; Qu, Xinghua

2015-01-01

An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

Multi-sensor investigation of the Sumatran Tsunami: observations and analysis of hydroacoustic, seismic, infrasonic, and tide gauge data

NASA Astrophysics Data System (ADS)

Bhattacharyya, J.; Pulli, J.; Gibson, R.; Upton, Z.

2005-05-01

We present an analysis of the acoustic signals from the December 26, 2004 Sumatra earthquakes, in conjunction with the seismic and tide gauge information from the event. The M9.0 mainshock and its aftershocks were recorded by a suite of seismic sensors around the globe, giving us information on its location and the source process. Recently installed sensor assets in the Indian Ocean have enabled us to study additional features of this significant event. Hydroacoustic signals were recorded by three hydrophone arrays, and the direction finding capability of these arrays allows us to examine the location, time and extent of the T-wave generation process. We detect a clear variation of the back-azimuth that is consistent with the spatial extent of the source rupture. Recordings from nearly co-located seismometers provide insights into the acoustic-to-seismic conversion process for T-waves at islands, along with the variation in signal characteristics with source size. Two separate infrasound arrays detect the atmospheric signals generated by the event, along with additional observations of the seismic surface wave and the T-phase. We will present a comparison of the signals from the mainshock, as a function of location and size, with those from aftershocks and similar events in the nearby region. Our acoustic observations compare favorably with model predictions of wave propagation in the region. For the hydroacoustic data, the azimuth, arrival time, and signal blockage characteristics, from three separate arrays, associate the onset of the signal with the mainshock and with a time extent consistent with the rupture propagation. Our analysis of the T-phase travel times suggests that the seismic-to-acoustic conversion occurs more than 100 km from the epicenter. The infrasound signal's arrival time and signal duration are consistent with both stratospheric and thermospheric propagation from a source region near the mainshock. We use the tide gauge data from stations around the Indian Ocean to identify the arrival time of the Tsunami. The acoustic and seismic signals associated with the earthquakes arrive at the remote stations significantly ahead of the Tsunami. We combine the information from the various sensors to investigate the ability of the acoustic stations to detect the Tsunami.

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics

PubMed Central

Herrero, Héctor; Outón, Jose Luis; Puerto, Mildred; Sallé, Damien; López de Ipiña, Karmele

2017-01-01

This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques. PMID:28561750

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics.

PubMed

Herrero, Héctor; Outón, Jose Luis; Puerto, Mildred; Sallé, Damien; López de Ipiña, Karmele

2017-05-31

This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

Reliability measurement during software development. [for a multisensor tracking system

NASA Technical Reports Server (NTRS)

Hecht, H.; Sturm, W. A.; Trattner, S.

1977-01-01

During the development of data base software for a multi-sensor tracking system, reliability was measured. The failure ratio and failure rate were found to be consistent measures. Trend lines were established from these measurements that provided good visualization of the progress on the job as a whole as well as on individual modules. Over one-half of the observed failures were due to factors associated with the individual run submission rather than with the code proper. Possible application of these findings for line management, project managers, functional management, and regulatory agencies is discussed. Steps for simplifying the measurement process and for use of these data in predicting operational software reliability are outlined.

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

NASA Technical Reports Server (NTRS)

Matsui, T.; Santanello, J.; Shi, J. J.; Tao, W.-K.; Wu, D.; Peters-Lidard, C.; Kemp, E.; Chin, M.; Starr, D.; Sekiguchi, M.;

Urban structure analysis of mega city Mexico City using multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Esch, T.; Wurm, M.; Thiel, M.; Ullmann, T.; Roth, A.; Schmidt, M.; Mehl, H.; Dech, S.

2008-10-01

Mega city Mexico City is ranked the third largest urban agglomeration to date around the globe. The large extension as well as dynamic urban transformation and sprawl processes lead to a lack of up-to-date and area-wide data and information to measure, monitor, and understand the urban situation. This paper focuses on the capabilities of multisensoral remotely sensed data to provide a broad range of products derived from one scientific field - remote sensing - to support urban managing and planning. Therefore optical data sets from the Landsat and Quickbird sensors as well as radar data from the Shuttle Radar Topography Mission (SRTM) and the TerraSAR-X sensor are utilised. Using the multi-sensoral data sets the analysis are scale-dependent. On the one hand change detection on city level utilising the derived urban footprints enables to monitor and to assess spatiotemporal urban transformation, areal dimension of urban sprawl, its direction, and the built-up density distribution over time. On the other hand, structural characteristics of an urban landscape - the alignment and types of buildings, streets and open spaces - provide insight in the very detailed physical pattern of urban morphology on higher scale. The results show high accuracies of the derived multi-scale products. The multi-scale analysis allows quantifying urban processes and thus leading to an assessment and interpretation of urban trends.

Fiber-optic temperature profiling for thermal protection system heat shields

NASA Astrophysics Data System (ADS)

Black, Richard J.; Costa, Joannes M.; Zarnescu, Livia; Hackney, Drew A.; Moslehi, Behzad; Peters, Kara J.

2016-11-01

To achieve better designs for spacecraft heat shields for missions requiring atmospheric aero-capture or entry/reentry, reliable thermal protection system (TPS) sensors are needed. Such sensors will provide both risk reduction and heat-shield mass minimization, which will facilitate more missions and enable increased payloads and returns. This paper discusses TPS thermal measurements provided by a temperature monitoring system involving lightweight, electromagnetic interference-immune, high-temperature resistant fiber Bragg grating (FBG) sensors with a thermal mass near that of TPS materials together with fast FBG sensor interrogation. Such fiber-optic sensing technology is highly sensitive and accurate, as well as suitable for high-volume production. Multiple sensing FBGs can be fabricated as arrays on a single fiber for simplified design and reduced cost. Experimental results are provided to demonstrate the temperature monitoring system using multisensor FBG arrays embedded in a small-size super-light ablator (SLA) coupon which was thermally loaded to temperatures in the vicinity of the SLA charring temperature. In addition, a high-temperature FBG array was fabricated and tested for 1000°C operation, and the temperature dependence considered over the full range (cryogenic to high temperature) for which silica fiber FBGs have been subjected.

The shallow boreholes at The AltotiBerina near fault Observatory (TABOO; northern Apennines of Italy)

NASA Astrophysics Data System (ADS)

Chiaraluce, L.; Collettini, C.; Cattaneo, M.; Monachesi, G.

2014-04-01

As part of an interdisciplinary research project, funded by the European Research Council and addressing the mechanics of weak faults, we drilled three 200-250 m-deep boreholes and installed an array of seismometers. The array augments TABOO (The AltotiBerina near fault ObservatOry), a scientific infrastructure managed by the Italian National Institute of Geophysics and Volcanology. The observatory, which consists of a geophysical network equipped with multi-sensor stations, is located in the northern Apennines (Italy) and monitors a large and active low-angle normal fault. The drilling operations started at the end of 2011 and were completed by July 2012. We instrumented the boreholes with three-component short-period (2 Hz) passive instruments at different depths. The seismometers are now fully operational and collecting waveforms characterised by a very high signal to noise ratio that is ideal for studying microearthquakes. The resulting increase in the detection capability of the seismic network will allow for a broader range of transients to be identified.

Earth Science Data Fusion with Event Building Approach

NASA Technical Reports Server (NTRS)

Lukashin, C.; Bartle, Ar.; Callaway, E.; Gyurjyan, V.; Mancilla, S.; Oyarzun, R.; Vakhnin, A.

2015-01-01

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

Reconnaissance Of The Year 2000 And Beyond

NASA Astrophysics Data System (ADS)

Dresser, M. M.

1981-12-01

The reconnaissance systems of the year 2000 and beyond may be merely an extension of current technology or may utilize bold new technology and concepts still in the embryonic stages. The five basic reconnaissance mission stages: collection, processing, interpretation, reporting, and dissemination, are reviewed in terms of the potential application of new and emerging technology such as high density multispectral focal plane arrays, new radar techniques, VLSI/VHSIC computational resources, artificial intelligence, multisensor integration, pattern and target recognition, image compression, advanced display and targeting techniques, and even new fields not thought of as exact sciences today. The application of these technologies is viewed in the context of the reconnaissance missions: targeting, damage assessment, order of battle assessment, terrain evaluation and planning. The traditional neeos for varying levels of detail and timeliness of reconnaissance data are shown to be largely removed by the use of the most advanced and highest development risk systems. Lower development risk systems show excellent capabilities with the potential for high capability at low cost. New fields may totally change or even eliminate reconnaissance as we know it today.

Multisensor signal processing techniques (hybrid GPS Loran-C with RAIM)

DOT National Transportation Integrated Search

1991-09-01

One of the major elements in alleviating existing problems in en route airspace is : to allow more aircraft to traverse a given volume of airspace. Recent developments : in navigation systems will support this effort by enabling user preferred routes...

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration.

PubMed

Pycinski, Bartlomiej; Czajkowska, Joanna; Badura, Pawel; Juszczyk, Jan; Pietka, Ewa

2016-01-01

A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

Applications of airborne ultrasound in human-computer interaction.

PubMed

Dahl, Tobias; Ealo, Joao L; Bang, Hans J; Holm, Sverre; Khuri-Yakub, Pierre

2014-09-01

Airborne ultrasound is a rapidly developing subfield within human-computer interaction (HCI). Touchless ultrasonic interfaces and pen tracking systems are part of recent trends in HCI and are gaining industry momentum. This paper aims to provide the background and overview necessary to understand the capabilities of ultrasound and its potential future in human-computer interaction. The latest developments on the ultrasound transducer side are presented, focusing on capacitive micro-machined ultrasonic transducers, or CMUTs. Their introduction is an important step toward providing real, low-cost multi-sensor array and beam-forming options. We also provide a unified mathematical framework for understanding and analyzing algorithms used for ultrasound detection and tracking for some of the most relevant applications. Copyright © 2014. Published by Elsevier B.V.

Multisensor systems today and tomorrow: Machine control, diagnosis and thermal compensation

NASA Astrophysics Data System (ADS)

Nunzio, D'Addea

2000-05-01

Multisensor techniques that deal with control of tribology test rig and with diagnosis and thermal error compensation of machine tools are the starting point for some consideration about the use of these techniques as in fuzzy and neural net systems. The author comes to conclusion that anticipatory systems and multisensor techniques will have in the next future a great improvement and a great development mainly in the thermal error compensation of machine tools.

Ultrasonic imaging of material flaws exploiting multipath information

NASA Astrophysics Data System (ADS)

Shen, Xizhong; Zhang, Yimin D.; Demirli, Ramazan; Amin, Moeness G.

2011-05-01

In this paper, we consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and classification of flaws inside a structure. Multipath exploitations provide extended virtual array apertures and, in turn, enhance imaging capability beyond the limitation of traditional multisensor approaches. We utilize reflections of ultrasonic signals which occur when encountering different media and interior discontinuities. The waveforms observed at the physical as well as virtual sensors yield additional measurements corresponding to different aspect angles. Exploitation of multipath information addresses unique issues observed in ultrasonic imaging. (1) Utilization of physical and virtual sensors significantly extends the array aperture for image enhancement. (2) Multipath signals extend the angle of view of the narrow beamwidth of the ultrasound transducers, allowing improved visibility and array design flexibility. (3) Ultrasonic signals experience difficulty in penetrating a flaw, thus the aspect angle of the observation is limited unless access to other sides is available. The significant extension of the aperture makes it possible to yield flaw observation from multiple aspect angles. We show that data fusion of physical and virtual sensor data significantly improves the detection and localization performance. The effectiveness of the proposed multipath exploitation approach is demonstrated through experimental studies.

Data Strategies to Support Automated Multi-Sensor Data Fusion in a Service Oriented Architecture

DTIC Science & Technology

2008-06-01

and employ vast quantities of content. This dissertation provides two software architectural patterns and an auto-fusion process that guide the...UDDI), Simple Order Access Protocol (SOAP), Java, Maritime Domain Awareness (MDA), Business Process Execution Language for Web Service (BPEL4WS) 16...content. This dissertation provides two software architectural patterns and an auto-fusion process that guide the development of a distributed

Improved blood glucose estimation through multi-sensor fusion.

PubMed

Xiong, Feiyu; Hipszer, Brian R; Joseph, Jeffrey; Kam, Moshe

2011-01-01

Continuous glucose monitoring systems are an integral component of diabetes management. Efforts to improve the accuracy and robustness of these systems are at the forefront of diabetes research. Towards this goal, a multi-sensor approach was evaluated in hospitalized patients. In this paper, we report on a multi-sensor fusion algorithm to combine glucose sensor measurements in a retrospective fashion. The results demonstrate the algorithm's ability to improve the accuracy and robustness of the blood glucose estimation with current glucose sensor technology.

Long-Term Large-Scale Bias-Adjusted Precipitation Estimates at High Spatial and Temporal Resolution Derived from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Precipitation Reanalysis over CONUS

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.; Stevens, S. E.; Seo, D. J.; Kim, B.

2014-12-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over Continental United States (CONUS) is nearly completed for the period covering from 2000 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network - Daily (GHCN-D) are used to adjust for those biases and to merge with the radar only product to provide a multi-sensor estimate. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. After assessing the bias and applying reduction or elimination techniques, we are investigating the kriging method and its variants such as simple kriging (SK), ordinary kriging (OK), and conditional bias-penalized Kriging (CBPK) among others. In addition we hope to generate estimates of uncertainty for the gridded estimate. In this work the methodology is presented as well as a comparison between the radar-only product and the final multi-sensor QPE product. The comparison is performed at various time scales from the sub-hourly, to annual. In addition, comparisons over the same period with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) and satellite products (TMPA, CMORPH, PERSIANN) are provided in order to give a detailed picture of the improvements and remaining challenges.

Calibrating a novel multi-sensor physical activity measurement system.

PubMed

John, D; Liu, S; Sasaki, J E; Howe, C A; Staudenmayer, J; Gao, R X; Freedson, P S

2011-09-01

Advancing the field of physical activity (PA) monitoring requires the development of innovative multi-sensor measurement systems that are feasible in the free-living environment. The use of novel analytical techniques to combine and process these multiple sensor signals is equally important. This paper describes a novel multi-sensor 'integrated PA measurement system' (IMS), the lab-based methodology used to calibrate the IMS, techniques used to predict multiple variables from the sensor signals, and proposes design changes to improve the feasibility of deploying the IMS in the free-living environment. The IMS consists of hip and wrist acceleration sensors, two piezoelectric respiration sensors on the torso, and an ultraviolet radiation sensor to obtain contextual information (indoors versus outdoors) of PA. During lab-based calibration of the IMS, data were collected on participants performing a PA routine consisting of seven different ambulatory and free-living activities while wearing a portable metabolic unit (criterion measure) and the IMS. Data analyses on the first 50 adult participants are presented. These analyses were used to determine if the IMS can be used to predict the variables of interest. Finally, physical modifications for the IMS that could enhance the feasibility of free-living use are proposed and refinement of the prediction techniques is discussed.

The Advanced Linked Extended Reconnaissance & Targeting Technology Demonstration project

NASA Astrophysics Data System (ADS)

Edwards, Mark

2008-04-01

The Advanced Linked Extended Reconnaissance & Targeting (ALERT) Technology Demonstration (TD) project is addressing many operational needs of the future Canadian Army's Surveillance and Reconnaissance forces. Using the surveillance system of the Coyote reconnaissance vehicle as an experimental platform, the ALERT TD project aims to significantly enhance situational awareness by fusing multi-sensor and tactical data, developing automated processes, and integrating beyond line-of-sight sensing. The project is exploiting important advances made in computer processing capability, displays technology, digital communications, and sensor technology since the design of the original surveillance system. As the major research area within the project, concepts are discussed for displaying and fusing multi-sensor and tactical data within an Enhanced Operator Control Station (EOCS). The sensor data can originate from the Coyote's own visible-band and IR cameras, laser rangefinder, and ground-surveillance radar, as well as from beyond line-of-sight systems such as mini-UAVs and unattended ground sensors. Video-rate image processing has been developed to assist the operator to detect poorly visible targets. As a second major area of research, automatic target cueing capabilities have been added to the system. These include scene change detection, automatic target detection and aided target recognition algorithms processing both IR and visible-band images to draw the operator's attention to possible targets. The merits of incorporating scene change detection algorithms are also discussed. In the area of multi-sensor data fusion, up to Joint Defence Labs level 2 has been demonstrated. The human factors engineering aspects of the user interface in this complex environment are presented, drawing upon multiple user group sessions with military surveillance system operators. The paper concludes with Lessons Learned from the project. The ALERT system has been used in a number of C4ISR field trials, most recently at Exercise Empire Challenge in China Lake CA, and at Trial Quest in Norway. Those exercises provided further opportunities to investigate operator interactions. The paper concludes with recommendations for future work in operator interface design.

Research on a Defects Detection Method in the Ferrite Phase Shifter Cementing Process Based on a Multi-Sensor Prognostic and Health Management (PHM) System.

PubMed

Wan, Bo; Fu, Guicui; Li, Yanruoyue; Zhao, Youhu

2016-08-10

The cementing manufacturing process of ferrite phase shifters has the defect that cementing strength is insufficient and fractures always appear. A detection method of these defects was studied utilizing the multi-sensors Prognostic and Health Management (PHM) theory. Aiming at these process defects, the reasons that lead to defects are analyzed in this paper. In the meanwhile, the key process parameters were determined and Differential Scanning Calorimetry (DSC) tests during the cure process of resin cementing were carried out. At the same time, in order to get data on changing cementing strength, multiple-group cementing process tests of different key process parameters were designed and conducted. A relational model of cementing strength and cure temperature, time and pressure was established, by combining data of DSC and process tests as well as based on the Avrami formula. Through sensitivity analysis for three process parameters, the on-line detection decision criterion and the process parameters which have obvious impact on cementing strength were determined. A PHM system with multiple temperature and pressure sensors was established on this basis, and then, on-line detection, diagnosis and control for ferrite phase shifter cementing process defects were realized. It was verified by subsequent process that the on-line detection system improved the reliability of the ferrite phase shifter cementing process and reduced the incidence of insufficient cementing strength defects.

Miniaturized Airborne Imaging Central Server System

NASA Technical Reports Server (NTRS)

Sun, Xiuhong

2011-01-01

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and data organization, fast aerial imaging applications, including the real time LWIR image mosaic for Google Earth, have been realized for NASA fs LWIR QWIP instrument. MAICSS is a significant improvement and miniaturization of current multisensor technologies. Structurally, it has a complete modular and solid-state design. Without rotating hard drives and other moving parts, it is operational at high altitudes and survivable in high-vibration environments. It is assembled from a suite of miniaturized, precision-machined, standardized, and stackable interchangeable embedded instrument modules. These stackable modules can be bolted together with the interconnection wires inside for the maximal simplicity and portability. Multiple modules are electronically interconnected as stacked. Alternatively, these dedicated modules can be flexibly distributed to fit the space constraints of a flying vehicle. As a flexibly configurable system, MAICSS can be tailored to interface a variety of multisensor packages. For example, with a 1,024x1,024 pixel LWIR and a 8,984x6,732 pixel EO payload, the complete MAICSS volume is approximately 7x9x11 in. (=18x23x28 cm), with a weight of 25 lb (=11.4 kg).

NASA Tech Briefs, January 2004

NASA Technical Reports Server (NTRS)

2004-01-01

Topics covered include: Multisensor Instrument for Real-Time Biological Monitoring; Sensor for Monitoring Nanodevice-Fabrication Plasmas; Backed Bending Actuator; Compact Optoelectronic Compass; Micro Sun Sensor for Spacecraft; Passive IFF: Autonomous Nonintrusive Rapid Identification of Friendly Assets; Finned-Ladder Slow-Wave Circuit for a TWT; Directional Radio-Frequency Identification Tag Reader; Integrated Solar-Energy-Harvesting and -Storage Device; Event-Driven Random-Access-Windowing CCD Imaging System; Stroboscope Controller for Imaging Helicopter Rotors; Software for Checking State-charts; Program Predicts Broadband Noise from a Turbofan Engine; Protocol for a Delay-Tolerant Data-Communication Network; Software Implements a Space-Mission File-Transfer Protocol; Making Carbon-Nanotube Arrays Using Block Copolymers: Part 2; Modular Rake of Pitot Probes; Preloading To Accelerate Slow-Crack-Growth Testing; Miniature Blimps for Surveillance and Collection of Samples; Hybrid Automotive Engine Using Ethanol-Burning Miller Cycle; Fabricating Blazed Diffraction Gratings by X-Ray Lithography; Freeze-Tolerant Condensers; The StarLight Space Interferometer; Champagne Heat Pump; Controllable Sonar Lenses and Prisms Based on ERFs; Measuring Gravitation Using Polarization Spectroscopy; Serial-Turbo-Trellis-Coded Modulation with Rate-1 Inner Code; Enhanced Software for Scheduling Space-Shuttle Processing; Bayesian-Augmented Identification of Stars in a Narrow View; Spacecraft Orbits for Earth/Mars-Lander Radio Relay; and Self-Inflatable/Self-Rigidizable Reflectarray Antenna.

Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

PubMed Central

Yuan, Xuebing; Yu, Shuai; Zhang, Shengzhi; Wang, Guoping; Liu, Sheng

2015-01-01

Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path. PMID:25961384

NASA GES DISC Level 2 Aerosol Analysis and Visualization Services

NASA Technical Reports Server (NTRS)

Wei, Jennifer; Petrenko, Maksym; Ichoku, Charles; Yang, Wenli; Johnson, James; Zhao, Peisheng; Kempler, Steve

2015-01-01

Overview of NASA GES DISC Level 2 aerosol analysis and visualization services: DQViz (Data Quality Visualization)MAPSS (Multi-sensor Aerosol Products Sampling System), and MAPSS_Explorer (Multi-sensor Aerosol Products Sampling System Explorer).

Multisensor system and artificial intelligence in housing for the elderly.

PubMed

Chan, M; Bocquet, H; Campo, E; Val, T; Estève, D; Pous, J

1998-01-01

To improve the safety of a growing proportion of elderly and disabled people in the developed countries, a multisensor system based on Artificial Intelligence (AI), Advanced Telecommunications (AT) and Information Technology (IT) has been devised and fabricated. Thus, the habits and behaviours of these populations will be recorded without disturbing their daily activities. AI will diagnose any abnormal behavior or change and the system will warn the professionals. Gerontology issues are presented together with the multisensor system, the AI-based learning and diagnosis methodology and the main functionalities.

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang, Indonesia, using multi-sensor satellite data

NASA Astrophysics Data System (ADS)

Husnayaen; Rimba, A. Besse; Osawa, Takahiro; Parwata, I. Nyoman Sudi; As-syakur, Abd. Rahman; Kasim, Faizal; Astarini, Ida Ayu

2018-04-01

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68 km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters = 53%, CVI 7 parameters = 93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7 km of the total 48.7 km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.

General software design for multisensor data fusion

NASA Astrophysics Data System (ADS)

Zhang, Junliang; Zhao, Yuming

1999-03-01

In this paper a general method of software design for multisensor data fusion is discussed in detail, which adopts object-oriented technology under UNIX operation system. The software for multisensor data fusion is divided into six functional modules: data collection, database management, GIS, target display and alarming data simulation etc. Furthermore, the primary function, the components and some realization methods of each modular is given. The interfaces among these functional modular relations are discussed. The data exchange among each functional modular is performed by interprocess communication IPC, including message queue, semaphore and shared memory. Thus, each functional modular is executed independently, which reduces the dependence among functional modules and helps software programing and testing. This software for multisensor data fusion is designed as hierarchical structure by the inheritance character of classes. Each functional modular is abstracted and encapsulated through class structure, which avoids software redundancy and enhances readability.

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

PubMed Central

Abu Bakr, Muhammad; Lee, Sukhan

2017-01-01

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted. PMID:29077035

Combining Sense and Intelligence for Smart Structures

NASA Technical Reports Server (NTRS)

2002-01-01

IFOS developed the I*Sense technology with assistance from a NASA Langley Research Center SBIR contract. NASA and IFOS collaborated to create sensing network designs that have high sensitivity, low power consumption, and significant potential for mass production. The joint- research effort led to the development of a module that is rugged, compact and light-weight, and immune to electromagnetic interference. These features make the I*Sense multisensor arrays favorable for smart structure applications, including smart buildings, bridges, highways, dams, power plants, ships, and oil tankers, as well as space vehicles, space stations, and other space structures. For instance, the system can be used as an early warning and detection device, with alarms being set to monitor the maximum allowable strain and stress values at various points of a given structure.

Interoperability Using Lightweight Metadata Standards: Service & Data Casting, OpenSearch, OPM Provenance, and Shared SciFlo Workflows

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E.

2011-12-01

Under several NASA grants, we are generating multi-sensor merged atmospheric datasets to enable the detection of instrument biases and studies of climate trends over decades of data. For example, under a NASA MEASURES grant we are producing a water vapor climatology from the A-Train instruments, stratified by the Cloudsat cloud classification for each geophysical scene. The generation and proper use of such multi-sensor climate data records (CDR's) requires a high level of openness, transparency, and traceability. To make the datasets self-documenting and provide access to full metadata and traceability, we have implemented a set of capabilities and services using known, interoperable protocols. These protocols include OpenSearch, OPeNDAP, Open Provenance Model, service & data casting technologies using Atom feeds, and REST-callable analysis workflows implemented as SciFlo (XML) documents. We advocate that our approach can serve as a blueprint for how to openly "document and serve" complex, multi-sensor CDR's with full traceability. The capabilities and services provided include: - Discovery of the collections by keyword search, exposed using OpenSearch protocol; - Space/time query across the CDR's granules and all of the input datasets via OpenSearch; - User-level configuration of the production workflows so that scientists can select additional physical variables from the A-Train to add to the next iteration of the merged datasets; - Efficient data merging using on-the-fly OPeNDAP variable slicing & spatial subsetting of data out of input netCDF and HDF files (without moving the entire files); - Self-documenting CDR's published in a highly usable netCDF4 format with groups used to organize the variables, CF-style attributes for each variable, numeric array compression, & links to OPM provenance; - Recording of processing provenance and data lineage into a query-able provenance trail in Open Provenance Model (OPM) format, auto-captured by the workflow engine; - Open Publishing of all of the workflows used to generate products as machine-callable REST web services, using the capabilities of the SciFlo workflow engine; - Advertising of the metadata (e.g. physical variables provided, space/time bounding box, etc.) for our prepared datasets as "datacasts" using the Atom feed format; - Publishing of all datasets via our "DataDrop" service, which exploits the WebDAV protocol to enable scientists to access remote data directories as local files on their laptops; - Rich "web browse" of the CDR's with full metadata and the provenance trail one click away; - Advertising of all services as Google-discoverable "service casts" using the Atom format. The presentation will describe our use of the interoperable protocols and demonstrate the capabilities and service GUI's.

The advanced linked extended reconnaissance and targeting technology demonstration project

NASA Astrophysics Data System (ADS)

Cruickshank, James; de Villers, Yves; Maheux, Jean; Edwards, Mark; Gains, David; Rea, Terry; Banbury, Simon; Gauthier, Michelle

2007-06-01

The Advanced Linked Extended Reconnaissance & Targeting (ALERT) Technology Demonstration (TD) project is addressing key operational needs of the future Canadian Army's Surveillance and Reconnaissance forces by fusing multi-sensor and tactical data, developing automated processes, and integrating beyond line-of-sight sensing. We discuss concepts for displaying and fusing multi-sensor and tactical data within an Enhanced Operator Control Station (EOCS). The sensor data can originate from the Coyote's own visible-band and IR cameras, laser rangefinder, and ground-surveillance radar, as well as beyond line-of-sight systems such as a mini-UAV and unattended ground sensors. The authors address technical issues associated with the use of fully digital IR and day video cameras and discuss video-rate image processing developed to assist the operator to recognize poorly visible targets. Automatic target detection and recognition algorithms processing both IR and visible-band images have been investigated to draw the operator's attention to possible targets. The machine generated information display requirements are presented with the human factors engineering aspects of the user interface in this complex environment, with a view to establishing user trust in the automation. The paper concludes with a summary of achievements to date and steps to project completion.

Multi-sensor Navigation System Design

DOT National Transportation Integrated Search

1971-03-01

This report treats the design of naviggation systems that collect data from two or more on-board measurement subsystems and precess this data in an on-board computer. Such systems are called Multi-sensor Navigation Systems. : The design begins with t...

Affordable multisensor digital video architecture for 360° situational awareness displays

NASA Astrophysics Data System (ADS)

Scheiner, Steven P.; Khan, Dina A.; Marecki, Alexander L.; Berman, David A.; Carberry, Dana

2011-06-01

One of the major challenges facing today's military ground combat vehicle operations is the ability to achieve and maintain full-spectrum situational awareness while under armor (i.e. closed hatch). Thus, the ability to perform basic tasks such as driving, maintaining local situational awareness, surveillance, and targeting will require a high-density array of real time information be processed, distributed, and presented to the vehicle operators and crew in near real time (i.e. low latency). Advances in display and sensor technologies are providing never before seen opportunities to supply large amounts of high fidelity imagery and video to the vehicle operators and crew in real time. To fully realize the advantages of these emerging display and sensor technologies, an underlying digital architecture must be developed that is capable of processing these large amounts of video and data from separate sensor systems and distributing it simultaneously within the vehicle to multiple vehicle operators and crew. This paper will examine the systems and software engineering efforts required to overcome these challenges and will address development of an affordable, integrated digital video architecture. The approaches evaluated will enable both current and future ground combat vehicle systems the flexibility to readily adopt emerging display and sensor technologies, while optimizing the Warfighter Machine Interface (WMI), minimizing lifecycle costs, and improve the survivability of the vehicle crew working in closed-hatch systems during complex ground combat operations.

An integrated approach for high spatial resolution mapping of water and carbon fluxes using multi-sensor data

USDA-ARS?s Scientific Manuscript database

In the last few years, modeling of surface processes, such as water and carbon balances, vegetation growth and energy budgets, has focused on integrated approaches that combine aspects of hydrology, biology and meteorology into unified analyses. In this context, remotely sensed data often have a cor...

Automated mosaicking of sub-canopy video incorporating ancillary data

Treesearch

E. Kee; N.E. Clark; A.L. Abbott

2002-01-01

This work investigates the process of mosaicking overlapping video frames of individual tree stems in sub-canopy scenes captured with a portable multisensor instrument. The robust commercial computer vision systems that are in use today typically rely on precisely controlled conditions. Inconsistent lighting as well as image distortion caused by varying interior and...

Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system

USGS Publications Warehouse

Bera, Maitreyee; Ortel, Terry W.

2018-01-12

The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.

Extended Kalman Doppler tracking and model determination for multi-sensor short-range radar

NASA Astrophysics Data System (ADS)

Mittermaier, Thomas J.; Siart, Uwe; Eibert, Thomas F.; Bonerz, Stefan

2016-09-01

A tracking solution for collision avoidance in industrial machine tools based on short-range millimeter-wave radar Doppler observations is presented. At the core of the tracking algorithm there is an Extended Kalman Filter (EKF) that provides dynamic estimation and localization in real-time. The underlying sensor platform consists of several homodyne continuous wave (CW) radar modules. Based on In-phase-Quadrature (IQ) processing and down-conversion, they provide only Doppler shift information about the observed target. Localization with Doppler shift estimates is a nonlinear problem that needs to be linearized before the linear KF can be applied. The accuracy of state estimation depends highly on the introduced linearization errors, the initialization and the models that represent the true physics as well as the stochastic properties. The important issue of filter consistency is addressed and an initialization procedure based on data fitting and maximum likelihood estimation is suggested. Models for both, measurement and process noise are developed. Tracking results from typical three-dimensional courses of movement at short distances in front of a multi-sensor radar platform are presented.

Solid-State Multi-Sensor Array System for Real Time Imaging of Magnetic Fields and Ferrous Objects

NASA Astrophysics Data System (ADS)

Benitez, D.; Gaydecki, P.; Quek, S.; Torres, V.

2008-02-01

In this paper the development of a solid-state sensors based system for real-time imaging of magnetic fields and ferrous objects is described. The system comprises 1089 magneto inductive solid state sensors arranged in a 2D array matrix of 33×33 files and columns, equally spaced in order to cover an approximate area of 300 by 300 mm. The sensor array is located within a large current-carrying coil. Data is sampled from the sensors by several DSP controlling units and finally streamed to a host computer via a USB 2.0 interface and the image generated and displayed at a rate of 20 frames per minute. The development of the instrumentation has been complemented by extensive numerical modeling of field distribution patterns using boundary element methods. The system was originally intended for deployment in the non-destructive evaluation (NDE) of reinforced concrete. Nevertheless, the system is not only capable of producing real-time, live video images of the metal target embedded within any opaque medium, it also allows the real-time visualization and determination of the magnetic field distribution emitted by either permanent magnets or geometries carrying current. Although this system was initially developed for the NDE arena, it could also have many potential applications in many other fields, including medicine, security, manufacturing, quality assurance and design involving magnetic fields.

NASA 1990 Multisensor Airborne Campaigns (MACs) for ecosystem and watershed studies

NASA Technical Reports Server (NTRS)

Wickland, Diane E.; Asrar, Ghassem; Murphy, Robert E.

1991-01-01

The Multisensor Airborne Campaign (MAC) focus within NASA's former Land Processes research program was conceived to achieve the following objectives: to acquire relatively complete, multisensor data sets for well-studied field sites, to add a strong remote sensing science component to ecology-, hydrology-, and geology-oriented field projects, to create a research environment that promotes strong interactions among scientists within the program, and to more efficiently utilize and compete for the NASA fleet of remote sensing aircraft. Four new MAC's were conducted in 1990: the Oregon Transect Ecosystem Research (OTTER) project along an east-west transect through central Oregon, the Forest Ecosystem Dynamics (FED) project at the Northern Experimental Forest in Howland, Maine, the MACHYDRO project in the Mahantango Creek watershed in central Pennsylvania, and the Walnut Gulch project near Tombstone, Arizona. The OTTER project is testing a model that estimates the major fluxes of carbon, nitrogen, and water through temperate coniferous forest ecosystems. The focus in the project is on short time-scale (days-year) variations in ecosystem function. The FED project is concerned with modeling vegetation changes of forest ecosystems using remotely sensed observations to extract biophysical properties of forest canopies. The focus in this project is on long time-scale (decades to millenia) changes in ecosystem structure. The MACHYDRO project is studying the role of soil moisture and its regulating effects on hydrologic processes. The focus of the study is to delineate soil moisture differences within a basin and their changes with respect to evapotranspiration, rainfall, and streamflow. The Walnut Gulch project is focused on the effects of soil moisture in the energy and water balance of arid and semiarid ecosystems and their feedbacks to the atmosphere via thermal forcing.

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

PubMed Central

Badura, Pawel; Juszczyk, Jan; Pietka, Ewa

2016-01-01

Purpose A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. Methods We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. Results The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. Conclusion The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers. PMID:27434396

Aided target recognition processing of MUDSS sonar data

NASA Astrophysics Data System (ADS)

Lau, Brian; Chao, Tien-Hsin

1998-09-01

The Mobile Underwater Debris Survey System (MUDSS) is a collaborative effort by the Navy and the Jet Propulsion Lab to demonstrate multi-sensor, real-time, survey of underwater sites for ordnance and explosive waste (OEW). We describe the sonar processing algorithm, a novel target recognition algorithm incorporating wavelets, morphological image processing, expansion by Hermite polynomials, and neural networks. This algorithm has found all planted targets in MUDSS tests and has achieved spectacular success upon another Coastal Systems Station (CSS) sonar image database.

Metal oxide based multisensor array and portable database for field analysis of antioxidants

PubMed Central

Sharpe, Erica; Bradley, Ryan; Frasco, Thalia; Jayathilaka, Dilhani; Marsh, Amanda; Andreescu, Silvana

2014-01-01

We report a novel chemical sensing array based on metal oxide nanoparticles as a portable and inexpensive paper-based colorimetric method for polyphenol detection and field characterization of antioxidant containing samples. Multiple metal oxide nanoparticles with various polyphenol binding properties were used as active sensing materials to develop the sensor array and establish a database of polyphenol standards that include epigallocatechin gallate, gallic acid, resveratrol, and Trolox among others. Unique charge-transfer complexes are formed between each polyphenol and each metal oxide on the surface of individual sensors in the array, creating distinct optically detectable signals which have been quantified and logged into a reference database for polyphenol identification. The field-portable Pantone/X-Rite© CapSure® color reader was used to create this database and to facilitate rapid colorimetric analysis. The use of multiple metal-oxide sensors allows for cross-validation of results and increases accuracy of analysis. The database has enabled successful identification and quantification of antioxidant constituents within real botanical extractions including green tea. Formation of charge-transfer complexes is also correlated with antioxidant activity exhibiting electron transfer capabilities of each polyphenol. The antioxidant activity of each sample was calculated and validated against the oxygen radical absorbance capacity (ORAC) assay showing good comparability. The results indicate that this method can be successfully used for a more comprehensive analysis of antioxidant containing samples as compared to conventional methods. This technology can greatly simplify investigations into plant phenolics and make possible the on-site determination of antioxidant composition and activity in remote locations. PMID:24610993

PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter.

PubMed

Li, Xiaohua; Li, Yaan; Yu, Jing; Chen, Xiao; Dai, Miao

2015-11-06

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.

Multichannel/Multisensor Signal Processing In Uncertain Environments With Application To Multitarget Tracking.

DTIC Science & Technology

1998-05-22

NUMBER PR-98-1 T. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) Office of Naval Research Ballston Center Tower One One North Quincy...unlimited. 12 b. DISTRIBUTION CODE 19980601 082 13. ABSTRACT (Maximum 200 words) This research project is concerned with two distinct aspects of analysis...Environments With Application To Multitarget Tracking This research project is concerned with two distinct aspects of analysis and processing of sig

Defense Small Business Innovation Research Program (SBIR). Volume 3. Air Force Abstracts of Phase 1 Awards 1991

DTIC Science & Technology

1991-01-01

The development of low-cost fabrication processes for high-performance composites is of paramount importance in the economical use of composites in...This proposal offers to evaluate the feasibility of marrying multiscale image processing techniques to multisensor image data. The product would be a...biotechnology to the production of 4-hydroxybenzocyclobutene will allow bulk manufacture of this polymer precursor by more economical means than is

A system for activity recognition using multi-sensor fusion.

PubMed

Gao, Lei; Bourke, Alan K; Nelson, John

2011-01-01

This paper proposes a system for activity recognition using multi-sensor fusion. In this system, four sensors are attached to the waist, chest, thigh, and side of the body. In the study we present two solutions for factors that affect the activity recognition accuracy: the calibration drift and the sensor orientation changing. The datasets used to evaluate this system were collected from 8 subjects who were asked to perform 8 scripted normal activities of daily living (ADL), three times each. The Naïve Bayes classifier using multi-sensor fusion is adopted and achieves 70.88%-97.66% recognition accuracies for 1-4 sensors.

Development of a Multi-Sensor Cancer Detection Probe Final Report CRADA No. TC-2026-01

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

Marion, J.; Hular, R.

This collaboration continued work started under a previous CRADA (TSB-2023-00) to take a detailed concept specification for a multi-sensor needle/probe suitable for breast cancer analysis and produce a prototype system suitable for human FDA trials.

Laboratory evaluation of dual-frequency multisensor capacitance probes to monitor soil water and salinity

USDA-ARS?s Scientific Manuscript database

Real-time information on salinity levels and transport of fertilizers are generally missing from soil profile knowledge bases. A dual-frequency multisensor capacitance probe (MCP) is now commercially available for sandy soils that simultaneously monitor volumetric soil water content (VWC, ') and sa...

Characterizing the Propagation of Uterine Electrophysiological Signals Recorded with a Multi-Sensor Abdominal Array in Term Pregnancies.

PubMed

Escalona-Vargas, Diana; Govindan, Rathinaswamy B; Furdea, Adrian; Murphy, Pam; Lowery, Curtis L; Eswaran, Hari

2015-01-01

The objective of this study was to quantify the number of segments that have contractile activity and determine the propagation speed from uterine electrophysiological signals recorded over the abdomen. The uterine magnetomyographic (MMG) signals were recorded with a 151 channel SARA (SQUID Array for Reproductive Assessment) system from 36 pregnant women between 37 and 40 weeks of gestational age. The MMG signals were scored and segments were classified based on presence of uterine contractile burst activity. The sensor space was then split into four quadrants and in each quadrant signal strength at each sample was calculated using center-of-gravity (COG). To this end, the cross-correlation analysis of the COG was performed to calculate the delay between pairwise combinations of quadrants. The relationship in propagation across the quadrants was quantified and propagation speeds were calculated from the delays. MMG recordings were successfully processed from 25 subjects and the average values of propagation speeds ranged from 1.3-9.5 cm/s, which was within the physiological range. The propagation was observed between both vertical and horizontal quadrants confirming multidirectional propagation. After the multiple pairwise test (99% CI), significant differences in speeds can be observed between certain vertical or horizontal combinations and the crossed pair combinations. The number of segments containing contractile activity in any given quadrant pair with a detectable delay was significantly higher in the lower abdominal pairwise combination as compared to all others. The quadrant-based approach using MMG signals provided us with high spatial-temporal information of the uterine contractile activity and will help us in the future to optimize abdominal electromyographic (EMG) recordings that are practical in a clinical setting.

Characterizing the Propagation of Uterine Electrophysiological Signals Recorded with a Multi-Sensor Abdominal Array in Term Pregnancies

PubMed Central

Escalona-Vargas, Diana; Govindan, Rathinaswamy B.; Furdea, Adrian; Murphy, Pam; Lowery, Curtis L.; Eswaran, Hari

2015-01-01

The objective of this study was to quantify the number of segments that have contractile activity and determine the propagation speed from uterine electrophysiological signals recorded over the abdomen. The uterine magnetomyographic (MMG) signals were recorded with a 151 channel SARA (SQUID Array for Reproductive Assessment) system from 36 pregnant women between 37 and 40 weeks of gestational age. The MMG signals were scored and segments were classified based on presence of uterine contractile burst activity. The sensor space was then split into four quadrants and in each quadrant signal strength at each sample was calculated using center-of-gravity (COG). To this end, the cross-correlation analysis of the COG was performed to calculate the delay between pairwise combinations of quadrants. The relationship in propagation across the quadrants was quantified and propagation speeds were calculated from the delays. MMG recordings were successfully processed from 25 subjects and the average values of propagation speeds ranged from 1.3–9.5 cm/s, which was within the physiological range. The propagation was observed between both vertical and horizontal quadrants confirming multidirectional propagation. After the multiple pairwise test (99% CI), significant differences in speeds can be observed between certain vertical or horizontal combinations and the crossed pair combinations. The number of segments containing contractile activity in any given quadrant pair with a detectable delay was significantly higher in the lower abdominal pairwise combination as compared to all others. The quadrant-based approach using MMG signals provided us with high spatial-temporal information of the uterine contractile activity and will help us in the future to optimize abdominal electromyographic (EMG) recordings that are practical in a clinical setting. PMID:26505624

Application of infrared uncooled cameras in surveillance systems

NASA Astrophysics Data System (ADS)

Dulski, R.; Bareła, J.; Trzaskawka, P.; PiÄ tkowski, T.

2013-10-01

The recent necessity to protect military bases, convoys and patrols gave serious impact to the development of multisensor security systems for perimeter protection. One of the most important devices used in such systems are IR cameras. The paper discusses technical possibilities and limitations to use uncooled IR camera in a multi-sensor surveillance system for perimeter protection. Effective ranges of detection depend on the class of the sensor used and the observed scene itself. Application of IR camera increases the probability of intruder detection regardless of the time of day or weather conditions. It also simultaneously decreased the false alarm rate produced by the surveillance system. The role of IR cameras in the system was discussed as well as technical possibilities to detect human being. Comparison of commercially available IR cameras, capable to achieve desired ranges was done. The required spatial resolution for detection, recognition and identification was calculated. The simulation of detection ranges was done using a new model for predicting target acquisition performance which uses the Targeting Task Performance (TTP) metric. Like its predecessor, the Johnson criteria, the new model bounds the range performance with image quality. The scope of presented analysis is limited to the estimation of detection, recognition and identification ranges for typical thermal cameras with uncooled microbolometer focal plane arrays. This type of cameras is most widely used in security systems because of competitive price to performance ratio. Detection, recognition and identification range calculations were made, and the appropriate results for the devices with selected technical specifications were compared and discussed.

The Canadian Forces ILDS: a militarily fielded multisensor vehicle-mounted teleoperated landmine detection system

NASA Astrophysics Data System (ADS)

McFee, John E.; Russell, Kevin L.; Chesney, Robert H.; Faust, Anthony A.; Das, Yogadhish

2006-05-01

The Improved Landmine Detection System (ILDS) is intended to meet Canadian military mine clearance requirements in rear area combat situations and peacekeeping on roads and tracks. The system consists of two teleoperated vehicles and a command vehicle. The teleoperated protection vehicle precedes, clearing antipersonnel mines and magnetic and tilt rod-fuzed antitank mines. It consists of an armoured personnel carrier with a forward looking infrared imager, a finger plow or roller and a magnetic signature duplicator. The teleoperated detection vehicle follows to detect antitank mines. The purpose-built vehicle carries forward looking infrared and visible imagers, a 3 m wide, down-looking sensitive electromagnetic induction detector array and a 3 m wide down-looking ground probing radar, which scan the ground in front of the vehicle. Sensor information is combined using navigation sensors and custom navigation, registration, spatial correspondence and data fusion algorithms. Suspicious targets are then confirmed by a thermal neutron activation detector. The prototype, designed and built by Defence R&D Canada, was completed in October 1997. General Dynamics Canada delivered four production units, based on the prototype concept and technologies, to the Canadian Forces (CF) in 2002. ILDS was deployed in Afghanistan in 2003, making the system the first militarily fielded, teleoperated, multi-sensor vehicle-mounted mine detector and the first with a fielded confirmation sensor. Performance of the prototype in Canadian and independent US trials is summarized and recent results from the production version of the confirmation sensor are discussed. CF operations with ILDS in Afghanistan are described.

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.

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

NASA Astrophysics Data System (ADS)

Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.

2017-12-01

Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation.

PubMed

Tkach, Itshak; Jevtić, Aleksandar; Nof, Shimon Y; Edan, Yael

2018-03-02

Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors' performance, tasks' priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation †

PubMed Central

Nof, Shimon Y.; Edan, Yael

2018-01-01

Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors’ performance, tasks’ priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems. PMID:29498683

A multi-sensor scenario for coastal surveillance

NASA Astrophysics Data System (ADS)

van den Broek, A. C.; van den Broek, S. P.; van den Heuvel, J. C.; Schwering, P. B. W.; van Heijningen, A. W. P.

2007-10-01

Maritime borders and coastal zones are susceptible to threats such as drug trafficking, piracy, undermining economical activities. At TNO Defence, Security and Safety various studies aim at improving situational awareness in a coastal zone. In this study we focus on multi-sensor surveillance of the coastal environment. We present a study on improving classification results for small sea surface targets using an advanced sensor suite and a scenario in which a small boat is approaching the coast. A next generation sensor suite mounted on a tower has been defined consisting of a maritime surveillance and tracking radar system, capable of producing range profiles and ISAR imagery of ships, an advanced infrared camera and a laser range profiler. For this suite we have developed a multi-sensor classification procedure, which is used to evaluate the capabilities for recognizing and identifying non-cooperative ships in coastal waters. We have found that the different sensors give complementary information. Each sensor has its own specific distance range in which it contributes most. A multi-sensor approach reduces the number of misclassifications and reliable classification results are obtained earlier compared to a single sensor approach.

Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

NASA Astrophysics Data System (ADS)

Yi, Cancan; Lv, Yong; Xiao, Han; Huang, Tao; You, Guanghui

2018-04-01

Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time-frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time-frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time-frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling bearing system and a gear system. The results show that the proposed multisensor matching synchronous squeezing wavelet transform (MMSWT) is superior to existing methods.

Regional distribution of forest height and biomass from multisensor data fusion

Treesearch

Yifan Yu; Sassan Saatch; Linda S. Heath; Elizabeth LaPoint; Ranga Myneni; Yuri Knyazikhin

2010-01-01

Elevation data acquired from radar interferometry at C-band from SRTM are used in data fusion techniques to estimate regional scale forest height and aboveground live biomass (AGLB) over the state of Maine. Two fusion techniques have been developed to perform post-processing and parameter estimations from four data sets: 1 arc sec National Elevation Data (NED), SRTM...

Development of a fusion approach selection tool

NASA Astrophysics Data System (ADS)

Pohl, C.; Zeng, Y.

2015-06-01

During the last decades number and quality of available remote sensing satellite sensors for Earth observation has grown significantly. The amount of available multi-sensor images along with their increased spatial and spectral resolution provides new challenges to Earth scientists. With a Fusion Approach Selection Tool (FAST) the remote sensing community would obtain access to an optimized and improved image processing technology. Remote sensing image fusion is a mean to produce images containing information that is not inherent in the single image alone. In the meantime the user has access to sophisticated commercialized image fusion techniques plus the option to tune the parameters of each individual technique to match the anticipated application. This leaves the operator with an uncountable number of options to combine remote sensing images, not talking about the selection of the appropriate images, resolution and bands. Image fusion can be a machine and time-consuming endeavour. In addition it requires knowledge about remote sensing, image fusion, digital image processing and the application. FAST shall provide the user with a quick overview of processing flows to choose from to reach the target. FAST will ask for available images, application parameters and desired information to process this input to come out with a workflow to quickly obtain the best results. It will optimize data and image fusion techniques. It provides an overview on the possible results from which the user can choose the best. FAST will enable even inexperienced users to use advanced processing methods to maximize the benefit of multi-sensor image exploitation.

Integrated circuit for SAW and MEMS sensors

NASA Astrophysics Data System (ADS)

Fischer, Wolf-Joachim; Koenig, Peter; Ploetner, Matthias; Hermann, Rudiger; Stab, Helmut

2001-11-01

The sensor processor circuit has been developed for hand-held devices used in industrial and environmental applications, such as on-line process monitoring. Thereby devices with SAW sensors or MEMS resonators will benefit from this processor especially. Up to 8 sensors can be connected to the circuit as multisensors or sensor arrays. Two sensor processors SP1 and SP2 for different applications are presented in this paper. The SP-1 chip has a PCMCIA interface which can be used for the program and data transfer. SAW sensors which are working in the frequency range from 80 MHz to 160 MHz can be connected to the processor directly. It is possible to use the new SP-2 chip fabricated in a 0.5(mu) CMOS process for SAW devices with a maximum frequency of 600 MHz. An on-chip analog-digital-converter (ADC) and 6 PWM modules support the development of high-miniaturized intelligent sensor systems We have developed a multi-SAW sensor system with this ASIC that manages the requirements on control as well as signal generation and storage and provides an interface to the PC and electronic devices on the board. Its low power consumption and its PCMCIA plug fulfil the requirements of small size and mobility. For this application sensors have been developed to detect hazardous gases in ambient air. Sensors with differently modified copper-phthalocyanine films are capable of detecting NO2 and O3, whereas those with a hyperbranched polyester film respond to NH3.

Development of a common biosensor format for an enzyme based biosensor array to monitor fruit quality.

PubMed

Jawaheer, Shobha; White, S F; Rughooputh, S D D V; Cullen, David C

2003-10-15

Individual enzyme-based biosensors involving three-electrode systems were developed for the detection of analytes comprising markers of the stage of maturity and quality in selected fruits of economic importance to tropical countries. Importantly, a common fabrication format has been developed to simplify manufacture and allow future integration of the individual sensors into a single multi-sensor array. Specifically, sensors for beta-D-glucose, total D-glucose, sucrose and ascorbic acid have been developed. Pectin, a natural polysaccharide present in plant cells, was used as a novel matrix to enhance enzyme entrapment and stabilisation in the sensors. Except for ascorbic acid, all the sensors function via the detection of enzymatically generated H2O2 at rhodinised carbon electrodes. Since ascorbic acid is electrochemically active at the working potential chosen (+350 mV vs. Ag/AgCl), it was measured directly. Enzyme sensors demonstrated expected response with respect to their substrates, typically 0-0.8 microA/20 mm2 electrode area response over analyte ranges of 0-7 mM. Interferences related to electrochemically active compounds present in fruits under study were significantly reduced by inclusion of a suitable cellulose acetate (CA) membrane or by enzymatic inactivation with ascorbate oxidase. Initial development was carried out into production of biosensor arrays. CA membranes were used to improve the linear range of the sensors, producing up to a fivefold improvement in the detection range compared to sensors without an additional diffusion barrier.

Information Measures for Multisensor Systems

DTIC Science & Technology

2013-12-11

permuted to generate spectra that were non- physical but preserved the entropy of the source spectra. Another 1000 spectra were constructed to mimic co...Research Laboratory (NRL) has yielded probabilistic models for spectral data that enable the computation of information measures such as entropy and...22308 Chemical sensing Information theory Spectral data Information entropy Information divergence Mass spectrometry Infrared spectroscopy Multisensor

SenseCube--A Novel Inexpensive Wireless Multisensor for Physics Lab Experimentations

ERIC Educational Resources Information Center

Mehta, Vedant; Lane, Charles D.

2018-01-01

SenseCube is a multisensor capable of measuring many different real-time events and changes in environment. Most conventional sensors used in introductory-physics labs use their own software and have wires that must be attached to a computer or an alternate device to analyze the data. This makes the standard sensors time consuming, tedious, and…

Adaptive Sensing and Fusion of Multi-Sensor Data and Historical Information

DTIC Science & Technology

2009-11-06

integrate MTL and semi-supervised learning into a single framework , thereby exploiting two forms of contextual information. A key new objective of the...this report we integrate MTL and semi-supervised learning into a single framework , thereby exploiting two forms of contextual information. A key new...process [8], denoted as X ∼ BeP (B), where B is a measure on Ω. If B is continuous, X is a Poisson process with intensity B and can be constructed as X = N

Particle Filter-Based Recursive Data Fusion With Sensor Indexing for Large Core Neutron Flux Estimation

NASA Astrophysics Data System (ADS)

Tamboli, Prakash Kumar; Duttagupta, Siddhartha P.; Roy, Kallol

2017-06-01

We introduce a sequential importance sampling particle filter (PF)-based multisensor multivariate nonlinear estimator for estimating the in-core neutron flux distribution for pressurized heavy water reactor core. Many critical applications such as reactor protection and control rely upon neutron flux information, and thus their reliability is of utmost importance. The point kinetic model based on neutron transport conveniently explains the dynamics of nuclear reactor. The neutron flux in the large core loosely coupled reactor is sensed by multiple sensors measuring point fluxes located at various locations inside the reactor core. The flux values are coupled to each other through diffusion equation. The coupling facilitates redundancy in the information. It is shown that multiple independent data about the localized flux can be fused together to enhance the estimation accuracy to a great extent. We also propose the sensor anomaly handling feature in multisensor PF to maintain the estimation process even when the sensor is faulty or generates data anomaly.

A multi-sensor remote sensing approach for measuring primary production from space

NASA Technical Reports Server (NTRS)

Gautier, Catherine

1989-01-01

It is proposed to develop a multi-sensor remote sensing method for computing marine primary productivity from space, based on the capability to measure the primary ocean variables which regulate photosynthesis. The three variables and the sensors which measure them are: (1) downwelling photosynthetically available irradiance, measured by the VISSR sensor on the GOES satellite, (2) sea-surface temperature from AVHRR on NOAA series satellites, and (3) chlorophyll-like pigment concentration from the Nimbus-7/CZCS sensor. These and other measured variables would be combined within empirical or analytical models to compute primary productivity. With this proposed capability of mapping primary productivity on a regional scale, we could begin realizing a more precise and accurate global assessment of its magnitude and variability. Applications would include supplementation and expansion on the horizontal scale of ship-acquired biological data, which is more accurate and which supplies the vertical components of the field, monitoring oceanic response to increased atmospheric carbon dioxide levels, correlation with observed sedimentation patterns and processes, and fisheries management.

Multisensor Network System for Wildfire Detection Using Infrared Image Processing

PubMed Central

Bosch, I.; Serrano, A.; Vergara, L.

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated. PMID:23843734

Multisensor network system for wildfire detection using infrared image processing.

PubMed

Bosch, I; Serrano, A; Vergara, L

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated.

STARR: shortwave-targeted agile Raman robot for the detection and identification of emplaced explosives

NASA Astrophysics Data System (ADS)

Gomer, Nathaniel R.; Gardner, Charles W.

2014-05-01

In order to combat the threat of emplaced explosives (land mines, etc.), ChemImage Sensor Systems (CISS) has developed a multi-sensor, robot mounted sensor capable of identification and confirmation of potential threats. The system, known as STARR (Shortwave-infrared Targeted Agile Raman Robot), utilizes shortwave infrared spectroscopy for the identification of potential threats, combined with a visible short-range standoff Raman hyperspectral imaging (HSI) system for material confirmation. The entire system is mounted onto a Talon UGV (Unmanned Ground Vehicle), giving the sensor an increased area search rate and reducing the risk of injury to the operator. The Raman HSI system utilizes a fiber array spectral translator (FAST) for the acquisition of high quality Raman chemical images, allowing for increased sensitivity and improved specificity. An overview of the design and operation of the system will be presented, along with initial detection results of the fusion sensor.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances

PubMed Central

Liu, Baoyu; Zhan, Xingqun; Zhu, Zheng H.

2017-01-01

As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed. In order to assess the influence of fusion structure on fusion performance, two fusion performance assessment parameters are defined as Fusion Distance and Fusion Index. Moreover, the formula for calculating the upper bounds of actual fused error covariances of the presented multisensor LE fusers is also provided. Demonstrated with simulation examples, the Fusion Index indicates fuser’s actual fused accuracy and its sensitivity to the sensor orders, as well as its robustness to the accuracy of newly added sensors. Compared to the LE fuser with sequential structure, the LE fusers with proposed parallel structures not only significantly improve their properties in these aspects, but also embrace better performances in consistency and computation efficiency. The presented multisensor LE fusers generally have better accuracies than covariance intersection (CI) fusion algorithm and are consistent when the local estimates are weakly correlated. PMID:28661442

Geocoding and stereo display of tropical forest multisensor datasets

NASA Technical Reports Server (NTRS)

Welch, R.; Jordan, T. R.; Luvall, J. C.

1990-01-01

Concern about the future of tropical forests has led to a demand for geocoded multisensor databases that can be used to assess forest structure, deforestation, thermal response, evapotranspiration, and other parameters linked to climate change. In response to studies being conducted at the Braulino Carrillo National Park, Costa Rica, digital satellite and aircraft images recorded by Landsat TM, SPOT HRV, Thermal Infrared Multispectral Scanner, and Calibrated Airborne Multispectral Scanner sensors were placed in register using the Landsat TM image as the reference map. Despite problems caused by relief, multitemporal datasets, and geometric distortions in the aircraft images, registration was accomplished to within + or - 20 m (+ or - 1 data pixel). A digital elevation model constructed from a multisensor Landsat TM/SPOT stereopair proved useful for generating perspective views of the rugged, forested terrain.

Multi-sensor image registration based on algebraic projective invariants.

PubMed

Li, Bin; Wang, Wei; Ye, Hao

2013-04-22

A new automatic feature-based registration algorithm is presented for multi-sensor images with projective deformation. Contours are firstly extracted from both reference and sensed images as basic features in the proposed method. Since it is difficult to design a projective-invariant descriptor from the contour information directly, a new feature named Five Sequential Corners (FSC) is constructed based on the corners detected from the extracted contours. By introducing algebraic projective invariants, we design a descriptor for each FSC that is ensured to be robust against projective deformation. Further, no gray scale related information is required in calculating the descriptor, thus it is also robust against the gray scale discrepancy between the multi-sensor image pairs. Experimental results utilizing real image pairs are presented to show the merits of the proposed registration method.

An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

NASA Astrophysics Data System (ADS)

Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

2018-05-01

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

A combined joint diagonalization-MUSIC algorithm for subsurface targets localization

NASA Astrophysics Data System (ADS)

Wang, Yinlin; Sigman, John B.; Barrowes, Benjamin E.; O'Neill, Kevin; Shubitidze, Fridon

2014-06-01

This paper presents a combined joint diagonalization (JD) and multiple signal classification (MUSIC) algorithm for estimating subsurface objects locations from electromagnetic induction (EMI) sensor data, without solving ill-posed inverse-scattering problems. JD is a numerical technique that finds the common eigenvectors that diagonalize a set of multistatic response (MSR) matrices measured by a time-domain EMI sensor. Eigenvalues from targets of interest (TOI) can be then distinguished automatically from noise-related eigenvalues. Filtering is also carried out in JD to improve the signal-to-noise ratio (SNR) of the data. The MUSIC algorithm utilizes the orthogonality between the signal and noise subspaces in the MSR matrix, which can be separated with information provided by JD. An array of theoreticallycalculated Green's functions are then projected onto the noise subspace, and the location of the target is estimated by the minimum of the projection owing to the orthogonality. This combined method is applied to data from the Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS). Examples of TEMTADS test stand data and field data collected at Spencer Range, Tennessee are analyzed and presented. Results indicate that due to its noniterative mechanism, the method can be executed fast enough to provide real-time estimation of objects' locations in the field.

Limited Scope Design Study for Multi-Sensor Towbody

DTIC Science & Technology

2016-06-01

FINAL REPORT Limited Scope Design Study for Multi-Sensor Towbody SERDP Project MR-2501 JUNE 2016 Dr. Kevin Williams Tim McGinnis...prepared under contract to the Department of Defense Strategic Environmental Research and Development Program (SERDP). The publication of this...Left Blank REPORT DOCUMENTATION PAGE Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 Form Approved OMB No. 0704-0188 The public

Formulating an image matching strategy for terrestrial stem data collection using a multisensor video system

Treesearch

Neil A. Clark

2001-01-01

A multisensor video system has been developed incorporating a CCD video camera, a 3-axis magnetometer, and a laser-rangefinding device, for the purpose of measuring individual tree stems. While preliminary results show promise, some changes are needed to improve the accuracy and efficiency of the system. Image matching is needed to improve the accuracy of length...

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

The US Army's Communications and Electronics Command through the auspices of its Night Vision and Electronics Sensors Directorate (CECOM-NVESD) is actively applying multisensor techniques to the detection of mine targets. This multisensor research results from the 'detection activity' with its broad range of operational conditions and targets. Multisensor operation justifies significant attention by yielding high target detection and low false alarm statistics. Furthermore, recent advances in sensor and computing technologies make its practical application realistic and affordable. The mine detection field-of-endeavor has since its WWI baptismal investigated the known spectra for applicable mine observation phenomena. Countless sensors, algorithms, processors, networks, and other techniques have been investigated to determine candidacy for mine detection. CECOM-NVESD efforts have addressed a wide range of sensors spanning the spectrum from gravity field perturbations, magentic field disturbances, seismic sounding, electromagnetic fields, earth penetrating radar imagery, and infrared/visible/ultraviolet surface imaging technologies. Supplementary analysis has considered sensor candidate applicability by testing under field conditions (versus laboratory), in determination of fieldability. As these field conditions directly effect the probability of detection and false alarms, sensor employment and design must be considered. Consequently, as a given sensor's performance is influenced directly by the operational conditions, tradeoffs are necessary. At present, mass produced and fielded mine detection techniques are limited to those incorporating a single sensor/processor methodology such as, pulse induction and megnetometry, as found in hand held detectors. The most sensitive fielded systems can detect minute metal components in small mine targets but result in very high false alarm rates reducing velocity in operation environments. Furthermore, the actual speed of advance for the entire mission (convoy, movement to engagement, etc.) is determined by the level of difficulty presented in clearance or avoidance activities required in response to the potential 'targets' marked throughout a detection activity. Therefore the application of fielded hand held systems to convoy operations in clearly impractical. CECOM-NVESD efforts are presently seeking to overcome these operational limitations by substantially increasing speed of detection while reducing the false alarm rate through the application of multisensor techniques. The CECOM-NVESD application of multisensor techniques through integration/fusion methods will be defined in this paper.

Analytical concepts for health management systems of liquid rocket engines

NASA Technical Reports Server (NTRS)

Williams, Richard; Tulpule, Sharayu; Hawman, Michael

1990-01-01

Substantial improvement in health management systems performance can be realized by implementing advanced analytical methods of processing existing liquid rocket engine sensor data. In this paper, such techniques ranging from time series analysis to multisensor pattern recognition to expert systems to fault isolation models are examined and contrasted. The performance of several of these methods is evaluated using data from test firings of the Space Shuttle main engines.

A Multi-Sensor Aerogeophysical Study of Afghanistan

DTIC Science & Technology

2007-01-01

magnetometer coupled with an Applied Physics 539 3-axis fluxgate mag- netometer for compensation of the aircraft field; • an Applanix DSS 301 digital...survey. DATA COlleCTION AND PROCeSSINg Photogrammetry More than 65,000 high-resolution photogram- metric images were collected using an Applanix Digital...HSI L-Band Polarimetric Imaging Radar KGPS Dual Gravity Meters Common Sensor Bomb-bay Pallet Applanix DSS Camera Sensor Suite • Magnetometer • Gravity

Integrated multi-sensor package (IMSP) for unmanned vehicle operations

NASA Astrophysics Data System (ADS)

Crow, Eddie C.; Reichard, Karl; Rogan, Chris; Callen, Jeff; Seifert, Elwood

2007-10-01

This paper describes recent efforts to develop integrated multi-sensor payloads for small robotic platforms for improved operator situational awareness and ultimately for greater robot autonomy. The focus is on enhancements to perception through integration of electro-optic, acoustic, and other sensors for navigation and inspection. The goals are to provide easier control and operation of the robot through fusion of multiple sensor outputs, to improve interoperability of the sensor payload package across multiple platforms through the use of open standards and architectures, and to reduce integration costs by embedded sensor data processing and fusion within the sensor payload package. The solutions investigated in this project to be discussed include: improved capture, processing and display of sensor data from multiple, non-commensurate sensors; an extensible architecture to support plug and play of integrated sensor packages; built-in health, power and system status monitoring using embedded diagnostics/prognostics; sensor payload integration into standard product forms for optimized size, weight and power; and the use of the open Joint Architecture for Unmanned Systems (JAUS)/ Society of Automotive Engineers (SAE) AS-4 interoperability standard. This project is in its first of three years. This paper will discuss the applicability of each of the solutions in terms of its projected impact to reducing operational time for the robot and teleoperator.

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

PubMed Central

Elfring, Jos; Appeldoorn, Rein; van den Dries, Sjoerd; Kwakkernaat, Maurice

2016-01-01

The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture. PMID:27727171

Adaptive multisensor fusion for planetary exploration rovers

NASA Technical Reports Server (NTRS)

Collin, Marie-France; Kumar, Krishen; Pampagnin, Luc-Henri

1992-01-01

The purpose of the adaptive multisensor fusion system currently being designed at NASA/Johnson Space Center is to provide a robotic rover with assured vision and safe navigation capabilities during robotic missions on planetary surfaces. Our approach consists of using multispectral sensing devices ranging from visible to microwave wavelengths to fulfill the needs of perception for space robotics. Based on the illumination conditions and the sensors capabilities knowledge, the designed perception system should automatically select the best subset of sensors and their sensing modalities that will allow the perception and interpretation of the environment. Then, based on reflectance and emittance theoretical models, the sensor data are fused to extract the physical and geometrical surface properties of the environment surface slope, dielectric constant, temperature and roughness. The theoretical concepts, the design and first results of the multisensor perception system are presented.

Concept of electro-optical sensor module for sniper detection system

NASA Astrophysics Data System (ADS)

Trzaskawka, Piotr; Dulski, Rafal; Kastek, Mariusz

2010-10-01

The paper presents an initial concept of the electro-optical sensor unit for sniper detection purposes. This unit, comprising of thermal and daylight cameras, can operate as a standalone device but its primary application is a multi-sensor sniper and shot detection system. Being a part of a larger system it should contribute to greater overall system efficiency and lower false alarm rate thanks to data and sensor fusion techniques. Additionally, it is expected to provide some pre-shot detection capabilities. Generally acoustic (or radar) systems used for shot detection offer only "after-the-shot" information and they cannot prevent enemy attack, which in case of a skilled sniper opponent usually means trouble. The passive imaging sensors presented in this paper, together with active systems detecting pointed optics, are capable of detecting specific shooter signatures or at least the presence of suspected objects in the vicinity. The proposed sensor unit use thermal camera as a primary sniper and shot detection tool. The basic camera parameters such as focal plane array size and type, focal length and aperture were chosen on the basis of assumed tactical characteristics of the system (mainly detection range) and current technology level. In order to provide costeffective solution the commercially available daylight camera modules and infrared focal plane arrays were tested, including fast cooled infrared array modules capable of 1000 fps image acquisition rate. The daylight camera operates as a support, providing corresponding visual image, easier to comprehend for a human operator. The initial assumptions concerning sensor operation were verified during laboratory and field test and some example shot recording sequences are presented.

Techniques for Sea Ice Characteristics Extraction and Sea Ice Monitoring Using Multi-Sensor Satellite Data in the Bohai Sea-Dragon 3 Programme Final Report (2012-2016)

NASA Astrophysics Data System (ADS)

Zhang, Xi; Zhang, Jie; Meng, Junmin

2016-08-01

The objectives of Dragon-3 programme (ID: 10501) are to develop methods for classification sea ice types and retrieving ice thickness based on multi-sensor data. In this final results paper, we give a briefly introduction for our research work and mainly results. Key words: the Bohai Sea ice, Sea ice, optical and

Hypothesis Testing Using Spatially Dependent Heavy Tailed Multisensor Data

DTIC Science & Technology

2014-12-01

Office of Research 113 Bowne Hall Syracuse, NY 13244 -1200 ABSTRACT HYPOTHESIS TESTING USING SPATIALLY DEPENDENT HEAVY-TAILED MULTISENSOR DATA Report...consistent with the null hypothesis of linearity and can be used to estimate the distribution of a test statistic that can discrimi- nate between the null... Test for nonlinearity. Histogram is generated using the surrogate data. The statistic of the original time series is represented by the solid line

Geometric Factors in Target Positioning and Tracking

DTIC Science & Technology

2009-07-01

Shalom and X.R. Li, Multitarget-Multisensor Tracking: Principles and Techniques, YBS Publishing, Storrs, CT, 1995. [2] S. Blackman and R. Popoli, Design...Multitarget-Multisensor Tracking: Applications and Advances, Vol.2, Y. Bar- Shalom (Ed.), 325-392, Artech House, Norwood, MA, 1999. [10] B. Ristic...R. Yarlagadda, I. Ali , N. Al-Dhahir, and J. Hershey, “GPS GDOP Metric,” IEE Proc. Radar, Sonar Navig, 147(5), Oct. 2000. [14] A. Kelly

Multisensor benchmark data for riot control

NASA Astrophysics Data System (ADS)

Jäger, Uwe; Höpken, Marc; Dürr, Bernhard; Metzler, Jürgen; Willersinn, Dieter

2008-10-01

Quick and precise response is essential for riot squads when coping with escalating violence in crowds. Often it is just a single person, known as the leader of the gang, who instigates other people and thus is responsible of excesses. Putting this single person out of action in most cases leads to a de-escalating situation. Fostering de-escalations is one of the main tasks of crowd and riot control. To do so, extensive situation awareness is mandatory for the squads and can be promoted by technical means such as video surveillance using sensor networks. To develop software tools for situation awareness appropriate input data with well-known quality is needed. Furthermore, the developer must be able to measure algorithm performance and ongoing improvements. Last but not least, after algorithm development has finished and marketing aspects emerge, meeting of specifications must be proved. This paper describes a multisensor benchmark which exactly serves this purpose. We first define the underlying algorithm task. Then we explain details about data acquisition and sensor setup and finally we give some insight into quality measures of multisensor data. Currently, the multisensor benchmark described in this paper is applied to the development of basic algorithms for situational awareness, e.g. tracking of individuals in a crowd.

NASA Tech Briefs, July 2004

NASA Technical Reports Server (NTRS)

2004-01-01

Topics: Optoelectronic Sensor System for Guidance in Docking; Hybrid Piezoelectric/Fiber-Optic Sensor Sheets; Multisensor Arrays for Greater Reliability and Accuracy; Integrated-Optic Oxygen Sensors; Ka-Band Autonomous Formation Flying Sensor; CMOS VLSI Active-Pixel Sensor for Tracking; Lightweight, Self-Deploying Foam Antenna Structures; Electrically Small Microstrip Quarter-Wave Monopole Antennas; A 2-to-28-MHz Phase-Locked Loop; Portable Electromyograph; Open-Source Software for Modeling of Nanoelectronic Devices; Software for Generating Strip Maps from SAR Data; Calibration Software for use with Jurassicprok; Software for Probabilistic Risk Reduction; Software Processes SAR Motion-Measurement Data; Improved Method of Purifying Carbon Nanotubes; Patterned Growth of Carbon Nanotubes or Nanofibers; Lightweight, Rack-Mountable Composite Cold Plate/Shelves; SiC-Based Miniature High-Temperature Cantilever Anemometer; Inlet Housing for a Partial-Admission Turbine; Lightweight Thermoformed Structural Components and Optics; Growing High-Quality InAs Quantum Dots for Infrared Lasers; Selected Papers on Protoplanetary Disks; Module for Oxygenating Water without Generating Bubbles; Coastal Research Imaging Spectrometer; Rapid Switching and Modulation by use of Coupled VCSELs; Laser-Induced-Fluorescence Photogrammetry and Videogrammetry; Laboratory Apparatus Generates Dual-Species Cold Atomic Beam; Laser Ablation of Materials for Propulsion of Spacecraft; Small Active Radiation Monitor; Hybrid Image-Plane/Stereo Manipulation; Partitioning a Gridded Rectangle into Smaller Rectangles; Digital Radar-Signal Processors Implemented in FPGAs; Part 1 of a Computational Study of a Drop-Laden Mixing Layer; and Some Improvements in Signal-Conditioning Circuits.

Diamond thin film temperature and heat-flux sensors

NASA Technical Reports Server (NTRS)

Aslam, M.; Yang, G. S.; Masood, A.; Fredricks, R.

1995-01-01

Diamond film temperature and heat-flux sensors are developed using a technology compatible with silicon integrated circuit processing. The technology involves diamond nucleation, patterning, doping, and metallization. Multi-sensor test chips were designed and fabricated to study the thermistor behavior. The minimum feature size (device width) for 1st and 2nd generation chips are 160 and 5 micron, respectively. The p-type diamond thermistors on the 1st generation test chip show temperature and response time ranges of 80-1270 K and 0.29-25 microseconds, respectively. An array of diamond thermistors, acting as heat flux sensors, was successfully fabricated on an oxidized Si rod with a diameter of 1 cm. Some problems were encountered in the patterning of the Pt/Ti ohmic contacts on the rod, due mainly to the surface roughness of the diamond film. The use of thermistors with a minimum width of 5 micron (to improve the spatial resolution of measurement) resulted in lithographic problems related to surface roughness of diamond films. We improved the mean surface roughness from 124 nm to 30 nm by using an ultra high nucleation density of 10(exp 11)/sq cm. To deposit thermistors with such small dimensions on a curved surface, a new 3-D diamond patterning technique is currently under development. This involves writing a diamond seed pattern directly on the curved surface by a computer-controlled nozzle.

Adding Semantics and OPM Ontology for the Provenance of Multi-sensor Merged Climate Data Records. Now What About Reproducibility?

NASA Astrophysics Data System (ADS)

Hua, H.; Wilson, B. D.; Manipon, G.; Pan, L.; Fetzer, E.

2011-12-01

Multi-decadal climate data records are critical to studying climate variability and change. These often also require merging data from multiple instruments such as those from NASA's A-Train that contain measurements covering a wide range of atmospheric conditions and phenomena. Multi-decadal climate data record of water vapor measurements from sensors on A-Train, operational weather, and other satellites are being assembled from existing data sources, or produced from well-established methods published in peer-reviewed literature. However, the immense volume and inhomogeneity of data often requires an "exploratory computing" approach to product generation where data is processed in a variety of different ways with varying algorithms, parameters, and code changes until an acceptable intermediate product is generated. This process is repeated until a desirable final merged product can be generated. Typically the production legacy is often lost due to the complexity of processing steps that were tried along the way. The data product information associated with source data, processing methods, parameters used, intermediate product outputs, and associated materials are often hidden in each of the trials and scattered throughout the processing system(s). We will discuss methods to help users better capture and explore the production legacy of the data, metadata, ancillary files, code, and computing environment changes used during the production of these merged and multi-sensor data products. By leveraging existing semantic and provenance tools, we can capture sufficient information to enable users to track, perform faceted searches, and visualize the provenance of the products and processing lineage. We will explore if sufficient provenance information can be captured to enable science reproducibility of these climate data records.

Detection of multiple airborne targets from multisensor data

NASA Astrophysics Data System (ADS)

Foltz, Mark A.; Srivastava, Anuj; Miller, Michael I.; Grenander, Ulf

1995-08-01

Previously we presented a jump-diffusion based random sampling algorithm for generating conditional mean estimates of scene representations for the tracking and recongition of maneuvering airborne targets. These representations include target positions and orientations along their trajectories and the target type associated with each trajectory. Taking a Bayesian approach, a posterior measure is defined on the parameter space by combining sensor models with a sophisticated prior based on nonlinear airplane dynamics. The jump-diffusion algorithm constructs a Markov process which visits the elements of the parameter space with frequencies proportional to the posterior probability. It consititutes both the infinitesimal, local search via a sample path continuous diffusion transform and the larger, global steps through discrete jump moves. The jump moves involve the addition and deletion of elements from the scene configuration or changes in the target type assoviated with each target trajectory. One such move results in target detection by the addition of a track seed to the inference set. This provides initial track data for the tracking/recognition algorithm to estimate linear graph structures representing tracks using the other jump moves and the diffusion process, as described in our earlier work. Target detection ideally involves a continuous research over a continuum of the observation space. In this work we conclude that for practical implemenations the search space must be discretized with lattice granularity comparable to sensor resolution, and discuss how fast Fourier transforms are utilized for efficient calcuation of sufficient statistics given our array models. Some results are also presented from our implementation on a networked system including a massively parallel machine architecture and a silicon graphics onyx workstation.

Stochastic model for threat assessment in multi-sensor defense system

NASA Astrophysics Data System (ADS)

Wang, Yongcheng; Wang, Hongfei; Jiang, Changsheng

2007-11-01

This paper puts forward a stochastic model for target detecting and tracking in multi-sensor defense systems and applies the Lanchester differential equations to threat assessment in combat. The two different modes of targets tracking and their respective Lanchester differential equations are analyzed and established. By use of these equations, we could briefly estimate the loss of each combat side and accordingly get the threat estimation results, given the situation analysis is accomplished.

A Vision for an International Multi-Sensor Snow Observing Mission

NASA Technical Reports Server (NTRS)

Kim, Edward

2015-01-01

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

NASA Technical Reports Server (NTRS)

Reith, Ernest; Roberts, Dar A.; Prentiss, Dylan

2001-01-01

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources.

PubMed

Gao, Xiang; Acar, Levent

2016-07-04

This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to follow an odor plume along its entire path, which is time consuming and may be especially difficult in a cluttered environment. To solve both of the above challenges, this paper proposes a novel algorithm that combines data from odor and anemometer sensors, and combine sensors' data at different positions. Initially, a multi-sensor integration method, together with the path of airflow was used to map the pattern of odor particle movement. Then, more sensors are introduced at specific regions to determine the probable location of the odor source. Finally, the results of odor source location simulation and a real experiment are presented.

Background characterization techniques for target detection using scene metrics and pattern recognition

NASA Astrophysics Data System (ADS)

Noah, Paul V.; Noah, Meg A.; Schroeder, John W.; Chernick, Julian A.

1990-09-01

The U.S. Army has a requirement to develop systems for the detection and identification of ground targets in a clutter environment. Autonomous Homing Munitions (AHM) using infrared, visible, millimeter wave and other sensors are being investigated for this application. Advanced signal processing and computational approaches using pattern recognition and artificial intelligence techniques combined with multisensor data fusion have the potential to meet the Army's requirements for next generation ARM.

BreedVision--a multi-sensor platform for non-destructive field-based phenotyping in plant breeding.

PubMed

Busemeyer, Lucas; Mentrup, Daniel; Möller, Kim; Wunder, Erik; Alheit, Katharina; Hahn, Volker; Maurer, Hans Peter; Reif, Jochen C; Würschum, Tobias; Müller, Joachim; Rahe, Florian; Ruckelshausen, Arno

2013-02-27

To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies.

BreedVision — A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding

PubMed Central

Busemeyer, Lucas; Mentrup, Daniel; Möller, Kim; Wunder, Erik; Alheit, Katharina; Hahn, Volker; Maurer, Hans Peter; Reif, Jochen C.; Würschum, Tobias; Müller, Joachim; Rahe, Florian; Ruckelshausen, Arno

2013-01-01

To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies. PMID:23447014

Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

NASA Astrophysics Data System (ADS)

Theologou, I.; Patelaki, M.; Karantzalos, K.

2015-04-01

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

Advantages and Challenges in using Multi-Sensor Data for Studying Aerosols from Space

NASA Astrophysics Data System (ADS)

Leptoukh, Gregory

We are living now in the golden era of numerous sensors measuring aerosols from space, e.g., MODIS, MISR, MERIS, OMI, POLDER, etc. Data from multiple sensors provide a more complete coverage of physical phenomena than data from a single sensor. These sensors are rather different from each other, are sensitive to various parts of the atmosphere, use different aerosol models and treat surface differently when retrieving aerosols. However, they complement each other thus providing more information about spatial, vertical and temporal distribution of aerosols. In addition to differences in instrumentation, retrieval algorithms and calibration, there are quite substantial differences in processing algorithms from Level 0 up to Level 3 and 4. Some of these differences in processing steps, at times not well documented and not widely known by users, can lead to quite significant differences in final products. Without documenting all the steps leading to the final product, data users will not trust the data and/or may use data incorrectly. Data by themselves without quality assessment and provenance are not sufficient to make accurate scientific conclusions. In this paper we provide examples of striking differences between aerosol optical depth data from MODIS, MISR, and MERIS that can be attributed to differences in a certain threshold, aggregation methods, and the dataday definition. We talk about challenges in developing processing provenance. Also, we address issues of harmonization of data, quality and provenance that is needed to guide the multi-sensor data usage and avoid apples-to-oranges comparison and fusion.

Multi-Sensor Documentation of Metric and Qualitative Information of Historic Stone Structures

NASA Astrophysics Data System (ADS)

Adamopoulos, E.; Tsilimantou, E.; Keramidas, V.; Apostolopoulou, M.; Karoglou, M.; Tapinaki, S.; Ioannidis, C.; Georgopoulos, A.; Moropoulou, A.

2017-08-01

This paper focuses on the integration of multi-sensor techniques regarding the acquisition, processing, visualisation and management of data regarding historic stone structures. The interdisciplinary methodology that is carried out here comprises of two parts. In the first part, the acquisition of qualitative and quantitative data concerning the geometry, the materials and the degradation of the tangible heritage asset each time, is discussed. The second part, refers to the analysis, management and visualization of the interrelated data by using spatial information technologies. Through the paradigm of the surveying of the ancient temple of Pythian Apollo at Acropolis of Rhodes, Rhodes Island, Greece, it is aimed to highlight the issues deriving from the separate application of documentation procedures and how the fusion of these methods can contribute effectively to ensure the completeness of the measurements for complex structures. The surveying results are further processed to be compatible and integrated with GIS. Also, the geometric documentation derivatives are combined with environmental data and the results of the application of non-destructive testing and evaluation techniques in situ and analytical techniques in lab after sampling. GIS operations are utilized to document the building materials but also to model and to analyse the decay extent and patterns. Detailed surface measurements and geo-processing analysis are executed. This integrated approach, helps the assessment of past interventions on the monument, identify main causes of damage and decay, and finally assist the decision making on the most compatible materials and techniques for protection and restoration works.

Design of a multisensor data fusion system for target detection

NASA Astrophysics Data System (ADS)

Thomopoulos, Stelios C.; Okello, Nickens N.; Kadar, Ivan; Lovas, Louis A.

1993-09-01

The objective of this paper is to discuss the issues that are involved in the design of a multisensor fusion system and provide a systematic analysis and synthesis methodology for the design of the fusion system. The system under consideration consists of multifrequency (similar) radar sensors. However, the fusion design must be flexible to accommodate additional dissimilar sensors such as IR, EO, ESM, and Ladar. The motivation for the system design is the proof of the fusion concept for enhancing the detectability of small targets in clutter. In the context of down-selecting the proper configuration for multisensor (similar and dissimilar, and centralized vs. distributed) data fusion, the issues of data modeling, fusion approaches, and fusion architectures need to be addressed for the particular application being considered. Although the study of different approaches may proceed in parallel, the interplay among them is crucial in selecting a fusion configuration for a given application. The natural sequence for addressing the three different issues is to begin from the data modeling, in order to determine the information content of the data. This information will dictate the appropriate fusion approach. This, in turn, will lead to a global fusion architecture. Both distributed and centralized fusion architectures are used to illustrate the design issues along with Monte-Carlo simulation performance comparison of a single sensor versus a multisensor centrally fused system.

Determination of urine ionic composition with potentiometric multisensor system.

PubMed

Yaroshenko, Irina; Kirsanov, Dmitry; Kartsova, Lyudmila; Sidorova, Alla; Borisova, Irina; Legin, Andrey

2015-01-01

The ionic composition of urine is a good indicator of patient's general condition and allows for diagnostics of certain medical problems such as e.g., urolithiasis. Due to environmental factors and malnutrition the number of registered urinary tract cases continuously increases. Most of the methods currently used for urine analysis are expensive, quite laborious and require skilled personnel. The present work deals with feasibility study of potentiometric multisensor system of 18 ion-selective and cross-sensitive sensors as an analytical tool for determination of urine ionic composition. In total 136 samples from patients of Urolithiasis Laboratory and healthy people were analyzed by the multisensor system as well as by capillary electrophoresis as a reference method. Various chemometric approaches were implemented to relate the data from electrochemical measurements with the reference data. Logistic regression (LR) was applied for classification of samples into healthy and unhealthy producing reasonable misclassification rates. Projection on Latent Structures (PLS) regression was applied for quantitative analysis of ionic composition from potentiometric data. Mean relative errors of simultaneous prediction of sodium, potassium, ammonium, calcium, magnesium, chloride, sulfate, phosphate, urate and creatinine from multisensor system response were in the range 3-13% for independent test sets. This shows a good promise for development of a fast and inexpensive alternative method for urine analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Autonomous collection of dynamically-cued multi-sensor imagery

NASA Astrophysics Data System (ADS)

Daniel, Brian; Wilson, Michael L.; Edelberg, Jason; Jensen, Mark; Johnson, Troy; Anderson, Scott

2011-05-01

The availability of imagery simultaneously collected from sensors of disparate modalities enhances an image analyst's situational awareness and expands the overall detection capability to a larger array of target classes. Dynamic cooperation between sensors is increasingly important for the collection of coincident data from multiple sensors either on the same or on different platforms suitable for UAV deployment. Of particular interest is autonomous collaboration between wide area survey detection, high-resolution inspection, and RF sensors that span large segments of the electromagnetic spectrum. The Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL) is building sensors with such networked communications capability and is conducting field tests to demonstrate the feasibility of collaborative sensor data collection and exploitation. Example survey / detection sensors include: NuSAR (NRL Unmanned SAR), a UAV compatible synthetic aperture radar system; microHSI, an NRL developed lightweight hyper-spectral imager; RASAR (Real-time Autonomous SAR), a lightweight podded synthetic aperture radar; and N-WAPSS-16 (Nighttime Wide-Area Persistent Surveillance Sensor-16Mpix), a MWIR large array gimbaled system. From these sensors, detected target cues are automatically sent to the NRL/SDL developed EyePod, a high-resolution, narrow FOV EO/IR sensor, for target inspection. In addition to this cooperative data collection, EyePod's real-time, autonomous target tracking capabilities will be demonstrated. Preliminary results and target analysis will be presented.

Multisensor System for Isotemporal Measurements to Assess Indoor Climatic Conditions in Poultry Farms

PubMed Central

Bustamante, Eliseo; Guijarro, Enrique; García-Diego, Fernando-Juan; Balasch, Sebastián; Hospitaler, Antonio; Torres, Antonio G.

2012-01-01

The rearing of poultry for meat production (broilers) is an agricultural food industry with high relevance to the economy and development of some countries. Periodic episodes of extreme climatic conditions during the summer season can cause high mortality among birds, resulting in economic losses. In this context, ventilation systems within poultry houses play a critical role to ensure appropriate indoor climatic conditions. The objective of this study was to develop a multisensor system to evaluate the design of the ventilation system in broiler houses. A measurement system equipped with three types of sensors: air velocity, temperature and differential pressure was designed and built. The system consisted in a laptop, a data acquisition card, a multiplexor module and a set of 24 air temperature, 24 air velocity and two differential pressure sensors. The system was able to acquire up to a maximum of 128 signals simultaneously at 5 second intervals. The multisensor system was calibrated under laboratory conditions and it was then tested in field tests. Field tests were conducted in a commercial broiler farm under four different pressure and ventilation scenarios in two sections within the building. The calibration curves obtained under laboratory conditions showed similar regression coefficients among temperature, air velocity and pressure sensors and a high goodness fit (R2 = 0.99) with the reference. Under field test conditions, the multisensor system showed a high number of input signals from different locations with minimum internal delay in acquiring signals. The variation among air velocity sensors was not significant. The developed multisensor system was able to integrate calibrated sensors of temperature, air velocity and differential pressure and operated succesfully under different conditions in a mechanically-ventilated broiler farm. This system can be used to obtain quasi-instantaneous fields of the air velocity and temperature, as well as differential pressure maps to assess the design and functioning of ventilation system and as a verification and validation (V&V) system of Computational Fluid Dynamics (CFD) simulations in poultry farms. PMID:22778611

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation.

PubMed

Zhang, Tao; Shi, Hongfei; Chen, Liping; Li, Yao; Tong, Jinwu

2016-03-11

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation

PubMed Central

Zhang, Tao; Shi, Hongfei; Chen, Liping; Li, Yao; Tong, Jinwu

2016-01-01

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL. PMID:26978361

The NASA Smart Probe Project for real-time multiple microsensor tissue recognition

NASA Technical Reports Server (NTRS)

Andrews, Russell J.; Mah, Robert W.

2003-01-01

BACKGROUND: Remote surgery requires automated sensors, effectors and sensor-effector communication. The NASA Smart Probe Project has focused on the sensor aspect. METHODS: The NASA Smart Probe uses neural networks and data from multiple microsensors for a unique tissue signature in real time. Animal and human trials use several probe configurations: (1) 8-microsensor probe (2.5 mm in diameter) for rodent studies (normal and subcutaneous mammary tumor tissues), and (2) 21-gauge needle probe with 3 spectroscopic fibers and an impedance microelectrode for breast cancer diagnosis in humans. Multisensor data are collected in real time (update 100 times/s) using PCs. RESULTS: Human data (collected by NASA licensee BioLuminate) from 15 women undergoing breast biopsy distinguished normal tissue from both benign tumors and breast carcinoma. Tumor margins and necrosis are rapidly detected. CONCLUSION: Real-time tissue identification is achievable. Potential applications, including probes incorporating nanoelectrode arrays, are presented. Copyright 2003 S. Karger AG, Basel.

Application of an e-tongue to the analysis of monovarietal and blends of white wines.

PubMed

Gutiérrez, Manuel; Llobera, Andreu; Ipatov, Andrey; Vila-Planas, Jordi; Mínguez, Santiago; Demming, Stefanie; Büttgenbach, Stephanus; Capdevila, Fina; Domingo, Carme; Jiménez-Jorquera, Cecilia

2011-01-01

This work presents a multiparametric system capable of characterizing and classifying white wines according to the grape variety and geographical origin. Besides, it quantifies specific parameters of interest for quality control in wine. The system, known as a hybrid electronic tongue, consists of an array of electrochemical microsensors-six ISFET based sensors, a conductivity sensor, a redox potential sensor and two amperometric electrodes, a gold microelectrode and a microelectrode for sensing electrochemical oxygen demand--and a miniaturized optofluidic system. The test sample set comprised eighteen Catalan monovarietal white wines from four different grape varieties, two Croatian monovarietal white wines and seven bi- and trivarietal mixtures prepared from the Catalan varieties. Different chemometric tools were used to characterize (i.e., Principal Component Analysis), classify (i.e., Soft Independent Modeling Class Analogy) and quantify (i.e., Partial-Least Squares) some parameters of interest. The results demonstrate the usefulness of the multisensor system for analysis of wine.

Application of an E-Tongue to the Analysis of Monovarietal and Blends of White Wines

PubMed Central

Gutiérrez, Manuel; Llobera, Andreu; Ipatov, Andrey; Vila-Planas, Jordi; Mínguez, Santiago; Demming, Stefanie; Büttgenbach, Stephanus; Capdevila, Fina; Domingo, Carme; Jiménez-Jorquera, Cecilia

2011-01-01

This work presents a multiparametric system capable of characterizing and classifying white wines according to the grape variety and geographical origin. Besides, it quantifies specific parameters of interest for quality control in wine. The system, known as a hybrid electronic tongue, consists of an array of electrochemical microsensors—six ISFET based sensors, a conductivity sensor, a redox potential sensor and two amperometric electrodes, a gold microelectrode and a microelectrode for sensing electrochemical oxygen demand—and a miniaturized optofluidic system. The test sample set comprised eighteen Catalan monovarietal white wines from four different grape varieties, two Croatian monovarietal white wines and seven bi- and trivarietal mixtures prepared from the Catalan varieties. Different chemometric tools were used to characterize (i.e., Principal Component Analysis), classify (i.e., Soft Independent Modeling Class Analogy) and quantify (i.e., Partial-Least Squares) some parameters of interest. The results demonstrate the usefulness of the multisensor system for analysis of wine. PMID:22163879

Atomic magnetometer for human magnetoencephalograpy.

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

Schwindt, Peter; Johnson, Cort N.

2010-12-01

We have developed a high sensitivity (<5 fTesla/{radical}Hz), fiber-optically coupled magnetometer to detect magnetic fields produced by the human brain. This is the first demonstration of a noncryogenic sensor that could replace cryogenic superconducting quantum interference device (SQUID) magnetometers in magnetoencephalography (MEG) and is an important advance in realizing cost-effective MEG. Within the sensor, a rubidium vapor is optically pumped with 795 laser light while field-induced optical rotations are measured with 780 nm laser light. Both beams share a single optical axis to maximize simplicity and compactness. In collaboration with neuroscientists at The Mind Research Network in Albuquerque, NM, themore » evoked responses resulting from median nerve and auditory stimulation were recorded with the atomic magnetometer and a commercial SQUID-based MEG system with signals comparing favorably. Multi-sensor operation has been demonstrated with two AMs placed on opposite sides of the head. Straightforward miniaturization would enable high-density sensor arrays for whole-head magnetoencephalography.« less

A Multitemporal, Multisensor Approach to Mapping the Canadian Boreal Forest

NASA Astrophysics Data System (ADS)

Reith, Ernest

The main anthropogenic source of CO2 emissions is the combustion of fossil fuels, while the clearing and burning of forests contribute significant amounts as well. Vegetation represents a major reservoir for terrestrial carbon stocks, and improving our ability to inventory vegetation will enhance our understanding of the impacts of land cover and climate change on carbon stocks and fluxes. These relationships may be an indication of a series of troubling biosphere-atmospheric feedback mechanisms that need to be better understood and modeled. Valuable land cover information can be provided to the global climate change modeling community using advanced remote sensing capabilities such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR). Individually and synergistically, data were successfully used to characterize the complex nature of the Canadian boreal forest land cover types. The multiple endmember spectral mixture analysis process was applied against seasonal AVIRIS data to produce species-level vegetated land cover maps of two study sites in the Canadian boreal forest: Old Black Spruce (OBS) and Old Jack Pine (OJP). The highest overall accuracy was assessed to be at least 66% accurate to the available reference map, providing evidence that high-quality, species-level land cover mapping of the Canadian boreal forest is achievable at accuracy levels greater than other previous research efforts in the region. Backscatter information from multichannel, polarimetric SAR utilizing a binary decision tree-based classification technique methodology was moderately successfully applied to AIRSAR to produce maps of the boreal land cover types at both sites, with overall accuracies at least 59%. A process, centered around noise whitening and principal component analysis features of the minimum noise fraction transform, was implemented to leverage synergies contained within spatially coregistered multitemporal and multisensor AVIRIS and AIRSAR data sets to successfully produce high-accuracy boreal forest land cover maps. Overall land cover map accuracies of 78% and 72% were assessed for OJP and OBS sites, respectively, for either seasonal or multitemporal data sets. High individual land cover accuracies appeared to be independent of site, season, or multisensor combination in the minimum-noise fraction-based approach.

Measurement of soil moisture trends with airborne scatterometers. [Guymon, Oklahoma and Dalhart, Texas

NASA Technical Reports Server (NTRS)

Jones, C. L.; Mcfarland, M. J.; Rosethal, W. D.; Theis, S. W. (Principal Investigator)

1982-01-01

In an effort to investigate aircraft multisensor responses to soil moisture and vegetation in agricultural fields, an intensive ground sampling program was conducted in Guymon, Oklahoma and Dalhart, Texas in conjunction with aircraft data collected for visible/infrared and passive and active microwave systems. Field selections, sampling techniques, data processing, and the aircraft schedule are discussed for both sites. Field notes are included along with final (normalized and corrected) data sets.

Optical Data Processing for Missile Guidance.

DTIC Science & Technology

1981-10-01

CORRELATORS The dynamic range of a digital system is well-known to be large. But moreso , these sys- tems can handle low modulation data by proper...nor (4). Rather, it is similar to (4), but with a lower bias level b than the one present in the original imagery of (1). Moreso , the t(x) input is...they are unisensor moreso than multisensor data. In such cases, HPF preprocessing will degrade the output correlation as noted in (5]. Thus, both cases

Multispectral multisensor image fusion using wavelet transforms

USGS Publications Warehouse

Lemeshewsky, George P.

1999-01-01

Fusion techniques can be applied to multispectral and higher spatial resolution panchromatic images to create a composite image that is easier to interpret than the individual images. Wavelet transform-based multisensor, multiresolution fusion (a type of band sharpening) was applied to Landsat thematic mapper (TM) multispectral and coregistered higher resolution SPOT panchromatic images. The objective was to obtain increased spatial resolution, false color composite products to support the interpretation of land cover types wherein the spectral characteristics of the imagery are preserved to provide the spectral clues needed for interpretation. Since the fusion process should not introduce artifacts, a shift invariant implementation of the discrete wavelet transform (SIDWT) was used. These results were compared with those using the shift variant, discrete wavelet transform (DWT). Overall, the process includes a hue, saturation, and value color space transform to minimize color changes, and a reported point-wise maximum selection rule to combine transform coefficients. The performance of fusion based on the SIDWT and DWT was evaluated with a simulated TM 30-m spatial resolution test image and a higher resolution reference. Simulated imagery was made by blurring higher resolution color-infrared photography with the TM sensors' point spread function. The SIDWT based technique produced imagery with fewer artifacts and lower error between fused images and the full resolution reference. Image examples with TM and SPOT 10-m panchromatic illustrate the reduction in artifacts due to the SIDWT based fusion.

Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements †

PubMed Central

2018-01-01

Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed. PMID:29351215

Fast Measurement and Reconstruction of Large Workpieces with Freeform Surfaces by Combining Local Scanning and Global Position Data

PubMed Central

Chen, Zhe; Zhang, Fumin; Qu, Xinghua; Liang, Baoqiu

2015-01-01

In this paper, we propose a new approach for the measurement and reconstruction of large workpieces with freeform surfaces. The system consists of a handheld laser scanning sensor and a position sensor. The laser scanning sensor is used to acquire the surface and geometry information, and the position sensor is utilized to unify the scanning sensors into a global coordinate system. The measurement process includes data collection, multi-sensor data fusion and surface reconstruction. With the multi-sensor data fusion, errors accumulated during the image alignment and registration process are minimized, and the measuring precision is significantly improved. After the dense accurate acquisition of the three-dimensional (3-D) coordinates, the surface is reconstructed using a commercial software piece, based on the Non-Uniform Rational B-Splines (NURBS) surface. The system has been evaluated, both qualitatively and quantitatively, using reference measurements provided by a commercial laser scanning sensor. The method has been applied for the reconstruction of a large gear rim and the accuracy is up to 0.0963 mm. The results prove that this new combined method is promising for measuring and reconstructing the large-scale objects with complex surface geometry. Compared with reported methods of large-scale shape measurement, it owns high freedom in motion, high precision and high measurement speed in a wide measurement range. PMID:26091396

Towards a High-Resolution Global Inundation Delineation Dataset

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.

2011-12-01

Although their importance for biodiversity, flow regulation and ecosystem service provision is widely recognized, wetlands and temporarily inundated landscapes remain poorly mapped globally because of their inherent elusive nature. Inventorying of wetland resources has been identified in international agreements as an essential component of appropriate conservation efforts and management initiatives of these threatened ecosystems. However, despite recent advances in remote sensing surface water monitoring, current inventories of surface water variations remain incomplete at the regional-to-global scale due to methodological limitations restricting truly global application. Remote sensing wetland applications such as SAR L-band are particularly constrained by image availability and heterogeneity of acquisition dates, while coarse resolution passive microwave and multi-sensor methods cannot discriminate distinct surface water bodies. As a result, the most popular global wetland dataset remains to this day the Global Lake & Wetland Database (Lehner and Doll, 2004) a spatially inconsistent database assembled from various existing data sources. The approach taken in this project circumvents the limitations of current global wetland monitoring methods by combining globally available topographic and hydrographic data to downscale coarse resolution global inundation data (Prigent et al., 2007) and thus create a superior inundation delineation map product. The developed procedure downscales inundation data from the coarse resolution (~27km) of current passive microwave sensors to the finer spatial resolution (~500m) of the topographic and hydrographic layers of HydroSHEDS' data suite (Lehner et al., 2006), while retaining the high temporal resolution of the multi-sensor inundation dataset. From the downscaling process emerges new information on the specific location of inundation, but also on its frequency and duration. The downscaling algorithm employs a decision tree classifier trained on regional remote sensing wetland maps, to derive inundation probability followed by a seeded region growing segmentation process to redistribute the inundated area at the finer resolution. Assessment of the algorithm's performance is accomplished by evaluating the level of agreement between its outputted downscaled inundation maps and existing regional remote sensing inundation delineation. Upon completion, this project's will offer a dynamic globally seamless inundation map at an unprecedented spatial and temporal scale, which will provide the baseline inventory long requested by the research community, and will open the door to a wide array of possible conservation and hydrological modeling applications which were until now data-restricted. Literature Lehner, B., K. Verdin, and A. Jarvis. 2008. New global hydrography derived from spaceborne elevation data. Eos 89, no. 10. Lehner, B, and P Doll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, no. 1-4: 1-22. Prigent, C., F. Papa, F. Aires, W. B. Rossow, and E. Matthews. 2007. Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research 112, no. D12: 1-13.

Large-Scale, Parallel, Multi-Sensor Data Fusion in the Cloud

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.

2012-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To efficiently assemble such decade-scale datasets in a timely manner, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. "SciReduce" is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, in which simple tuples (keys & values) are passed between the map and reduce functions, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Thus, SciReduce uses the native datatypes (geolocated grids, swaths, and points) that geo-scientists are familiar with. We are deploying within SciReduce a versatile set of python operators for data lookup, access, subsetting, co-registration, mining, fusion, and statistical analysis. All operators take in sets of geo-located arrays and generate more arrays. Large, multi-year satellite and model datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of granules) can be compared or fused in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP or webification URLs, thereby minimizing the size of the stored input and intermediate datasets. A typical map function might assemble and quality control AIRS Level-2 water vapor profiles for a year of data in parallel, then a reduce function would average the profiles in lat/lon bins (again, in parallel), and a final reduce would aggregate the climatology and write it to output files. We are using SciReduce to automate the production of multiple versions of a multi-year water vapor climatology (AIRS & MODIS), stratified by Cloudsat cloud classification, and compare it to models (ECMWF & MERRA reanalysis). We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing huge datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer.

Large-Scale, Parallel, Multi-Sensor Data Fusion in the Cloud

NASA Astrophysics Data System (ADS)

Wilson, B.; Manipon, G.; Hua, H.

2012-04-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To efficiently assemble such decade-scale datasets in a timely manner, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. "SciReduce" is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, in which simple tuples (keys & values) are passed between the map and reduce functions, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Thus, SciReduce uses the native datatypes (geolocated grids, swaths, and points) that geo-scientists are familiar with. We are deploying within SciReduce a versatile set of python operators for data lookup, access, subsetting, co-registration, mining, fusion, and statistical analysis. All operators take in sets of geo-arrays and generate more arrays. Large, multi-year satellite and model datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of granules) can be compared or fused in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP or webification URLs, thereby minimizing the size of the stored input and intermediate datasets. A typical map function might assemble and quality control AIRS Level-2 water vapor profiles for a year of data in parallel, then a reduce function would average the profiles in bins (again, in parallel), and a final reduce would aggregate the climatology and write it to output files. We are using SciReduce to automate the production of multiple versions of a multi-year water vapor climatology (AIRS & MODIS), stratified by Cloudsat cloud classification, and compare it to models (ECMWF & MERRA reanalysis). We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing huge datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer.

Multisensor data fusion for physical activity assessment.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John W; Freedson, Patty S

2012-03-01

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods.

Study on parallel and distributed management of RS data based on spatial database

NASA Astrophysics Data System (ADS)

Chen, Yingbiao; Qian, Qinglan; Wu, Hongqiao; Liu, Shijin

2009-10-01

With the rapid development of current earth-observing technology, RS image data storage, management and information publication become a bottle-neck for its appliance and popularization. There are two prominent problems in RS image data storage and management system. First, background server hardly handle the heavy process of great capacity of RS data which stored at different nodes in a distributing environment. A tough burden has put on the background server. Second, there is no unique, standard and rational organization of Multi-sensor RS data for its storage and management. And lots of information is lost or not included at storage. Faced at the above two problems, the paper has put forward a framework for RS image data parallel and distributed management and storage system. This system aims at RS data information system based on parallel background server and a distributed data management system. Aiming at the above two goals, this paper has studied the following key techniques and elicited some revelatory conclusions. The paper has put forward a solid index of "Pyramid, Block, Layer, Epoch" according to the properties of RS image data. With the solid index mechanism, a rational organization for different resolution, different area, different band and different period of Multi-sensor RS image data is completed. In data storage, RS data is not divided into binary large objects to be stored at current relational database system, while it is reconstructed through the above solid index mechanism. A logical image database for the RS image data file is constructed. In system architecture, this paper has set up a framework based on a parallel server of several common computers. Under the framework, the background process is divided into two parts, the common WEB process and parallel process.

Study on parallel and distributed management of RS data based on spatial data base

NASA Astrophysics Data System (ADS)

Chen, Yingbiao; Qian, Qinglan; Liu, Shijin

2006-12-01

With the rapid development of current earth-observing technology, RS image data storage, management and information publication become a bottle-neck for its appliance and popularization. There are two prominent problems in RS image data storage and management system. First, background server hardly handle the heavy process of great capacity of RS data which stored at different nodes in a distributing environment. A tough burden has put on the background server. Second, there is no unique, standard and rational organization of Multi-sensor RS data for its storage and management. And lots of information is lost or not included at storage. Faced at the above two problems, the paper has put forward a framework for RS image data parallel and distributed management and storage system. This system aims at RS data information system based on parallel background server and a distributed data management system. Aiming at the above two goals, this paper has studied the following key techniques and elicited some revelatory conclusions. The paper has put forward a solid index of "Pyramid, Block, Layer, Epoch" according to the properties of RS image data. With the solid index mechanism, a rational organization for different resolution, different area, different band and different period of Multi-sensor RS image data is completed. In data storage, RS data is not divided into binary large objects to be stored at current relational database system, while it is reconstructed through the above solid index mechanism. A logical image database for the RS image data file is constructed. In system architecture, this paper has set up a framework based on a parallel server of several common computers. Under the framework, the background process is divided into two parts, the common WEB process and parallel process.

Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation

NASA Astrophysics Data System (ADS)

Kim, Beomgeun; Seo, Dong-Jun; Noh, Seong Jin; Prat, Olivier P.; Nelson, Brian R.

2018-01-01

A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC > 0.9 and hourly gauge precipitation is > 60 mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12 mm while the reduction in RMSE by OCK over RO is about 7 mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance.

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications †

PubMed Central

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Muroyama, Masanori

2017-01-01

Robot tactile sensation can enhance human–robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as “sensor platform LSI”) as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated. PMID:29061954

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications.

PubMed

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Nonomura, Yutaka; Muroyama, Masanori

2017-08-28

Robot tactile sensation can enhance human-robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as "sensor platform LSI") as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated.

Novel Multisensor Probe for Monitoring Bladder Temperature During Locoregional Chemohyperthermia for Nonmuscle-Invasive Bladder Cancer: Technical Feasibility Study

PubMed Central

Geijsen, Debby E.; Zum Vörde Sive Vörding, Paul J.; Schooneveldt, Gerben; Sijbrands, Jan; Hulshof, Maarten C.; de la Rosette, Jean; de Reijke, Theo M.; Crezee, Hans

2013-01-01

Abstract Background and Purpose: The effectiveness of locoregional hyperthermia combined with intravesical instillation of mitomycin C to reduce the risk of recurrence and progression of intermediate- and high-risk nonmuscle-invasive bladder cancer is currently investigated in clinical trials. Clinically effective locoregional hyperthermia delivery necessitates adequate thermal dosimetry; thus, optimal thermometry methods are needed to monitor accurately the temperature distribution throughout the bladder wall. The aim of the study was to evaluate the technical feasibility of a novel intravesical device (multi-sensor probe) developed to monitor the local bladder wall temperatures during loco-regional C-HT. Materials and Methods: A multisensor thermocouple probe was designed for deployment in the human bladder, using special sensors to cover the bladder wall in different directions. The deployment of the thermocouples against the bladder wall was evaluated with visual, endoscopic, and CT imaging in bladder phantoms, porcine models, and human bladders obtained from obduction for bladder volumes and different deployment sizes of the probe. Finally, porcine bladders were embedded in a phantom and subjected to locoregional heating to compare probe temperatures with additional thermometry inside and outside the bladder wall. Results: The 7.5 cm thermocouple probe yielded optimal bladder wall contact, adapting to different bladder volumes. Temperature monitoring was shown to be accurate and representative for the actual bladder wall temperature. Conclusions: Use of this novel multisensor probe could yield a more accurate monitoring of the bladder wall temperature during locoregional chemohyperthermia. PMID:24112045

Acousto-ultrasonic system for the inspection of composite armored vehicles

NASA Astrophysics Data System (ADS)

Godinez, Valery F.; Carlos, Mark F.; Delamere, Michael; Hoch, William; Fotopoulos, Christos; Dai, Weiming; Raju, Basavaraju B.

2001-04-01

In this paper the design and implementation of a unique acousto-ultrasonics system for the inspection of composite armored vehicles is discussed. The system includes a multi-sensor probe with a position-tracking device mounted on a computer controlled scanning bridge. The system also includes an arbitrary waveform generator with a multiplexer and a multi-channel acoustic emission board capable of simultaneously collecting and processing up to four acoustic signals in real time. C-scans of an armored vehicle panel with defects are presented.

Validation of a multi-sensor hotwire probe for boundary layer enstrophy measurements

NASA Astrophysics Data System (ADS)

Zimmerman, Spencer; Morrill-Winter, Caleb; Klewicki, Joseph

2016-11-01

A multi-sensor hotwire probe capable of measuring the velocity and vorticity vectors has been designed and implemented in a turbulent boundary layer with the goal of educing the means by which the associated momentum transport is maintained under increasing scale separation between the velocity and vorticity fields with increasing Reynolds number. The capacity of this sensor to accurately measure each component of velocity and vorticity is first evaluated via synthetic experiment. The three-dimensional velocity field from the DNS of Sillero et al. is used to compute effective cooling for each sensor element, and the resulting signals are interpreted via two-dimensional calibration surfaces such as would be used to process physical experimental data. Results from this virtual validation experiment are presented and suggest the sensor is capable of resolving key features of the velocity and vorticity fields at physically achievable spatial resolutions. Results from measurements collected at the Flow Physics Facility (FPF) at the University of New Hampshire are presented alongside these projections and exhibit very good agreement in trend, but with some differences in magnitude. The support of the Australian Research Council and the National Science Foundation is gratefully acknowledged.

Assessment of Bias in the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Reanalysis Radar-Only Estimate

NASA Astrophysics Data System (ADS)

Nelson, B. R.; Prat, O. P.; Stevens, S. E.; Seo, D. J.; Zhang, J.; Howard, K.

2014-12-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is nearly completed for the period covering from 2001 to 2012. Reanalysis data are available at 1-km and 5-minute resolution. An important step in generating the best possible precipitation data is to assess the bias in the radar-only product. In this work, we use data from a combination of rain gauge networks to assess the bias in the NMQ reanalysis. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network Daily (GHCN-D) are combined for use in the assessment. These rain gauge networks vary in spatial density and temporal resolution. The challenge hence is to optimally utilize them to assess the bias at the finest resolution possible. For initial assessment, we propose to subset the CONUS data in climatologically representative domains, and perform bias assessment using information in the Q2 dataset on precipitation type and phase.

The ODAS Italia 1 buoy: More than forty years of activity in the Ligurian Sea

NASA Astrophysics Data System (ADS)

Canepa, Elisa; Pensieri, Sara; Bozzano, Roberto; Faimali, Marco; Traverso, Pierluigi; Cavaleri, Luigi

2015-06-01

The Ligurian Sea plays a relevant role in driving both the circulation of the Western Mediterranean Sea and the weather and climate of the area. In order to better understand the peculiarities of this basin, the Oceanographic Data Acquisition System (ODAS) Italia 1 buoy was developed and deployed in the early '70s. Throughout the years, the buoy has been fitted with updated measuring and data acquiring systems. Since 2003 the buoy has been part of the Mediterranean Moored Multi-sensor Array network of fixed open ocean observatories with the W1-M3A identifier and presently constitutes one of the Mediterranean sites of the European FixO3 network. Recently, a deep-ocean sub-surface mooring line was, and is, deployed close to it in relation to specific projects. This multidisciplinary observing system is able to perform both long-term operational and ad-hoc monitoring from the lower atmosphere to the deep ocean. It is used for analysis of air-sea interaction processes, study of the physical proprieties of the water column, bio-geo-chemical monitoring of the sea, meteorological and oceanographic model evaluation, calibration of remotely sensed measurements, and development of innovative marine monitoring technologies. After reporting some historical notes and the description of the observing system, this paper summarises and reviews the main oceanographic and atmospheric studies performed during the last 15 years using the data acquired on board.

Mapping Palm Swamp Wetland Ecosystems in the Peruvian Amazon: a Multi-Sensor Remote Sensing Approach

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schroeder, R.; Pinto, N.; Zimmerman, R.; Horna, V.

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. Spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

MATSurv: multisensor air traffic surveillance system

NASA Astrophysics Data System (ADS)

Yeddanapudi, Murali; Bar-Shalom, Yaakov; Pattipati, Krishna R.; Gassner, Richard R.

1995-09-01

This paper deals with the design and implementation of MATSurv 1--an experimental Multisensor Air Traffic Surveillance system. The proposed system consists of a Kalman filter based state estimator used in conjunction with a 2D sliding window assignment algorithm. Real data from two FAA radars is used to evaluate the performance of this algorithm. The results indicate that the proposed algorithm provides a superior classification of the measurements into tracks (i.e., the most likely aircraft trajectories) when compared to the aircraft trajectories obtained using the measurement IDs (squawk or IFF code).

The use of multisensor images for Earth Science applications

NASA Technical Reports Server (NTRS)

Evans, D.; Stromberg, B.

1983-01-01

The use of more than one remote sensing technique is particularly important for Earth Science applications because of the compositional and textural information derivable from the images. The ability to simultaneously analyze images acquired by different sensors requires coregistration of the multisensor image data sets. In order to insure pixel to pixel registration in areas of high relief, images must be rectified to eliminate topographic distortions. Coregistered images can be analyzed using a variety of multidimensional techniques and the acquired knowledge of topographic effects in the images can be used in photogeologic interpretations.

AOD furnace splash soft-sensor in the smelting process based on improved BP neural network

NASA Astrophysics Data System (ADS)

Ma, Haitao; Wang, Shanshan; Wu, Libin; Yu, Ying

2017-11-01

In view of argon oxygen refining low carbon ferrochrome production process, in the splash of smelting process as the research object, based on splash mechanism analysis in the smelting process , using multi-sensor information fusion and BP neural network modeling techniques is proposed in this paper, using the vibration signal, the audio signal and the flame image signal in the furnace as the characteristic signal of splash, the vibration signal, the audio signal and the flame image signal in the furnace integration and modeling, and reconstruct splash signal, realize the splash soft measurement in the smelting process, the simulation results show that the method can accurately forecast splash type in the smelting process, provide a new method of measurement for forecast splash in the smelting process, provide more accurate information to control splash.

Evidence of Mineral Dust Altering Cloud Microphysics and Precipitation

NASA Technical Reports Server (NTRS)

Min, Qilong; Li, Rui; Lin, Bing; Joseph, Everette; Wang, Shuyu; Hu, Yongxiang; Morris, Vernon; Chang, F.

2008-01-01

Multi-platform and multi-sensor observations are employed to investigate the impact of mineral dust on cloud microphysical and precipitation processes in mesoscale convective systems. It is clearly evident that for a given convection strength,small hydrometeors were more prevalent in the stratiform rain regions with dust than in those regions that were dust free. Evidence of abundant cloud ice particles in the dust sector, particularly at altitudes where heterogeneous nucleation process of mineral dust prevails, further supports the observed changes of precipitation. The consequences of the microphysical effects of the dust aerosols were to shift the precipitation size spectrum from heavy precipitation to light precipitation and ultimately suppressing precipitation.

The 1990 forest ecosystem dynamics multisensor aircraft campaign

NASA Technical Reports Server (NTRS)

Williams, Darrel L.; Ranson, K. Jon

1991-01-01

The overall objective of the Forest Ecosystem Dynamics (FED) research activity is to develop a better understanding of the dynamics of forest ecosystem evolution over a variety of temporal and spatial scales. Primary emphasis is being placed on assessing the ecosystem dynamics associated with the transition zone between northern hardwood forests in eastern North America and the predominantly coniferous forests of the more northerly boreal biome. The approach is to combine ground-based, airborne, and satellite observations with an integrated forest pattern and process model which is being developed to link together existing models of forest growth and development, soil processes, and radiative transfer.

Introduction: Special issue on advances in topobathymetric mapping, models, and applications

USGS Publications Warehouse

Gesch, Dean B.; Brock, John C.; Parrish, Christopher E.; Rogers, Jeffrey N.; Wright, C. Wayne

2016-01-01

Detailed knowledge of near-shore topography and bathymetry is required for many geospatial data applications in the coastal environment. New data sources and processing methods are facilitating development of seamless, regional-scale topobathymetric digital elevation models. These elevation models integrate disparate multi-sensor, multi-temporal topographic and bathymetric datasets to provide a coherent base layer for coastal science applications such as wetlands mapping and monitoring, sea-level rise assessment, benthic habitat mapping, erosion monitoring, and storm impact assessment. The focus of this special issue is on recent advances in the source data, data processing and integration methods, and applications of topobathymetric datasets.

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

NASA Technical Reports Server (NTRS)

Masunaga, Hirohiko; Matsui, Toshihisa; Tao, Wei-Kuo; Hou, Arthur Y.; Kummerow, Christian D.; Nakajima, Teruyuki; Bauer, Peter; Olson, William S.; Sekiguchi, Miho; Nakajima, Teruyuki

2010-01-01

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

RadMAP: The Radiological Multi-sensor Analysis Platform

NASA Astrophysics Data System (ADS)

Bandstra, Mark S.; Aucott, Timothy J.; Brubaker, Erik; Chivers, Daniel H.; Cooper, Reynold J.; Curtis, Joseph C.; Davis, John R.; Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J.; Srinivasan, Shreyas; Zakhor, Avideh; Zhang, Richard; Vetter, Kai

2016-12-01

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

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

Medical decision-making inspired from aerospace multisensor data fusion concepts.

PubMed

Pombo, Nuno; Bousson, Kouamana; Araújo, Pedro; Viana, Joaquim

2015-01-01

In recent years, Internet-delivered treatments have been largely used for pain monitoring, offering healthcare professionals and patients the ability to interact anywhere and at any time. Electronic diaries have been increasingly adopted as the preferred methodology to collect data related to pain intensity and symptoms, replacing traditional pen-and-paper diaries. This article presents a multisensor data fusion methodology based on the capabilities provided by aerospace systems to evaluate the effects of electronic and pen-and-paper diaries on pain. We examined English-language studies of randomized controlled trials that use computerized systems and the Internet to collect data about chronic pain complaints. These studies were obtained from three data sources: BioMed Central, PubMed Central and ScienceDirect from the year 2000 until 30 June 2012. Based on comparisons of the reported pain intensity collected during pre- and post-treatment in both the control and intervention groups, the proposed multisensor data fusion model revealed that the benefits of technology and pen-and-paper are qualitatively equivalent [Formula: see text]. We conclude that the proposed model is suitable, intelligible, easy to implement, time efficient and resource efficient.

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

PubMed

Zhu, Qingyuan; Xiao, Chunsheng; Hu, Huosheng; Liu, Yuanhui; Wu, Jinjin

2018-01-13

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy.

Concepts for the Design of a Diagnostic Device to Detect Malignancies in Human Tissues Final Report CRADA No. TSB-2023-00

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

DaSilva, L.; Marion, J.; Chase, C.

BioLuminate, Inc. planned to develop, produce and market a revolutionary diagnostic device for early breast cancer diagnosis. The device was originally invented by NASA; and exclusively licensed to BioLuminate for commercialization. At the time of the CRADA, eighty-five percent (85%) of all biopsies in the United States were found negative each year. The number of biopsies cost the health care system $23 billio n annually. A multi-sensor probe would allow surgeons to improve breast cancer scre ening and significantly reduce the number of biopsies. BioLuminate was developing an in-vivo system for the detection of cancer using a multi-sensor needle/probe. Themore » first system would be developed for the detection of breast cancer. LLNL, in collaboration with BioLuminate worked toward a detailed concept specification for the prototype multi-sensor needle/probe suitable for breast cancer analysis. BioLuminate in collaboration with LLNL, worked to develop a new version of the needle probe that would be the same size as needles commonly used to draw blood.« less

SVM-based multi-sensor fusion for free-living physical activity assessment.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty S

2011-01-01

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on the support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multi-sensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which the activity types and related energy expenditures are derived. The result shows that the method correctly recognized the 13 activity types 84.7% of the time, which is 26% higher than using a hip accelerometer alone. Also, the method predicted the associated energy expenditure with a root mean square error of 0.43 METs, 43% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor was added to the fusion model. These results demonstrate that the multi-sensor fusion technique presented is more effective in assessing activities of varying intensities than the traditional accelerometer-alone based methods.

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles

PubMed Central

Xiao, Chunsheng; Liu, Yuanhui; Wu, Jinjin

2018-01-01

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy. PMID:29342850

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

PubMed

Deng, Xinyang; Jiang, Wen

2017-09-12

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

PubMed Central

Deng, Xinyang

2017-01-01

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model. PMID:28895905

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

DOE PAGES

Meng, Ran; Wu, Jin; Zhao, Feng; ...

2018-06-01

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery wouldmore » enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

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

Meng, Ran; Wu, Jin; Zhao, Feng

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. For this work, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1m simultaneous airborne imaging spectroscopy and LiDAR and 2m satellite multi-spectral imagery) to separate canopy recovery from understory recovery wouldmore » enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal domains. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management, and for constraining/benchmarking fire effect schemes in ecological process models.« less

Air Enquirer's multi-sensor boxes as a tool for High School Education and Atmospheric Research

NASA Astrophysics Data System (ADS)

Morguí, Josep-Anton; Font, Anna; Cañas, Lidia; Vázquez-García, Eusebi; Gini, Andrea; Corominas, Ariadna; Àgueda, Alba; Lobo, Agustin; Ferraz, Carlos; Nofuentes, Manel; Ulldemolins, Delmir; Roca, Alex; Kamnang, Armand; Grossi, Claudia; Curcoll, Roger; Batet, Oscar; Borràs, Silvia; Occhipinti, Paola; Rodó, Xavier

2016-04-01

An educational tool was designed with the aim of making more comprehensive the research done on Greenhouse Gases (GHGs) in the ClimaDat Spanish network of atmospheric observation stations (www.climadat.es). This tool is called Air Enquirer and it consist of a multi-sensor box. It is envisaged to build more than two hundred boxes to yield them to the Spanish High Schools through the Education department (www.educaixa.com) of the "Obra Social 'La Caixa'", who funds this research. The starting point for the development of the Air Enquirers was the experience at IC3 (www.ic3.cat) in the CarboSchools+ FP7 project (www.carboschools.cat, www.carboschools.eu). The Air Enquirer's multi-sensor box is based in Arduino's architecture and contains sensors for CO2, temperature, relative humidity, pressure, and both infrared and visible luminance. The Air Enquirer is designed for taking continuous measurements. Every Air Enquirer ensemble of measurements is used to convert values to standard units (water content in ppmv, and CO2 in ppmv_dry). These values are referred to a calibration made with Cavity Ring Down Spectrometry (Picarro®) under different temperature, pressure, humidity and CO2 concentrations. Multiple sets of Air Enquirers are intercalibrated for its use in parallel during the experiments. The different experiments proposed to the students will be outdoor (observational) or indoor (experimental, in the lab) focusing on understanding the biogeochemistry of GHGs in the ecosystems (mainly CO2), the exchange (flux) of gases, the organic matter production, respiration and decomposition processes, the influence of the anthropogenic activities on the gases (and particles) exchanges, and their interaction with the structure and composition of the atmosphere (temperature, water content, cooling and warming processes, radiative forcing, vertical gradients and horizontal patterns). In order to ensure Air Enquirers a high-profile research performance the experimental designs and the device have been tested under research conditions by professional instruments. Results from several experiments are shown here: i) from vertical profiles obtained by drones (www.hemav.com) over Ebre Delta crops, ii) from measurements on lagoons, salt marshes and marine coastal research in the ClimaDat DEC3 station, iii) from horizontal patterns of variability over and under canopy, related to ecosystem patchiness in the highly instrumented Valderejo ClimaDat mountain station (www.modpow.es) and iv) from urban transects to reveal the urban atmosphere dynamic processes.

Geology

NASA Technical Reports Server (NTRS)

Stewart, R. K.; Sabins, F. F., Jr.; Rowan, L. C.; Short, N. M.

1975-01-01

Papers from private industry reporting applications of remote sensing to oil and gas exploration were presented. Digitally processed LANDSAT images were successfully employed in several geologic interpretations. A growing interest in digital image processing among the geologic user community was shown. The papers covered a wide geographic range and a wide technical and application range. Topics included: (1) oil and gas exploration, by use of radar and multisensor studies as well as by use of LANDSAT imagery or LANDSAT digital data, (2) mineral exploration, by mapping from LANDSAT and Skylab imagery and by LANDSAT digital processing, (3) geothermal energy studies with Skylab imagery, (4) environmental and engineering geology, by use of radar or LANDSAT and Skylab imagery, (5) regional mapping and interpretation, and digital and spectral methods.

Steps toward a CONUS-wide reanalysis with archived NEXRAD data using National Mosaic and Multisensor Quantitative Precipitation Estimation (NMQ/Q2) algorithms

NASA Astrophysics Data System (ADS)

Stevens, S. E.; Nelson, B. R.; Langston, C.; Qi, Y.

2012-12-01

The National Mosaic and Multisensor QPE (NMQ/Q2) software suite, developed at NOAA's National Severe Storms Laboratory (NSSL) in Norman, OK, addresses a large deficiency in the resolution of currently archived precipitation datasets. Current standards, both radar- and satellite-based, provide for nationwide precipitation data with a spatial resolution of up to 4-5 km, with a temporal resolution as fine as one hour. Efforts are ongoing to process archived NEXRAD data for the period of record (1996 - present), producing a continuous dataset providing precipitation data at a spatial resolution of 1 km, on a timescale of only five minutes. In addition, radar-derived precipitation data are adjusted hourly using a wide variety of automated gauge networks spanning the United States. Applications for such a product range widely, from emergency management and flash flood guidance, to hydrological studies and drought monitoring. Results are presented from a subset of the NEXRAD dataset, providing basic statistics on the distribution of rainrates, relative frequency of precipitation types, and several other variables which demonstrate the variety of output provided by the software. Precipitation data from select case studies are also presented to highlight the increased resolution provided by this reanalysis and the possibilities that arise from the availability of data on such fine scales. A previously completed pilot project and steps toward a nationwide implementation are presented along with proposed strategies for managing and processing such a large dataset. Reprocessing efforts span several institutions in both North Carolina and Oklahoma, and data/software coordination are key in producing a homogeneous record of precipitation to be archived alongside NOAA's other Climate Data Records. Methods are presented for utilizing supercomputing capability in expediting processing, to allow for the iterative nature of a reanalysis effort.

Rupture Dynamics and Scaling Behavior of Hydraulically Stimulated Micro-Earthquakes in a Shale Reservoir

NASA Astrophysics Data System (ADS)

Viegas, G. F.; Urbancic, T.; Baig, A. M.

2014-12-01

In hydraulic fracturing completion programs fluids are injected under pressure into fractured rock formations to open escape pathways for trapped hydrocarbons along pre-existing and newly generated fractures. To characterize the failure process, we estimate static and dynamic source and rupture parameters, such as dynamic and static stress drop, radiated energy, seismic efficiency, failure modes, failure plane orientations and dimensions, and rupture velocity to investigate the rupture dynamics and scaling relations of micro-earthquakes induced during a hydraulic fracturing shale completion program in NE British Columbia, Canada. The relationships between the different parameters combined with the in-situ stress field and rock properties provide valuable information on the rupture process giving insights into the generation and development of the fracture network. Approximately 30,000 micro-earthquakes were recorded using three multi-sensor arrays of high frequency geophones temporarily placed close to the treatment area at reservoir depth (~2km). On average the events have low radiated energy, low dynamic stress and low seismic efficiency, consistent with the obtained slow rupture velocities. Events fail in overshoot mode (slip weakening failure model), with fluids lubricating faults and decreasing friction resistance. Events occurring in deeper formations tend to have faster rupture velocities and are more efficient in radiating energy. Variations in rupture velocity tend to correlate with variation in depth, fault azimuth and elapsed time, reflecting a dominance of the local stress field over other factors. Several regions with different characteristic failure modes are identifiable based on coherent stress drop, seismic efficiency, rupture velocities and fracture orientations. Variations of source parameters with rock rheology and hydro-fracture fluids are also observed. Our results suggest that the spatial and temporal distribution of events with similar characteristic rupture behaviors can be used to determine reservoir geophysical properties, constrain reservoir geo-mechanical models, classify dynamic rupture processes for fracture models and improve fracture treatment designs.

Machine processing of remotely sensed data - quantifying global process: Models, sensor systems, and analytical methods; Proceedings of the Eleventh International Symposium, Purdue University, West Lafayette, IN, June 25-27, 1985

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

Mengel, S.K.; Morrison, D.B.

1985-01-01

Consideration is given to global biogeochemical issues, image processing, remote sensing of tropical environments, global processes, geology, landcover hydrology, and ecosystems modeling. Topics discussed include multisensor remote sensing strategies, geographic information systems, radars, and agricultural remote sensing. Papers are presented on fast feature extraction; a computational approach for adjusting TM imagery terrain distortions; the segmentation of a textured image by a maximum likelihood classifier; analysis of MSS Landsat data; sun angle and background effects on spectral response of simulated forest canopies; an integrated approach for vegetation/landcover mapping with digital Landsat images; geological and geomorphological studies using an image processing technique;more » and wavelength intensity indices in relation to tree conditions and leaf-nutrient content.« less

A Comparison of Multisensor Precipitation Estimation Methods in Complex Terrain for Flash Flood Warning and Mitigation

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, C. V.; Willie, D.; Reynolds, D.; Campbell, C.; Zhang, Y.; Sukovich, E.

2012-12-01

Investigating the uncertainties and improving the accuracy of quantitative precipitation estimation (QPE) is a critical mission of the National Oceanic and Atmospheric Administration (NOAA). QPE is extremely challenging in regions of complex terrain like the western U.S. because of the sparse coverage of ground-based radar, complex orographic precipitation processes, and the effects of beam blockages (e.g., Westrick et al. 1999). In addition, the rain gauge density in complex terrain is often inadequate to capture spatial variability in the precipitation patterns. The NOAA Hydrometeorology Testbed (HMT) conducts research on precipitation and weather conditions that can lead to flooding, and fosters transition of scientific advances and new tools into forecasting operations (see hmt.noaa.gov). The HMT program consists of a series of demonstration projects in different geographical regions to enhance understanding of region specific processes related to precipitation, including QPE. There are a number of QPE systems that are widely used across NOAA for precipitation estimation (e.g., Cifelli et al. 2011; Chandrasekar et al. 2012). Two of these systems have been installed at the NOAA Earth System Research Laboratory: Multisensor Precipitation Estimator (MPE) and National Mosaic and Multi-sensor QPE (NMQ) developed by NWS and NSSL, respectively. Both provide gridded QPE products that include radar-only, gauge-only and gauge-radar-merged, etc; however, these systems often provide large differences in QPE (in terms of amounts and spatial patterns) due to differences in Z-R selection, vertical profile of reflectivity correction, and gauge interpolation procedures. Determining the appropriate QPE product and quantification of QPE uncertainty is critical for operational applications, including water management decisions and flood warnings. For example, hourly QPE is used to correct radar based rain rates used by the Flash Flood Monitoring and Prediction (FFMP) package in the NWS forecast offices for issuance of flash flood warnings. This study will evaluate the performance of MPE and NMQ QPE products using independent gauges, object identification techniques for spatial verification and impact on surface runoff using a distributed hydrologic model. The effort will consist of baseline evaluations of these QPE systems to determine which combination of algorithm features is appropriate as well as investigate new methods for combining the gage and radar data. The Russian River Basin in California is used to demonstrate the comparison methodology with data collected from several rainfall events in March 2012.

Multi-Sensor Systems and Data Fusion for Telecommunications, Remote Sensing and Radar (les Systemes multi-senseurs et le fusionnement des donnees pour les telecommunications, la teledetection et les radars)

DTIC Science & Technology

1998-04-01

The result of the project is a demonstration of the fusion process, the sensors management and the real-time capabilities using simulated sensors...demonstrator (TAD) is a system that demonstrates the core ele- ment of a battlefield ground surveillance system by simulation in near real-time. The core...Management and Sensor/Platform simulation . The surveillance system observes the real world through a non-collocated heterogene- ous multisensory system

Design of a Customized Multipurpose Nano-Enabled Implantable System for In-Vivo Theranostics

PubMed Central

Juanola-Feliu, Esteve; Miribel-Català, Pere Ll.; Páez Avilés, Cristina; Colomer-Farrarons, Jordi; González-Piñero, Manel; Samitier, Josep

2014-01-01

The first part of this paper reviews the current development and key issues on implantable multi-sensor devices for in vivo theranostics. Afterwards, the authors propose an innovative biomedical multisensory system for in vivo biomarker monitoring that could be suitable for customized theranostics applications. At this point, findings suggest that cross-cutting Key Enabling Technologies (KETs) could improve the overall performance of the system given that the convergence of technologies in nanotechnology, biotechnology, micro&nanoelectronics and advanced materials permit the development of new medical devices of small dimensions, using biocompatible materials, and embedding reliable and targeted biosensors, high speed data communication, and even energy autonomy. Therefore, this article deals with new research and market challenges of implantable sensor devices, from the point of view of the pervasive system, and time-to-market. The remote clinical monitoring approach introduced in this paper could be based on an array of biosensors to extract information from the patient. A key contribution of the authors is that the general architecture introduced in this paper would require minor modifications for the final customized bio-implantable medical device. PMID:25325336

Towards a cyber-physical era: soft computing framework based multi-sensor array for water quality monitoring

NASA Astrophysics Data System (ADS)

Bhardwaj, Jyotirmoy; Gupta, Karunesh K.; Gupta, Rajiv

2018-02-01

New concepts and techniques are replacing traditional methods of water quality parameter measurement systems. This paper introduces a cyber-physical system (CPS) approach for water quality assessment in a distribution network. Cyber-physical systems with embedded sensors, processors and actuators can be designed to sense and interact with the water environment. The proposed CPS is comprised of sensing framework integrated with five different water quality parameter sensor nodes and soft computing framework for computational modelling. Soft computing framework utilizes the applications of Python for user interface and fuzzy sciences for decision making. Introduction of multiple sensors in a water distribution network generates a huge number of data matrices, which are sometimes highly complex, difficult to understand and convoluted for effective decision making. Therefore, the proposed system framework also intends to simplify the complexity of obtained sensor data matrices and to support decision making for water engineers through a soft computing framework. The target of this proposed research is to provide a simple and efficient method to identify and detect presence of contamination in a water distribution network using applications of CPS.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

The Effect of a Global, Subject, and Device-Specific Model on a Noninvasive Glucose Monitoring Multisensor System.

PubMed

Caduff, Andreas; Zanon, Mattia; Mueller, Martin; Zakharov, Pavel; Feldman, Yuri; De Feo, Oscar; Donath, Marc; Stahel, Werner A; Talary, Mark S

2015-07-01

We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate. Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived. We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals. We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches. © 2015 Diabetes Technology Society.

Non-invasive glucose monitoring in patients with Type 1 diabetes: a Multisensor system combining sensors for dielectric and optical characterisation of skin.

PubMed

Caduff, Andreas; Talary, Mark S; Mueller, Martin; Dewarrat, Francois; Klisic, Jelena; Donath, Marc; Heinemann, Lutz; Stahel, Werner A

2009-05-15

In vivo variations of blood glucose (BG) are affecting the biophysical characteristics (e.g. dielectric and optical) of skin and underlying tissue (SAUT) at various frequencies. However, the skin impedance spectra for instance can also be affected by other factors, perturbing the glucose related information, factors such as temperature, skin moisture and sweat, blood perfusion as well as body movements affecting the sensor-skin contact. In order to be able to correct for such perturbing factors, a Multisensor system was developed including sensors to measure the identified factors. To evaluate the quality of glucose monitoring, the Multisensor was applied in 10 patients with Type 1 diabetes. Glucose was administered orally to induce hyperglycaemic excursions at two different study visits. For analysis of the sensor signals, a global multiple linear regression model was derived. The respective coefficients of the variables were determined from the sensor signals of this first study visit (R(2)=0.74, MARD=18.0%--mean absolute relative difference). The identical set of modelling coefficients of the first study visit was re-applied to the test data of the second study visit to evaluate the predictive power of the model (R(2)=0.68, MARD=27.3%). It appears as if the Multisensor together with the global linear regression model applied, allows for tracking glucose changes non-invasively in patients with diabetes without requiring new model coefficients for each visit. Confirmation of these findings in a larger study group and under less experimentally controlled conditions is required for understanding whether a global parameterisation routine is feasible.

Data fusion: principles and applications in air defense

NASA Astrophysics Data System (ADS)

Maltese, Dominique; Lucas, Andre

1998-07-01

Within a Surveillance and Reconnaissance System, the Fusion Process is an essential part of the software package since the different sensors measurements are combined by this process; each sensor sends its data to a fusion center whose task is to elaborate the best tactical situation. In this paper, a practical algorithm of data fusion applied to a military application context is presented; the case studied here is a medium-range surveillance situation featuring a dual-sensor platform which combines a surveillance Radar and an IRST; both sensors are collocated. The presented performances were obtained on validation scenarios via simulations performed by SAGEM with the ESSOR ('Environnement de Simulation de Senseurs Optroniques et Radar') multisensor simulation test bench.

Surveillance and reconnaissance ground system architecture

NASA Astrophysics Data System (ADS)

Devambez, Francois

2001-12-01

Modern conflicts induces various modes of deployment, due to the type of conflict, the type of mission, and phase of conflict. It is then impossible to define fixed architecture systems for surveillance ground segments. Thales has developed a structure for a ground segment based on the operational functions required, and on the definition of modules and networks. Theses modules are software and hardware modules, including communications and networks. This ground segment is called MGS (Modular Ground Segment), and is intended for use in airborne reconnaissance systems, surveillance systems, and U.A.V. systems. Main parameters for the definition of a modular ground image exploitation system are : Compliance with various operational configurations, Easy adaptation to the evolution of theses configurations, Interoperability with NATO and multinational forces, Security, Multi-sensors, multi-platforms capabilities, Technical modularity, Evolutivity Reduction of life cycle cost The general performances of the MGS are presented : type of sensors, acquisition process, exploitation of images, report generation, data base management, dissemination, interface with C4I. The MGS is then described as a set of hardware and software modules, and their organization to build numerous operational configurations. Architectures are from minimal configuration intended for a mono-sensor image exploitation system, to a full image intelligence center, for a multilevel exploitation of multi-sensor.

Analysis of evolution of meddies in the North Atlantic using float experiments and multi - sensor data

NASA Astrophysics Data System (ADS)

Jo, Y.; Yan, X.; Zheng, Q.; Klemas, V. V.; Liu, W.

2002-05-01

We analyzed the interactions of Mediterranean eddies (meddies) in the North Atlantic with large - and meso - scale dynamic processes. The study focuses on the baroclinic instability due to the surface wind forcing, topographical Rossby wave (TRW) and the meddies' signals in multi-sensor data. The Hilbert - Huang's Energy - Frequency - Time spectrum was employed to estimate the dominant frequency. The major power peak of the surface wind forcing and sea surface height anomaly occurs every 33 months and relates to horizontal translation of the southwestward meddies. This frequency is quite close to M\\x81ler and Siedler's (1992) zonal variability with periods of 3 - 4 years. The subsequent power peaks in the vertical displacement of the meddies are at 5 day and 10 day intervals as derived from AMUSE and SEMAPHORE experiments (1993 - 1995). These 5 and 10 day periods may be caused by the intrusion of dense Mediterranean water. The contributions of the rotation speed, thermal expansion, and vertical fluctuation in the meddies' signals were estimated using the data taken by the AMUSE and the SEMAPHORE experiments. Consequently, mean monthly climatological meddies' signals from the multi-sensor analysis and float measurements show that the meddies mean kinetic energy is related to topographic scales.

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.

Low cost, multiscale and multi-sensor application for flooded area mapping

NASA Astrophysics Data System (ADS)

Giordan, Daniele; Notti, Davide; Villa, Alfredo; Zucca, Francesco; Calò, Fabiana; Pepe, Antonio; Dutto, Furio; Pari, Paolo; Baldo, Marco; Allasia, Paolo

2018-05-01

Flood mapping and estimation of the maximum water depth are essential elements for the first damage evaluation, civil protection intervention planning and detection of areas where remediation is needed. In this work, we present and discuss a methodology for mapping and quantifying flood severity over floodplains. The proposed methodology considers a multiscale and multi-sensor approach using free or low-cost data and sensors. We applied this method to the November 2016 Piedmont (northwestern Italy) flood. We first mapped the flooded areas at the basin scale using free satellite data from low- to medium-high-resolution from both the SAR (Sentinel-1, COSMO-Skymed) and multispectral sensors (MODIS, Sentinel-2). Using very- and ultra-high-resolution images from the low-cost aerial platform and remotely piloted aerial system, we refined the flooded zone and detected the most damaged sector. The presented method considers both urbanised and non-urbanised areas. Nadiral images have several limitations, in particular in urbanised areas, where the use of terrestrial images solved this limitation. Very- and ultra-high-resolution images were processed with structure from motion (SfM) for the realisation of 3-D models. These data, combined with an available digital terrain model, allowed us to obtain maps of the flooded area, maximum high water area and damaged infrastructures.

Integrated LTCC pressure/flow/temperature multisensor for compressed air diagnostics.

PubMed

Fournier, Yannick; Maeder, Thomas; Boutinard-Rouelle, Grégoire; Barras, Aurélie; Craquelin, Nicolas; Ryser, Peter

2010-01-01

We present a multisensor designed for industrial compressed air diagnostics and combining the measurement of pressure, flow, and temperature, integrated with the corresponding signal conditioning electronics in a single low-temperature co-fired ceramic (LTCC) package. The developed sensor may be soldered onto an integrated electro-fluidic platform by using standard surface mount device (SMD) technology, e.g., as a standard electronic component would be on a printed circuit board, obviating the need for both wires and tubes and thus paving the road towards low-cost integrated electro-fluidic systems. Several performance aspects of this device are presented and discussed, together with electronics design issues.

Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.

PubMed

Sarkar, Anjan; Banerjee, Anjan; Banerjee, Nilanjan; Brahma, Siddhartha; Kartikeyan, B; Chakraborty, Manab; Majumder, K L

2005-05-01

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.

Application of adaptive optics in complicated and integrated spatial multisensor system and its measurement analysis

NASA Astrophysics Data System (ADS)

Ding, Quanxin; Guo, Chunjie; Cai, Meng; Liu, Hua

2007-12-01

Adaptive Optics Expand System is a kind of new concept spatial equipment, which concerns system, cybernetics and informatics deeply, and is key way to improve advanced sensors ability. Traditional Zernike Phase Contrast Method is developed, and Accelerated High-level Phase Contrast Theory is established. Integration theory and mathematical simulation is achieved. Such Equipment, which is based on some crucial components, such as, core optical system, multi mode wavefront sensor and so on, is established for AOES advantageous configuration and global design. Studies on Complicated Spatial Multisensor System Integratation and measurement Analysis including error analysis are carried out.

Integrated LTCC Pressure/Flow/Temperature Multisensor for Compressed Air Diagnostics†

PubMed Central

Fournier, Yannick; Maeder, Thomas; Boutinard-Rouelle, Grégoire; Barras, Aurélie; Craquelin, Nicolas; Ryser, Peter

2010-01-01

We present a multisensor designed for industrial compressed air diagnostics and combining the measurement of pressure, flow, and temperature, integrated with the corresponding signal conditioning electronics in a single low-temperature co-fired ceramic (LTCC) package. The developed sensor may be soldered onto an integrated electro-fluidic platform by using standard surface mount device (SMD) technology, e.g., as a standard electronic component would be on a printed circuit board, obviating the need for both wires and tubes and thus paving the road towards low-cost integrated electro-fluidic systems. Several performance aspects of this device are presented and discussed, together with electronics design issues. PMID:22163518

Optimum Sensors Integration for Multi-Sensor Multi-Target Environment for Ballistic Missile Defense Applications

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

Imam, Neena; Barhen, Jacob; Glover, Charles Wayne

2012-01-01

Multi-sensor networks may face resource limitations in a dynamically evolving multiple target tracking scenario. It is necessary to task the sensors efficiently so that the overall system performance is maximized within the system constraints. The central sensor resource manager may control the sensors to meet objective functions that are formulated to meet system goals such as minimization of track loss, maximization of probability of target detection, and minimization of track error. This paper discusses the variety of techniques that may be utilized to optimize sensor performance for either near term gain or future reward over a longer time horizon.

NEWTON - NEW portable multi-sensor scienTific instrument for non-invasive ON-site characterization of rock from planetary surface and sub-surfaces

NASA Astrophysics Data System (ADS)

Díaz-Michelena, M.; de Frutos, J.; Ordóñez, A. A.; Rivero, M. A.; Mesa, J. L.; González, L.; Lavín, C.; Aroca, C.; Sanz, M.; Maicas, M.; Prieto, J. L.; Cobos, P.; Pérez, M.; Kilian, R.; Baeza, O.; Langlais, B.; Thébault, E.; Grösser, J.; Pappusch, M.

2017-09-01

In space instrumentation, there is currently no instrument dedicated to susceptibly or complete magnetization measurements of rocks. Magnetic field instrument suites are generally vector (or scalar) magnetometers, which locally measure the magnetic field. When mounted on board rovers, the electromagnetic perturbations associated with motors and other elements make it difficult to reap the benefits from the inclusion of such instruments. However, magnetic characterization is essential to understand key aspects of the present and past history of planetary objects. The work presented here overcomes the limitations currently existing in space instrumentation by developing a new portable and compact multi-sensor instrument for ground breaking high-resolution magnetic characterization of planetary surfaces and sub-surfaces. This new technology introduces for the first time magnetic susceptometry (real and imaginary parts) as a complement to existing compact vector magnetometers for planetary exploration. This work aims to solve the limitations currently existing in space instrumentation by means of providing a new portable and compact multi-sensor instrument for use in space, science and planetary exploration to solve some of the open questions on the crustal and more generally planetary evolution within the Solar System.

Case-Based Multi-Sensor Intrusion Detection

NASA Astrophysics Data System (ADS)

Schwartz, Daniel G.; Long, Jidong

2009-08-01

Multi-sensor intrusion detection systems (IDSs) combine the alerts raised by individual IDSs and possibly other kinds of devices such as firewalls and antivirus software. A critical issue in building a multi-sensor IDS is alert-correlation, i.e., determining which alerts are caused by the same attack. This paper explores a novel approach to alert correlation using case-based reasoning (CBR). Each case in the CBR system's library contains a pattern of alerts raised by some known attack type, together with the identity of the attack. Then during run time, the alert streams gleaned from the sensors are compared with the patterns in the cases, and a match indicates that the attack described by that case has occurred. For this purpose the design of a fast and accurate matching algorithm is imperative. Two such algorithms were explored: (i) the well-known Hungarian algorithm, and (ii) an order-preserving matching of our own device. Tests were conducted using the DARPA Grand Challenge Problem attack simulator. These showed that the both matching algorithms are effective in detecting attacks; but the Hungarian algorithm is inefficient; whereas the order-preserving one is very efficient, in fact runs in linear time.

Multi-Sensor Distributive On-Line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

NASA Technical Reports Server (NTRS)

Teng, William; Berrick, Steve; Leptuokh, Gregory; Liu, Zhong; Rui, Hualan; Pham, Long; Shen, Suhung; Zhu, Tong

2004-01-01

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM On-line Visualization and Analysis System precipitation and other satellite data products and services. AIS outputs will be ,integrated into existing operational decision support system for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that ,allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical ,parameters, area of interest, and time period; and the system generates an output ,on screen in a matter of seconds.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

In-process deformation measurements of translucent high speed fibre-reinforced disc rotors

NASA Astrophysics Data System (ADS)

Philipp, Katrin; Filippatos, Angelos; Koukourakis, Nektarios; Kuschmierz, Robert; Leithold, Christoph; Langkamp, Albert; Fischer, Andreas; Czarske, Jürgen

2015-07-01

The high stiffness to weight ratio of glass fibre-reinforced polymers (GFRP) makes them an attractive material for rotors e.g. in the aerospace industry. We report on recent developments towards non-contact, in-situ deformation measurements with temporal resolution up to 200 µs and micron measurement uncertainty. We determine the starting point of damage evolution inside the rotor material through radial expansion measurements. This leads to a better understanding of dynamic material behaviour regarding damage evolution and the prediction of damage initiation and propagation. The measurements are conducted using a novel multi-sensor system consisting of four laser Doppler distance (LDD) sensors. The LDD sensor, a two-wavelength Mach-Zehnder interferometer was already successfully applied for dynamic deformation measurements at metallic rotors. While translucency of the GFRP rotor material limits the applicability of most optical measurement techniques due to speckles from both surface and volume of the rotor, the LDD profits from speckles and is not disturbed by backscattered laser light from the rotor volume. The LDD sensor evaluates only signals from the rotor surface. The anisotropic glass fibre-reinforcement results in a rotationally asymmetric dynamic deformation. A novel signal processing algorithm is applied for the combination of the single sensor signals to obtain the shape of the investigated rotors. In conclusion, the applied multi-sensor system allows high temporal resolution dynamic deformation measurements. First investigations regarding damage evolution inside GFRP are presented as an important step towards a fundamental understanding of the material behaviour and the prediction of damage initiation and propagation.

Sled-Mounted Geophone Arrays for Near-Surface (0-4m) Seismic Profiling in Highly-attenuating Sedimentary Facies: Atchafalaya Basin Indian Bayou, Louisiana

NASA Astrophysics Data System (ADS)

Lorenzo, J. M.; Saanumi, A. A.; Westbrook, C. C.; Egnew, S. F.; Bentley, S. J.

2004-12-01

Towed land-geophone seismic arrays have the potential to increase markedly the efficiency for collecting near-surface (0-100m) high-resolution seismic data, but viable cases are few and have been limited to a narrow range of near-surface sedimentary facies. During November 2003 through June 2004 we conducted extensive seismic tests with traditional geophones mounted on low-cost Π -shaped sleds. We targeted human habitation surfaces within the upper few meters of a crevasse splay complex in the Atchafalaya Basin study area, Indian Bayou Wildlife Management Area, Louisiana, U.S. For seismic-to-core correlation, sealed, continuous test cores were run through a multi-sensor to test for magnetic susceptibility, bulk sediment density and electrical resistivity. We compared 24-channel seismic data using a variety of seismic source-receiver combinations. Sources comprised a 12-gauge pipe-gun, a 0.22 caliber-powered piston gun, an accelerated weight drop, and a small claw hammer. Commercial blanks, 2g-black-powder, and primer-only shells were fired by the pipe gun. Receivers included 100-Hz vertical-, and 14-Hz-horizontal-component geophones. For comparison, both ground-planted and geophones mounted on wooden and iron sleds 0.3 and 1.2m long respectively. Geophones mounted on steel sleds produced data of adequate quality. Whereas traditional ground-planted geophones showed better data quality, time and cost efficiency make mounted phones more feasible for regional studies as traditional arrays are prohibitively expensive. Because of the high seismic attenuation, only horizontal-component geophones mounted on heavy (9-kg) steel sleds provided useful data, although the shallowest reflection observed in the shear wave data came from a boundary at ~ 19m depth, too far below the target depth of 4-5 m. Instead, we forward-modeled refraction traveltime data to derive the acoustic and SH velocity structure.

The New Pelagic Operational Observatory of the Catalan Sea (OOCS) for the Multisensor Coordinated Measurement of Atmospheric and Oceanographic Conditions

PubMed Central

Bahamon, Nixon; Aguzzi, Jacopo; Bernardello, Raffaele; Ahumada-Sempoal, Miguel-Angel; Puigdefabregas, Joan; Cateura, Jordi; Muñoz, Eduardo; Velásquez, Zoila; Cruzado, Antonio

2011-01-01

The new pelagic Operational Observatory of the Catalan Sea (OOCS) for the coordinated multisensor measurement of atmospheric and oceanographic conditions has been recently installed (2009) in the Catalan Sea (41°39′N, 2°54′E; Western Mediterranean) and continuously operated (with minor maintenance gaps) until today. This multiparametric platform is moored at 192 m depth, 9.3 km off Blanes harbour (Girona, Spain). It is composed of a buoy holding atmospheric sensors and a set of oceanographic sensors measuring the water conditions over the upper 100 m depth. The station is located close to the head of the Blanes submarine canyon where an important multispecies pelagic and demersal fishery gives the station ecological and economic relevance. The OOCS provides important records on atmospheric and oceanographic conditions, the latter through the measurement of hydrological and biogeochemical parameters, at depths with a time resolution never attained before for this area of the Mediterranean. Twenty four moored sensors and probes operating in a coordinated fashion provide important data on Essential Ocean Variables (EOVs; UNESCO) such as temperature, salinity, pressure, dissolved oxygen, chlorophyll fluorescence, and turbidity. In comparison with other pelagic observatories presently operating in other world areas, OOCS also measures photosynthetic available radiation (PAR) from above the sea surface and at different depths in the upper 50 m. Data are recorded each 30 min and transmitted in real-time to a ground station via GPRS. This time series is published and automatically updated at the frequency of data collection on the official OOCS website (http://www.ceab.csic.es/~oceans). Under development are embedded automated routines for the in situ data treatment and assimilation into numerical models, in order to provide a reliable local marine processing forecast. In this work, our goal is to detail the OOCS multisensor architecture in relation to the coordinated capability for the remote, continuous and prolonged monitoring of atmospheric and oceanographic conditions, including data communication and storage. Accordingly, time series of measurements for a number of biological parameters will be presented for the summer months of 2011. Marine hindcast outputs from the numerical models implemented for simulating the conditions over the study area are shown. The strong changes of atmospheric conditions recorded in the last years over the area have altered the marine conditions of living organisms, but the dimension of the impact remains unclear. The OOCS multisensor coordinated monitoring has been specifically designed to address this issue, thus contributing to better understand the present environmental fluctuations and to provide a sound basis for a more accurate marine forecast system. PMID:22247664

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.

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Optimum Multisensor, Multitarget Localization and Tracking.

DTIC Science & Technology

1983-06-07

parameter vector t is given by (see Equation (3.5.1-7)’ the simul- taneous solution of A(e) N B G --1 ae &j’ -4n-in (fk’ ijn k3jn ~ k )kjn kjn - knn =1 k...the coefficient of mutual dependence given by M = 12 -(K-2) 121M12 :(3l 11J12 ) (K-2 and Jij is given by (see Equation (6.4.1-2)) - - (_ I R knN kn K...Transactions on Acoustic, Speech and Signal Processing, Vol ASSP-29, No. 3, June 1981. 17. B. Friedlander, "An ARMA Modeling Approach to Multitarget Tracking

Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots

NASA Astrophysics Data System (ADS)

Emter, Thomas; Petereit, Janko

2014-05-01

An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

A scale space feature based registration technique for fusion of satellite imagery

NASA Technical Reports Server (NTRS)

Raghavan, Srini; Cromp, Robert F.; Campbell, William C.

1997-01-01

Feature based registration is one of the most reliable methods to register multi-sensor images (both active and passive imagery) since features are often more reliable than intensity or radiometric values. The only situation where a feature based approach will fail is when the scene is completely homogenous or densely textural in which case a combination of feature and intensity based methods may yield better results. In this paper, we present some preliminary results of testing our scale space feature based registration technique, a modified version of feature based method developed earlier for classification of multi-sensor imagery. The proposed approach removes the sensitivity in parameter selection experienced in the earlier version as explained later.

Sensors, Volume 1, Fundamentals and General Aspects

NASA Astrophysics Data System (ADS)

Grandke, Thomas; Ko, Wen H.

1996-12-01

'Sensors' is the first self-contained series to deal with the whole area of sensors. It describes general aspects, technical and physical fundamentals, construction, function, applications and developments of the various types of sensors. This volume deals with the fundamentals and common principles of sensors and covers the wide areas of principles, technologies, signal processing, and applications. Contents include: Sensor Fundamentals, e.g. Sensor Parameters, Modeling, Design and Packaging; Basic Sensor Technologies, e.g. Thin and Thick Films, Integrated Magnetic Sensors, Optical Fibres and Intergrated Optics, Ceramics and Oxides; Sensor Interfaces, e.g. Signal Processing, Multisensor Signal Processing, Smart Sensors, Interface Systems; Sensor Applications, e.g. Automotive: On-board Sensors, Traffic Surveillance and Control, Home Appliances, Environmental Monitoring, etc. This volume is an indispensable reference work and text book for both specialits and newcomers, researchers and developers.

Multisensor data fusion for enhanced respiratory rate estimation in thermal videos.

PubMed

Pereira, Carina B; Xinchi Yu; Blazek, Vladimir; Venema, Boudewijn; Leonhardt, Steffen

2016-08-01

Scientific studies have demonstrated that an atypical respiratory rate (RR) is frequently one of the earliest and major indicators of physiological distress. However, it is also described in the literature as "the neglected vital parameter", mainly due to shortcomings of clinical available monitoring techniques, which require attachment of sensors to the patient's body. The current paper introduces a novel approach that uses multisensor data fusion for an enhanced RR estimation in thermal videos. It considers not only the temperature variation around nostrils and mouth, but the upward and downward movement of both shoulders. In order to analyze the performance of our approach, two experiments were carried out on five healthy candidates. While during phase A, the subjects breathed normally, during phase B they simulated different breathing patterns. Thoracic effort was the gold standard elected to validate our algorithm. Our results show an excellent agreement between infrared thermography (IRT) and ground truth. While in phase A a mean correlation of 0.983 and a root-mean-square error of 0.240 bpm (breaths per minute) was obtained, in phase B they hovered around 0.995 and 0.890 bpm, respectively. In sum, IRT may be a promising clinical alternative to conventional sensors. Additionally, multisensor data fusion contributes to an enhancement of RR estimation and robustness.

ATR architecture for multisensor fusion

NASA Astrophysics Data System (ADS)

Hamilton, Mark K.; Kipp, Teresa A.

1996-06-01

The work of the U.S. Army Research Laboratory (ARL) in the area of algorithms for the identification of static military targets in single-frame electro-optical (EO) imagery has demonstrated great potential in platform-based automatic target identification (ATI). In this case, the term identification is used to mean being able to tell the difference between two military vehicles -- e.g., the M60 from the T72. ARL's work includes not only single-sensor forward-looking infrared (FLIR) ATI algorithms, but also multi-sensor ATI algorithms. We briefly discuss ARL's hybrid model-based/data-learning strategy for ATI, which represents a significant step forward in ATI algorithm design. For example, in the case of single sensor FLIR it allows the human algorithm designer to build directly into the algorithm knowledge that can be adequately modeled at this time, such as the target geometry which directly translates into the target silhouette in the FLIR realm. In addition, it allows structure that is not currently well understood (i.e., adequately modeled) to be incorporated through automated data-learning algorithms, which in a FLIR directly translates into an internal thermal target structure signature. This paper shows the direct applicability of this strategy to both the single-sensor FLIR as well as the multi-sensor FLIR and laser radar.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

2012-01-01

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Long-Term Quantitative Precipitation Estimates (QPE) at High Spatial and Temporal Resolution over CONUS: Bias-Adjustment of the Radar-Only National Mosaic and Multi-sensor QPE (NMQ/Q2) Precipitation Reanalysis (2001-2012)

NASA Astrophysics Data System (ADS)

Prat, Olivier; Nelson, Brian; Stevens, Scott; Seo, Dong-Jun; Kim, Beomgeun

2015-04-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is completed for the period covering from 2001 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Several in-situ datasets are available to assess the biases of the radar-only product and to adjust for those biases to provide a multi-sensor QPE. The rain gauge networks that are used such as the Global Historical Climatology Network-Daily (GHCN-D), the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), and the Climate Reference Network (CRN), have different spatial density and temporal resolution. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. The objective of this work is threefold. First, we investigate how the different in-situ networks can impact the precipitation estimates as a function of the spatial density, sensor type, and temporal resolution. Second, we assess conditional and un-conditional biases of the radar-only QPE for various time scales (daily, hourly, 5-min) using in-situ precipitation observations. Finally, after assessing the bias and applying reduction or elimination techniques, we are using a unique in-situ dataset merging the different RG networks (CRN, ASOS, HADS, GHCN-D) to adjust the radar-only QPE product via an Inverse Distance Weighting (IDW) approach. In addition, we also investigate alternate adjustment techniques such as the kriging method and its variants (Simple Kriging: SK; Ordinary Kriging: OK; Conditional Bias-Penalized Kriging: CBPK). From this approach, we also hope to generate estimates of uncertainty for the gridded bias-adjusted QPE. Further comparison with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) and satellite products (TMPA, CMORPH, PERSIANN) is also provided in order to give a detailed picture of the improvements and remaining challenges.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors.

PubMed

Chowdhury, Enhad A; Western, Max J; Nightingale, Thomas E; Peacock, Oliver J; Thompson, Dylan

2017-01-01

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors

PubMed Central

Chowdhury, Enhad A.; Western, Max J.; Nightingale, Thomas E.; Peacock, Oliver J.; Thompson, Dylan

2017-01-01

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required. PMID:28234979

Observability considerations for multi-sensor and product fusion: Bias, information content, and validation (Invited)

NASA Astrophysics Data System (ADS)

Reid, J. S.; Zhang, J.; Hyer, E. J.; Campbell, J. R.; Christopher, S. A.; Ferrare, R. A.; Leptoukh, G. G.; Stackhouse, P. W.

2009-12-01

With the successful development of many aerosol products from the NASA A-train as well as new operational geostationary and polar orbiting sensors, the scientific community now has a host of new parameters to use in their analyses. The variety and quality of products has reached a point where the community has moved from basic observation-based science to sophisticated multi-component research that addresses the complex atmospheric environment. In order for these satellite data contribute to the science their uncertainty levels must move from semi-quantitative to quantitative. Initial attempts to quantify uncertainties have led to some recent debate in the community as to the efficacy of aerosol products from current and future NASA satellite sensors. In an effort to understand the state of satellite product fidelity, the Naval Research Laboratory and a newly reformed Global Energy and Water Cycle Experiment (GEWEX) aerosol panel have both initiated assessments of the nature of aerosol remote sensing uncertainty and bias. In this talk we go over areas of specific concern based on the authors’ experiences with the data, emphasizing the multi-sensor problem. We first enumerate potential biases, including retrieval, sampling/contextual, and cognitive bias. We show examples of how these biases can subsequently lead to the pitfalls of correlated/compensating errors, tautology, and confounding. The nature of bias is closely related to the information content of the sensor signal and its subsequent application to the derived aerosol quantity of interest (e.g., optical depth, flux, index of refraction, etc.). Consequently, purpose-specific validation methods must be employed, especially when generating multi-sensor products. Indeed, cloud and lower boundary condition biases in particular complicate the more typical methods of regressional bias elimination and histogram matching. We close with a discussion of sequestration of uncertainty in multi-sensor applications of these products in both pair-wise and fused fashions.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Barrow real-time sea ice mass balance data: ingestion, processing, dissemination and archival of multi-sensor data

NASA Astrophysics Data System (ADS)

Grimes, J.; Mahoney, A. R.; Heinrichs, T. A.; Eicken, H.

2012-12-01

Sensor data can be highly variable in nature and also varied depending on the physical quantity being observed, sensor hardware and sampling parameters. The sea ice mass balance site (MBS) operated in Barrow by the University of Alaska Fairbanks (http://seaice.alaska.edu/gi/observatories/barrow_sealevel) is a multisensor platform consisting of a thermistor string, air and water temperature sensors, acoustic altimeters above and below the ice and a humidity sensor. Each sensor has a unique specification and configuration. The data from multiple sensors are combined to generate sea ice data products. For example, ice thickness is calculated from the positions of the upper and lower ice surfaces, which are determined using data from downward-looking and upward-looking acoustic altimeters above and below the ice, respectively. As a data clearinghouse, the Geographic Information Network of Alaska (GINA) processes real time data from many sources, including the Barrow MBS. Doing so requires a system that is easy to use, yet also offers the flexibility to handle data from multisensor observing platforms. In the case of the Barrow MBS, the metadata system needs to accommodate the addition of new and retirement of old sensors from year to year as well as instrument configuration changes caused by, for example, spring melt or inquisitive polar bears. We also require ease of use for both administrators and end users. Here we present the data and processing steps of using sensor data system powered by the NoSQL storage engine, MongoDB. The system has been developed to ingest, process, disseminate and archive data from the Barrow MBS. Storing sensor data in a generalized format, from many different sources, is a challenging task, especially for traditional SQL databases with a set schema. MongoDB is a NoSQL (not only SQL) database that does not require a fixed schema. There are several advantages using this model over the traditional relational database management system (RDBMS) model databases. The lack of a required schema allows flexibility in how the data can be ingested into the database. For example, MongoDB imposes no restrictions on field names. For researchers using the system, this means that the name they have chosen for the sensor is carried through the database, any processing, and to the final output helping to preserve data integrity. Also, MongoDB allows the data to be pushed to it dynamically meaning that field attributes can be defined at the point of ingestion. This allows any sensor data to be ingested as a document and for this functionality to be transferred to the user interface, allowing greater adaptability to different use-case scenarios. In presenting the MondoDB data system being developed for the Barrow MBS, we demonstrate the versatility of this approach and its suitability as the foundation of a Barrow node of the Arctic Observing Network. Authors Jason Grimes - Geographic Information Network of Alaska - jason@gina.alaska.edu Andy Mahony - Geophysical Institute - mahoney@gi.alaska.edu Hajo Eiken - Geophysical Institute - Hajo.Eicken@gi.alaska.edu Tom Heinrichs - Geographic Information Network of Alaska - Tom.Heinrichs@alaska.edu

Adaptive Multi-sensor Data Fusion Model for In-situ Exploration of Mars

NASA Astrophysics Data System (ADS)

Schneiderman, T.; Sobron, P.

2014-12-01

Laser Raman spectroscopy (LRS) and laser-induced breakdown spectroscopy (LIBS) can be used synergistically to characterize the geochemistry and mineralogy of potential microbial habitats and biosignatures. The value of LRS and LIBS has been recognized by the planetary science community: (i) NASA's Mars2020 mission features a combined LRS-LIBS instrument, SuperCam, and an LRS instrument, SHERLOC; (ii) an LRS instrument, RLS, will fly on ESA's 2018 ExoMars mission. The advantages of combining LRS and LIBS are evident: (1) LRS/LIBS can share hardware components; (2) LIBS reveals the relative concentration of major (and often trace) elements present in a sample; and (3) LRS yields information on the individual mineral species and their chemical/structural nature. Combining data from LRS and LIBS enables definitive mineral phase identification with precise chemical characterization of major, minor, and trace mineral species. New approaches to data processing are needed to analyze large amounts of LRS+LIBS data efficiently and maximize the scientific return of integrated measurements. Multi-sensor data fusion (MSDF) is a method that allows for robust sample identification through automated acquisition, processing, and combination of data. It optimizes information usage, yielding a more robust characterization of a target than could be acquired through single sensor use. We have developed a prototype fuzzy logic adaptive MSDF model aimed towards the unsupervised characterization of Martian habitats and their biosignatures using LRS and LIBS datasets. Our model also incorporates fusion of microimaging (MI) data - critical for placing analyses in geological and spatial context. Here, we discuss the performance of our novel MSDF model and demonstrate that automated quantification of the salt abundance in sulfate/clay/phyllosilicate mixtures is possible through data fusion of collocated LRS, LIBS, and MI data.

An approach for combining airborne LiDAR and high-resolution aerial color imagery using Gaussian processes

NASA Astrophysics Data System (ADS)

Liu, Yansong; Monteiro, Sildomar T.; Saber, Eli

2015-10-01

Changes in vegetation cover, building construction, road network and traffic conditions caused by urban expansion affect the human habitat as well as the natural environment in rapidly developing cities. It is crucial to assess these changes and respond accordingly by identifying man-made and natural structures with accurate classification algorithms. With the increase in use of multi-sensor remote sensing systems, researchers are able to obtain a more complete description of the scene of interest. By utilizing multi-sensor data, the accuracy of classification algorithms can be improved. In this paper, we propose a method for combining 3D LiDAR point clouds and high-resolution color images to classify urban areas using Gaussian processes (GP). GP classification is a powerful non-parametric classification method that yields probabilistic classification results. It makes predictions in a way that addresses the uncertainty of real world. In this paper, we attempt to identify man-made and natural objects in urban areas including buildings, roads, trees, grass, water and vehicles. LiDAR features are derived from the 3D point clouds and the spatial and color features are extracted from RGB images. For classification, we use the Laplacian approximation for GP binary classification on the new combined feature space. The multiclass classification has been implemented by using one-vs-all binary classification strategy. The result of applying support vector machines (SVMs) and logistic regression (LR) classifier is also provided for comparison. Our experiments show a clear improvement of classification results by using the two sensors combined instead of each sensor separately. Also we found the advantage of applying GP approach to handle the uncertainty in classification result without compromising accuracy compared to SVM, which is considered as the state-of-the-art classification method.

Multi-parameter Observations and Validation of Pre-earthquake Atmospheric Signals

NASA Astrophysics Data System (ADS)

Ouzounov, D.; Pulinets, S. A.; Hattori, K.; Mogi, T.; Kafatos, M.

2014-12-01

We are presenting the latest development in multi-sensors observations of short-term pre-earthquake phenomena preceding major earthquakes. We are exploring the potential of pre-seismic atmospheric and ionospheric signals to alert for large earthquakes. To achieve this, we start validating anomalous ionospheric /atmospheric signals in retrospective and prospective modes. The integrated satellite and terrestrial framework (ISTF) is our method for validation and is based on a joint analysis of several physical and environmental parameters (Satellite thermal infrared radiation (OLR), electron concentration in the ionosphere (GPS/TEC), VHF-bands radio waves, radon/ion activities, air temperature and seismicity patterns) that were found to be associated with earthquakes. The science rationale for multidisciplinary analysis is based on concept Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) [Pulinets and Ouzounov, 2011], which explains the synergy of different geospace processes and anomalous variations, usually named short-term pre-earthquake anomalies. Our validation processes consist in two steps: (1) A continuous retrospective analysis preformed over two different regions with high seismicity- Taiwan and Japan for 2003-2009 The retrospective tests (100+ major earthquakes, M>5.9, Taiwan and Japan) show OLR anomalous behavior before all of these events with no false negatives. False alarm ratio for false positives is less then 25%. (2) Prospective testing using multiple parameters with potential for M5.5+ events. The initial testing shows systematic appearance of atmospheric anomalies in advance (days) to the M5.5+ events for Taiwan and Japan (Honshu and Hokkaido areas). Our initial prospective results suggest that our approach show a systematic appearance of atmospheric anomalies, one to several days prior to the largest earthquakes That feature could be further studied and tested for advancing the multi-sensors detection of pre-earthquake atmospheric signals.

Efficient processing of two-dimensional arrays with C or C++

USGS Publications Warehouse

Donato, David I.

2017-07-20

Because fast and efficient serial processing of raster-graphic images and other two-dimensional arrays is a requirement in land-change modeling and other applications, the effects of 10 factors on the runtimes for processing two-dimensional arrays with C and C++ are evaluated in a comparative factorial study. This study’s factors include the choice among three C or C++ source-code techniques for array processing; the choice of Microsoft Windows 7 or a Linux operating system; the choice of 4-byte or 8-byte array elements and indexes; and the choice of 32-bit or 64-bit memory addressing. This study demonstrates how programmer choices can reduce runtimes by 75 percent or more, even after compiler optimizations. Ten points of practical advice for faster processing of two-dimensional arrays are offered to C and C++ programmers. Further study and the development of a C and C++ software test suite are recommended.Key words: array processing, C, C++, compiler, computational speed, land-change modeling, raster-graphic image, two-dimensional array, software efficiency

Precision of EM Simulation Based Wireless Location Estimation in Multi-Sensor Capsule Endoscopy

PubMed Central

Ye, Yunxing; Aisha, Ain-Ul; Swar, Pranay; Pahlavan, Kaveh

2018-01-01

In this paper, we compute and examine two-way localization limits for an RF endoscopy pill as it passes through an individuals gastrointestinal (GI) tract. We obtain finite-difference time-domain and finite element method-based simulation results position assessment employing time of arrival (TOA). By means of a 3-D human body representation from a full-wave simulation software and lognormal models for TOA propagation from implant organs to body surface, we calculate bounds on location estimators in three digestive organs: stomach, small intestine, and large intestine. We present an investigation of the causes influencing localization precision, consisting of a range of organ properties; peripheral sensor array arrangements, number of pills in cooperation, and the random variations in transmit power of sensor nodes. We also perform a localization precision investigation for the situation where the transmission signal of the antenna is arbitrary with a known probability distribution. The computational solver outcome shows that the number of receiver antennas on the exterior of the body has higher impact on the precision of the location than the amount of capsules in collaboration within the GI region. The large intestine is influenced the most by the transmitter power probability distribution. PMID:29651364

A multi-sensor land mine detection system: hardware and architectural outline of the Australian RRAMNS CTD system

NASA Astrophysics Data System (ADS)

Abeynayake, Canicious; Chant, Ian; Kempinger, Siegfried; Rye, Alan

2005-06-01

The Rapid Route Area and Mine Neutralisation System (RRAMNS) Capability Technology Demonstrator (CTD) is a countermine detection project undertaken by DSTO and supported by the Australian Defence Force (ADF). The limited time and budget for this CTD resulted in some difficult strategic decisions with regard to hardware selection and system architecture. Although the delivered system has certain limitations arising from its experimental status, many lessons have been learned which illustrate a pragmatic path for future development. RRAMNS a similar sensor suite to other systems, in that three complementary sensors are included. These are Ground Probing Radar, Metal Detector Array, and multi-band electro-optic sensors. However, RRAMNS uses a unique imaging system and a network based real-time control and sensor fusion architecture. The relatively simple integration of each of these components could be the basis for a robust and cost-effective operational system. The RRAMNS imaging system consists of three cameras which cover the visible spectrum, the mid-wave and long-wave infrared region. This subsystem can be used separately as a scouting sensor. This paper describes the system at its mid-2004 status, when full integration of all detection components was achieved.

Monitoring human health behaviour in one's living environment: a technological review.

PubMed

Lowe, Shane A; Ólaighin, Gearóid

2014-02-01

The electronic monitoring of human health behaviour using computer techniques has been an active research area for the past few decades. A wide array of different approaches have been investigated using various technologies including inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. It is only in recent years that research has turned towards a sensor fusion approach using several different technologies in single systems or devices. These systems allow for an increased volume of data to be collected and for activity data to be better used as measures of behaviour. This change may be due to decreasing hardware costs, smaller sensors, increased power efficiency or increases in portability. This paper is intended to act as a reference for the design of multi-sensor behaviour monitoring systems. The range of technologies that have been used in isolation for behaviour monitoring both in research and commercial devices are reviewed and discussed. Filtering, range, sensitivity, usability and other considerations of different technologies are discussed. A brief overview of commercially available activity monitors and their technology is also included. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Precision of EM Simulation Based Wireless Location Estimation in Multi-Sensor Capsule Endoscopy.

PubMed

Khan, Umair; Ye, Yunxing; Aisha, Ain-Ul; Swar, Pranay; Pahlavan, Kaveh

2018-01-01

In this paper, we compute and examine two-way localization limits for an RF endoscopy pill as it passes through an individuals gastrointestinal (GI) tract. We obtain finite-difference time-domain and finite element method-based simulation results position assessment employing time of arrival (TOA). By means of a 3-D human body representation from a full-wave simulation software and lognormal models for TOA propagation from implant organs to body surface, we calculate bounds on location estimators in three digestive organs: stomach, small intestine, and large intestine. We present an investigation of the causes influencing localization precision, consisting of a range of organ properties; peripheral sensor array arrangements, number of pills in cooperation, and the random variations in transmit power of sensor nodes. We also perform a localization precision investigation for the situation where the transmission signal of the antenna is arbitrary with a known probability distribution. The computational solver outcome shows that the number of receiver antennas on the exterior of the body has higher impact on the precision of the location than the amount of capsules in collaboration within the GI region. The large intestine is influenced the most by the transmitter power probability distribution.

Multi-sensor millimeter-wave system for hidden objects detection by non-collaborative screening

NASA Astrophysics Data System (ADS)

Zouaoui, Rhalem; Czarny, Romain; Diaz, Frédéric; Khy, Antoine; Lamarque, Thierry

2011-05-01

In this work, we present the development of a multi-sensor system for the detection of objects concealed under clothes using passive and active millimeter-wave (mmW) technologies. This study concerns both the optimization of a commercial passive mmW imager at 94 GHz using a phase mask and the development of an active mmW detector at 77 GHz based on synthetic aperture radar (SAR). A first wide-field inspection is done by the passive imager while the person is walking. If a suspicious area is detected, the active imager is switched-on and focused on this area in order to obtain more accurate data (shape of the object, nature of the material ...).

Low Cost Multi-Sensor Robot Laser Scanning System and its Accuracy Investigations for Indoor Mapping Application

NASA Astrophysics Data System (ADS)

Chen, C.; Zou, X.; Tian, M.; Li, J.; Wu, W.; Song, Y.; Dai, W.; Yang, B.

2017-11-01

In order to solve the automation of 3D indoor mapping task, a low cost multi-sensor robot laser scanning system is proposed in this paper. The multiple-sensor robot laser scanning system includes a panorama camera, a laser scanner, and an inertial measurement unit and etc., which are calibrated and synchronized together to achieve simultaneously collection of 3D indoor data. Experiments are undertaken in a typical indoor scene and the data generated by the proposed system are compared with ground truth data collected by a TLS scanner showing an accuracy of 99.2% below 0.25 meter, which explains the applicability and precision of the system in indoor mapping applications.

Multisensor data fusion for IED threat detection

NASA Astrophysics Data System (ADS)

Mees, Wim; Heremans, Roel

2012-10-01

In this paper we present the multi-sensor registration and fusion algorithms that were developed for a force protection research project in order to detect threats against military patrol vehicles. The fusion is performed at object level, using a hierarchical evidence aggregation approach. It first uses expert domain knowledge about the features used to characterize the detected threats, that is implemented in the form of a fuzzy expert system. The next level consists in fusing intra-sensor and inter-sensor information. Here an ordered weighted averaging operator is used. The object level fusion between candidate threats that are detected asynchronously on a moving vehicle by sensors with different imaging geometries, requires an accurate sensor to world coordinate transformation. This image registration will also be discussed in this paper.

Optical processing for landmark identification

NASA Technical Reports Server (NTRS)

Casasent, D.; Luu, T. K.

1981-01-01

A study of optical pattern recognition techniques, available components and airborne optical systems for use in landmark identification was conducted. A data base of imagery exhibiting multisensor, seasonal, snow and fog cover, exposure, and other differences was assembled. These were successfully processed in a scaling optical correlator using weighted matched spatial filter synthesis. Distinctive data classes were defined and a description of the data (with considerable input information and content information) emerged from this study. It has considerable merit with regard to the preprocessing needed and the image difference categories advanced. A optical pattern recognition airborne applications was developed, assembled and demontrated. It employed a laser diode light source and holographic optical elements in a new lensless matched spatial filter architecture with greatly reduced size and weight, as well as component positioning toleranced.

Falling Person Detection Using Multi-Sensor Signal Processing

NASA Astrophysics Data System (ADS)

Toreyin, B. Ugur; Soyer, A. Birey; Onaran, Ibrahim; Cetin, E. Enis

2007-12-01

Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.

Improved particle swarm optimization algorithm for android medical care IOT using modified parameters.

PubMed

Sung, Wen-Tsai; Chiang, Yen-Chun

2012-12-01

This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

Development of ECT/UT inspection system for bottom mounted instrumentation nozzle of PWR reactor vessels

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

Tanaka, H.; Fukui, S.; Iwahashi, Y.

1994-12-31

The development of inspection technique and tool for Bottom Mounted Instrument (BMI) nozzle of PWR plant was performed for countermeasure of leakage accident at incore instrument nozzle of Hamaoka-1 (BWR). MHI achieved the following development, of which object was PWR Plant R/V: (1) development of ECT/UT Multi-sensored Probe; (2) development of Inspection System (3) development of Data Processing System. The Inspection System had been functionally tested using full scale mock-up. As the result of the functional test, this system was confirmed to be very effective, and assumed to be hopeful for the actual application on site.

Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain

PubMed Central

Garcia, Gabriel J.; Corrales, Juan A.; Pomares, Jorge; Torres, Fernando

2009-01-01

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors. PMID:22303146

A miniature electronic nose system based on an MWNT-polymer microsensor array and a low-power signal-processing chip.

PubMed

Chiu, Shih-Wen; Wu, Hsiang-Chiu; Chou, Ting-I; Chen, Hsin; Tang, Kea-Tiong

2014-06-01

This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760 μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.

IMM estimator with out-of-sequence measurements

NASA Astrophysics Data System (ADS)

Bar-Shalom, Yaakov; Chen, Huimin

2004-08-01

In multisensor tracking systems that operate in a centralized information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence. In order to avoid either a delay in the output or the need for reordering and reprocessing an entire sequence of measurements, such measurements have to be processed as out-of-sequence measurements (OOSM). Recent work developed procedures for incorporating OOSMs into a Kalman filter (KF). Since the state of the art tracker for real (maneuvering) targets is the Interacting Multiple Model (IMM) estimator, this paper presents the algorithm for incorporating OOSMs into an IMM estimator. Both data association and estimation are considered. Simulation results are presented for two realistic problems using measurements from two airborne GMTI sensors. It is shown that the proposed algorithm for incorporating OOSMs into an IMM estimator yields practically the same performance as the reordering and in-sequence reprocessing of the measurements.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

PubMed Central

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-01-01

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

Multianalyte imaging in one-shot format sensors for natural waters.

PubMed

Lapresta-Fernández, A; Huertas, Rafael; Melgosa, Manuel; Capitán-Vallvey, L F

2009-03-23

A one-shot multisensor based on ionophore-chromoionophore chemistry for optical monitoring of potassium, magnesium and hardness in water is presented. The analytical procedure uses a black and white non-cooled CCD camera for image acquisition of the one-shot multisensor after reaction, followed by data treatment for quantitation using the grey value pixel average from a defined region of interest from each sensing area to build the analytical parameter 1-alpha. In optimised experimental conditions, the procedure shows a large linear range, up to 6 orders using the linearised model and good detection limits: 9.92 x 10(-5)mM, 1.86 x 10(-3)mM and 1.30 x 10(-2)mgL(-1) of CaCO(3) for potassium, magnesium and hardness, respectively. This analysis system exhibits good precision in terms of relative standard deviation (RSD%) from 2.3 to 3.8 for potassium, from 5.0 to 6.8 for magnesium and from 5.4 to 5.9 for hardness. The trueness of this multisensor procedure was demonstrated comparing it with results obtained by a DAD spectrophotometer used as a reference. Finally, it was satisfactorily applied to the analysis of these analytes in miscellaneous samples, such as water and beverage samples from different origins, validating the results against atomic absorption spectrometry (AAS) as the reference procedure.

A-Train Aerosol Observations Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-Sky Estimates

NASA Technical Reports Server (NTRS)

Redemann, J.; Livingston, J.; Shinozuka, Y.; Kacenelenbogen, M.; Russell, P.; LeBlanc, S.; Vaughan, M.; Ferrare, R.; Hostetler, C.; Rogers, R.;

Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles.

PubMed

Xing, Boyang; Zhu, Quanmin; Pan, Feng; Feng, Xiaoxue

2018-05-25

A novel multi-sensor fusion indoor localization algorithm based on ArUco marker is designed in this paper. The proposed ArUco mapping algorithm can build and correct the map of markers online with Grubbs criterion and K-mean clustering, which avoids the map distortion due to lack of correction. Based on the conception of multi-sensor information fusion, the federated Kalman filter is utilized to synthesize the multi-source information from markers, optical flow, ultrasonic and the inertial sensor, which can obtain a continuous localization result and effectively reduce the position drift due to the long-term loss of markers in pure marker localization. The proposed algorithm can be easily implemented in a hardware of one Raspberry Pi Zero and two STM32 micro controllers produced by STMicroelectronics (Geneva, Switzerland). Thus, a small-size and low-cost marker-based localization system is presented. The experimental results show that the speed estimation result of the proposed system is better than Px4flow, and it has the centimeter accuracy of mapping and positioning. The presented system not only gives satisfying localization precision, but also has the potential to expand other sensors (such as visual odometry, ultra wideband (UWB) beacon and lidar) to further improve the localization performance. The proposed system can be reliably employed in Micro Aerial Vehicle (MAV) visual localization and robotics control.

Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2014-09-02

Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers. Copyright © 2014 Elsevier B.V. All rights reserved.

Automatic parameter selection for feature-based multi-sensor image registration

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

2006-05-01

Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

PubMed

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-08-04

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

Development of a MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the NSRDB

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

Maclaurin, Galen; Sengupta, Manajit; Xie, Yu

A significant source of bias in the transposition of global horizontal irradiance to plane-of-array (POA) irradiance arises from inaccurate estimations of surface albedo. The current physics-based model used to produce the National Solar Radiation Database (NSRDB) relies on model estimations of surface albedo from a reanalysis climatalogy produced at relatively coarse spatial resolution compared to that of the NSRDB. As an input to spectral decomposition and transposition models, more accurate surface albedo data from remotely sensed imagery at finer spatial resolutions would improve accuracy in the final product. The National Renewable Energy Laboratory (NREL) developed an improved white-sky (bi-hemispherical reflectance)more » broadband (0.3-5.0 ..mu..m) surface albedo data set for processing the NSRDB from two existing data sets: a gap-filled albedo product and a daily snow cover product. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites have provided high-quality measurements of surface albedo at 30 arc-second spatial resolution and 8-day temporal resolution since 2001. The high spatial and temporal resolutions and the temporal coverage of the MODIS sensor will allow for improved modeling of POA irradiance in the NSRDB. However, cloud and snow cover interfere with MODIS observations of ground surface albedo, and thus they require post-processing. The MODIS production team applied a gap-filling methodology to interpolate observations obscured by clouds or ephemeral snow. This approach filled pixels with ephemeral snow cover because the 8-day temporal resolution is too coarse to accurately capture the variability of snow cover and its impact on albedo estimates. However, for this project, accurate representation of daily snow cover change is important in producing the NSRDB. Therefore, NREL also used the Integrated Multisensor Snow and Ice Mapping System data set, which provides daily snow cover observations of the Northern Hemisphere for the temporal extent of the NSRDB (1998-2015). We provide a review of validation studies conducted on these two products and describe the methodology developed by NREL to remap the data products to the NSRDB grid and integrate them into a seamless daily data set.« less

Accuracy Assessment of Professional Grade Unmanned Systems for High Precision Airborne Mapping

NASA Astrophysics Data System (ADS)

Mostafa, M. M. R.

2017-08-01

Recently, sophisticated multi-sensor systems have been implemented on-board modern Unmanned Aerial Systems. This allows for producing a variety of mapping products for different mapping applications. The resulting accuracies match the traditional well engineered manned systems. This paper presents the results of a geometric accuracy assessment project for unmanned systems equipped with multi-sensor systems for direct georeferencing purposes. There are a number of parameters that either individually or collectively affect the quality and accuracy of a final airborne mapping product. This paper focuses on identifying and explaining these parameters and their mutual interaction and correlation. Accuracy Assessment of the final ground object positioning accuracy is presented through real-world 8 flight missions that were flown in Quebec, Canada. The achievable precision of map production is addressed in some detail.

Multi-Sensor Distributive On-line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

NASA Astrophysics Data System (ADS)

Teng, W.; Berrick, S.; Leptoukh, G.; Liu, Z.; Rui, H.; Pham, L.; Shen, S.; Zhu, T.

2004-12-01

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Archive Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide precipitation and other satellite data products and services. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical parameters, area of interest, and time period; and the system generates an output on screen in a matter of seconds. The currently available output options are (1) area plot - averaged or accumulated over any available data period for any rectangular area; (2) time plot - time series averaged over any rectangular area; (3) Hovmoller plots - longitude-time and latitude-time plots; (4) ASCII output - for all plot types; and (5) image animation - for area plot. Planned output options for the near-future include correlation plots and GIS-compatible outputs. The AIS will enable the remote, interoperable access to distributed data, because the current Giovanni implementation incorporates the GrADS-DODS Server (GDS), a stable, secure data server that provides subsetting and analysis services across the Internet, for any GrADS-readable data set. The subsetting capability allows users to retrieve a specified spatial region from a large data set, eliminating the need to first download the entire data set. The analysis capability allows users to retrieve the results of an operation applied to one or more data sets on the server. The Giovanni-GDS technology allows the serving of data, through convenient on-line analysis tools, from any location where GDS and a few GrADS scripts are installed. The GES-DISC implementation of this technology is unique in the way it enables multi-sensor processing and analysis.

A Self-Sustained Wireless Multi-Sensor Platform Integrated with Printable Organic Sensors for Indoor Environmental Monitoring

PubMed Central

Wu, Chun-Chang; Chuang, Wen-Yu; Wu, Ching-Da; Su, Yu-Cheng; Huang, Yung-Yang; Huang, Yang-Jing; Peng, Sheng-Yu; Yu, Shih-An; Lin, Chih-Ting; Lu, Shey-Shi

2017-01-01

A self-sustained multi-sensor platform for indoor environmental monitoring is proposed in this paper. To reduce the cost and power consumption of the sensing platform, in the developed platform, organic materials of PEDOT:PSS and PEDOT:PSS/EB-PANI are used as the sensing films for humidity and CO2 detection, respectively. Different from traditional gas sensors, these organic sensing films can operate at room temperature without heating processes or infrared transceivers so that the power consumption of the developed humidity and the CO2 sensors can be as low as 10 μW and 5 μW, respectively. To cooperate with these low-power sensors, a Complementary Metal-Oxide-Semiconductor (CMOS) system-on-chip (SoC) is designed to amplify and to read out multiple sensor signals with low power consumption. The developed SoC includes an analog-front-end interface circuit (AFE), an analog-to-digital convertor (ADC), a digital controller and a power management unit (PMU). Scheduled by the digital controller, the sensing circuits are power gated with a small duty-cycle to reduce the average power consumption to 3.2 μW. The designed PMU converts the power scavenged from a dye sensitized solar cell (DSSC) module into required supply voltages for SoC circuits operation under typical indoor illuminance conditions. To our knowledge, this is the first multiple environmental parameters (Temperature/CO2/Humidity) sensing platform that demonstrates a true self-powering functionality for long-term operations. PMID:28353680

Physical property data from the ICDP-USGS Eyreville cores A and B, Chesapeake Bay impact structure, Virginia, USA, acquired using a multisensor core logger

USGS Publications Warehouse

Pierce, H.A.; Murray, J.B.

2009-01-01

The International Continental Scientific Drilling Program (ICDP) and the U.S. Geological Survey (USGS) drilled three core holes to a composite depth of 1766 m within the moat of the Chesapeake Bay impact structure. Core recovery rates from the drilling were high (??90%), but problems with core hole collapse limited the geophysical downhole logging to natural-gamma and temperature logs. To supplement the downhole logs, ??5% of the Chesapeake Bay impact structure cores was processed through the USGS GeoTek multisensor core logger (MSCL) located in Menlo Park, California. The measured physical properties included core thickness (cm), density (g cm-3), P-wave velocity (m s-1), P-wave amplitude (%), magnetic susceptibility (cgs), and resistivity (ohm-m). Fractional porosity was a secondary calculated property. The MSCL data-sampling interval for all core sections was 1 cm longitudinally. Photos of each MSCL sampled core section were imbedded with the physical property data for direct comparison. These data have been used in seismic, geologic, thermal history, magnetic, and gravity models of the Chesapeake Bay impact structure. Each physical property curve has a unique signature when viewed over the full depth of the Chesapeake Bay impact structure core holes. Variations in the measured properties reflect differences in pre-impact target-rock lithologies and spatial variations in impact-related deformation during late-stage crater collapse and ocean resurge. ?? 2009 The Geological Society of America.

A Self-Sustained Wireless Multi-Sensor Platform Integrated with Printable Organic Sensors for Indoor Environmental Monitoring.

PubMed

Wu, Chun-Chang; Chuang, Wen-Yu; Wu, Ching-Da; Su, Yu-Cheng; Huang, Yung-Yang; Huang, Yang-Jing; Peng, Sheng-Yu; Yu, Shih-An; Lin, Chih-Ting; Lu, Shey-Shi

2017-03-29

A self-sustained multi-sensor platform for indoor environmental monitoring is proposed in this paper. To reduce the cost and power consumption of the sensing platform, in the developed platform, organic materials of PEDOT:PSS and PEDOT:PSS/EB-PANI are used as the sensing films for humidity and CO₂ detection, respectively. Different from traditional gas sensors, these organic sensing films can operate at room temperature without heating processes or infrared transceivers so that the power consumption of the developed humidity and the CO₂ sensors can be as low as 10 μW and 5 μW, respectively. To cooperate with these low-power sensors, a Complementary Metal-Oxide-Semiconductor (CMOS) system-on-chip (SoC) is designed to amplify and to read out multiple sensor signals with low power consumption. The developed SoC includes an analog-front-end interface circuit (AFE), an analog-to-digital convertor (ADC), a digital controller and a power management unit (PMU). Scheduled by the digital controller, the sensing circuits are power gated with a small duty-cycle to reduce the average power consumption to 3.2 μW. The designed PMU converts the power scavenged from a dye sensitized solar cell (DSSC) module into required supply voltages for SoC circuits operation under typical indoor illuminance conditions. To our knowledge, this is the first multiple environmental parameters (Temperature/CO₂/Humidity) sensing platform that demonstrates a true self-powering functionality for long-term operations.

Multi-Sensor Observations of Earthquake Related Atmospheric Signals over Major Geohazard Validation Sites

NASA Technical Reports Server (NTRS)

Ouzounov, D.; Pulinets, S.; Davindenko, D.; Hattori, K.; Kafatos, M.; Taylor, P.

2012-01-01

We are conducting a scientific validation study involving multi-sensor observations in our investigation of phenomena preceding major earthquakes. Our approach is based on a systematic analysis of several atmospheric and environmental parameters, which we found, are associated with the earthquakes, namely: thermal infrared radiation, outgoing long-wavelength radiation, ionospheric electron density, and atmospheric temperature and humidity. For first time we applied this approach to selected GEOSS sites prone to earthquakes or volcanoes. This provides a new opportunity to cross validate our results with the dense networks of in-situ and space measurements. We investigated two different seismic aspects, first the sites with recent large earthquakes, viz.- Tohoku-oki (M9, 2011, Japan) and Emilia region (M5.9, 2012,N. Italy). Our retrospective analysis of satellite data has shown the presence of anomalies in the atmosphere. Second, we did a retrospective analysis to check the re-occurrence of similar anomalous behavior in atmosphere/ionosphere over three regions with distinct geological settings and high seismicity: Taiwan, Japan and Kamchatka, which include 40 major earthquakes (M>5.9) for the period of 2005-2009. We found anomalous behavior before all of these events with no false negatives; false positives were less then 10%. Our initial results suggest that multi-instrument space-borne and ground observations show a systematic appearance of atmospheric anomalies near the epicentral area that could be explained by a coupling between the observed physical parameters and earthquake preparation processes.

A model for the distributed storage and processing of large arrays

NASA Technical Reports Server (NTRS)

Mehrota, P.; Pratt, T. W.

1983-01-01

A conceptual model for parallel computations on large arrays is developed. The model provides a set of language concepts appropriate for processing arrays which are generally too large to fit in the primary memories of a multiprocessor system. The semantic model is used to represent arrays on a concurrent architecture in such a way that the performance realities inherent in the distributed storage and processing can be adequately represented. An implementation of the large array concept as an Ada package is also described.

75 FR 13730 - Marine Mammals; File No. 14118

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-23

...) extended fine-scale behavioral ecology studies using multi-sensor data recording packages. Initial efforts..., photography and video both above water and underwater, and collection of sloughed skin. Other animals...

Downsampling Photodetector Array with Windowing

NASA Technical Reports Server (NTRS)

Patawaran, Ferze D.; Farr, William H.; Nguyen, Danh H.; Quirk, Kevin J.; Sahasrabudhe, Adit

2012-01-01

In a photon counting detector array, each pixel in the array produces an electrical pulse when an incident photon on that pixel is detected. Detection and demodulation of an optical communication signal that modulated the intensity of the optical signal requires counting the number of photon arrivals over a given interval. As the size of photon counting photodetector arrays increases, parallel processing of all the pixels exceeds the resources available in current application-specific integrated circuit (ASIC) and gate array (GA) technology; the desire for a high fill factor in avalanche photodiode (APD) detector arrays also precludes this. Through the use of downsampling and windowing portions of the detector array, the processing is distributed between the ASIC and GA. This allows demodulation of the optical communication signal incident on a large photon counting detector array, as well as providing architecture amenable to algorithmic changes. The detector array readout ASIC functions as a parallel-to-serial converter, serializing the photodetector array output for subsequent processing. Additional downsampling functionality for each pixel is added to this ASIC. Due to the large number of pixels in the array, the readout time of the entire photodetector is greater than the time between photon arrivals; therefore, a downsampling pre-processing step is done in order to increase the time allowed for the readout to occur. Each pixel drives a small counter that is incremented at every detected photon arrival or, equivalently, the charge in a storage capacitor is incremented. At the end of a user-configurable counting period (calculated independently from the ASIC), the counters are sampled and cleared. This downsampled photon count information is then sent one counter word at a time to the GA. For a large array, processing even the downsampled pixel counts exceeds the capabilities of the GA. Windowing of the array, whereby several subsets of pixels are designated for processing, is used to further reduce the computational requirements. The grouping of the designated pixel frame as the photon count information is sent one word at a time to the GA, the aggregation of the pixels in a window can be achieved by selecting only the designated pixel counts from the serial stream of photon counts, thereby obviating the need to store the entire frame of pixel count in the gate array. The pixel count se quence from each window can then be processed, forming lower-rate pixel statistics for each window. By having this processing occur in the GA rather than in the ASIC, future changes to the processing algorithm can be readily implemented. The high-bandwidth requirements of a photon counting array combined with the properties of the optical modulation being detected by the array present a unique problem that has not been addressed by current CCD or CMOS sensor array solutions.

Magnetoencephalography with a Cs-based high-sensitivity compact atomic magnetometer

NASA Astrophysics Data System (ADS)

Sheng, Jingwei; Wan, Shuangai; Sun, Yifan; Dou, Rongshe; Guo, Yuhao; Wei, Kequan; He, Kaiyan; Qin, Jie; Gao, Jia-Hong

2017-09-01

In recent years, substantial progress has been made in developing a new generation of magnetoencephalography (MEG) with a spin-exchange relaxation free (SERF)-based atomic magnetometer (AM). An AM employs alkali atoms to detect weak magnetic fields. A compact AM array with high sensitivity is crucial to the design; however, most proposed compact AMs are potassium (K)- or rubidium (Rb)-based with single beam configurations. In the present study, a pump-probe two beam configuration with a Cesium (Cs)-based AM (Cs-AM) is introduced to detect human neuronal magnetic fields. The length of the vapor cell is 4 mm, which can fully satisfy the need of designing a compact sensor array. Compared with state-of-the-art compact AMs, our new Cs-AM has two advantages. First, it can be operated in a SERF regime, requiring much lower heating temperature, which benefits the sensor with a closer distance to scalp due to ease of thermal insulation and less electric heating noise interference. Second, the two-beam configuration in the design can achieve higher sensitivity. It is free of magnetic modulation, which is necessary in one-beam AMs; however, such modulation may cause other interference in multi-channel circumstances. In the frequency band between 10 Hz and 30 Hz, the noise level of the proposed Cs-AM is approximately 10 f T/Hz1/2, which is comparable with state-of-the-art K- or Rb-based compact AMs. The performance of the Cs-AM was verified by measuring human auditory evoked fields (AEFs) in reference to commercial superconducting quantum interference device (SQUID) channels. By using a Cs-AM, we observed a clear peak in AEFs around 100 ms (M100) with a much larger amplitude compared with that of a SQUID, and the temporal profiles of the two devices were in good agreement. The results indicate the possibility of using the compact Cs-AM for MEG recordings, and the current Cs-AM has the potential to be designed for multi-sensor arrays and gradiometers for future neuroscience studies.

Materials Development for Auxiliary Components for Large Compact Mo/Au TES Arrays

NASA Technical Reports Server (NTRS)

Finkbeiner, F. m.; Chervenak, J. A.; Bandler, S. R.; Brekosky, R.; Brown, A. D.; Figueroa-Feliciano, E.; Iyomoto, N.; Kelley, R. L.; Kilbourne, C. A.; Porter, F. S.;

ASIC Readout Circuit Architecture for Large Geiger Photodiode Arrays

NASA Technical Reports Server (NTRS)

Vasile, Stefan; Lipson, Jerold

2012-01-01

The objective of this work was to develop a new class of readout integrated circuit (ROIC) arrays to be operated with Geiger avalanche photodiode (GPD) arrays, by integrating multiple functions at the pixel level (smart-pixel or active pixel technology) in 250-nm CMOS (complementary metal oxide semiconductor) processes. In order to pack a maximum of functions within a minimum pixel size, the ROIC array is a full, custom application-specific integrated circuit (ASIC) design using a mixed-signal CMOS process with compact primitive layout cells. The ROIC array was processed to allow assembly in bump-bonding technology with photon-counting infrared detector arrays into 3-D imaging cameras (LADAR). The ROIC architecture was designed to work with either common- anode Si GPD arrays or common-cathode InGaAs GPD arrays. The current ROIC pixel design is hardwired prior to processing one of the two GPD array configurations, and it has the provision to allow soft reconfiguration to either array (to be implemented into the next ROIC array generation). The ROIC pixel architecture implements the Geiger avalanche quenching, bias, reset, and time to digital conversion (TDC) functions in full-digital design, and uses time domain over-sampling (vernier) to allow high temporal resolution at low clock rates, increased data yield, and improved utilization of the laser beam.

Microphone Array

NASA Astrophysics Data System (ADS)

Bader, Rolf

This chapter deals with microphone arrays. It is arranged according to the different methods available to proceed through the different problems and through the different mathematical methods. After discussing general properties of different array types, such as plane arrays, spherical arrays, or scanning arrays, it proceeds to the signal processing tools that are most used in speech processing. In the third section, backpropagating methods based on the Helmholtz-Kirchhoff integral are discussed, which result in spatial radiation patterns of vibrating bodies or air.

The Use of a Microcomputer Based Array Processor for Real Time Laser Velocimeter Data Processing

NASA Technical Reports Server (NTRS)

Meyers, James F.

1990-01-01

The application of an array processor to laser velocimeter data processing is presented. The hardware is described along with the method of parallel programming required by the array processor. A portion of the data processing program is described in detail. The increase in computational speed of a microcomputer equipped with an array processor is illustrated by comparative testing with a minicomputer.

Research relative to automated multisensor image registration

NASA Technical Reports Server (NTRS)

Kanal, L. N.

1983-01-01

The basic aproaches to image registration are surveyed. Three image models are presented as models of the subpixel problem. A variety of approaches to the analysis of subpixel analysis are presented using these models.

ASPECT (Airborne Spectral Photometric Environmental Collection Technology) Fact Sheet

EPA Pesticide Factsheets

This multi-sensor screening tool provides infrared and photographic images with geospatial, chemical, and radiological data within minutes to support emergency responses, home-land security missions, environmental surveys, and climate monitoring missions.

SAMuS: Service-Oriented Architecture for Multisensor Surveillance in Smart Homes

PubMed Central

Van de Walle, Rik

2014-01-01

The design of a service-oriented architecture for multisensor surveillance in smart homes is presented as an integrated solution enabling automatic deployment, dynamic selection, and composition of sensors. Sensors are implemented as Web-connected devices, with a uniform Web API. RESTdesc is used to describe the sensors and a novel solution is presented to automatically compose Web APIs that can be applied with existing Semantic Web reasoners. We evaluated the solution by building a smart Kinect sensor that is able to dynamically switch between IR and RGB and optimizing person detection by incorporating feedback from pressure sensors, as such demonstrating the collaboration among sensors to enhance detection of complex events. The performance results show that the platform scales for many Web APIs as composition time remains limited to a few hundred milliseconds in almost all cases. PMID:24778579

ArrayBridge: Interweaving declarative array processing with high-performance computing

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

Xing, Haoyuan; Floratos, Sofoklis; Blanas, Spyros

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aimsmore » to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.« less

Towards Simpler Custom and OpenSearch Services for Voluminous NEWS Merged A-Train Data (Invited)

NASA Astrophysics Data System (ADS)

Hua, H.; Fetzer, E.; Braverman, A. J.; Lewis, S.; Henderson, M. L.; Guillaume, A.; Lee, S.; de La Torre Juarez, M.; Dang, H. T.

2010-12-01

To simplify access to large and complex satellite data sets for climate analysis and model verification, we developed web services that is used to study long-term and global-scale trends in climate, water and energy cycle, and weather variability. A related NASA Energy and Water Cycle Study (NEWS) task has created a merged NEWS Level 2 data from multiple instruments in NASA’s A-Train constellation of satellites. We used this data to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for simple and well-designed services so that users can perform analysis in their own familiar computing environments. Custom on-demand services were developed to improve data accessibility of voluminous multi-sensor data. Services enabling geospatial, geographical, and multi-sensor parameter subsets of the data, as well a custom time-averaged Level 3 service will be presented. We will also show how a Level 3Q data reduction approach can be used to help “browse” the voluminous multi-sensor Level 2 data. An OpenSearch capability with full text + space + time search of data products will also be presented as an approach to facilitated interoperability with other data systems. We will present our experiences for improving user usability as well as strategies for facilitating interoperability with other data systems.

Direct Aerosol Radiative Forcing from Combined A-Train Observations - Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-sky Estimates

NASA Astrophysics Data System (ADS)

Redemann, J.; Livingston, J. M.; Shinozuka, Y.; Kacenelenbogen, M. S.; Russell, P. B.; LeBlanc, S. E.; Vaughan, M.; Ferrare, R. A.; Hostetler, C. A.; Rogers, R. R.; Burton, S. P.; Torres, O.; Remer, L. A.; Stier, P.; Schutgens, N.

2014-12-01

We describe a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) retrievals for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the recently released MODIS Collection 6 data for aerosol optical depths derived with the dark target and deep blue algorithms has extended the coverage of the multi-sensor estimates towards higher latitudes. Initial calculations of seasonal clear-sky aerosol radiative forcing based on our multi-sensor aerosol retrievals compare well with over-ocean and top of the atmosphere IPCC-2007 model-based results, and with more recent assessments in the "Climate Change Science Program Report: Atmospheric Aerosol Properties and Climate Impacts" (2009). For the first time, we present comparisons of our multi-sensor aerosol direct radiative forcing estimates to values derived from a subset of models that participated in the latest AeroCom initiative. We discuss the major challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed.

Cooperative multisensor system for real-time face detection and tracking in uncontrolled conditions

NASA Astrophysics Data System (ADS)

Marchesotti, Luca; Piva, Stefano; Turolla, Andrea; Minetti, Deborah; Regazzoni, Carlo S.

2005-03-01

The presented work describes an innovative architecture for multi-sensor distributed video surveillance applications. The aim of the system is to track moving objects in outdoor environments with a cooperative strategy exploiting two video cameras. The system also exhibits the capacity of focusing its attention on the faces of detected pedestrians collecting snapshot frames of face images, by segmenting and tracking them over time at different resolution. The system is designed to employ two video cameras in a cooperative client/server structure: the first camera monitors the entire area of interest and detects the moving objects using change detection techniques. The detected objects are tracked over time and their position is indicated on a map representing the monitored area. The objects" coordinates are sent to the server sensor in order to point its zooming optics towards the moving object. The second camera tracks the objects at high resolution. As well as the client camera, this sensor is calibrated and the position of the object detected on the image plane reference system is translated in its coordinates referred to the same area map. In the map common reference system, data fusion techniques are applied to achieve a more precise and robust estimation of the objects" track and to perform face detection and tracking. The work novelties and strength reside in the cooperative multi-sensor approach, in the high resolution long distance tracking and in the automatic collection of biometric data such as a person face clip for recognition purposes.

From Multi-Sensors Observations Towards Cross-Disciplinary Study of Pre-Earthquake Signals. What have We Learned from the Tohoku Earthquake?

NASA Technical Reports Server (NTRS)

Ouzounov, D.; Pulinets, S.; Papadopoulos, G.; Kunitsyn, V.; Nesterov, I.; Hayakawa, M.; Mogi, K.; Hattori, K.; Kafatos, M.; Taylor, P.

2012-01-01

The lessons we have learned from the Great Tohoku EQ (Japan, 2011) how this knowledge will affect our future observation and analysis is the main focus of this presentation.We present multi-sensors observations and multidisciplinary research in our investigation of phenomena preceding major earthquakes. These observations revealed the existence of atmospheric and ionospheric phenomena occurring prior to theM9.0 Tohoku earthquake of March 11, 2011, which indicates s new evidence of a distinct coupling between the lithosphere and atmosphere/ionosphere, as related to underlying tectonic activity. Similar results have been reported before the catastrophic events in Chile (M8.8, 2010), Italy (M6.3, 2009) and Sumatra (M9.3, 2004). For the Tohoku earthquake, our analysis shows a synergy between several independent observations characterizing the state of the lithosphere /atmosphere coupling several days before the onset of the earthquakes, namely: (i) Foreshock sequence change (rate, space and time); (ii) Outgoing Long wave Radiation (OLR) measured at the top of the atmosphere; and (iii) Anomalous variations of ionospheric parameters revealed by multi-sensors observations. We are presenting a cross-disciplinary analysis of the observed pre-earthquake anomalies and will discuss current research in the detection of these signals in Japan. We expect that our analysis will shed light on the underlying physics of pre-earthquake signals associated with some of the largest earthquake events

Electronic nose for the identification of pig feeding and ripening time in Iberian hams.

PubMed

Santos, J P; García, M; Aleixandre, M; Horrillo, M C; Gutiérrez, J; Sayago, I; Fernández, M J; Arés, L

2004-03-01

An electronic nose system to control the processing of dry-cured Iberian ham is presented. The sensors involved are tin oxide semiconductors thin films. They were prepared by RF sputtering. Some of the sensors were doped with metal catalysts as Pt and Pd, in order to improve the selectivity of the sensors. The multisensor with 16 semiconductor sensors, gave different responses from two types of dry-cured Iberian hams which differ in the feeding and curing time. The data has been analysed using the PCA (principal component analysis) and backpropagation and probabilistic neural networks. The analysis shows that different types of Iberian ham can be discriminated and identified successfully.

The determination of measures of software reliability

NASA Technical Reports Server (NTRS)

Maxwell, F. D.; Corn, B. C.

1978-01-01

Measurement of software reliability was carried out during the development of data base software for a multi-sensor tracking system. The failure ratio and failure rate were found to be consistent measures. Trend lines could be established from these measurements that provide good visualization of the progress on the job as a whole as well as on individual modules. Over one-half of the observed failures were due to factors associated with the individual run submission rather than with the code proper. Possible application of these findings for line management, project managers, functional management, and regulatory agencies is discussed. Steps for simplifying the measurement process and for use of these data in predicting operational software reliability are outlined.

A Multimodal Database for a Home Remote Medical Care Application

NASA Astrophysics Data System (ADS)

Medjahed, Hamid; Istrate, Dan; Boudy, Jerome; Steenkeste, François; Baldinger, Jean-Louis; Dorizzi, Bernadette

The home remote monitoring systems aim to make a protective contribution to the well being of individuals (patients, elderly persons) requiring moderate amounts of support for independent living spaces, and improving their everyday life. Existing researches of these systems suffer from lack of experimental data and a standard medical database intended for their validation and improvement. This paper presents a multi-sensors environment for acquiring and recording a multimodal medical database, which includes physiological data (cardiac frequency, activity or agitation, posture, fall), environment sounds and localization data. It provides graphical interface functions to manage, process and index these data. The paper focuses on the system implementation, its usage and it points out possibilities for future work.

Risk Identification in a Smart Monitoring System Used to Preserve Artefacts Based on Textile Materials

NASA Astrophysics Data System (ADS)

Diaconescu, V. D.; Scripcariu, L.; Mătăsaru, P. D.; Diaconescu, M. R.; Ignat, C. A.

2018-06-01

Exhibited textile-materials-based artefacts can be affected by the environmental conditions. A smart monitoring system that commands an adaptive automatic environment control system is proposed for indoor exhibition spaces containing various textile artefacts. All exhibited objects are monitored by many multi-sensor nodes containing temperature, relative humidity and light sensors. Data collected periodically from the entire sensor network is stored in a database and statistically processed in order to identify and classify the environment risk. Risk consequences are analyzed depending on the risk class and the smart system commands different control measures in order to stabilize the indoor environment conditions to the recommended values and prevent material degradation.

Design and Fabrication Highlights Enabling a 2 mm, 128 Element Bolometer Array for GISMO

NASA Technical Reports Server (NTRS)

Allen, Christine; Benford, Dominic; Miller, Timothy; Staguhn, Johannes; Wollack, Edward; Moseley, Harvey

2007-01-01

The Backshort-Under-Grid (BUG) superconducting bolometer array architecture is intended to be highly versatile, operating in a large range of wavelengths and background conditions. We have undertaken a three-year program to develop key technologies and processes required to build kilopixel arrays. To validate the basic array design and to demonstrate its applicability for future kilopixel arrays, we have chosen to demonstrate a 128 element bolometer array optimized for 2 mm wavelength using a newly built Goddard instrument, GISMO (Goddard /RAM Superconducting 2-millimeter Observer). The arrays are fabricated using batch wafer processing developed and optimized for high pixel yield, low noise, and high uniformity. The molybdenum-gold superconducting transition edge sensors are fabricated using batch sputter deposition and are patterned using dry etch techniques developed at Goddard. With a detector pitch of 2 mm 8x16 array for GISMO occupies nearly one half of the processing area of a 100 mm silicon-on-insulator starting wafer. Two such arrays are produced from a single wafer along with witness samples for process characterization. To provide thermal isolation for the detector elements, at the end of the process over 90% of the silicon must be removed using deep reactive ion etching techniques. The electrical connections for each bolometer element are patterned on the top edge of the square grid supporting the array. The design considerations unique to GISMO, key fabrication challenges, and laboratory experimental results will be presented.

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

Breath alcohol, multisensor arrays, and electronic noses

NASA Astrophysics Data System (ADS)

Paulsson, Nils; Winquist, Fredrik

1997-01-01

The concept behind a volatile compound mapper, or electronic nose, is to use the combination of multiple gas sensors and pattern recognition techniques to detect and quantify substances in gas mixtures. There are several different kinds of sensors which have been developed during recent years of which the base techniques are conducting polymers, piezo electrical crystals and solid state devices. In this work we have used a combination of gas sensitive field effect devices and semiconducting metal oxides. The most useful pattern recognition routine was found to be ANNs, which is a mathematical approximation of the human neural network. The aim of this work is to evaluate the possibility of using electronic noses in field instruments to detect drugs, arson residues, explosives etc. As a test application we have chosen breath alcohol measurements. There are several reasons for this. Breath samples are a quite complex mixture contains between 200 and 300 substances at trace levels. The alcohol level is low but still possible to handle. There are needs for replacing large and heavy mobile instruments with smaller devices. Current instrumentation is rather sensitive to interfering substances. The work so far has dealt with sampling, how to introduce ethanol and other substances in the breath, correlation measurements between the electronic nose and headspace GC, and how to evaluate the sensor signals.

Site characterization at the Rabbit Valley Geophysical Performance Evaluation Range

NASA Astrophysics Data System (ADS)

Koppenjan, S.,; Martinez, M.

The United States Department of Energy (US DOE) is developing a Geophysical Performance Evaluation Range (GPER) at Rabbit Valley located 30 miles west of Grand Junction, Colorado. The purpose of the range is to provide a test area for geophysical instruments and survey procedures. Assessment of equipment accuracy and resolution is accomplished through the use of static and dynamic physical models. These models include targets with fixed configurations and targets that can be re-configured to simulate specific specifications. Initial testing (1991) combined with the current tests at the Rabbit Valley GPER will establish baseline data and will provide performance criteria for the development of geophysical technologies and techniques. The US DOE's Special Technologies Laboratory (STL) staff has conducted a Ground Penetrating Radar (GPR) survey of the site with its stepped FM-CW GPR. Additionally, STL contracted several other geophysical tests. These include an airborne GPR survey incorporating a 'chirped' FM-CW GPR system and a magnetic survey with a surfaced-towed magnetometer array unit Ground-based and aerial video and still frame pictures were also acquired. STL compiled and analyzed all of the geophysical maps and created a site characterization database. This paper discusses the results of the multi-sensor geophysical studies performed at Rabbit Valley and the future plans for the site.

Site characterization at the Rabbit Valley Geophysical Performance Evaluation Range

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

Koppenjan, S,; Martinez, M.

1994-06-01

The United States Department of Energy (US DOE) is developing a Geophysical Performance Evaluation Range (GPER) at Rabbit Valley located 30 miles west of Grand Junction, Colorado. The purpose of the range is to provide a test area for geophysical instruments and survey procedures. Assessment of equipment accuracy and resolution is accomplished through the use of static and dynamic physical models. These models include targets with fixed configurations and targets that can be re-configured to simulate specific specifications. Initial testing (1991) combined with the current tests at the Rabbit Valley GPER will establish baseline data and will provide performance criteriamore » for the development of geophysical technologies and techniques. The US DOE`s Special Technologies Laboratory (STL) staff has conducted a Ground Penetrating Radar (GPR) survey of the site with its stepped FM-CW GPR. Additionally, STL contracted several other geophysical tests. These include an airborne GPR survey incorporating a ``chirped`` FM-CW GPR system and a magnetic survey with a surfaced-towed magnetometer array unit Ground-based and aerial video and still frame pictures were also acquired. STL compiled and analyzed all of the geophysical maps and created a site characterization database. This paper discusses the results of the multi-sensor geophysical studies performed at Rabbit Valley and the future plans for the site.« less

Charge coupled devices

NASA Technical Reports Server (NTRS)

Walker, J. W.; Hornbeck, L. J.; Stubbs, D. P.

1977-01-01

The results are presented of a program to design, fabricate, and test CCD arrays suitable for operation in an electron-bombarded mode. These intensified charge coupled devices have potential application to astronomy as photon-counting arrays. The objectives of this program were to deliver arrays of 250 lines of 400 pixels each and some associated electronics. Some arrays were delivered on tube-compatible headers and some were delivered after incorporation in vacuum tubes. Delivery of these devices required considerable improvements to be made in the processing associated with intensified operation. These improvements resulted in a high yield in the thinning process, reproducible results in the accumulation process, elimination of a dark current source in the accumulation process, solution of a number of header related problems, and the identification of a remaining major source of dark current. Two systematic failure modes were identified and protective measures established. The effects of tube processing on the arrays in the delivered ICCDs were determined and are reported along with the characterization data on the arrays.

Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

NASA Technical Reports Server (NTRS)

Navarro, Robert

2006-01-01

The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

A programmable computational image sensor for high-speed vision

NASA Astrophysics Data System (ADS)

Yang, Jie; Shi, Cong; Long, Xitian; Wu, Nanjian

2013-08-01

In this paper we present a programmable computational image sensor for high-speed vision. This computational image sensor contains four main blocks: an image pixel array, a massively parallel processing element (PE) array, a row processor (RP) array and a RISC core. The pixel-parallel PE is responsible for transferring, storing and processing image raw data in a SIMD fashion with its own programming language. The RPs are one dimensional array of simplified RISC cores, it can carry out complex arithmetic and logic operations. The PE array and RP array can finish great amount of computation with few instruction cycles and therefore satisfy the low- and middle-level high-speed image processing requirement. The RISC core controls the whole system operation and finishes some high-level image processing algorithms. We utilize a simplified AHB bus as the system bus to connect our major components. Programming language and corresponding tool chain for this computational image sensor are also developed.

Radar Array Processing of Experimental Data Via the Scan-MUSIC Algorithm

DTIC Science & Technology

2004-06-01

Radar Array Processing of Experimental Data Via the Scan- MUSIC Algorithm by Canh Ly ARL-TR-3135 June 2004...Processing of Experimental Data Via the Scan- MUSIC Algorithm Canh Ly Sensors and Electron Devices Directorate, ARL...NUMBER 5b. GRANT NUMBER 4. TITLE AND SUBTITLE Radar Array Processing of Experimental Data Via the Scan- MUSIC Algorithm 5c. PROGRAM ELEMENT NUMBER 5d

Pseudo-orthogonal frequency coded wireless SAW RFID temperature sensor tags.

PubMed

Saldanha, Nancy; Malocha, Donald C

2012-08-01

SAW sensors are ideal for various wireless, passive multi-sensor applications because they are small, rugged, radiation hard, and offer a wide range of material choices for operation over broad temperature ranges. The readable distance of a tag in a multi-sensor environment is dependent on the insertion loss of the device and the processing gain of the system. Single-frequency code division multiple access (CDMA) tags that are used in high-volume commercial applications must have universal coding schemes and large numbers of codes. The use of a large number of bits at the common center frequency to achieve sufficient code diversity in CDMA tags necessitates reflector banks with >30 dB loss. Orthogonal frequency coding is a spread-spectrum approach that employs frequency and time diversity to achieve enhanced tag properties. The use of orthogonal frequency coded (OFC) SAW tags reduces adjacent reflector interactions for low insertion loss, increased range, complex coding, and system processing gain. This work describes a SAW tag-sensor platform that reduces device loss by implementing long reflector banks with optimized spectral coding. This new pseudo-OFC (POFC) coding is defined and contrasted with the previously defined OFC coding scheme. Auto- and cross-correlation properties of the chips and their relation to reflectivity per strip and reflector length are discussed. Results at 250 MHz of 8-chip OFC and POFC SAW tags will be compared. The key parameters of insertion loss, cross-correlation, and autocorrelation of the two types of frequency-coded tags will be analyzed, contrasted, and discussed. It is shown that coded reflector banks can be achieved with near-zero loss and still maintain good coding properties. Experimental results and results predicted by the coupling of modes model are presented for varying reflector designs and codes. A prototype 915-MHz POFC sensor tag is used as a wireless temperature sensor and the results are shown.

Prospective Validation of Pre-earthquake Atmospheric Signals and Their Potential for Short–term Earthquake Forecasting

NASA Astrophysics Data System (ADS)

Ouzounov, Dimitar; Pulinets, Sergey; Hattori, Katsumi; Lee, Lou; Liu, Tiger; Kafatos, Menas

2015-04-01

We are presenting the latest development in multi-sensors observations of short-term pre-earthquake phenomena preceding major earthquakes. Our challenge question is: "Whether such pre-earthquake atmospheric/ionospheric signals are significant and could be useful for early warning of large earthquakes?" To check the predictive potential of atmospheric pre-earthquake signals we have started to validate anomalous ionospheric / atmospheric signals in retrospective and prospective modes. The integrated satellite and terrestrial framework (ISTF) is our method for validation and is based on a joint analysis of several physical and environmental parameters (Satellite thermal infrared radiation (STIR), electron concentration in the ionosphere (GPS/TEC), radon/ion activities, air temperature and seismicity patterns) that were found to be associated with earthquakes. The science rationale for multidisciplinary analysis is based on concept Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) [Pulinets and Ouzounov, 2011], which explains the synergy of different geospace processes and anomalous variations, usually named short-term pre-earthquake anomalies. Our validation processes consist in two steps: (1) A continuous retrospective analysis preformed over two different regions with high seismicity- Taiwan and Japan for 2003-2009 (2) Prospective testing of STIR anomalies with potential for M5.5+ events. The retrospective tests (100+ major earthquakes, M>5.9, Taiwan and Japan) show STIR anomalous behavior before all of these events with false negatives close to zero. False alarm ratio for false positives is less then 25%. The initial prospective testing for STIR shows systematic appearance of anomalies in advance (1-30 days) to the M5.5+ events for Taiwan, Kamchatka-Sakhalin (Russia) and Japan. Our initial prospective results suggest that our approach show a systematic appearance of atmospheric anomalies, one to several days prior to the largest earthquakes That feature could be further studied and tested for prospective early warnings based on the multi-sensors detection of pre-earthquake atmospheric signals.

Embry-Riddle Aeronautical University multispectral sensor and data fusion laboratory: a model for distributed research and education

NASA Astrophysics Data System (ADS)

McMullen, Sonya A. H.; Henderson, Troy; Ison, David

2017-05-01

The miniaturization of unmanned systems and spacecraft, as well as computing and sensor technologies, has opened new opportunities in the areas of remote sensing and multi-sensor data fusion for a variety of applications. Remote sensing and data fusion historically have been the purview of large government organizations, such as the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), and National Geospatial-Intelligence Agency (NGA) due to the high cost and complexity of developing, fielding, and operating such systems. However, miniaturized computers with high capacity processing capabilities, small and affordable sensors, and emerging, commercially available platforms such as UAS and CubeSats to carry such sensors, have allowed for a vast range of novel applications. In order to leverage these developments, Embry-Riddle Aeronautical University (ERAU) has developed an advanced sensor and data fusion laboratory to research component capabilities and their employment on a wide-range of autonomous, robotic, and transportation systems. This lab is unique in several ways, for example, it provides a traditional campus laboratory for students and faculty to model and test sensors in a range of scenarios, process multi-sensor data sets (both simulated and experimental), and analyze results. Moreover, such allows for "virtual" modeling, testing, and teaching capability reaching beyond the physical confines of the facility for use among ERAU Worldwide students and faculty located around the globe. Although other institutions such as Georgia Institute of Technology, Lockheed Martin, University of Dayton, and University of Central Florida have optical sensor laboratories, the ERAU virtual concept is the first such lab to expand to multispectral sensors and data fusion, while focusing on the data collection and data products and not on the manufacturing aspect. Further, the initiative is a unique effort among Embry-Riddle faculty to develop multi-disciplinary, cross-campus research to facilitate faculty- and student-driven research. Specifically, the ERAU Worldwide Campus, with locations across the globe and delivering curricula online, will be leveraged to provide novel approaches to remote sensor experimentation and simulation. The purpose of this paper and presentation is to present this new laboratory, research, education, and collaboration process.

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).

Advances in Multi-Sensor Scanning and Visualization of Complex Plants: the Utmost Case of a Reactor Building

NASA Astrophysics Data System (ADS)

Hullo, J.-F.; Thibault, G.; Boucheny, C.

2015-02-01

In a context of increased maintenance operations and workers generational renewal, a nuclear owner and operator like Electricité de France (EDF) is interested in the scaling up of tools and methods of "as-built virtual reality" for larger buildings and wider audiences. However, acquisition and sharing of as-built data on a large scale (large and complex multi-floored buildings) challenge current scientific and technical capacities. In this paper, we first present a state of the art of scanning tools and methods for industrial plants with very complex architecture. Then, we introduce the inner characteristics of the multi-sensor scanning and visualization of the interior of the most complex building of a power plant: a nuclear reactor building. We introduce several developments that made possible a first complete survey of such a large building, from acquisition, processing and fusion of multiple data sources (3D laser scans, total-station survey, RGB panoramic, 2D floor plans, 3D CAD as-built models). In addition, we present the concepts of a smart application developed for the painless exploration of the whole dataset. The goal of this application is to help professionals, unfamiliar with the manipulation of such datasets, to take into account spatial constraints induced by the building complexity while preparing maintenance operations. Finally, we discuss the main feedbacks of this large experiment, the remaining issues for the generalization of such large scale surveys and the future technical and scientific challenges in the field of industrial "virtual reality".

Reliability and validity of electrothermometers and associated thermocouples.

PubMed

Jutte, Lisa S; Knight, Kenneth L; Long, Blaine C

2008-02-01

Examine thermocouple model uncertainty (reliability+validity). First, a 3x3 repeated measures design with independent variables electrothermometers and thermocouple model. Second, a 1x3 repeated measures design with independent variable subprobe. Three electrothermometers, 3 thermocouple models, a multi-sensor probe and a mercury thermometer measured a stable water bath. Temperature and absolute temperature differences between thermocouples and a mercury thermometer. Thermocouple uncertainty was greater than manufactures'claims. For all thermocouple models, validity and reliability were better in the Iso-Themex than the Datalogger, but there were no practical differences between models within an electrothermometers. Validity of multi-sensor probes and thermocouples within a probe were not different but were greater than manufacturers'claims. Reliability of multiprobes and thermocouples within a probe were within manufacturers claims. Thermocouple models vary in reliability and validity. Scientists should test and report the uncertainty of their equipment rather than depending on manufactures' claims.

SenseCube—a novel inexpensive wireless multisensor for physics lab experimentations

NASA Astrophysics Data System (ADS)

Mehta, Vedant; Lane, Charles D.

2018-07-01

SenseCube is a multisensor capable of measuring many different real-time events and changes in environment. Most conventional sensors used in introductory-physics labs use their own software and have wires that must be attached to a computer or an alternate device to analyze the data. This makes the standard sensors time consuming, tedious, and space-constricted. SenseCube was developed to overcome these limitations. This research was focused on developing a device that is all-encompassing, cost-effective, wireless, and compact, yet can perform the same tasks as the multiple standard sensors normally used in physics labs. It measures more than twenty distinct types of real-time events and transfers the data via Bluetooth. Both Windows and Mac software were developed so that the data from this device can be retrieved and/or saved on either platform. This paper describes the sensor itself, its development, its capabilities, and its cost comparison with standard sensors.

Information-based approach to performance estimation and requirements allocation in multisensor fusion for target recognition

NASA Astrophysics Data System (ADS)

Harney, Robert C.

1997-03-01

A novel methodology offering the potential for resolving two of the significant problems of implementing multisensor target recognition systems, i.e., the rational selection of a specific sensor suite and optimal allocation of requirements among sensors, is presented. Based on a sequence of conjectures (and their supporting arguments) concerning the relationship of extractable information content to recognition performance of a sensor system, a set of heuristics (essentially a reformulation of Johnson's criteria applicable to all sensor and data types) is developed. An approach to quantifying the information content of sensor data is described. Coupling this approach with the widely accepted Johnson's criteria for target recognition capabilities results in a quantitative method for comparing the target recognition ability of diverse sensors (imagers, nonimagers, active, passive, electromagnetic, acoustic, etc.). Extension to describing the performance of multiple sensors is straightforward. The application of the technique to sensor selection and requirements allocation is discussed.

A mobile ferromagnetic shape detection sensor using a Hall sensor array and magnetic imaging.

PubMed

Misron, Norhisam; Shin, Ng Wei; Shafie, Suhaidi; Marhaban, Mohd Hamiruce; Mailah, Nashiren Farzilah

2011-01-01

This paper presents a mobile Hall sensor array system for the shape detection of ferromagnetic materials that are embedded in walls or floors. The operation of the mobile Hall sensor array system is based on the principle of magnetic flux leakage to describe the shape of the ferromagnetic material. Two permanent magnets are used to generate the magnetic flux flow. The distribution of magnetic flux is perturbed as the ferromagnetic material is brought near the permanent magnets and the changes in magnetic flux distribution are detected by the 1-D array of the Hall sensor array setup. The process for magnetic imaging of the magnetic flux distribution is done by a signal processing unit before it displays the real time images using a netbook. A signal processing application software is developed for the 1-D Hall sensor array signal acquisition and processing to construct a 2-D array matrix. The processed 1-D Hall sensor array signals are later used to construct the magnetic image of ferromagnetic material based on the voltage signal and the magnetic flux distribution. The experimental results illustrate how the shape of specimens such as square, round and triangle shapes is determined through magnetic images based on the voltage signal and magnetic flux distribution of the specimen. In addition, the magnetic images of actual ferromagnetic objects are also illustrated to prove the functionality of mobile Hall sensor array system for actual shape detection. The results prove that the mobile Hall sensor array system is able to perform magnetic imaging in identifying various ferromagnetic materials.

A Mobile Ferromagnetic Shape Detection Sensor Using a Hall Sensor Array and Magnetic Imaging

PubMed Central

Misron, Norhisam; Shin, Ng Wei; Shafie, Suhaidi; Marhaban, Mohd Hamiruce; Mailah, Nashiren Farzilah

2011-01-01

This paper presents a Mobile Hall Sensor Array system for the shape detection of ferromagnetic materials that are embedded in walls or floors. The operation of the Mobile Hall Sensor Array system is based on the principle of magnetic flux leakage to describe the shape of the ferromagnetic material. Two permanent magnets are used to generate the magnetic flux flow. The distribution of magnetic flux is perturbed as the ferromagnetic material is brought near the permanent magnets and the changes in magnetic flux distribution are detected by the 1-D array of the Hall sensor array setup. The process for magnetic imaging of the magnetic flux distribution is done by a signal processing unit before it displays the real time images using a netbook. A signal processing application software is developed for the 1-D Hall sensor array signal acquisition and processing to construct a 2-D array matrix. The processed 1-D Hall sensor array signals are later used to construct the magnetic image of ferromagnetic material based on the voltage signal and the magnetic flux distribution. The experimental results illustrate how the shape of specimens such as square, round and triangle shapes is determined through magnetic images based on the voltage signal and magnetic flux distribution of the specimen. In addition, the magnetic images of actual ferromagnetic objects are also illustrated to prove the functionality of Mobile Hall Sensor Array system for actual shape detection. The results prove that the Mobile Hall Sensor Array system is able to perform magnetic imaging in identifying various ferromagnetic materials. PMID:22346653

Training site statistics from Landsat and Seasat satellite imagery registered to a common map base

NASA Technical Reports Server (NTRS)

Clark, J.

1981-01-01

Landsat and Seasat satellite imagery and training site boundary coordinates were registered to a common Universal Transverse Mercator map base in the Newport Beach area of Orange County, California. The purpose was to establish a spatially-registered, multi-sensor data base which would test the use of Seasat synthetic aperture radar imagery to improve spectral separability of channels used for land use classification of an urban area. Digital image processing techniques originally developed for the digital mosaics of the California Desert and the State of Arizona were adapted to spatially register multispectral and radar data. Techniques included control point selection from imagery and USGS topographic quadrangle maps, control point cataloguing with the Image Based Information System, and spatial and spectral rectifications of the imagery. The radar imagery was pre-processed to reduce its tendency toward uniform data distributions, so that training site statistics for selected Landsat and pre-processed Seasat imagery indicated good spectral separation between channels.

A novel scalable manufacturing process for the production of hydrogel-forming microneedle arrays.

PubMed

Lutton, Rebecca E M; Larrañeta, Eneko; Kearney, Mary-Carmel; Boyd, Peter; Woolfson, A David; Donnelly, Ryan F

2015-10-15

A novel manufacturing process for fabricating microneedle arrays (MN) has been designed and evaluated. The prototype is able to successfully produce 14×14 MN arrays and is easily capable of scale-up, enabling the transition from laboratory to industry and subsequent commercialisation. The method requires the custom design of metal MN master templates to produce silicone MN moulds using an injection moulding process. The MN arrays produced using this novel method was compared with centrifugation, the traditional method of producing aqueous hydrogel-forming MN arrays. The results proved that there was negligible difference between either methods, with each producing MN arrays with comparable quality. Both types of MN arrays can be successfully inserted in a skin simulant. In both cases the insertion depth was approximately 60% of the needle length and the height reduction after insertion was in both cases approximately 3%. Copyright © 2015 Elsevier B.V. All rights reserved.

A novel scalable manufacturing process for the production of hydrogel-forming microneedle arrays

PubMed Central

Lutton, Rebecca E.M.; Larrañeta, Eneko; Kearney, Mary-Carmel; Boyd, Peter; Woolfson, A.David; Donnelly, Ryan F.

2015-01-01

A novel manufacturing process for fabricating microneedle arrays (MN) has been designed and evaluated. The prototype is able to successfully produce 14 × 14 MN arrays and is easily capable of scale-up, enabling the transition from laboratory to industry and subsequent commercialisation. The method requires the custom design of metal MN master templates to produce silicone MN moulds using an injection moulding process. The MN arrays produced using this novel method was compared with centrifugation, the traditional method of producing aqueous hydrogel-forming MN arrays. The results proved that there was negligible difference between either methods, with each producing MN arrays with comparable quality. Both types of MN arrays can be successfully inserted in a skin simulant. In both cases the insertion depth was approximately 60% of the needle length and the height reduction after insertion was in both cases approximately 3%. PMID:26302858

Array signal processing in the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Pham, Timothy T.; Jongeling, Andre P.

2004-01-01

In this paper, we will describe the benefits of arraying and past as well as expected future use of this application. The signal processing aspects of array system are described. Field measurements via actual tracking spacecraft are also presented.

Development of a monolithic ferrite memory array

NASA Technical Reports Server (NTRS)

Heckler, C. H., Jr.; Bhiwandker, N. C.

1972-01-01

The results of the development and testing of ferrite monolithic memory arrays are presented. This development required the synthesis of ferrite materials having special magnetic and physical characteristics and the development of special processes; (1) for making flexible sheets (laminae) of the ferrite composition, (2) for embedding conductors in ferrite, and (3) bonding ferrite laminae together to form a monolithic structure. Major problems encountered in each of these areas and their solutions are discussed. Twenty-two full-size arrays were fabricated and fired during the development of these processes. The majority of these arrays were tested for their memory characteristics as well as for their physical characteristics and the results are presented. The arrays produced during this program meet the essential goals and demonstrate the feasibility of fabricating monolithic ferrite memory arrays by the processes developed.

Effects of Data Quality on the Characterization of Aerosol Properties from Multiple Sensors

NASA Technical Reports Server (NTRS)

Petrenko, Maksym; Ichoku, Charles; Leptoukh, Gregory

2011-01-01

Cross-comparison of aerosol properties between ground-based and spaceborne measurements is an important validation technique that helps to investigate the uncertainties of aerosol products acquired using spaceborne sensors. However, it has been shown that even minor differences in the cross-characterization procedure may significantly impact the results of such validation. Of particular consideration is the quality assurance I quality control (QA/QC) information - an auxiliary data indicating a "confidence" level (e.g., Bad, Fair, Good, Excellent, etc.) conferred by the retrieval algorithms on the produced data. Depending on the treatment of available QA/QC information, a cross-characterization procedure has the potential of filtering out invalid data points, such as uncertain or erroneous retrievals, which tend to reduce the credibility of such comparisons. However, under certain circumstances, even high QA/QC values may not fully guarantee the quality of the data. For example, retrievals in proximity of a cloud might be particularly perplexing for an aerosol retrieval algorithm, resulting in an invalid data that, nonetheless, could be assigned a high QA/QC confidence. In this presentation, we will study the effects of several QA/QC parameters on cross-characterization of aerosol properties between the data acquired by multiple spaceborne sensors. We will utilize the Multi-sensor Aerosol Products Sampling System (MAPSS) that provides a consistent platform for multi-sensor comparison, including collocation with measurements acquired by the ground-based Aerosol Robotic Network (AERONET), The multi-sensor spaceborne data analyzed include those acquired by the Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and CalipsoCALIOP satellite instruments.

The role of multisensor data fusion in neuromuscular control of a sagittal arm with a pair of muscles using actor-critic reinforcement learning method.

PubMed

Golkhou, V; Parnianpour, M; Lucas, C

2004-01-01

In this study, we consider the role of multisensor data fusion in neuromuscular control using an actor-critic reinforcement learning method. The model we use is a single link system actuated by a pair of muscles that are excited with alpha and gamma signals. Various physiological sensor information such as proprioception, spindle sensors, and Golgi tendon organs have been integrated to achieve an oscillatory movement with variable amplitude and frequency, while achieving a stable movement with minimum metabolic cost and coactivation. The system is highly nonlinear in all its physical and physiological attributes. Transmission delays are included in the afferent and efferent neural paths to account for a more accurate representation of the reflex loops. This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by the State-Action-Reward-State-Action (SARSA) method. The Critic will train the Actor by supervisory learning based on previous experiences. The reinforcement signal in SARSA is evaluated based on available alternatives concerning the concept of multisensor data fusion. The effectiveness and the biological plausibility of the present model are demonstrated by several simulations. The system showed excellent tracking capability when we integrated the available sensor information. Addition of a penalty for activation of muscles resulted in much lower muscle coactivation while keeping the movement stable.

Fabricating interlocking support walls, with an adjustable backshort, in a TES bolometer array for far-infrared astronomy

NASA Astrophysics Data System (ADS)

Miller, Timothy M.; Abrahams, John H.; Allen, Christine A.

2006-04-01

We report a fabrication process for deep etching silicon to different depths with a single masking layer, using standard masking and exposure techniques. Using this technique, we have incorporated a deep notch in the support walls of a transition-edge-sensor (TES) bolometer array during the detector back-etch, while simultaneously creating a cavity behind the detector. The notches serve to receive the support beams of a separate component, the Backshort-Under-Grid (BUG), an array of adjustable height quarter-wave backshorts that fill the cavities behind each pixel in the detector array. The backshort spacing, set prior to securing to the detector array, can be controlled from 25 to 300 μm by adjusting only a few process steps. In addition to backshort spacing, the interlocking beams and notches provide positioning and structural support for the ˜1 mm pitch, 8×8 array. This process is being incorporated into developing a TES bolometer array with an adjustable backshort for use in far-infrared astronomy. The masking technique and machining process used to fabricate the interlocking walls will be discussed.

PERKINELMER ELM

EPA Science Inventory

The PerkinElmer Elm (formerly the AirBase CanarIT) is a multi-sensor air quality monitoring device that measures particulate matter (PM), total volatile organic compounds (VOCs), nitrogen dioxide (NO₂), and several other atmospheric components. PM, VOCs, and NO₂

ALMA Array Operations Group process overview

NASA Astrophysics Data System (ADS)

Barrios, Emilio; Alarcon, Hector

2016-07-01

ALMA Science operations activities in Chile are responsibility of the Department of Science Operations, which consists of three groups, the Array Operations Group (AOG), the Program Management Group (PMG) and the Data Management Group (DMG). The AOG includes the Array Operators and have the mission to provide support for science observations, operating safely and efficiently the array. The poster describes the AOG process, management and operational tools.

Computer controlled multisensor thermocouple apparatus for invasive measurement of temperature.

PubMed

Hanus, J; Záhora, J; Volenec, K

1996-01-01

The computer controlled apparatus for invasive measurement of temperature profile of biological systems based on original miniature multithermocouple probe is described in this article. The main properties of measuring system were verified by using the original testing device.

Quantitative multiplex detection of pathogen biomarkers

DOEpatents

Mukundan, Harshini; Xie, Hongzhi; Swanson, Basil I.; Martinez, Jennifer; Grace, Wynne K.

2016-02-09

The present invention addresses the simultaneous detection and quantitative measurement of multiple biomolecules, e.g., pathogen biomarkers through either a sandwich assay approach or a lipid insertion approach. The invention can further employ a multichannel, structure with multi-sensor elements per channel.

Quantitative multiplex detection of pathogen biomarkers

DOEpatents

Mukundan, Harshini; Xie, Hongzhi; Swanson, Basil I; Martinez, Jennifer; Grace, Wynne K

2014-10-14

The present invention addresses the simultaneous detection and quantitative measurement of multiple biomolecules, e.g., pathogen biomarkers through either a sandwich assay approach or a lipid insertion approach. The invention can further employ a multichannel, structure with multi-sensor elements per channel.

The data array, a tool to interface the user to a large data base

NASA Technical Reports Server (NTRS)

Foster, G. H.

1974-01-01

Aspects of the processing of spacecraft data is considered. Use of the data array in a large address space as an intermediate form in data processing for a large scientific data base is advocated. Techniques for efficient indexing in data arrays are reviewed and the data array method for mapping an arbitrary structure onto linear address space is shown. A compromise between the two forms is given. The impact of the data array on the user interface are considered along with implementation.

Modeling Array Stations in SIG-VISA

NASA Astrophysics Data System (ADS)

Ding, N.; Moore, D.; Russell, S.

2013-12-01

We add support for array stations to SIG-VISA, a system for nuclear monitoring using probabilistic inference on seismic signals. Array stations comprise a large portion of the IMS network; they can provide increased sensitivity and more accurate directional information compared to single-component stations. Our existing model assumed that signals were independent at each station, which is false when lots of stations are close together, as in an array. The new model removes that assumption by jointly modeling signals across array elements. This is done by extending our existing Gaussian process (GP) regression models, also known as kriging, from a 3-dimensional single-component space of events to a 6-dimensional space of station-event pairs. For each array and each event attribute (including coda decay, coda height, amplitude transfer and travel time), we model the joint distribution across array elements using a Gaussian process that learns the correlation lengthscale across the array, thereby incorporating information of array stations into the probabilistic inference framework. To evaluate the effectiveness of our model, we perform ';probabilistic beamforming' on new events using our GP model, i.e., we compute the event azimuth having highest posterior probability under the model, conditioned on the signals at array elements. We compare the results from our probabilistic inference model to the beamforming currently performed by IMS station processing.

Multi-Sensors Observations of Pre-Earthquake Signals. What We Learned from the Great Tohoku Earthquake?

NASA Technical Reports Server (NTRS)

Ouzonounov, D.; Pulinets, S.; Papadopoulos, G.; Kunitsyn, V.; Nesterov, I.; Hattori, K.; Kafatos, M.; Taylor, P.

2012-01-01

The lessons learned from the Great Tohoku EQ (Japan, 2011) will affect our future observations and an analysis is the main focus of this presentation. Multi-sensors observations and multidisciplinary research is presented in our study of the phenomena preceding major earthquakes Our approach is based on a systematic analysis of several physical and environmental parameters, which been reported by others in connections with earthquake processes: thermal infrared radiation; temperature; concentration of electrons in the ionosphere; radon/ion activities; and atmospheric temperature/humidity [Ouzounov et al, 2011]. We used the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model, one of several possible paradigms [Pulinets and Ouzounov, 2011] to interpret our observations. We retrospectively analyzed the temporal and spatial variations of three different physical parameters characterizing the state of the atmosphere, ionosphere the ground surface several days before the March 11, 2011 M9 Tohoku earthquake Namely: (i) Outgoing Long wave Radiation (OLR) measured at the top of the atmosphere; (ii) Anomalous variations of ionospheric parameters revealed by multi-sensors observations; and (iii) The change in the foreshock sequence (rate, space and time); Our results show that on March 8th, 2011 a rapid increase of emitted infrared radiation was observed and an anomaly developed near the epicenter with largest value occurring on March 11 at 07.30 LT. The GPS/TEC data indicate an increase and variation in electron density reaching a maximum value on March 8. Starting from this day in the lower ionosphere there was also observed an abnormal TEC variation over the epicenter. From March 3 to 11 a large increase in electron concentration was recorded at all four Japanese ground-based ionosondes, which returned to normal after the main earthquake. We use the Japanese GPS network stations and method of Radio Tomography to study the spatiotemporal structure of ionospheric perturbations, and to distinguish ionospheric responses to processes of EQ preparation against the effects of other factors. The 2-D snapshots of the electron density over Japan showed abnormal increase over the maximum stress during the night, a few hours before the main shock. Our results from recording atmospheric and ionospheric conditions during the earthquake indicate the presence of anomalies in the atmosphere and ionospheres occurring consistently over regions of maximum stress near the epicenter. Due to their long duration (hours and days) and spatial appearance (only over the Sendai region) these results do not appear to be caused by meteorological or magnetic activity. They reveal the existence of atmospheric and ionospheric phenomena occurring prior to the earthquake, which indicates new evidence of a distinct coupling between the lithosphere and atmosphere/ionosphere. Similar results have been reported before the catastrophic events in Chile (M8.8, 2010), Italy (M6.3, 2009) and Sumatra (M9.3, 2004).

CMOS-micromachined, two-dimenisional transistor arrays for neural recording and stimulation.

PubMed

Lin, J S; Chang, S R; Chang, C H; Lu, S C; Chen, H

2007-01-01

In-plane microelectrode arrays have proven to be useful tools for studying the connectivities and the functions of neural tissues. However, seldom microelectrode arrays are monolithically-integrated with signal-processing circuits, without which the maximum number of electrodes is limited by the compromise with routing complexity and interferences. This paper proposes a CMOS-compatible, two-dimensional array of oxide-semiconductor field-effect transistors(OSFETs), capable of both recording and stimulating neuronal activities. The fabrication of the OSFETs not only requires simply die-level, post-CMOS micromachining process, but also retains metal layers for monolithic integration with signal-processing circuits. A CMOS microsystem containing the OSFET arrays and gain-programmable recording circuits has been fabricated and tested. The preliminary testing results are presented and discussed.

Matched Field Processing Based on Least Squares with a Small Aperture Hydrophone Array.

PubMed

Wang, Qi; Wang, Yingmin; Zhu, Guolei

2016-12-30

The receiver hydrophone array is the signal front-end and plays an important role in matched field processing, which usually covers the whole water column from the sea surface to the bottom. Such a large aperture array is very difficult to realize. To solve this problem, an approach called matched field processing based on least squares with a small aperture hydrophone array is proposed, which decomposes the received acoustic fields into depth function matrix and amplitudes of the normal modes at the beginning. Then all the mode amplitudes are estimated using the least squares in the sense of minimum norm, and the amplitudes estimated are used to recalculate the received acoustic fields of the small aperture array, which means the recalculated ones contain more environmental information. In the end, lots of numerical experiments with three small aperture arrays are processed in the classical shallow water, and the performance of matched field passive localization is evaluated. The results show that the proposed method can make the recalculated fields contain more acoustic information of the source, and the performance of matched field passive localization with small aperture array is improved, so the proposed algorithm is proved to be effective.

Matched Field Processing Based on Least Squares with a Small Aperture Hydrophone Array

PubMed Central

Wang, Qi; Wang, Yingmin; Zhu, Guolei

2016-01-01

The receiver hydrophone array is the signal front-end and plays an important role in matched field processing, which usually covers the whole water column from the sea surface to the bottom. Such a large aperture array is very difficult to realize. To solve this problem, an approach called matched field processing based on least squares with a small aperture hydrophone array is proposed, which decomposes the received acoustic fields into depth function matrix and amplitudes of the normal modes at the beginning. Then all the mode amplitudes are estimated using the least squares in the sense of minimum norm, and the amplitudes estimated are used to recalculate the received acoustic fields of the small aperture array, which means the recalculated ones contain more environmental information. In the end, lots of numerical experiments with three small aperture arrays are processed in the classical shallow water, and the performance of matched field passive localization is evaluated. The results show that the proposed method can make the recalculated fields contain more acoustic information of the source, and the performance of matched field passive localization with small aperture array is improved, so the proposed algorithm is proved to be effective. PMID:28042828

Fabrication and evaluation of an improved polymer-based cochlear electrode array for atraumatic insertion.

PubMed

Gwon, Tae Mok; Min, Kyou Sik; Kim, Jin Ho; Oh, Seung Ha; Lee, Ho Sun; Park, Min-Hyun; Kim, Sung June

2015-04-01

An atraumatic cochlear electrode array has become indispensable to high-performance cochlear implants such as electric acoustic stimulation (EAS), wherein the preservation of residual hearing is significant. For an atraumatic implantation, we propose and demonstrate a new improved design of a cochlear electrode array based on liquid crystal polymer (LCP), which can be fabricated by precise batch processes and a thermal lamination process, in contrast to conventional wire-based cochlear electrode arrays. Using a thin-film process of LCP-film-mounted silicon wafer and thermal press lamination, we devise a multi-layered structure with variable layers of LCP films to achieve a sufficient degree of basal rigidity and a flexible tip. A peripheral blind via and self-aligned silicone elastomer molding process can reduce the width of the array. Measuring the insertion and extraction forces in a human scala tympani model, we investigate five human temporal bone insertion trials and record electrically evoked auditory brainstem responses (EABR) acutely in a guinea pig model. The diameters of the finalized electrode arrays are 0.3 mm (tip) and 0.75 mm (base). The insertion force with a displacement of 8 mm from a round window and the maximum extraction force are 2.4 mN and 34.0 mN, respectively. The electrode arrays can be inserted from 360° to 630° without trauma at the basal turn. The EABR data confirm the efficacy of the array. A new design of LCP-based cochlear electrode array for atraumatic implantation is fabricated. Verification indicates that foretells the development of an atraumatic cochlear electrode array and clinical implant.

Array servo scanning micro EDM of 3D micro cavities

NASA Astrophysics Data System (ADS)

Tong, Hao; Li, Yong; Yi, Futing

2011-05-01

Micro electro discharge machining (Micro EDM) is a non-traditional processing technology with the special advantages of low set-up cost and few cutting force in machining any conductive materials regardless of their hardness. As well known, die-sinking EDM is unsuitable for machining the complex 3D micro cavity less than 1mm due to the high-priced fabrication of 3D microelectrode itself and its serous wear during EDM process. In our former study, a servo scanning 3D micro-EDM (3D SSMEDM) method was put forward, and our experiments showed it was available to fabricate complex 3D micro-cavities. In this study, in order to improve machining efficiency and consistency accuracy for array 3D micro-cavities, an array-servo-scanning 3D micro EDM (3D ASSMEDM) method is presented considering the complementary advantages of the 3D SSMEDM and the array micro electrodes with simple cross-section. During 3D ASSMEDM process, the array cavities designed by CAD / CAM system can be batch-manufactured by servo scanning layer by layer using array-rod-like micro tool electrodes, and the axial wear of the array electrodes is compensated in real time by keeping discharge gap. To verify the effectiveness of the 3D ASSMEDM, the array-triangle-micro cavities (side length 630 μm) are batch-manufactured on P-doped silicon by applying the array-micro-electrodes with square-cross-section fabricated by LIGA process. Our exploratory experiment shows that the 3D ASSMEDM provides a feasible approach for the batch-manufacture of 3D array-micro-cavities of conductive materials.

Electrothermal actuators fabricated in four-level planarized surface-miromachined polycrystalline silicon

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

Comtois, J.H.; Michalicek, A.; Barron, C.C.

1997-11-01

This paper presents the results of tests performed on a variety of electrochemical microactuators and arrays of these actuators fabricated in the SUMMiT process at the U.S. Department of Energy`s Sandia National Laboratories. These results are intended to aid designers of thermally actuated mechanisms, and they apply to similar actuators made in other polysilicon MEMS processes such as the MUMPS process. Measurements include force and deflection versus input power, maximum operating frequency, effects of long term operation, and ideal actuator and array geometries for different applications` force requirements. Also, different methods of arraying these actuators together are compared. It ismore » found that a method using rotary joints, enabled by the advanced features of the SUMMiT fabrication process, is the most efficient array design. The design and operation of a thermally actuated stepper motor is explained to illustrate a useful application of these arrays.« less

Multi-sensor sheets based on large-area electronics for advanced structural health monitoring of civil infrastructure.

DOT National Transportation Integrated Search

2014-09-01

Structural Health Monitoring has a great potential to provide valuable information about the actual structural : condition and can help optimize the management activities. However, few eective and robust monitoring technology exist which hinders a...

Field Demonstration of Multi-Sensor Technology for Condition Assessment of Wastewater Collection Systems (Abstract)

EPA Science Inventory

The purpose of the field demonstration program is to gather technically reliable cost and performance information on selected condition assessment technologies under defined field conditions. The selected technologies include zoom camera, focused electrode leak location (FELL), ...

Design and grayscale fabrication of beamfanners in a silicon substrate

NASA Astrophysics Data System (ADS)

Ellis, Arthur Cecil

2001-11-01

This dissertation addresses important first steps in the development of a grayscale fabrication process for multiple phase diffractive optical elements (DOS's) in silicon. Specifically, this process was developed through the design, fabrication, and testing of 1-2 and 1-4 beamfanner arrays for 5-micron illumination. The 1-2 beamfanner arrays serve as a test-of- concept and basic developmental step toward the construction of the 1-4 beamfanners. The beamfanners are 50 microns wide, and have features with dimensions of between 2 and 10 microns. The Iterative Annular Spectrum Approach (IASA) method, developed by Steve Mellin of UAH, and the Boundary Element Method (BEM) are the design and testing tools used to create the beamfanner profiles and predict their performance. Fabrication of the beamfanners required the techniques of grayscale photolithography and reactive ion etching (RIE). A 2-3micron feature size 1-4 silicon beamfanner array was fabricated, but the small features and contact photolithographic techniques available prevented its construction to specifications. A second and more successful attempt was made in which both 1-4 and 1-2 beamfanner arrays were fabricated with a 5-micron minimum feature size. Photolithography for the UAH array was contracted to MEMS-Optical of Huntsville, Alabama. A repeatability study was performed, using statistical techniques, of 14 photoresist arrays and the subsequent RIE process used to etch the arrays in silicon. The variance in selectivity between the 14 processes was far greater than the variance between the individual etched features within each process. Specifically, the ratio of the variance of the selectivities averaged over each of the 14 etch processes to the variance of individual feature selectivities within the processes yielded a significance level below 0.1% by F-test, indicating that good etch-to-etch process repeatability was not attained. One of the 14 arrays had feature etch-depths close enough to design specifications for optical testing, but 5- micron IR illumination of the 1-4 and 1-2 beamfanners yielded no convincing results of beam splitting in the detector plane 340 microns from the surface of the beamfanner array.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

PubMed

Hsu, Yu-Liang; Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-07-15

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

a Multi-Data Source and Multi-Sensor Approach for the 3d Reconstruction and Visualization of a Complex Archaelogical Site: the Case Study of Tolmo de Minateda

NASA Astrophysics Data System (ADS)

Torres-Martínez, J. A.; Seddaiu, M.; Rodríguez-Gonzálvez, P.; Hernández-López, D.; González-Aguilera, D.

2015-02-01

The complexity of archaeological sites hinders to get an integral modelling using the actual Geomatic techniques (i.e. aerial, closerange photogrammetry and terrestrial laser scanner) individually, so a multi-sensor approach is proposed as the best solution to provide a 3D reconstruction and visualization of these complex sites. Sensor registration represents a riveting milestone when automation is required and when aerial and terrestrial dataset must be integrated. To this end, several problems must be solved: coordinate system definition, geo-referencing, co-registration of point clouds, geometric and radiometric homogeneity, etc. Last but not least, safeguarding of tangible archaeological heritage and its associated intangible expressions entails a multi-source data approach in which heterogeneous material (historical documents, drawings, archaeological techniques, habit of living, etc.) should be collected and combined with the resulting hybrid 3D of "Tolmo de Minateda" located models. The proposed multi-data source and multi-sensor approach is applied to the study case of "Tolmo de Minateda" archaeological site. A total extension of 9 ha is reconstructed, with an adapted level of detail, by an ultralight aerial platform (paratrike), an unmanned aerial vehicle, a terrestrial laser scanner and terrestrial photogrammetry. In addition, the own defensive nature of the site (i.e. with the presence of three different defensive walls) together with the considerable stratification of the archaeological site (i.e. with different archaeological surfaces and constructive typologies) require that tangible and intangible archaeological heritage expressions can be integrated with the hybrid 3D models obtained, to analyse, understand and exploit the archaeological site by different experts and heritage stakeholders.

Development of a multi-sensor based urban discharge forecasting system using remotely sensed data: A case study of extreme rainfall in South Korea

NASA Astrophysics Data System (ADS)

Yoon, Sunkwon; Jang, Sangmin; Park, Kyungwon

2017-04-01

Extreme weather due to changing climate is a main source of water-related disasters such as flooding and inundation and its damage will be accelerated somewhere in world wide. To prevent the water-related disasters and mitigate their damage in urban areas in future, we developed a multi-sensor based real-time discharge forecasting system using remotely sensed data such as radar and satellite. We used Communication, Ocean and Meteorological Satellite (COMS) and Korea Meteorological Agency (KMA) weather radar for quantitative precipitation estimation. The Automatic Weather System (AWS) and McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) were used for verification of rainfall accuracy. The optimal Z-R relation was applied the Tropical Z-R relationship (Z=32R1.65), it has been confirmed that the accuracy is improved in the extreme rainfall events. In addition, the performance of blended multi-sensor combining rainfall was improved in 60mm/h rainfall and more strong heavy rainfall events. Moreover, we adjusted to forecast the urban discharge using Storm Water Management Model (SWMM). Several statistical methods have been used for assessment of model simulation between observed and simulated discharge. In terms of the correlation coefficient and r-squared discharge between observed and forecasted were highly correlated. Based on this study, we captured a possibility of real-time urban discharge forecasting system using remotely sensed data and its utilization for real-time flood warning. Acknowledgement This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.

A wireless sensor network deployment for rural and forest fire detection and verification.

PubMed

Lloret, Jaime; Garcia, Miguel; Bri, Diana; Sendra, Sandra

2009-01-01

Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

PubMed Central

Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-01-01

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment. PMID:28714884

A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification

PubMed Central

Lloret, Jaime; Garcia, Miguel; Bri, Diana; Sendra, Sandra

2009-01-01

Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world. PMID:22291533

Brightness field distributions of microlens arrays using micro molding.

PubMed

Cheng, Hsin-Chung; Huang, Chiung-Fang; Lin, Yi; Shen, Yung-Kang

2010-12-20

This study describes the brightness field distributions of microlens arrays fabricated by micro injection molding (μIM) and micro injection-compression molding (μICM). The process for fabricating microlens arrays used room-temperature imprint lithography, photoresist reflow, electroforming, μIM, μICM, and optical properties measurement. Analytical results indicate that the brightness field distribution of the molded microlens arrays generated by μICM is better than those made using μIM. Our results further demonstrate that mold temperature is the most important processing parameter for brightness field distribution of molded microlens arrays made by μIM or μICM.

Comparison of Computational and Experimental Microphone Array Results for an 18%-Scale Aircraft Model

NASA Technical Reports Server (NTRS)

Lockard, David P.; Humphreys, William M.; Khorrami, Mehdi R.; Fares, Ehab; Casalino, Damiano; Ravetta, Patricio A.

2015-01-01

An 18%-scale, semi-span model is used as a platform for examining the efficacy of microphone array processing using synthetic data from numerical simulations. Two hybrid RANS/LES codes coupled with Ffowcs Williams-Hawkings solvers are used to calculate 97 microphone signals at the locations of an array employed in the NASA LaRC 14x22 tunnel. Conventional, DAMAS, and CLEAN-SC array processing is applied in an identical fashion to the experimental and computational results for three different configurations involving deploying and retracting the main landing gear and a part span flap. Despite the short time records of the numerical signals, the beamform maps are able to isolate the noise sources, and the appearance of the DAMAS synthetic array maps is generally better than those from the experimental data. The experimental CLEAN-SC maps are similar in quality to those from the simulations indicating that CLEAN-SC may have less sensitivity to background noise. The spectrum obtained from DAMAS processing of synthetic array data is nearly identical to the spectrum of the center microphone of the array, indicating that for this problem array processing of synthetic data does not improve spectral comparisons with experiment. However, the beamform maps do provide an additional means of comparison that can reveal differences that cannot be ascertained from spectra alone.

An integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes.

PubMed

Chiaradia, Enrico Antonio; Facchi, Arianna; Masseroni, Daniele; Ferrari, Daniele; Bischetti, Gian Battista; Gharsallah, Olfa; Cesari de Maria, Sandra; Rienzner, Michele; Naldi, Ezio; Romani, Marco; Gandolfi, Claudio

2015-09-01

The cultivation of rice, one of the most important staple crops worldwide, has very high water requirements. A variety of irrigation practices are applied, whose pros and cons, both in terms of water productivity and of their effects on the environment, are not completely understood yet. The continuous monitoring of irrigation and rainfall inputs, as well as of soil water dynamics, is a very important factor in the analysis of these practices. At the same time, however, it represents a challenging and costly task because of the complexity of the processes involved, of the difference in nature and magnitude of the driving variables and of the high variety of field conditions. In this paper, we present the prototype of an integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes. The system consists of the following: (1) flow measurement devices for the monitoring of irrigation supply and tailwater drainage; (2) piezometers for groundwater level monitoring; (3) level gauges for monitoring the flooding depth; (4) multilevel tensiometers and moisture sensor clusters to monitor soil water status; (5) eddy covariance station for the estimation of evapotranspiration fluxes and (6) wireless transmission devices and software interface for data transfer, storage and control from remote computer. The system is modular and it is replicable in different field conditions. It was successfully applied over a 2-year period in three experimental plots in Northern Italy, each one with a different water management strategy. In the paper, we present information concerning the different instruments selected, their interconnections and their integration in a common remote control scheme. We also provide considerations and figures on the material and labour costs of the installation and management of the system.

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor

PubMed Central

Li, Zhi; Wei, Henglu; Zhou, Wei; Duan, Zhemin

2018-01-01

Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 ∘C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times. PMID:29393862

a Comparison among Different Optimization Levels in 3d Multi-Sensor Models. a Test Case in Emergency Context: 2016 Italian Earthquake

NASA Astrophysics Data System (ADS)

Chiabrando, F.; Sammartano, G.; Spanò, A.

2017-02-01

In sudden emergency contexts that affect urban centres and built heritage, the latest Geomatics technique solutions must enable the demands of damage documentation, risk assessment, management and data sharing as efficiently as possible, in relation to the danger condition, to the accessibility constraints of areas and to the tight deadlines needs. In recent times, Unmanned Vehicle System (UAV) equipped with cameras are more and more involved in aerial survey and reconnaissance missions, and they are behaving in a very cost-effective way in the direction of 3D documentation and preliminary damage assessment. More and more UAV equipment with low-cost sensors must become, in the future, suitable in every situation of documentation, but above all in damages and uncertainty frameworks. Rapidity in acquisition times and low-cost sensors are challenging marks, and they could be taken into consideration maybe with time spending processing. The paper will analyze and try to classify the information content in 3D aerial and terrestrial models and the importance of metric and non-metric withdrawable information that should be suitable for further uses, as the structural analysis one. The test area is an experience of Team Direct from Politecnico di Torino in centre Italy, where a strong earthquake occurred in August 2016. This study is carried out on a stand-alone damaged building in Pescara del Tronto (AP), with a multi-sensor 3D survey. The aim is to evaluate the contribution of terrestrial and aerial quick documentation by a SLAM based LiDAR and a camera equipped multirotor UAV, for a first reconnaissance inspection and modelling in terms of level of details, metric and non-metric information.

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.

PubMed

Li, Xin; Ou, Xingtao; Li, Zhi; Wei, Henglu; Zhou, Wei; Duan, Zhemin

2018-02-02

Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 ∘ C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

A multi-sensor monitoring system of human physiology and daily activities.

PubMed

Doherty, Sean T; Oh, Paul

2012-04-01

To present the design and pilot test results of a continuous multi-sensor monitoring system of real-world physiological conditions and daily life (activities, travel, exercise, and food consumption), culminating in a Web-based graphical decision-support interface. The system includes a set of wearable sensors wirelessly connected to a "smartphone" with a continuously running software application that compresses and transmits the data to a central server. Sensors include a Global Positioning System (GPS) receiver, electrocardiogram (ECG), three-axis accelerometer, and continuous blood glucose monitor. A food/medicine diary and prompted recall activity diary were also used. The pilot test involved 40 type 2 diabetic patients monitored over a 72-h period. All but three subjects were successfully monitored for the full study period. Smartphones proved to be an effective hub for managing multiple streams of data but required attention to data compression and battery consumption issues. ECG, accelerometer, and blood glucose devices performed adequately as long as subjects wore them. GPS tracking for a full day was feasible, although significant efforts are needed to impute missing data. Activity detection algorithms were successful in identifying activities and trip modes but could benefit by incorporating accelerometer data. The prompted recall diary was an effective tool for augmenting algorithm results, although subjects reported some difficulties with it. The food and medicine diary was completed fully, although end times and medicine dosages were occasionally missing. The unique combination of sensors holds promise for increasing accuracy and reducing burden associated with collecting individual-level activity and physiological data under real-world conditions, but significant data processing issues remain. Such data will provide new opportunities to explore the impacts of human geography and daily lifestyle on health at a fine spatial/temporal scale.

Breadboard linear array scan imager using LSI solid-state technology

NASA Technical Reports Server (NTRS)

Tracy, R. A.; Brennan, J. A.; Frankel, D. G.; Noll, R. E.

1976-01-01

The performance of large scale integration photodiode arrays in a linear array scan (pushbroom) breadboard was evaluated for application to multispectral remote sensing of the earth's resources. The technical approach, implementation, and test results of the program are described. Several self scanned linear array visible photodetector focal plane arrays were fabricated and evaluated in an optical bench configuration. A 1728-detector array operating in four bands (0.5 - 1.1 micrometer) was evaluated for noise, spectral response, dynamic range, crosstalk, MTF, noise equivalent irradiance, linearity, and image quality. Other results include image artifact data, temporal characteristics, radiometric accuracy, calibration experience, chip alignment, and array fabrication experience. Special studies and experimentation were included in long array fabrication and real-time image processing for low-cost ground stations, including the use of computer image processing. High quality images were produced and all objectives of the program were attained.

Integrating Scientific Array Processing into Standard SQL

NASA Astrophysics Data System (ADS)

Misev, Dimitar; Bachhuber, Johannes; Baumann, Peter

2014-05-01

We live in a time that is dominated by data. Data storage is cheap and more applications than ever accrue vast amounts of data. Storing the emerging multidimensional data sets efficiently, however, and allowing them to be queried by their inherent structure, is a challenge many databases have to face today. Despite the fact that multidimensional array data is almost always linked to additional, non-array information, array databases have mostly developed separately from relational systems, resulting in a disparity between the two database categories. The current SQL standard and SQL DBMS supports arrays - and in an extension also multidimensional arrays - but does so in a very rudimentary and inefficient way. This poster demonstrates the practicality of an SQL extension for array processing, implemented in a proof-of-concept multi-faceted system that manages a federation of array and relational database systems, providing transparent, efficient and scalable access to the heterogeneous data in them.

A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays

NASA Technical Reports Server (NTRS)

Brooks, Thomas F.; Humphreys, William M.

2006-01-01

Current processing of acoustic array data is burdened with considerable uncertainty. This study reports an original methodology that serves to demystify array results, reduce misinterpretation, and accurately quantify position and strength of acoustic sources. Traditional array results represent noise sources that are convolved with array beamform response functions, which depend on array geometry, size (with respect to source position and distributions), and frequency. The Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) method removes beamforming characteristics from output presentations. A unique linear system of equations accounts for reciprocal influence at different locations over the array survey region. It makes no assumption beyond the traditional processing assumption of statistically independent noise sources. The full rank equations are solved with a new robust iterative method. DAMAS is quantitatively validated using archival data from a variety of prior high-lift airframe component noise studies, including flap edge/cove, trailing edge, leading edge, slat, and calibration sources. Presentations are explicit and straightforward, as the noise radiated from a region of interest is determined by simply summing the mean-squared values over that region. DAMAS can fully replace existing array processing and presentations methodology in most applications. It appears to dramatically increase the value of arrays to the field of experimental acoustics.

Geostationary Lightning Mapper for GOES-R

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2007-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Parallel processing in a host plus multiple array processor system for radar

NASA Technical Reports Server (NTRS)

Barkan, B. Z.

1983-01-01

Host plus multiple array processor architecture is demonstrated to yield a modular, fast, and cost-effective system for radar processing. Software methodology for programming such a system is developed. Parallel processing with pipelined data flow among the host, array processors, and discs is implemented. Theoretical analysis of performance is made and experimentally verified. The broad class of problems to which the architecture and methodology can be applied is indicated.

SAR processing on the MPP

NASA Technical Reports Server (NTRS)

Batcher, K. E.; Eddey, E. E.; Faiss, R. O.; Gilmore, P. A.

1981-01-01

The processing of synthetic aperture radar (SAR) signals using the massively parallel processor (MPP) is discussed. The fast Fourier transform convolution procedures employed in the algorithms are described. The MPP architecture comprises an array unit (ARU) which processes arrays of data; an array control unit which controls the operation of the ARU and performs scalar arithmetic; a program and data management unit which controls the flow of data; and a unique staging memory (SM) which buffers and permutes data. The ARU contains a 128 by 128 array of bit-serial processing elements (PE). Two-by-four surarrays of PE's are packaged in a custom VLSI HCMOS chip. The staging memory is a large multidimensional-access memory which buffers and permutes data flowing with the system. Efficient SAR processing is achieved via ARU communication paths and SM data manipulation. Real time processing capability can be realized via a multiple ARU, multiple SM configuration.

PROGRAM ASPECT - FOR REMOTE SENSING OF AIRBORNE PLUMES

EPA Science Inventory

The SAFEGUARD program is a multi-sensor program for the detection and imaging of chemical plumes and vapors. The system is composed of an airborne sensor suite including an infrared line scanner and a high-speed fourier transform infrared spectrometer. Both systems are integrat...

Military Performance and Health Monitoring in Extreme Environments

DTIC Science & Technology

2009-10-01

radiation and wind to give a true temperature reading . At high ambient temperatures, in particular in combination with solar radiation, objects may...Equivital multi-sensor unit enabling the real-time, parallel and continuous assessment of EKG (and heart rate), respiration (and respiration rate), skin

Multi-sensor Improved Sea Surface Temperature (MISST) for GODAE

DTIC Science & Technology

2007-09-30

NAVOCEANO has improved on its methodology to add retrieval error information to the US Navy operational data stream. Quantitative estimates of...hycom.rsmas.miami.edu/ “ POSITIV : Prototype Operational System – ISAR – Temperature Instrumentation for the VOS fleet” CIRA/CSU Joint Hurricane Testbed

Wide Area UXO Screening with the Multi-Sensor Fixed-Wing Airborne System MARS

DTIC Science & Technology

2008-02-01

snakes, lizards, and spiders may contain sufficient poison to warrant medical attention. In addition, ticks can spread Rocky Mountain spotted fever ...is extremely serious • Systemic hypothermia manifests itself in five stages of symptoms, including: (1) shivering ; (2) apathy, listlessness

A MULTI-SENSOR APPROACH FOR MONITORING RIVER CHEMICAL TANK BARGE EMISSIONS

EPA Science Inventory

The nation’s waterways are critical avenues for transporting petroleum products and chemicals. These chemicals are often volatile and emissions from the tanker barges carrying these products are a problem. Large population centers exist along the routes of these tank barges, crea...

Transmission Line Security Monitor

ScienceCinema

None

2017-12-09

The Transmission Line Security Monitor is a multi-sensor monitor that mounts directly on high-voltage transmission lines to detect, characterize and communicate terrorist activity, human tampering and threatening conditions around support towers. For more information about INL's critical infrastructure protection research, visit http://www.facebook.com/idahonationallaboratory.

Testing and evaluation of tactical electro-optical sensors

NASA Astrophysics Data System (ADS)

Middlebrook, Christopher T.; Smith, John G.

2002-07-01

As integrated electro-optical sensor payloads (multi- sensors) comprised of infrared imagers, visible imagers, and lasers advance in performance, the tests and testing methods must also advance in order to fully evaluate them. Future operational requirements will require integrated sensor payloads to perform missions at further ranges and with increased targeting accuracy. In order to meet these requirements sensors will require advanced imaging algorithms, advanced tracking capability, high-powered lasers, and high-resolution imagers. To meet the U.S. Navy's testing requirements of such multi-sensors, the test and evaluation group in the Night Vision and Chemical Biological Warfare Department at NAVSEA Crane is developing automated testing methods, and improved tests to evaluate imaging algorithms, and procuring advanced testing hardware to measure high resolution imagers and line of sight stabilization of targeting systems. This paper addresses: descriptions of the multi-sensor payloads tested, testing methods used and under development, and the different types of testing hardware and specific payload tests that are being developed and used at NAVSEA Crane.

Deep learning decision fusion for the classification of urban remote sensing data

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Hyperspectral landcover classification for the Yakima Training Center, Yakima, Washington

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

Steinmaus, K.L.; Perry, E.M.; Petrie, G.M.

1998-04-01

The US Department of Energy`s (DOE`s) Pacific Northwest National Laboratory (PNNL) was tasked in FY97-98 to conduct a multisensor feature extraction project for the Terrain Modeling Project Office (TMPO) of the National Imagery and Mapping Agency (NIMA). The goal of this research is the development of near-autonomous methods to remotely classify and characterize regions of military interest, in support of the TMPO of NIMA. These methods exploit remotely sensed datasets including hyperspectral (HYDICE) imagery, near-infrared and thermal infrared (Daedalus 3600), radar, and terrain datasets. The study site for this project is the US Army`s Yakima Training Center (YTC), a 326,741-acremore » training area located near Yakima, Washington. Two study areas at the YTC were selected to conduct and demonstrate multisensor feature extraction, the 2-km x 2-km Cantonment Area and the 3-km x 3-km Choke Point area. Classification of the Cantonment area afforded a comparison of classification results at different scales.« less

A High Performance Computing Study of a Scalable FISST-Based Approach to Multi-Target, Multi-Sensor Tracking

NASA Astrophysics Data System (ADS)

Hussein, I.; Wilkins, M.; Roscoe, C.; Faber, W.; Chakravorty, S.; Schumacher, P.

2016-09-01

Finite Set Statistics (FISST) is a rigorous Bayesian multi-hypothesis management tool for the joint detection, classification and tracking of multi-sensor, multi-object systems. Implicit within the approach are solutions to the data association and target label-tracking problems. The full FISST filtering equations, however, are intractable. While FISST-based methods such as the PHD and CPHD filters are tractable, they require heavy moment approximations to the full FISST equations that result in a significant loss of information contained in the collected data. In this paper, we review Smart Sampling Markov Chain Monte Carlo (SSMCMC) that enables FISST to be tractable while avoiding moment approximations. We study the effect of tuning key SSMCMC parameters on tracking quality and computation time. The study is performed on a representative space object catalog with varying numbers of RSOs. The solution is implemented in the Scala computing language at the Maui High Performance Computing Center (MHPCC) facility.

Estimating Precipitation Susceptibility in Warm Marine Clouds Using Multi-sensor Aerosol and Cloud Products from A-Train Satellites

NASA Astrophysics Data System (ADS)

Bai, H.; Gong, C.; Wang, M.; Zhang, Z.

2017-12-01

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from CALIPSO, CloudSat, MODIS, and AMSR-E from June 2006 to April 2011 are analyzed to estimate precipitation susceptibility (including precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR) in warm marine clouds. Our results show that SPOP demonstrates relatively robust features throughout independent LWP products and diverse rain products. In contrast, the behaviors of SI are more subject to LWP or rain products. Our results further show that SPOP strongly depends on atmospherics stability, with larger value under more stable environment. Precipitation susceptibility calculated with respect to cloud droplet number concentration (CDNC) is generally much larger than that estimated with respect to aerosol index (AI), which results from the weak dependency of CDNC on AI.

Game Design to Measure Reflexes and Attention Based on Biofeedback Multi-Sensor Interaction

PubMed Central

Ortiz-Vigon Uriarte, Inigo de Loyola; Garcia-Zapirain, Begonya; Garcia-Chimeno, Yolanda

2015-01-01

This paper presents a multi-sensor system for implementing biofeedback as a human-computer interaction technique in a game involving driving cars in risky situations. The sensors used are: Eye Tracker, Kinect, pulsometer, respirometer, electromiography (EMG) and galvanic skin resistance (GSR). An algorithm has been designed which gives rise to an interaction logic with the game according to the set of physiological constants obtained from the sensors. The results reflect a 72.333 response to the System Usability Scale (SUS), a significant difference of p = 0.026 in GSR values in terms of the difference between the start and end of the game, and an r = 0.659 and p = 0.008 correlation while playing with the Kinect between the breathing level and the energy and joy factor. All the sensors used had an impact on the end results, whereby none of them should be disregarded in future lines of research, even though it would be interesting to obtain separate breathing values from that of the cardio. PMID:25789493

Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.

USGS Publications Warehouse

Brown, Jesslyn F.; Miura, T.; Wardlow, B.; Gu, Yingxin

2011-01-01

Droughts occur repeatedly in the United States resulting in billions of dollars of damage. Monitoring and reporting on drought conditions is a necessary function of government agencies at multiple levels. A team of Federal and university partners developed a drought decision- support tool with higher spatial resolution relative to traditional climate-based drought maps. The Vegetation Drought Response Index (VegDRI) indicates general canopy vegetation condition assimilation of climate, satellite, and biophysical data via geospatial modeling. In VegDRI, complementary drought-related data are merged to provide a comprehensive, detailed representation of drought stress on vegetation. Time-series data from daily polar-orbiting earth observing systems [Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)] providing global measurements of land surface conditions are ingested into VegDRI. Inter-sensor compatibility is required to extend multi-sensor data records; thus, translations were developed using overlapping observations to create consistent, long-term data time series. 

Automatic Construction of Wi-Fi Radio Map Using Smartphones

NASA Astrophysics Data System (ADS)

Liu, Tao; Li, Qingquan; Zhang, Xing

2016-06-01

Indoor positioning could provide interesting services and applications. As one of the most popular indoor positioning methods, location fingerprinting determines the location of mobile users by matching the received signal strength (RSS) which is location dependent. However, fingerprinting-based indoor positioning requires calibration and updating of the fingerprints which is labor-intensive and time-consuming. In this paper, we propose a visual-based approach for the construction of radio map for anonymous indoor environments without any prior knowledge. This approach collects multi-sensors data, e.g. video, accelerometer, gyroscope, Wi-Fi signals, etc., when people (with smartphones) walks freely in indoor environments. Then, it uses the multi-sensor data to restore the trajectories of people based on an integrated structure from motion (SFM) and image matching method, and finally estimates location of sampling points on the trajectories and construct Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m.

Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices

PubMed Central

Guiry, John J.; van de Ven, Pepijn; Nelson, John

2014-01-01

In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living. A feasibility study involving N = 10 participants was carried out to evaluate the devices' ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Naïve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances. PMID:24662406

Multi-sensor system for in situ shape monitoring and damage identification of high-speed composite rotors

NASA Astrophysics Data System (ADS)

Philipp, K.; Filippatos, A.; Kuschmierz, R.; Langkamp, A.; Gude, M.; Fischer, A.; Czarske, J.

2016-08-01

Glass fibre-reinforced polymer (GFRP) composites offer a higher stiffness-to-weight ratio than conventional rotor materials used in turbomachinery. However, the material behaviour of GFRP high-speed rotors is difficult to predict due to the complexity of the composite material and the dynamic loading conditions. Consequently dynamic expansion measurements of GRFP rotors are required in situ and with micron precision. However, the whirling motion amplitude is about two orders of magnitude higher than the desired precision. To overcome this problem, a multi-sensor system capable of separating rotor expansion and whirling motion is proposed. High measurement rates well above the rotational frequency and micron uncertainty are achieved at whirling amplitudes up to 120μm and surface velocities up to 300 m/s. The dynamic elliptical expansion of a GFRP rotor is investigated in a rotor loading test rig under vacuum conditions. In situ measurements identified not only the introduced damage but also damage initiation and propagation.

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

NASA Astrophysics Data System (ADS)

Brilli, L.; Chiesi, M.; Maselli, F.; Moriondo, M.; Gioli, B.; Toscano, P.; Zaldei, A.; Bindi, M.

2013-08-01

We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010-2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

PubMed

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

2015-11-13

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices.

PubMed

Guiry, John J; van de Ven, Pepijn; Nelson, John

2014-03-21

In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living. A feasibility study involving N = 10 participants was carried out to evaluate the devices' ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Naïve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances.

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements.

PubMed

Tannous, Halim; Istrate, Dan; Benlarbi-Delai, Aziz; Sarrazin, Julien; Gamet, Didier; Ho Ba Tho, Marie Christine; Dao, Tien Tuan

2016-11-15

Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject's movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

Design and testing of a multi-sensor pedestrian location and navigation platform.

PubMed

Morrison, Aiden; Renaudin, Valérie; Bancroft, Jared B; Lachapelle, Gérard

2012-01-01

Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided.

DFT algorithms for bit-serial GaAs array processor architectures

NASA Technical Reports Server (NTRS)

Mcmillan, Gary B.

1988-01-01

Systems and Processes Engineering Corporation (SPEC) has developed an innovative array processor architecture for computing Fourier transforms and other commonly used signal processing algorithms. This architecture is designed to extract the highest possible array performance from state-of-the-art GaAs technology. SPEC's architectural design includes a high performance RISC processor implemented in GaAs, along with a Floating Point Coprocessor and a unique Array Communications Coprocessor, also implemented in GaAs technology. Together, these data processors represent the latest in technology, both from an architectural and implementation viewpoint. SPEC has examined numerous algorithms and parallel processing architectures to determine the optimum array processor architecture. SPEC has developed an array processor architecture with integral communications ability to provide maximum node connectivity. The Array Communications Coprocessor embeds communications operations directly in the core of the processor architecture. A Floating Point Coprocessor architecture has been defined that utilizes Bit-Serial arithmetic units, operating at very high frequency, to perform floating point operations. These Bit-Serial devices reduce the device integration level and complexity to a level compatible with state-of-the-art GaAs device technology.

Fabrication of five-level ultraplanar micromirror arrays by flip-chip assembly

NASA Astrophysics Data System (ADS)

Michalicek, M. Adrian; Bright, Victor M.

2001-10-01

This paper reports a detailed study of the fabrication of various piston, torsion, and cantilever style micromirror arrays using a novel, simple, and inexpensive flip-chip assembly technique. Several rectangular and polar arrays were commercially prefabricated in the MUMPs process and then flip-chip bonded to form advanced micromirror arrays where adverse effects typically associated with surface micromachining were removed. These arrays were bonded by directly fusing the MUMPs gold layers with no complex preprocessing. The modules were assembled using a computer-controlled, custom-built flip-chip bonding machine. Topographically opposed bond pads were designed to correct for slight misalignment errors during bonding and typically result in less than 2 micrometers of lateral alignment error. Although flip-chip micromirror performance is briefly discussed, the means used to create these arrays is the focus of the paper. A detailed study of flip-chip process yield is presented which describes the primary failure mechanisms for flip-chip bonding. Studies of alignment tolerance, bonding force, stress concentration, module planarity, bonding machine calibration techniques, prefabrication errors, and release procedures are presented in relation to specific observations in process yield. Ultimately, the standard thermo-compression flip-chip assembly process remains a viable technique to develop highly complex prototypes of advanced micromirror arrays.

Multi-Sensor Fusion of Infrared and Electro-Optic Signals for High Resolution Night Images

PubMed Central

Huang, Xiaopeng; Netravali, Ravi; Man, Hong; Lawrence, Victor

2012-01-01

Electro-optic (EO) image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR) image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF) proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available. PMID:23112602

Multi-sensor fusion of infrared and electro-optic signals for high resolution night images.

PubMed

Huang, Xiaopeng; Netravali, Ravi; Man, Hong; Lawrence, Victor

2012-01-01

Electro-optic (EO) image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR) image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF) proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

Automated Hybridization of X-ray Absorber Elements-A Path to Large Format Microcalorimeter Arrays

NASA Technical Reports Server (NTRS)

Moseley, S.; Kelley, R.; Allen, C.; Kilbourne, C.; Costen, N.; Miller, T.

2007-01-01

In the design of microcalorimeters, it is often desirable to produce the X-ray absorber separately from the detector element. In this case, the attachment of the absorber to the detector element with the required thermal and mechanical characteristics is a major challenge. In such arrays, the attachment has been done by hand. This process is not easily extended to the large format arrays required for future X- ray astronomy missions such as the New x-ray Telescope or NeXT. In this paper we present an automated process for attaching absorber tiles to the surface of a large-scale X-ray detector array. The absorbers are attached with stycast epoxy to a thermally isolating polymer structure made of SU-8. SU-8 is a negative epoxy based photo resist produced by Microchem. We describe the fabrication of the X-ray absorbers and their suspension on a handle die in an adhesive matrix. We describe the production process for the polymer isolators on the detector elements. We have developed a new process for the alignment, and simultaneous bonding of the absorber tiles to an entire detector array. This process uses equipment and techniques used in the flip-chip bonding industry and approaches developed in the fabrication of the XRS-2 instrument. XRS-2 was an X-ray spectrometer that was launched on the Suzaku telescope in July 10, 2005. We describe the process and show examples of sample arrays produced by this process. Arrays with up to 300 elements have been bonded. The present tests have used dummy absorbers made of Si. In future work, we will demonstrate bonding of HgTe absorbers.

Python Winding Itself Around Datacubes: How to Access Massive Multi-Dimensional Arrays in a Pythonic Way

NASA Astrophysics Data System (ADS)

Merticariu, Vlad; Misev, Dimitar; Baumann, Peter

2017-04-01

While python has developed into the lingua franca in Data Science there is often a paradigm break when accessing specialized tools. In particular for one of the core data categories in science and engineering, massive multi-dimensional arrays, out-of-memory solutions typically employ their own, different models. We discuss this situation on the example of the scalable open-source array engine, rasdaman ("raster data manager") which offers access to and processing of Petascale multi-dimensional arrays through an SQL-style array query language, rasql. Such queries are executed in the server on a storage engine utilizing adaptive array partitioning and based on a processing engine implementing a "tile streaming" paradigm to allow processing of arrays massively larger than server RAM. The rasdaman QL has acted as blueprint for forthcoming ISO Array SQL and the Open Geospatial Consortium (OGC) geo analytics language, Web Coverage Processing Service, adopted in 2008. Not surprisingly, rasdaman is OGC and INSPIRE Reference Implementation for their "Big Earth Data" standards suite. Recently, rasdaman has been augmented with a python interface which allows to transparently interact with the database (credits go to Siddharth Shukla's Master Thesis at Jacobs University). Programmers do not need to know the rasdaman query language, as the operators are silently transformed, through lazy evaluation, into queries. Arrays delivered are likewise automatically transformed into their python representation. In the talk, the rasdaman concept will be illustrated with the help of large-scale real-life examples of operational satellite image and weather data services, and sample python code.

Method of varying a characteristic of an optical vertical cavity structure formed by metalorganic vapor phase epitaxy

DOEpatents

Hou, Hong Q.; Coltrin, Michael E.; Choquette, Kent D.

2001-01-01

A process for forming an array of vertical cavity optical resonant structures wherein the structures in the array have different detection or emission wavelengths. The process uses selective area growth (SAG) in conjunction with annular masks of differing dimensions to control the thickness and chemical composition of the materials in the optical cavities in conjunction with a metalorganic vapor phase epitaxy (MOVPE) process to build these arrays.

Vision technology/algorithms for space robotics applications

NASA Technical Reports Server (NTRS)

Krishen, Kumar; Defigueiredo, Rui J. P.

1987-01-01

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed.

Advances in the Application of Surface Drifters.

PubMed

Lumpkin, Rick; Özgökmen, Tamay; Centurioni, Luca

2017-01-03

Surface drifting buoys, or drifters, are used in oceanographic and climate research, oil spill tracking, weather forecasting, search and rescue operations, calibration and validation of velocities from high-frequency radar and from altimeters, iceberg tracking, and support of offshore drilling operations. In this review, we present a brief history of drifters, from the message in a bottle to the latest satellite-tracked, multisensor drifters. We discuss the different types of drifters currently used for research and operations as well as drifter designs in development. We conclude with a discussion of the various properties that can be observed with drifters, with heavy emphasis on a critical process that cannot adequately be observed by any other instrument: dispersion in the upper ocean, driven by turbulence at scales from waves through the submesoscale to the large-scale geostrophic eddies.

Process for forming transparent aerogel insulating arrays

DOEpatents

Tewari, Param H.; Hunt, Arlon J.

1986-01-01

An improved supercritical drying process for forming transparent silica aerogel arrays is described. The process is of the type utilizing the steps of hydrolyzing and condensing aloxides to form alcogels. A subsequent step removes the alcohol to form aerogels. The improvement includes the additional step, after alcogels are formed, of substituting a solvent, such as CO.sub.2, for the alcohol in the alcogels, the solvent having a critical temperature less than the critical temperature of the alcohol. The resulting gels are dried at a supercritical temperature for the selected solvent, such as CO.sub.2, to thereby provide a transparent aerogel array within a substantially reduced (days-to-hours) time period. The supercritical drying occurs at about 40.degree. C. instead of at about 270.degree. C. The improved process provides increased yields of large scale, structurally sound arrays. The transparent aerogel array, formed in sheets or slabs, as made in accordance with the improved process, can replace the air gap within a double glazed window, for example, to provide a substantial reduction in heat transfer. The thus formed transparent aerogel arrays may also be utilized, for example, in windows of refrigerators and ovens, or in the walls and doors thereof or as the active material in detectors for analyzing high energy elementry particles or cosmic rays.

Process for forming transparent aerogel insulating arrays

DOEpatents

Tewari, P.H.; Hunt, A.J.

1985-09-04

An improved supercritical drying process for forming transparent silica aerogel arrays is described. The process is of the type utilizing the steps of hydrolyzing and condensing aloxides to form alcogels. A subsequent step removes the alcohol to form aerogels. The improvement includes the additional step, after alcogels are formed, of substituting a solvent, such as CO/sub 2/, for the alcohol in the alcogels, the solvent having a critical temperature less than the critical temperature of the alcohol. The resulting gels are dried at a supercritical temperature for the selected solvent, such as CO/sub 2/, to thereby provide a transparent aerogel array within a substantially reduced (days-to-hours) time period. The supercritical drying occurs at about 40/sup 0/C instead of at about 270/sup 0/C. The improved process provides increased yields of large scale, structurally sound arrays. The transparent aerogel array, formed in sheets or slabs, as made in accordance with the improved process, can replace the air gap within a double glazed window, for example, to provide a substantial reduction in heat transfer. The thus formed transparent aerogel arrays may also be utilized, for example, in windows of refrigerators and ovens, or in the walls and doors thereof or as the active material in detectors for analyzing high energy elementary particles or cosmic rays.

Solution-processed single-wall carbon nanotube transistor arrays for wearable display backplanes

NASA Astrophysics Data System (ADS)

Kang, Byeong-Cheol; Ha, Tae-Jun

2018-01-01

In this paper, we demonstrate solution-processed single-wall carbon nanotube thin-film transistor (SWCNT-TFT) arrays with polymeric gate dielectrics on the polymeric substrates for wearable display backplanes, which can be directly attached to the human body. The optimized SWCNT-TFTs without any buffer layer on flexible substrates exhibit a linear field-effect mobility of 1.5cm2/V-s and a threshold voltage of around 0V. The statistical plot of the key device metrics extracted from 35 SWCNT-TFTs which were fabricated in different batches at different times conclusively support that we successfully demonstrated high-performance solution-processed SWCNT-TFT arrays which demand excellent uniformity in the device performance. We also investigate the operational stability of wearable SWCNT-TFT arrays against an applied strain of up to 40%, which is the essential for a harsh degree of strain on human body. We believe that the demonstration of flexible SWCNT-TFT arrays which were fabricated by all solution-process except the deposition of metal electrodes at process temperature below 130oC can open up new routes for wearable display backplanes.

Redundant Disk Arrays in Transaction Processing Systems. Ph.D. Thesis, 1993

NASA Technical Reports Server (NTRS)

Mourad, Antoine Nagib

1994-01-01

We address various issues dealing with the use of disk arrays in transaction processing environments. We look at the problem of transaction undo recovery and propose a scheme for using the redundancy in disk arrays to support undo recovery. The scheme uses twin page storage for the parity information in the array. It speeds up transaction processing by eliminating the need for undo logging for most transactions. The use of redundant arrays of distributed disks to provide recovery from disasters as well as temporary site failures and disk crashes is also studied. We investigate the problem of assigning the sites of a distributed storage system to redundant arrays in such a way that a cost of maintaining the redundant parity information is minimized. Heuristic algorithms for solving the site partitioning problem are proposed and their performance is evaluated using simulation. We also develop a heuristic for which an upper bound on the deviation from the optimal solution can be established.

In situ formation of a ZnO/ZnSe nanonail array as a photoelectrode for enhanced photoelectrochemical water oxidation performance

NASA Astrophysics Data System (ADS)

Wang, Liyang; Tian, Guohui; Chen, Yajie; Xiao, Yuting; Fu, Honggang

2016-04-01

In this study, a ZnO/ZnSe nanonail array was prepared via a two-step sequential hydrothermal synthetic route. In this synthetic process, the ZnO nanorod array was first grown on a fluorine-doped tin oxide (FTO) substrate using a seed-mediated growth approach via the hydrothermal process. Then, the ZnO nanonail array was obtained via in situ growth of ZnSe nano caps onto the ZnO nanorod array via a hydrothermal process in the presence of a Se source. The surface morphology and amount of ZnSe grown on the surface of the ZnO nanorods can be regulated by varying the reaction time and reactant concentration. Compared with pure ZnO nanorods, this unique nanonail array heterostructure exhibits enhanced visible light absorption. The transient photocurrent condition, in combination with steady-state and time-resolved photoluminescence spectroscopy, reveals that the ZnO/ZnSe nanonail array electrode has the highest charge separation rate, highest electron injection efficiency, and highest chemical stability. The photocurrent density of the ZnO/ZnSe nanonail array heterostructure reaches 1.01 mA cm-2 at an applied potential of 0.1 V (vs. Ag/AgCl), which is much higher than that of the ZnO/ZnSe nanorod array (0.71 mA cm-2), the pristine ZnO nanorod array (0.39 mA cm-2), and the ZnSe electrode (0.21 mA cm-2), indicating its significant visible light driven activities for photoelectrochemical water oxidation. This unique morphology of nail-capped nanorods might be important for providing better insight into the correlation between heterostructure and photoelectrochemical activity.In this study, a ZnO/ZnSe nanonail array was prepared via a two-step sequential hydrothermal synthetic route. In this synthetic process, the ZnO nanorod array was first grown on a fluorine-doped tin oxide (FTO) substrate using a seed-mediated growth approach via the hydrothermal process. Then, the ZnO nanonail array was obtained via in situ growth of ZnSe nano caps onto the ZnO nanorod array via a hydrothermal process in the presence of a Se source. The surface morphology and amount of ZnSe grown on the surface of the ZnO nanorods can be regulated by varying the reaction time and reactant concentration. Compared with pure ZnO nanorods, this unique nanonail array heterostructure exhibits enhanced visible light absorption. The transient photocurrent condition, in combination with steady-state and time-resolved photoluminescence spectroscopy, reveals that the ZnO/ZnSe nanonail array electrode has the highest charge separation rate, highest electron injection efficiency, and highest chemical stability. The photocurrent density of the ZnO/ZnSe nanonail array heterostructure reaches 1.01 mA cm-2 at an applied potential of 0.1 V (vs. Ag/AgCl), which is much higher than that of the ZnO/ZnSe nanorod array (0.71 mA cm-2), the pristine ZnO nanorod array (0.39 mA cm-2), and the ZnSe electrode (0.21 mA cm-2), indicating its significant visible light driven activities for photoelectrochemical water oxidation. This unique morphology of nail-capped nanorods might be important for providing better insight into the correlation between heterostructure and photoelectrochemical activity. Electronic supplementary information (ESI) available: SEM, EDS, XPS and photocurrent test. See DOI: 10.1039/c6nr01969b

Multi-Sensor Improved Sea Surface Temperature (MISST) for GODAE

DTIC Science & Technology

2007-01-01

new data streams. NAVOCEANO has improved on its methodology to add retrieval error information to the US Navy operational data stream. Quantitative ...HYCOM)”: http://hycom.rsmas.miami.edu/ “ POSITIV : Prototype Operational System – ISAR – Temperature Instrumentation for the VOS fleet” CIRA/CSU Joint

Multi-Sensor Improved Sea Surface Temperature (MISST) for GODAE

DTIC Science & Technology

2008-01-01

its methodology to add 3 retrieval error information to the US Navy operational data stream. Quantitative estimates of reliability are added to...hycom.rsmas.miami.edu/ “ POSITIV : Prototype Operational System – ISAR – Temperature Instrumentation for the VOS fleet” CIRA/CSU Joint Hurricane Testbed project

Middle-term Metropolitan Water Availability Index Assessment Based on Synergistic Potentials of Multi-sensor Data

EPA Science Inventory

The impact of recent drought and water pollution episodes results in an acute need to project future water availability to assist water managers in water utility infrastructure management within many metropolitan regions. Separate drought and water quality indices previously deve...

MULTI-SENSOR REPORTER CELL TECHNOLOGY TO ASSESS HAZARD INVOLVING ENDOCRINE SIGNALING PATHWAYS

EPA Science Inventory

Results will define an experimental approach that can be used in a high-throughput format to evaluate the response of hormone signaling pathways and networks to individual chemicals or mixtures. The assay also will have application across species and would significantly reduce...

Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

2014-05-01

Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety of land cover types. Remote sensing data from the Tropical Rainfall Measuring Mission satellite (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensors were obtained for the period from 2000 to 2012, and observation data from 99 weather stations, 441 streamflow gauges, as well as the gridded observation data from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) were obtained for validation. The objective blends of multiple indicators helped better assessment of various types of drought, and can be useful for drought early warning system. Since the improved SDCI is based on remotely sensed data, it can be easily applied to regions with limited or no observation data for drought assessment and monitoring.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Highly uniform and vertically aligned SnO2 nanochannel arrays for photovoltaic applications

NASA Astrophysics Data System (ADS)

Kim, Jae-Yup; Kang, Jin Soo; Shin, Junyoung; Kim, Jin; Han, Seung-Joo; Park, Jongwoo; Min, Yo-Sep; Ko, Min Jae; Sung, Yung-Eun

2015-04-01

Nanostructured electrodes with vertical alignment have been considered ideal structures for electron transport and interfacial contact with redox electrolytes in photovoltaic devices. Here, we report large-scale vertically aligned SnO2 nanochannel arrays with uniform structures, without lateral cracks fabricated by a modified anodic oxidation process. In the modified process, ultrasonication is utilized to avoid formation of partial compact layers and lateral cracks in the SnO2 nanochannel arrays. Building on this breakthrough, we first demonstrate the photovoltaic application of these vertically aligned SnO2 nanochannel arrays. These vertically aligned arrays were directly and successfully applied in quasi-solid state dye-sensitized solar cells (DSSCs) as photoanodes, yielding reasonable conversion efficiency under back-side illumination. In addition, a significantly short process time (330 s) for achieving the optimal thickness (7.0 μm) and direct utilization of the anodized electrodes enable a simple, rapid and low-cost fabrication process. Furthermore, a TiO2 shell layer was coated on the SnO2 nanochannel arrays by the atomic layer deposition (ALD) process for enhancement of dye-loading and prolonging the electron lifetime in the DSSC. Owing to the presence of the ALD TiO2 layer, the short-circuit photocurrent density (Jsc) and conversion efficiency were increased by 20% and 19%, respectively, compared to those of the DSSC without the ALD TiO2 layer. This study provides valuable insight into the development of efficient SnO2-based photoanodes for photovoltaic application by a simple and rapid fabrication process.Nanostructured electrodes with vertical alignment have been considered ideal structures for electron transport and interfacial contact with redox electrolytes in photovoltaic devices. Here, we report large-scale vertically aligned SnO2 nanochannel arrays with uniform structures, without lateral cracks fabricated by a modified anodic oxidation process. In the modified process, ultrasonication is utilized to avoid formation of partial compact layers and lateral cracks in the SnO2 nanochannel arrays. Building on this breakthrough, we first demonstrate the photovoltaic application of these vertically aligned SnO2 nanochannel arrays. These vertically aligned arrays were directly and successfully applied in quasi-solid state dye-sensitized solar cells (DSSCs) as photoanodes, yielding reasonable conversion efficiency under back-side illumination. In addition, a significantly short process time (330 s) for achieving the optimal thickness (7.0 μm) and direct utilization of the anodized electrodes enable a simple, rapid and low-cost fabrication process. Furthermore, a TiO2 shell layer was coated on the SnO2 nanochannel arrays by the atomic layer deposition (ALD) process for enhancement of dye-loading and prolonging the electron lifetime in the DSSC. Owing to the presence of the ALD TiO2 layer, the short-circuit photocurrent density (Jsc) and conversion efficiency were increased by 20% and 19%, respectively, compared to those of the DSSC without the ALD TiO2 layer. This study provides valuable insight into the development of efficient SnO2-based photoanodes for photovoltaic application by a simple and rapid fabrication process. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr00202h

Use of multi-sensor active fire detections to map fires in the United States: the future of monitoring trends in burn severity

USGS Publications Warehouse

Picotte, Joshua J.; Coan, Michael; Howard, Stephen M.

2014-01-01

The effort to utilize satellite-based MODIS, AVHRR, and GOES fire detections from the Hazard Monitoring System (HMS) to identify undocumented fires in Florida and improve the Monitoring Trends in Burn Severity (MTBS) mapping process has yielded promising results. This method was augmented using regression tree models to identify burned/not-burned pixels (BnB) in every Landsat scene (1984–2012) in Worldwide Referencing System 2 Path/Rows 16/40, 17/39, and 1839. The burned area delineations were combined with the HMS detections to create burned area polygons attributed with their date of fire detection. Within our study area, we processed 88,000 HMS points (2003–2012) and 1,800 Landsat scenes to identify approximately 300,000 burned area polygons. Six percent of these burned area polygons were larger than the 500-acre MTBS minimum size threshold. From this study, we conclude that the process can significantly improve understanding of fire occurrence and improve the efficiency and timeliness of assessing its impacts upon the landscape.

Correlation and registration of ERTS multispectral imagery. [by a digital processing technique

NASA Technical Reports Server (NTRS)

Bonrud, L. O.; Henrikson, P. J.

1974-01-01

Examples of automatic digital processing demonstrate the feasibility of registering one ERTS multispectral scanner (MSS) image with another obtained on a subsequent orbit, and automatic matching, correlation, and registration of MSS imagery with aerial photography (multisensor correlation) is demonstrated. Excellent correlation was obtained with patch sizes exceeding 16 pixels square. Qualities which lead to effective control point selection are distinctive features, good contrast, and constant feature characteristics. Results of the study indicate that more than 300 degrees of freedom are required to register two standard ERTS-1 MSS frames covering 100 by 100 nautical miles to an accuracy of 0.6 pixel mean radial displacement error. An automatic strip processing technique demonstrates 600 to 1200 degrees of freedom over a quater frame of ERTS imagery. Registration accuracies in the range of 0.3 pixel to 0.5 pixel mean radial error were confirmed by independent error analysis. Accuracies in the range of 0.5 pixel to 1.4 pixel mean radial error were demonstrated by semi-automatic registration over small geographic areas.

50 years of progress in microphone arrays for speech processing

NASA Astrophysics Data System (ADS)

Elko, Gary W.; Frisk, George V.

2004-10-01

In the early 1980s, Jim Flanagan had a dream of covering the walls of a room with microphones. He occasionally referred to this concept as acoustic wallpaper. Being a new graduate in the field of acoustics and signal processing, it was fortunate that Bell Labs was looking for someone to investigate this area of microphone arrays for telecommunication. The job interview was exciting, with all of the big names in speech signal processing and acoustics sitting in the audience, many of whom were the authors of books and articles that were seminal contributions to the fields of acoustics and signal processing. If there ever was an opportunity of a lifetime, this was it. Fortunately, some of the work had already begun, and Sessler and West had already laid the groundwork for directional electret microphones. This talk will describe some of the very early work done at Bell Labs on microphone arrays and reflect on some of the many systems, from large 400-element arrays, to small two-microphone arrays. These microphone array systems were built under Jim Flanagan's leadership in an attempt to realize his vision of seamless hands-free speech communication between people and the communication of people with machines.

A front-end wafer-level microsystem packaging technique with micro-cap array

NASA Astrophysics Data System (ADS)

Chiang, Yuh-Min

2002-09-01

The back-end packaging process is the remaining challenge for the micromachining industry to commercialize microsystem technology (MST) devices at low cost. This dissertation presents a novel wafer level protection technique as a final step of the front-end fabrication process for MSTs. It facilitates improved manufacturing throughput and automation in package assembly, wafer level testing of devices, and enhanced device performance. The method involves the use of a wafer-sized micro-cap array, which consists of an assortment of small caps micro-molded onto a material with adjustable shapes and sizes to serve as protective structures against the hostile environments during packaging. The micro-cap array is first constructed by a micromachining process with micro-molding technique, then sealed to the device wafer at wafer level. Epoxy-based wafer-level micro cap array has been successfully fabricated and showed good compatibility with conventional back-end packaging processes. An adhesive transfer technique was demonstrated to seal the micro cap array with a MEMS device wafer. No damage or gross leak was observed while wafer dicing or later during a gross leak test. Applications of the micro cap array are demonstrated on MEMS, microactuators fabricated using CRONOS MUMPS process. Depending on the application needs, the micro-molded cap can be designed and modified to facilitate additional component functions, such as optical, electrical, mechanical, and chemical functions, which are not easily achieved in the device by traditional means. Successful fabrication of a micro cap array comprised with microlenses can provide active functions as well as passive protection. An optical tweezer array could be one possibility for applications of a micro cap with microlenses. The micro cap itself could serve as micro well for DNA or bacteria amplification as well.

Cross-coherent vector sensor processing for spatially distributed glider networks.

PubMed

Nichols, Brendan; Sabra, Karim G

2015-09-01

Autonomous underwater gliders fitted with vector sensors can be used as a spatially distributed sensor array to passively locate underwater sources. However, to date, the positional accuracy required for robust array processing (especially coherent processing) is not achievable using dead-reckoning while the gliders remain submerged. To obtain such accuracy, the gliders can be temporarily surfaced to allow for global positioning system contact, but the acoustically active sea surface introduces locally additional sensor noise. This letter demonstrates that cross-coherent array processing, which inherently mitigates the effects of local noise, outperforms traditional incoherent processing source localization methods for this spatially distributed vector sensor network.

Polymer Waveguide Fabrication Techniques

NASA Astrophysics Data System (ADS)

Ramey, Delvan A.

1985-01-01

The ability of integrated optic systems to compete in signal processing aplications with more traditional analog and digital electronic systems is discussed. The Acousto-Optic Spectrum Analyzer is an example which motivated the particular work discussed herein. Provided real time processing is more critical than absolute accuracy, such integrated optic systems fulfill a design need. Fan-out waveguide arrays allow crosstalk in system detector arrays to be controlled without directly limiting system resolution. A polyurethane pattern definition process was developed in order to demonstrate fan-out arrays. This novel process is discussed, along with further research needs. Integrated optic system market penetration would be enhanced by development of commercial processes of this type.

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

NASA Astrophysics Data System (ADS)

Zhang, Zhifen; Chen, Huabin; Xu, Yanling; Zhong, Jiyong; Lv, Na; Chen, Shanben

2015-08-01

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.

The Design and Development of an Omni-Directional Mobile Robot Oriented to an Intelligent Manufacturing System

PubMed Central

Qian, Jun; Zi, Bin; Ma, Yangang; Zhang, Dan

2017-01-01

In order to transport materials flexibly and smoothly in a tight plant environment, an omni-directional mobile robot based on four Mecanum wheels was designed. The mechanical system of the mobile robot is made up of three separable layers so as to simplify its combination and reorganization. Each modularized wheel was installed on a vertical suspension mechanism, which ensures the moving stability and keeps the distances of four wheels invariable. The control system consists of two-level controllers that implement motion control and multi-sensor data processing, respectively. In order to make the mobile robot navigate in an unknown semi-structured indoor environment, the data from a Kinect visual sensor and four wheel encoders were fused to localize the mobile robot using an extended Kalman filter with specific processing. Finally, the mobile robot was integrated in an intelligent manufacturing system for material conveying. Experimental results show that the omni-directional mobile robot can move stably and autonomously in an indoor environment and in industrial fields. PMID:28891964

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

NASA Technical Reports Server (NTRS)

Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

2017-01-01

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Integrative Multi-Spectral Sensor Device for Far-Infrared and Visible Light Fusion

NASA Astrophysics Data System (ADS)

Qiao, Tiezhu; Chen, Lulu; Pang, Yusong; Yan, Gaowei

2018-06-01

Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.

Advanced integrated enhanced vision systems

NASA Astrophysics Data System (ADS)

Kerr, J. R.; Luk, Chiu H.; Hammerstrom, Dan; Pavel, Misha

2003-09-01

In anticipation of its ultimate role in transport, business and rotary wing aircraft, we clarify the role of Enhanced Vision Systems (EVS): how the output data will be utilized, appropriate architecture for total avionics integration, pilot and control interfaces, and operational utilization. Ground-map (database) correlation is critical, and we suggest that "synthetic vision" is simply a subset of the monitor/guidance interface issue. The core of integrated EVS is its sensor processor. In order to approximate optimal, Bayesian multi-sensor fusion and ground correlation functionality in real time, we are developing a neural net approach utilizing human visual pathway and self-organizing, associative-engine processing. In addition to EVS/SVS imagery, outputs will include sensor-based navigation and attitude signals as well as hazard detection. A system architecture is described, encompassing an all-weather sensor suite; advanced processing technology; intertial, GPS and other avionics inputs; and pilot and machine interfaces. Issues of total-system accuracy and integrity are addressed, as well as flight operational aspects relating to both civil certification and military applications in IMC.

Minefield reconnaissance and detector system

DOEpatents

Butler, M.T.; Cave, S.P.; Creager, J.D.; Johnson, C.M.; Mathes, J.B.; Smith, K.J.

1994-04-26

A multi-sensor system is described for detecting the presence of objects on the surface of the ground or buried just under the surface, such as anti-personnel or anti-tank mines or the like. A remote sensor platform has a plurality of metal detector sensors and a plurality of short pulse radar sensors. The remote sensor platform is remotely controlled from a processing and control unit and signals from the remote sensor platform are sent to the processing and control unit where they are individually evaluated in separate data analysis subprocess steps to obtain a probability score for each of the pluralities of sensors. These probability scores are combined in a fusion subprocess step by comparing score sets to a probability table which is derived based upon the historical incidence of object present conditions given that score set. A decision making rule is applied to provide an output which is optionally provided to a marker subprocess for controlling a marker device to mark the location of found objects. 7 figures.

The Design and Development of an Omni-Directional Mobile Robot Oriented to an Intelligent Manufacturing System.

PubMed

Qian, Jun; Zi, Bin; Wang, Daoming; Ma, Yangang; Zhang, Dan

2017-09-10

In order to transport materials flexibly and smoothly in a tight plant environment, an omni-directional mobile robot based on four Mecanum wheels was designed. The mechanical system of the mobile robot is made up of three separable layers so as to simplify its combination and reorganization. Each modularized wheel was installed on a vertical suspension mechanism, which ensures the moving stability and keeps the distances of four wheels invariable. The control system consists of two-level controllers that implement motion control and multi-sensor data processing, respectively. In order to make the mobile robot navigate in an unknown semi-structured indoor environment, the data from a Kinect visual sensor and four wheel encoders were fused to localize the mobile robot using an extended Kalman filter with specific processing. Finally, the mobile robot was integrated in an intelligent manufacturing system for material conveying. Experimental results show that the omni-directional mobile robot can move stably and autonomously in an indoor environment and in industrial fields.

SU-D-BRD-07: Evaluation of the Effectiveness of Statistical Process Control Methods to Detect Systematic Errors For Routine Electron Energy Verification

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

Parker, S

2015-06-15

Purpose: To evaluate the ability of statistical process control methods to detect systematic errors when using a two dimensional (2D) detector array for routine electron beam energy verification. Methods: Electron beam energy constancy was measured using an aluminum wedge and a 2D diode array on four linear accelerators. Process control limits were established. Measurements were recorded in control charts and compared with both calculated process control limits and TG-142 recommended specification limits. The data was tested for normality, process capability and process acceptability. Additional measurements were recorded while systematic errors were intentionally introduced. Systematic errors included shifts in the alignmentmore » of the wedge, incorrect orientation of the wedge, and incorrect array calibration. Results: Control limits calculated for each beam were smaller than the recommended specification limits. Process capability and process acceptability ratios were greater than one in all cases. All data was normally distributed. Shifts in the alignment of the wedge were most apparent for low energies. The smallest shift (0.5 mm) was detectable using process control limits in some cases, while the largest shift (2 mm) was detectable using specification limits in only one case. The wedge orientation tested did not affect the measurements as this did not affect the thickness of aluminum over the detectors of interest. Array calibration dependence varied with energy and selected array calibration. 6 MeV was the least sensitive to array calibration selection while 16 MeV was the most sensitive. Conclusion: Statistical process control methods demonstrated that the data distribution was normally distributed, the process was capable of meeting specifications, and that the process was centered within the specification limits. Though not all systematic errors were distinguishable from random errors, process control limits increased the ability to detect systematic errors using routine measurement of electron beam energy constancy.« less

Application specific serial arithmetic arrays

NASA Technical Reports Server (NTRS)

Winters, K.; Mathews, D.; Thompson, T.

1990-01-01

High performance systolic arrays of serial-parallel multiplier elements may be rapidly constructed for specific applications by applying hardware description language techniques to a library of full-custom CMOS building blocks. Single clock pre-charged circuits have been implemented for these arrays at clock rates in excess of 100 Mhz using economical 2-micron (minimum feature size) CMOS processes, which may be quickly configured for a variety of applications. A number of application-specific arrays are presented, including a 2-D convolver for image processing, an integer polynomial solver, and a finite-field polynomial solver.

Exploring the use of multi-sensor data fusion for daily evapotranspiration mapping at field scale

USDA-ARS?s Scientific Manuscript database

Modern practices of water management in agriculture can significantly benefit from accurate mapping of crop water consumption at field scale. Assuming that actual evapotranspiration (ET) is the main water loss in land hydrological balance, remote sensing data represent an invaluable tool for water u...

Multi-Sensor Image Fusion for Target Recognition in the Environment of Network Decision Support Systems

DTIC Science & Technology

2015-12-01

FOV Field of view GEO Geosynchronous, or geostationary , earth orbit HEO Highly elliptical earth orbit HTTP Hypertext transfer protocol HTTPS...orbit (MEO), geosynchronous or geostationary earth orbit (GEO), and highly elliptical earth orbit (HEO) [38]. Furthermore, if we consider the actual

Low cost sensors: Field evaluations and multi-sensor approaches for emissions factors

EPA Science Inventory

The development, and application of low cost sensors to measure both particulate and gas-phase air pollutants is poised to explode over the next several years. The need for the sensors is driven by poor air quality experienced in inhabited regions throughout the world, in both de...

Batch-processed semiconductor gas sensor array for the selective detection of NOx in automotive exhaust gas

NASA Astrophysics Data System (ADS)

Jang, Hani; Kim, Minki; Kim, Yongjun

2016-12-01

This paper reports on a semiconductor gas sensor array to detect nitrogen oxides (NOx) in automotive exhaust gas. The proposed semiconductor gas sensor array consisted of one common electrode and three individual electrodes to minimize the size of the sensor array, and three sensing layers [TiO2 + SnO2 (15 wt%), SnO2, and Ga2O3] were deposited using screen printing. In addition, sensing materials were sintered under the same conditions in order to take advantage of batch processing. The sensing properties of the proposed sensor array were verified by experimental measurements, and the selectivity improved by using pattern recognition.

Co-Prime Frequency and Aperture Design for HF Surveillance, Wideband Radar Imaging, and Nonstationary Array Processing

DTIC Science & Technology

2018-03-01

offset designs . Particularly, the proposed CA-CFO is compared with uniform linear array and uniform frequency offset (ULA-UFO). Uniform linear array...and Aperture Design for HF Surveillance, Wideband Radar Imaging, and Nonstationary Array Processing (Grant No. N00014-13-1-0061) Submitted to...Contents 1. Executive Summary …………………………………………………………………………. 1 1.1. Generalized Co-Prime Array Design ………………………………………………… 1 1.2. Wideband

Multisensor Fusion for Change Detection

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B.

2005-12-01

Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach with detecting surface elevation changes on the Byrd Glacier, Antarctica, with aerial imagery from 1980s and ICESat laser altimetry data from 2003-05. Change detection from such disparate data sets is an intricate fusion problem, beginning with sensor alignment, and on to reasoning with spatial information as to where changes occurred and to what extent.

Bottom-up assessment of the Net Ecosystem Carbon Balance of Russian forests in 2010 for comparison to Top-down estimates.

NASA Astrophysics Data System (ADS)

Maksyutov, S. S.; Shvidenko, A.; Shchepashchenko, D.

2014-12-01

The verified full carbon assessment of Russian forests (FCA) is based on an Integrated Land Information System (ILIS) that includes a multi-layer and multi-scale GIS with basic resolution of 1 km and corresponding attributive databases. The ILIS aggregates all available information about ecosystems and landscapes, sets of empirical and semi-empirical data and aggregations, data of different inventories and surveys, and multi-sensor remote sensing data. The ILIS serves as an information base for application of the landscape-ecosystem approach (LEA) of the FCA and as a systems design for comparison and mutual constraints with other methods of study of carbon cycling of forest ecosystems (eddy covariance; process models; inverse modeling; and multi-sensor application of remote sensing). The LEA is based on a complimentary use of the flux-based method with some elements of the pool-based method. Introduction of climatic parameters of individual years in the LEA, as well as some process-based elements, allows providing a substantial decrease of the uncertainties of carbon cycling yearly indicators of forest ecosystems. Major carbon pools (live biomass, coarse woody debris, soil organic carbon) are estimated based on data on areas, distribution and major biometric characteristics of Russian forests presented in form of the ILIS for the country. The major fluxes accounted for include Net Primary Production (NPP), Soil Heterotrophic Respiration (SHR), as well as fluxes caused by decomposition of Coarse Woody Debris (CWD), harvest and use of forest products, fluxes caused by natural disturbances (fire, insect outbreaks, impacts of unfavorable environment) and lateral fluxes to hydrosphere and lithosphere. Use of landscape-ecosystem approach resulted in the NECB at 573±140 Tg C yr-1 (CI 0.9). While the total carbon sink is high, large forest areas, particularly on permafrost, serve as a carbon source. The ratio between net primary production and soil heterotrophic respiration, together with natural and human-induced disturbances are major drivers of the magnitude and spatial distribution of the NECB of forest ecosystems. We also present comparison to the recent top-down estimates of the Siberian carbon sink.

Novel fabrication method of microlens arrays with High OLED outcoupling efficiency

NASA Astrophysics Data System (ADS)

Kim, Hyun Soo; Moon, Seong Il; Hwang, Dong Eui; Jeong, Ki Won; Kim, Chang Kyo; Moon, Dae-Gyu; Hong, Chinsoo

2016-03-01

We presented a novel fabrication method of pyramidal and hemispherical polymethylmethacrylate (PMMA) microlens arrays to improve the outcoupling efficiency. Pyramidal microlens arrays were fabricated by replica molding processes using concave-pyramidal silicon molds prepared by the wet etching method. Concave-hemispherical PMMA thin film was used as a template for fabrication of the hemispherical microlens array. The concave-hemispherical PMMA template was prepared by blowing a N2 gas stream onto the thin PMMA film suspended on a silicon pedestal. A PMMA microlens arrays with hemispherical structure were fabricated by a replica molding process. The outcoupling efficiency of the hemispherical microlens array was greater than that of the pyramidal microlens array. The outcoupling efficiency of hemispherical microlens arrays with a higher contact angle was larger than that of those with lower contact angle. This indicates that, for the hemispherical microlens with larger contact angle, more light can be extracted from the OLEDs due to the decrease in the incident angle of the light at the interface between an air and a hemispherical microlens arrays. After attaching a hemispherical microlens array with contact angle of 50.4° onto the OLEDs, the luminance was enhanced by approximately 117%.

ProSens: integrated production control by automated inspection planning and efficient multisensor metrology

NASA Astrophysics Data System (ADS)

Glaser, Ulf; Li, Zhichao; Bichmann, Stephan, II; Pfeifer, Tilo

2003-05-01

By China's entry into the WTO, Chinese as well as German companies are facing the question, how to minimize the risk of unfamiliar cooperation partners when developing products. The rise of customer demands concerning quality, product diversity and the reduction of expenses require flexibility and efficiency with reliable component suppliers. In order to build and strengthen sino-german cooperations, a manufacturing control using homogenized and efficient measures to assure high quality is of vital importance. Lack of unifications may cause identical measurements conducted at subcontractors or customers to be carried out with different measurement processes which leads to incomparable results. Rapidly growing company cooperations and simultaneously decreasing of manufacturing scope cause substantial difficulties when coordinating joint quality control activities. "ProSens," a sino-german project consortium consisting of industrial users, technology producers and research institutes, aims at improving selected production processes by: Creation of a homogeneous quality awareness in sino-german cooperations. Sensitization for process accompanying metrology at an early stage of product development. Increase of the process performance by the use of integrated metrology. Reduction of production time and cost. Unification of quality control of complex products by means of efficient measurement strategies and CAD-based inspection planning.

AltiVec performance increases for autonomous robotics for the MARSSCAPE architecture program

NASA Astrophysics Data System (ADS)

Gothard, Benny M.

2002-02-01

One of the main tall poles that must be overcome to develop a fully autonomous vehicle is the inability of the computer to understand its surrounding environment to a level that is required for the intended task. The military mission scenario requires a robot to interact in a complex, unstructured, dynamic environment. Reference A High Fidelity Multi-Sensor Scene Understanding System for Autonomous Navigation The Mobile Autonomous Robot Software Self Composing Adaptive Programming Environment (MarsScape) perception research addresses three aspects of the problem; sensor system design, processing architectures, and algorithm enhancements. A prototype perception system has been demonstrated on robotic High Mobility Multi-purpose Wheeled Vehicle and All Terrain Vehicle testbeds. This paper addresses the tall pole of processing requirements and the performance improvements based on the selected MarsScape Processing Architecture. The processor chosen is the Motorola Altivec-G4 Power PC(PPC) (1998 Motorola, Inc.), a highly parallized commercial Single Instruction Multiple Data processor. Both derived perception benchmarks and actual perception subsystems code will be benchmarked and compared against previous Demo II-Semi-autonomous Surrogate Vehicle processing architectures along with desktop Personal Computers(PC). Performance gains are highlighted with progress to date, and lessons learned and future directions are described.

A Versatile Multichannel Digital Signal Processing Module for Microcalorimeter Arrays

NASA Astrophysics Data System (ADS)

Tan, H.; Collins, J. W.; Walby, M.; Hennig, W.; Warburton, W. K.; Grudberg, P.

2012-06-01

Different techniques have been developed for reading out microcalorimeter sensor arrays: individual outputs for small arrays, and time-division or frequency-division or code-division multiplexing for large arrays. Typically, raw waveform data are first read out from the arrays using one of these techniques and then stored on computer hard drives for offline optimum filtering, leading not only to requirements for large storage space but also limitations on achievable count rate. Thus, a read-out module that is capable of processing microcalorimeter signals in real time will be highly desirable. We have developed multichannel digital signal processing electronics that are capable of on-board, real time processing of microcalorimeter sensor signals from multiplexed or individual pixel arrays. It is a 3U PXI module consisting of a standardized core processor board and a set of daughter boards. Each daughter board is designed to interface a specific type of microcalorimeter array to the core processor. The combination of the standardized core plus this set of easily designed and modified daughter boards results in a versatile data acquisition module that not only can easily expand to future detector systems, but is also low cost. In this paper, we first present the core processor/daughter board architecture, and then report the performance of an 8-channel daughter board, which digitizes individual pixel outputs at 1 MSPS with 16-bit precision. We will also introduce a time-division multiplexing type daughter board, which takes in time-division multiplexing signals through fiber-optic cables and then processes the digital signals to generate energy spectra in real time.

Servo scanning 3D micro EDM for array micro cavities using on-machine fabricated tool electrodes

NASA Astrophysics Data System (ADS)

Tong, Hao; Li, Yong; Zhang, Long

2018-02-01

Array micro cavities are useful in many fields including in micro molds, optical devices, biochips and so on. Array servo scanning micro electro discharge machining (EDM), using array micro electrodes with simple cross-sectional shape, has the advantage of machining complex 3D micro cavities in batches. In this paper, the machining errors caused by offline-fabricated array micro electrodes are analyzed in particular, and then a machining process of array servo scanning micro EDM is proposed by using on-machine fabricated array micro electrodes. The array micro electrodes are fabricated on-machine by combined procedures including wire electro discharge grinding, array reverse copying and electrode end trimming. Nine-array tool electrodes with Φ80 µm diameter and 600 µm length are obtained. Furthermore, the proposed process is verified by several machining experiments for achieving nine-array hexagonal micro cavities with top side length of 300 µm, bottom side length of 150 µm, and depth of 112 µm or 120 µm. In the experiments, a chip hump accumulates on the electrode tips like the built-up edge in mechanical machining under the conditions of brass workpieces, copper electrodes and the dielectric of deionized water. The accumulated hump can be avoided by replacing the water dielectric by an oil dielectric.

Lithographically patterned electrodeposition of gold, silver, and nickel nanoring arrays with widely tunable near-infrared plasmonic resonances.

PubMed

Halpern, Aaron R; Corn, Robert M

2013-02-26

A novel low-cost nanoring array fabrication method that combines the process of lithographically patterned nanoscale electrodeposition (LPNE) with colloidal lithography is described. Nanoring array fabrication was accomplished in three steps: (i) a thin (70 nm) sacrificial nickel or silver film was first vapor-deposited onto a plasma-etched packed colloidal monolayer; (ii) the polymer colloids were removed from the surface, a thin film of positive photoresist was applied, and a backside exposure of the photoresist was used to create a nanohole electrode array; (iii) this array of nanoscale cylindrical electrodes was then used for the electrodeposition of gold, silver, or nickel nanorings. Removal of the photoresist and sacrificial metal film yielded a nanoring array in which all of the nanoring dimensions were set independently: the inter-ring spacing was fixed by the colloidal radius, the radius of the nanorings was controlled by the plasma etching process, and the width of the nanorings was controlled by the electrodeposition process. A combination of scanning electron microscopy (SEM) measurements and Fourier transform near-infrared (FT-NIR) absorption spectroscopy were used to characterize the nanoring arrays. Nanoring arrays with radii from 200 to 400 nm exhibited a single strong NIR plasmonic resonance with an absorption maximum wavelength that varied linearly from 1.25 to 3.33 μm as predicted by a simple standing wave model linear antenna theory. This simple yet versatile nanoring array fabrication method was also used to electrodeposit concentric double gold nanoring arrays that exhibited multiple NIR plasmonic resonances.

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters - Part 2: Aerosols

NASA Astrophysics Data System (ADS)

Wind, Galina; da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-07-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a "simulated radiance" product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land-ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters . Part 2; Aerosols

NASA Technical Reports Server (NTRS)

Wind, Galina; Da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-01-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Nanohole Arrays of Mixed Designs and Microwriting for Simultaneous and Multiple Protein Binding Studies

PubMed Central

Ji, Jin; Yang, Jiun-Chan; Larson, Dale N.

2009-01-01

We demonstrate using nanohole arrays of mixed designs and a microwriting process based on dip-pen nanolithography to monitor multiple, different protein binding events simultaneously in real time based on the intensity of Extraordinary Optical Transmission of nanohole arrays. The microwriting process and small footprint of the individual nanohole arrays enabled us to observe different binding events located only 16μm apart, achieving high spatial resolution. We also present a novel concept that incorporates nanohole arrays of different designs to improve confidence and accuracy of binding studies. For proof of concept, two types of nanohole arrays, designed to exhibit opposite responses to protein bindings, were fabricated on one transducer. Initial studies indicate that the mixed designs could help to screen out artifacts such as protein intrinsic signals, providing improved accuracy of binding interpretation. PMID:19297143

Fabrication of polymer micro-lens array with pneumatically diaphragm-driven drop-on-demand inkjet technology.

PubMed

Xie, Dan; Zhang, Honghai; Shu, Xiayun; Xiao, Junfeng

2012-07-02

The paper reports an effective method to fabricate micro-lens arrays with the ultraviolet-curable polymer, using an original pneumatically diaphragm-driven drop-on-demand inkjet system. An array of plano convex micro-lenses can be formed on the glass substrate due to surface tension and hydrophobic effect. The micro-lens arrays have uniform focusing function, smooth and real planar surface. The fabrication process showed good repeatability as well, fifty micro-lenses randomly selected form 9 × 9 miro-lens array with an average diameter of 333.28μm showed 1.1% variations. Also, the focal length, the surface roughness and optical property of the fabricated micro-lenses are measured, analyzed and proved satisfactory. The technique shows great potential for fabricating polymer micro-lens arrays with high flexibility, simple technological process and low production cost.

Lithographically Patterned Nanoscale Electrodeposition of Plasmonic, Bimetallic, Semiconductor, Magnetic, and Polymer Nanoring Arrays

PubMed Central

2015-01-01

Large area arrays of magnetic, semiconducting, and insulating nanorings were created by coupling colloidal lithography with nanoscale electrodeposition. This versatile nanoscale fabrication process allows for the independent tuning of the spacing, diameter, and width of the nanorings with typical values of 1.0 μm, 750 nm, and 100 nm, respectively, and was used to form nanorings from a host of materials: Ni, Co, bimetallic Ni/Au, CdSe, and polydopamine. These nanoring arrays have potential applications in memory storage, optical materials, and biosensing. A modified version of this nanoscale electrodeposition process was also used to create arrays of split gold nanorings. The size of the split nanoring opening was controlled by the angle of photoresist exposure during the fabrication process and could be varied from 50% down to 10% of the ring circumference. The large area (cm2 scale) gold split nanoring array surfaces exhibited strong polarization-dependent plasmonic absorption bands for wavelengths from 1 to 5 μm. Plasmonic nanoscale split ring arrays are potentially useful as tunable dichroic materials throughout the infrared and near-infrared spectral regions. PMID:25553204

Estimation of forest biomass using remote sensing

NASA Astrophysics Data System (ADS)

Sarker, Md. Latifur Rahman

Forest biomass estimation is essential for greenhouse gas inventories, terrestrial carbon accounting and climate change modelling studies. The availability of new SAR, (C-band RADARSAT-2 and L-band PALSAR) and optical sensors (SPOT-5 and AVNIR-2) has opened new possibilities for biomass estimation because these new SAR sensors can provide data with varying polarizations, incidence angles and fine spatial resolutions. 'Therefore, this study investigated the potential of two SAR sensors (RADARSAT-2 with C-band and PALSAR with L-band) and two optical sensors (SPOT-5 and AVNIR2) for the estimation of biomass in Hong Kong. Three common major processing steps were used for data processing, namely (i) spectral reflectance/intensity, (ii) texture measurements and (iii) polarization or band ratios of texture parameters. Simple linear and stepwise multiple regression models were developed to establish a relationship between the image parameters and the biomass of field plots. The results demonstrate the ineffectiveness of raw data. However, significant improvements in performance (r2) (RADARSAT-2=0.78; PALSAR=0.679; AVNIR-2=0.786; SPOT-5=0.854; AVNIR-2 + SPOT-5=0.911) were achieved using texture parameters of all sensors. The performances were further improved and very promising performances (r2) were obtained using the ratio of texture parameters (RADARSAT-2=0.91; PALSAR=0.823; PALSAR two-date=0.921; AVNIR-2=0.899; SPOT-5=0.916; AVNIR-2 + SPOT-5=0.939). These performances suggest four main contributions arising from this research, namely (i) biomass estimation can be significantly improved by using texture parameters, (ii) further improvements can be obtained using the ratio of texture parameters, (iii) multisensor texture parameters and their ratios have more potential than texture from a single sensor, and (iv) biomass can be accurately estimated far beyond the previously perceived saturation levels of SAR and optical data using texture parameters or the ratios of texture parameters. A further important contribution resulting from the fusion of SAR & optical images produced accuracies (r2) of 0.706 and 0.77 from the simple fusion, and the texture processing of the fused image, respectively. Although these performances were not as attractive as the performances obtained from the other four processing steps, the wavelet fusion procedure improved the saturation level of the optical (AVNIR-2) image very significantly after fusion with SAR, image. Keywords: biomass, climate change, SAR, optical, multisensors, RADARSAT-2, PALSAR, AVNIR-2, SPOT-5, texture measurement, ratio of texture parameters, wavelets, fusion, saturation

Fabrication of Microstripline Wiring for Large Format Transition Edge Sensor Arrays

NASA Technical Reports Server (NTRS)

Chervenak, James A.; Adams, J. M.; Bailey, C. N.; Bandler, S.; Brekosky, R. P.; Eckart, M. E.; Erwin, A. E.; Finkbeiner, F. M.; Kelley, R. L.; Kilbourne, C. A.;

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

Kay, Randolph R; Campbell, David V; Shinde, Subhash L

A modular, scalable focal plane array is provided as an array of integrated circuit dice, wherein each die includes a given amount of modular pixel array circuitry. The array of dice effectively multiplies the amount of modular pixel array circuitry to produce a larger pixel array without increasing die size. Desired pixel pitch across the enlarged pixel array is preserved by forming die stacks with each pixel array circuitry die stacked on a separate die that contains the corresponding signal processing circuitry. Techniques for die stack interconnections and die stack placement are implemented to ensure that the desired pixel pitchmore » is preserved across the enlarged pixel array.« less

Manufacture of Micromirror Arrays Using a CMOS-MEMS Technique

PubMed Central

Kao, Pin-Hsu; Dai, Ching-Liang; Hsu, Cheng-Chih; Wu, Chyan-Chyi

2009-01-01

In this study we used the commercial 0.35 μm CMOS (complementary metal oxide semiconductor) process and simple maskless post-processing to fabricate an array of micromirrors exhibiting high natural frequency. The micromirrors were manufactured from aluminum; the sacrificial layer was silicon dioxide. Because we fabricated the micromirror arrays using the standard CMOS process, they have the potential to be integrated with circuitry on a chip. For post-processing we used an etchant to remove the sacrificial layer and thereby suspend the micromirrors. The micromirror array contained a circular membrane and four fixed beams set symmetrically around and below the circular mirror; these four fan-shaped electrodes controlled the tilting of the micromirror. A MEMS (microelectromechanical system) motion analysis system and a confocal 3D-surface topography were used to characterize the properties and configuration of the micromirror array. Each micromirror could be rotated in four independent directions. Experimentally, we found that the micromirror had a tilting angle of about 2.55° when applying a driving voltage of 40 V. The natural frequency of the micromirrors was 59.1 kHz. PMID:22454581

Manufacture of Micromirror Arrays Using a CMOS-MEMS Technique.

PubMed

Kao, Pin-Hsu; Dai, Ching-Liang; Hsu, Cheng-Chih; Wu, Chyan-Chyi

2009-01-01

In this study we used the commercial 0.35 μm CMOS (complementary metal oxide semiconductor) process and simple maskless post-processing to fabricate an array of micromirrors exhibiting high natural frequency. The micromirrors were manufactured from aluminum; the sacrificial layer was silicon dioxide. Because we fabricated the micromirror arrays using the standard CMOS process, they have the potential to be integrated with circuitry on a chip. For post-processing we used an etchant to remove the sacrificial layer and thereby suspend the micromirrors. The micromirror array contained a circular membrane and four fixed beams set symmetrically around and below the circular mirror; these four fan-shaped electrodes controlled the tilting of the micromirror. A MEMS (microelectromechanical system) motion analysis system and a confocal 3D-surface topography were used to characterize the properties and configuration of the micromirror array. Each micromirror could be rotated in four independent directions. Experimentally, we found that the micromirror had a tilting angle of about 2.55° when applying a driving voltage of 40 V. The natural frequency of the micromirrors was 59.1 kHz.

Low Noise Infrasonic Sensor System with High Reduction of Natural Background Noise

DTIC Science & Technology

2006-05-01

local processing allows a variety of options both in the array geometry and signal processing. A generic geometry is indicated in Figure 2. Geometric...higher frequency sound detected . Table 1 provides a comparison of piezocable and microbarograph based arrays . Piezocable Sensor Local Signal ...aliasing associated with the current infrasound sensors used at large spacing in the present designs of infrasound monitoring arrays , particularly in the

Technical Objective Document. Fiscal Year 1989

DTIC Science & Technology

1987-12-01

other special interest areas/technologies; and throuch a " delphi " process with the Center Technical Investment Committee *develop a "puts and takes...radar and larce optical systems in space, the detection and trackina of low observables, and the operation of sensors for tracking objects in space for...for reducing the processing time for adaptive beamforming in receive arrays, self-coherina techniques in larce distributed arrays and array self

Removing Background Noise with Phased Array Signal Processing

NASA Technical Reports Server (NTRS)

Podboy, Gary; Stephens, David

2015-01-01

Preliminary results are presented from a test conducted to determine how well microphone phased array processing software could pull an acoustic signal out of background noise. The array consisted of 24 microphones in an aerodynamic fairing designed to be mounted in-flow. The processing was conducted using Functional Beam forming software developed by Optinav combined with cross spectral matrix subtraction. The test was conducted in the free-jet of the Nozzle Acoustic Test Rig at NASA GRC. The background noise was produced by the interaction of the free-jet flow with the solid surfaces in the flow. The acoustic signals were produced by acoustic drivers. The results show that the phased array processing was able to pull the acoustic signal out of the background noise provided the signal was no more than 20 dB below the background noise level measured using a conventional single microphone equipped with an aerodynamic forebody.

Fabrication of nano-gap electrode arrays by the construction and selective chemical etching of nano-crosswire stacks

NASA Technical Reports Server (NTRS)

Prokopuk, Nicholas (Inventor); Son, Kyung-Ah (Inventor)

2008-01-01

Methods of fabricating nano-gap electrode structures in array configurations, and the structures so produced. The fabrication method involves depositing first and second pluralities of electrodes comprising nanowires using processes such as lithography, deposition of metals, lift-off processes, and chemical etching that can be performed using conventional processing tools applicable to electronic materials processing. The gap spacing in the nano-gap electrode array is defined by the thickness of a sacrificial spacer layer that is deposited between the first and second pluralities of electrodes. The sacrificial spacer layer is removed by etching, thereby leaving a structure in which the distance between pairs of electrodes is substantially equal to the thickness of the sacrificial spacer layer. Electrode arrays with gaps measured in units of nanometers are produced. In one embodiment, the first and second pluralities of electrodes are aligned in mutually orthogonal orientations.

High-Resolution Spin-on-Patterning of Perovskite Thin Films for a Multiplexed Image Sensor Array.

PubMed

Lee, Woongchan; Lee, Jongha; Yun, Huiwon; Kim, Joonsoo; Park, Jinhong; Choi, Changsoon; Kim, Dong Chan; Seo, Hyunseon; Lee, Hakyong; Yu, Ji Woong; Lee, Won Bo; Kim, Dae-Hyeong

2017-10-01

Inorganic-organic hybrid perovskite thin films have attracted significant attention as an alternative to silicon in photon-absorbing devices mainly because of their superb optoelectronic properties. However, high-definition patterning of perovskite thin films, which is important for fabrication of the image sensor array, is hardly accomplished owing to their extreme instability in general photolithographic solvents. Here, a novel patterning process for perovskite thin films is described: the high-resolution spin-on-patterning (SoP) process. This fast and facile process is compatible with a variety of spin-coated perovskite materials and perovskite deposition techniques. The SoP process is successfully applied to develop a high-performance, ultrathin, and deformable perovskite-on-silicon multiplexed image sensor array, paving the road toward next-generation image sensor arrays. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Integration of Multi-sensor Data for Desertification Monitoring

NASA Astrophysics Data System (ADS)

Lin, S.; Kim, J.

2010-12-01

The desert area has been rapidly expanding globally due to reasons such as climate change, uninhibited human activities, etc. The continuous desertification has seriously affected in (and near) desert area all over the world. As sand dune activity has been recognised as an essential indicator of desertification (it is the signature and the consequence of desertification), an accurate monitoring of desert dune movement hence becomes crucial for understanding and modelling the progress of desertification. In order to determine dune’s moving speed and tendency, also to understand the propagation occurring in transition region between desert and soil rich area, a monitoring system applying multi-temporal and multi-sensor remote sensed data are proposed and implemented. Remote sensed data involved in the monitoring scheme include space-borne optical image, Synthetic Aperture Radar (SAR) data, multi- and hyper-spectral image, and terrestrial close range image. In order to determine the movement of dunes, a reference terrain surface is required. To this end, a digital terrain model (DTM) covering the test site is firstly produced using high resolution optical stereo satellite images. Subsequently, ERS-1/2 SAR imagery are employed as another resource for dune field observation. Through the interferometric SAR (InSAR) technique combining with image-based stereo DTM, the surface displacements are obtained. From which the movement and speed of the dunes can be determined. To understand the effect of desertification combating activities, the correlation between dune activities and the landcover change is also an important issue to be covered in the monitoring scheme. The task is accomplished by tracing soil and vegetation canopy variation with the multi and hyper spectral image analysis using Hyperion and Ali imagery derived from Earth Observation Mission 1 (EO-1). As a result, the correlation between the soil restorations, expanding of vegetation canopy and the ceasing of dune activities can be clearly revealed. For the very detailed measurement, a terrestrial system applying close range photogrammetry will be set up in the test sites to acquire sequential images and used to generate 4D model of the dunes in future. Finally, all the outputs from the multi-sensor data will be crossly verified and compiled to model the desertification process and the consequences. A desertification combating activity which is performed by Korea-China NGO alliance has been conducted in Qubuqi desert in Nei Mongol, China. The method and system proposed above will be established and applied to monitor the dune mobility occurring in this area. The results are expected to be of great value to demonstrate the first case of remote sensing monitoring over the combat desertification activities.