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Harshlata V.,Center for Remote Sensing, Inc.
Materials Today: Proceedings | Year: 2017

The protection, consistency and effectiveness of rotating machinery are of a key apprehension in industries. Condition monitoring of a machines helps to retain the effectiveness and performance of a machine to its optimal level. The condition monitoring of a rotating machine is efficient, but often it is difficult and labour intensive task for maintenance crew to troubleshoot the machine. Vibration analysis is a method used for condition monitoring of the machine. Effective vibration signal extracting techniques have a critical part in diagnosing a rotating machine. Many vibration signal extracting techniques have been proposed during past some years. The paper presents review of some vibration feature extraction methods applied to different types of rotating machines. © 2017 Elsevier Ltd. All rights reserved.


Schmidt M.,Center for Remote Sensing, Inc. | Schmidt M.,University of Queensland | Lucas R.,University of New South Wales | Bunting P.,Aberystwyth University | And 3 more authors.
Remote Sensing of Environment | Year: 2015

High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011) with an 8-day interval of a 30m spatial resolution data was generated by the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Landsat sensor observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data as input. The time series showed a close relationship over homogeneous forested and grassland sites, with r2 values of 0.99 between Landsat and the closest STARFM simulated data; and values of 0.84 and 0.94 between MODIS and STARFM. The time and magnitude of clearing and re-clearing events were estimated through a phenological breakpoint analysis, with 96.2% of the estimated breakpoints of the clearing event and 83.6% of the re-clearing event being within 40days of the true clearing. The study highlights the benefits of using these moderate resolution data for quantifying and understanding land cover change in open forest environments. © 2014 Elsevier Inc.


Lar U.A.,University of Jos | Agene J.I.,Center for Remote Sensing, Inc. | Umar A.I.,Federal Ministry of Mines and Steel Development
Environmental Geochemistry and Health | Year: 2015

Geophagy is a common practice among certain cultural groups especially women in some rural communities in Nigeria. The safety of eating such clays in terms of their heavy metal composition has not been ascertained, neither is the link between them and disease conditions established in geophagists. The analysis of field survey data reveals that the majority (about 90 %) of the women did not go beyond secondary school education. The geology of an area has a direct influence on the chemical composition of the soils. Therefore, this research was carried out to determine the mineralogical and the heavy metal content of some geophagic clay materials from Nigeria. All the geophagic clay materials are hydrated silicates of either Al, (Na and Ca), (Al and Mg), or/and (Mg and Fe). The concentration levels of Na, Al, Ca, Fe, Mg, Cu, and Zn are tolerable and apparently could serve as a veritable source of mineral nutrients deficient in the human body. An assessment of the level of contamination of heavy metals on the basis of the index of geo-accumulation (Igeo) shows that Cr, Cu, Zn, Co, and Ni (all with Igeo < 1) did not contaminate the clay materials. On the contrary, they are extremely contaminated by As, Cd and Se (Igeo = >5), and are moderately to strongly contaminated by Pb and Sb (Igeo = 2–3). In terms of health risk assessment, the presence of heavy metals such as As, Cd, Pb, Se, and Sb with a health risk index (HRI) >1, renders the geophagic clays unsafe for human consumption. Similarly, Al, Fe, and Na are in excess in the clay (HRI ⋙ 1) posing serious human health risks. Thus, the ingestion of geophagic clay materials by pregnant women and children when it contains heavy metals like Pb, As, Cd, Se, and Sb poses the risk of some medical disorders and should therefore be considered a public health problem. Since geophagic practice will persist despite civilization, we advocate finding ways of reducing heavy metal pollutants in geophagic clays through suitable remediation technology. © 2014, Springer Science+Business Media Dordrecht.


Samad O.E.,Lebanese Atomic Energy Commission | Baydoun R.,Lebanese Atomic Energy Commission | Nsouli B.,Lebanese Atomic Energy Commission | Darwish T.,Center for Remote Sensing, Inc.
Journal of Environmental Radioactivity | Year: 2013

The concentrations of natural and artificial radionuclides at 57 sampling locations along the North Province of Lebanon are reported. The samples were collected from uncultivated areas in a region not previously reported. The samples were analyzed by gamma spectrometers with High Purity Germanium detectors of 30% and 40% relative efficiency. The activity concentrations of primordial naturally occurring radionuclides of 238U, 232Th, and 40K varied between 4-73Bqkg-1, 5-50Bqkg-1, and 57-554Bqkg-1 respectively. The surface activity concentrations due to the presence of these radionuclides were calculated and Kriging-geostatistical method was used to plot the obtained data on the Lebanese radioactive map. The results for 238U, 232Th, and 40K ranged from 0.2kBqm-2 to 9kBqm-2, from 0.2kBqm-2 to 3kBqm-2, and from 3kBqm-2 to 29kBqm-2 respectively. For the anthropogenic radionuclides, the activity concentrations of 137Cs founded in soil ranged from 2Bqkg-1 to 113Bqkg-1, and the surface activity concentration from 0.1kBqm-2 to 5kBqm-2. The total absorbed gamma dose rates in air from natural and artificial radionuclides in these locations were calculated. The minimum value was 6nGyh-1 and the highest one was 135nGyh-1 with an average of 55nGyh-1 in which the natural terrestrial radiation contributes in 99% and the artificial radionuclides mainly 137Cs contributes only in 1%. The total effective dose calculated varied in the range of 7μSvy-1 and 166μSvy-1 while the average value was 69μSvy-1 which is below the permissible limit 1000μSvy-1. © 2013 Elsevier Ltd.


Cheddadi R.,Montpellier University | Khater C.,Center for Remote Sensing, Inc.
Quaternary Science Reviews | Year: 2016

In this study, we quantified the mean January temperature (Tjan) and both winter (Pw) and summer (Ps) precipitation from three fossil pollen records from Lebanon. Tjan showed a strong correlation with the global temperature changes retrieved in the NGRIP Greenland ice core. The amplitude of ca. 8 °C between the Younger Dryas (YD) period and the Holocene is coherent with climate reconstructions from the Eastern Mediterranean. The overall amount of precipitation was also lower during the YD than during the Holocene but the contrast between Pw and Ps was much more reduced (less than 2 times) during the YD than during the Holocene (up to 8 times). Such different seasonal contrast compare to the present day is coherent with some climate proxies from the Levant that tend to indicate the presence of moisture during the last glacial period. In effect, the low Pw during the YD reflects the replacement of the forest ecosystem by a more shrubby or herbaceous vegetation. Concomitantly, the occurrence of an amount of precipitation higher than the current one during the summer season, along with a reduced evaporation, due to lower temperature, may have contributed to some local observed high lake levels in the area. During the last glacial period, Lebanon was not under a typical Mediterranean climate such as the one we know today, i.e. with a strong precipitation and temperature contrast between summer and winter seasons, but rather under a less contrasted climate. Mediterranean species persisted in this area due to the low amplitude of temperature change between the last glacial period and the Holocene as well as to an availability of moisture throughout the year instead of an occurrence mainly during the winter season as is the case today. © 2016 Elsevier Ltd


Eustace A.H.,Center for Remote Sensing, Inc. | Pringle M.J.,Center for Remote Sensing, Inc. | Denham R.J.,Center for Remote Sensing, Inc.
European Journal of Soil Science | Year: 2011

In central Queensland, Australia, relatively little is known about where gullies occur ('gully presence'). This is despite a general acceptance among scientists and politicians that gully erosion in the region is an ecologically important process, exacerbated by grazing pressure. We aimed to create a risk map of gully presence for a 4.86 × 106-ha area of central Queensland dominated by grazing and thought to be particularly prone to gully erosion. We achieved this by using (i) light detection and ranging (lidar) technology (vertical accuracy < 0.15 m; spatial resolution 0.5 m) to observe topography on transects at eight selected sites within the study area, (ii) object-oriented classification to derive gully presence from lidar observations and (iii) a random forest to model the relationship between gully presence and a set of readily available explanatory variables (comprising soil, topography, and vegetation information; finest spatial resolution 25 m) and (iv) extrapolating the model to unsampled locations. Cross-validation indicated that the predictive ability of the model was modest, with an average area under the receiver operating characteristic curve of 0.62 (where 1.0 is a perfect model and 0.5 is no better than chance). The greatest risk of gully presence was associated with areas of large topographic variation, and where, coincidentally, there was relatively little long-term vegetation cover. Ultimately, however, we acknowledge that the quality of the map is limited by the small area of observed lidar data relative to the study area, the relatively coarse spatial resolution of the explanatory variables and the possibility that gully presence is the result of different processes at different locations. © 2011 The Authors. Journal compilation © 2011 British Society of Soil Science.


Goodwin N.R.,Center for Remote Sensing, Inc. | Collett L.J.,Center for Remote Sensing, Inc. | Denham R.J.,Center for Remote Sensing, Inc. | Flood N.,Center for Remote Sensing, Inc. | Tindall D.,Center for Remote Sensing, Inc.
Remote Sensing of Environment | Year: 2013

The 20. + year collection and moderate resolution of Landsat Thematic Mapper (TM) imagery and Enhanced Thematic Mapper (ETM. +) imagery provide a crucial data source for analysing land surface change over time for a range of applications. In Queensland, Australia, a number of government policies, natural resource management programmes and research activities are reliant on large-area, multi-temporal land cover monitoring applications based on Landsat satellite imagery. However, clouds and associated cloud shadows frequently obstruct the view of the land surface. The restriction of analyses to cloud-free imagery will reduce the opportunities to sample the land surface and limit the analysis of trends in reflectance over time. This study presents a new automated method to screen cloud and cloud shadow and is intended for application to entire time series of Landsat imagery rather than single images processed in near- real time. The method uses a hierarchical approach and takes advantage of spectral, temporal, and contextual information. Outliers are located relative to the time series of land surface reflectance by smoothing time series information using minimum and median filters which are then used in multi-temporal image differencing. Seeded region grow and morphological dilation (pixel buffering) filters are then applied to map a larger spatial extent of the cloud/cloud shadow. Spectral and contextual rules were developed empirically using calibration and validation data derived from six Landsat WRS Path/Rows (number of images= 60) with varying climatic and land surface characteristics across the state of Queensland, Australia. The validation demonstrates that cloud contaminated pixels were accurately classified with producer's, user's and overall accuracies of 98, 87 and 97%, respectively. The ability to detect cloud shadow was less accurate, in comparison, with producer's, user's and overall accuracies of 90, 62 and 97%, respectively. The pixel buffer was found to be the largest source of commission error for cloud and cloud shadow in the final classifications. However, for many applications removing additional cloud/shadow at the expense of higher commission errors may be desirable. The performance of the method was also compared with the published Fmask (Function of mask) method. This demonstrated a moderate improvement in the detection of cloud (producer's accuracies: time series 98% and Fmask 90%; and an equivalent user's accuracy of 87%), and a significant improvement in the detection of cloud shadow (producer's accuracies: time series 90% and Fmask 78%; user's accuracies: time series 62% and Fmask 50%). Importantly, the results indicate that this automated method is robust and that temporal information can improve the detection of cloud and cloud shadow, although shadow detection above cropping areas is limited. The calibration/validation of the method has been restricted to Queensland, Australia. With further development there is potential for this method or one using a similar framework to have wider application in other landscapes. © 2013.


Awad M.M.,Center for Remote Sensing, Inc.
International Journal of Remote Sensing | Year: 2012

Image segmentation is a central process in image processing. There are many segmentation methods such as region growing, edge detection, split and merge and artificial neural networks (ANNs). However, the most important and popular are clustering methods. Normally, clustering methods select cluster centres randomly to segment an image into disjoint and homogeneous regions. The use of random cluster centres without a priori knowledge leads to degradation in the accuracy of the obtained results. However, combined with edge detection, shape representation can help in improving the clustering methods. The improvement is obtained by knowing the optimal location of the cluster centres at the beginning of the image segmentation process. In this article, a new geometric model for high-resolution satellite image segmentation is implemented that can overcome the problem encountered in random clustering processes. The proposed model uses Canny-Deriche edge detection and the modified non-uniform rational B-spline (NURBS) methods to generate the control points of the edges. These points are used to identify cluster centres that are necessary to create the population of the hybrid dynamic genetic algorithm (HDGA). The new geometric model is compared with the self-organizing maps (SOMs) method, which is an efficient unsupervised ANN method. Two experiments are conducted using high-resolution satellite images, and the results prove the high accuracy and reliability of the new evolutionary geometric model. © 2012 Copyright Taylor and Francis Group, LLC.


Goodwin N.R.,Center for Remote Sensing, Inc. | Collett L.J.,Center for Remote Sensing, Inc.
Remote Sensing of Environment | Year: 2014

Remote sensing can quantify past and present fire activity at spatial scales useful for a range of fire and vegetation management applications. In this study, we present a new automated approach to classifying burnt areas across the state of Queensland, Australia. The method is applied to complete time series of Landsat TM/ETM. + imagery rather than single images and considers spectral (band 4, B4, and bands 4. +. 5, B45), thermal, temporal and contextual information within a hierarchical framework. To maximise the available observations and the burnt area detected, we used imagery containing up to 60% cloud that was screened during pre-processing. Median filters were applied to smooth the time series and multi-date change detection used to locate negative outliers (large declines in reflectance relative to the median-smoothed time series). Watershed region growing was used to segment and map a larger spatial extent of the change while minimising commission errors. These segmented change objects were attributed as either burnt or unburnt using their thermal, reflective and contextual characteristics in a classification tree. Thermal information was found to be more important than reflective indices in the change attribution. Algorithm calibration used training data from ten Path/Rows located strategically across Queensland with four images sampled per path row (n= 40). Thresholds were optimised to maximise the burnt area detected while limiting under/over-growing of burnt area. Validation data covered a range of burnt areas from ten independent Path/Rows with ten images sampled across a range of burnt area fractions per Path/Row (n= 100). The results for burnt area mapping demonstrated an average producer's accuracy of 85% (range of 28 to 100% for individual images) and average user's accuracy of 71% (range of 4 to 99% for individual images). A morphological dilation of one pixel restricted to locations exhibiting a decline in B45 over time, increased the producer's accuracy by 4% but reduced the user's accuracy by 8%. The total accuracy for the burnt area classification was greater than 99%, however this is more a reflection of the small fraction of landscape represented by burnt area rather than the ability to detect burnt area. Areas frequently misclassified were related to areas of high spectral/land use change which included areas of cropping, frequently inundated land, and moisture/ground cover variations over dark soils. In this study, we applied a crop and water mask to minimise commission errors. Significantly, the results of this study demonstrate that an automated time series method for mapping burnt areas can be successfully applied across a diversity of land cover types. The method may be applied in similar savanna dominated environments but is likely to require modification to be applicable in other landscapes. © 2014 Elsevier Inc.


Grant
Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase I | Award Amount: 99.79K | Year: 2011

With the rapid strides in various avionics-related technologies, the need for advanced simulators will increase. Anti-jam receiver development and future improvements in PNT are critically dependent on the availability of advanced simulators. The needs include: flexible, accurate, adaptable, programmable, user-friendly, hardware in the loop operation, precise wavefront simulation, high dynamics, environment and jamming simulation, etc. The simulator for the 21st century will have to precisely accurately represent various environments ranging from urban canyons, nuclear and plasma effects for the complete wavefront. Large update rates and significant processing power are required. A navigation simulator with these features and capable of generating all current and future signals (CA, P, M, L2C, L5, Jammer waveforms,GNSS, etc.) is proposed. CRS has developed a modern wavefront simulator for all GNSS signals.This proposal details adapting the simulator to meet MDA objectives and optimize the system for cost/performance ratio. This system will be capable of multi-satellite, multi-interferer real-time RF output for multiple antennas. It will be capable of integration with a variety of other hardware simulation tools and GPS receivers. The proposed approach will leverage CRS"s current capabilities and expertise and will result, at the end of Phase II, in a fully functioning working system

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