SENSE-MAKING TECHNIQUES IN EDUCATIONAL PROCESS AND THEIR IMPACT ON THE PERSONAL CHARACTERISTICS OF STUDENTS

Directory of Open Access Journals (Sweden)

Irina V. Abakumova

2017-12-01

Full Text Available This study looks into psychotechnics used in education and contributing to initiating logic among students, their personal growth and characterizes psychological features of “sense-deducting”. Here you will find a review of the sense-making techniques considering as one of the categories of psychotechnics. The described techniques are based on the human psychology, they improve the quality of instruction, create a favorable and unique system of values, take into account the individual characteristics of all types of education, and influence the sense-making process development among children. Sense-making techniques are stated in the author’s classification and extended by practical methods. The study of psychological features of influence of sense-making techniques on the personality of a student lets us see new patterns in personal, subjective and “meta-subjective” results of acquiring of the school program via transformation and development of value/logic consciousness of a child. The work emphasizes that the use of sense-making techniques is effective in the educational and after-school activities of the educational organization. The achieved results make it possible to understand, to substantiate the naturalness and relevance of the sense-technical approach according to personal and academic indicators of students. In the process of competent and correct use of the semantic techniques, we see the possibility of conveying the best, productive and quality pedagogical experience, as well as the perspective of innovative developments in the psychological and pedagogical sciences. For children and adolescents, information, thanks to sense-techniques, starts to be personal in nature, knowledge is objectified, learning activity becomes an individual need.

Multidimensional Signal Processing for Sensing & Communications

Science.gov (United States)

2015-07-29

Spectrum Sensing,” submitted to IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico , 13-16 Dec. 2015...Sensing,” submitted to IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico , 13-16 Dec. 2015...diversity in echolocating mammals ,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 65- 75, Jan. 2009. DISTRIBUTION A: Distribution approved for

Search asymmetry: a diagnostic for preattentive processing of separable features.

Science.gov (United States)

Treisman, A; Souther, J

1985-09-01

The search rate for a target among distractors may vary dramatically depending on which stimulus plays the role of target and which that of distractors. For example, the time required to find a circle distinguished by an intersecting line is independent of the number of regular circles in the display, whereas the time to find a regular circle among circles with lines increases linearly with the number of distractors. The pattern of performance suggests parallel processing when the target has a unique distinguishing feature and serial self-terminating search when the target is distinguished only by the absence of a feature that is present in all the distractors. The results are consistent with feature-integration theory (Treisman & Gelade, 1980), which predicts that a single feature should be detected by the mere presence of activity in the relevant feature map, whereas tasks that require subjects to locate multiple instances of a feature demand focused attention. Search asymmetries may therefore offer a new diagnostic to identify the primitive features of early vision. Several candidate features are examined in this article: Colors, line ends or terminators, and closure (in the sense of a partly or wholly enclosed area) appear to be functional features; connectedness, intactness (absence of an intersecting line), and acute angles do not.

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Directory of Open Access Journals (Sweden)

Yansheng Li

2016-08-01

Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

Ontology-aided feature correlation for multi-modal urban sensing

Science.gov (United States)

Misra, Archan; Lantra, Zaman; Jayarajah, Kasthuri

2016-05-01

The paper explores the use of correlation across features extracted from different sensing channels to help in urban situational understanding. We use real-world datasets to show how such correlation can improve the accuracy of detection of city-wide events by combining metadata analysis with image analysis of Instagram content. We demonstrate this through a case study on the Singapore Haze. We show that simple ontological relationships and reasoning can significantly help in automating such correlation-based understanding of transient urban events.

MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

Directory of Open Access Journals (Sweden)

Y. Di

2017-05-01

Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

Selective Sensing of Gas Mixture via a Temperature Modulation Approach: New Strategy for Potentiometric Gas Sensor Obtaining Satisfactory Discriminating Features.

Science.gov (United States)

Li, Fu-An; Jin, Han; Wang, Jinxia; Zou, Jie; Jian, Jiawen

2017-03-12

A new strategy to discriminate four types of hazardous gases is proposed in this research. Through modulating the operating temperature and the processing response signal with a pattern recognition algorithm, a gas sensor consisting of a single sensing electrode, i.e., ZnO/In₂O₃ composite, is designed to differentiate NO₂, NH₃, C₃H₆, CO within the level of 50-400 ppm. Results indicate that with adding 15 wt.% ZnO to In₂O₃, the sensor fabricated at 900 °C shows optimal sensing characteristics in detecting all the studied gases. Moreover, with the aid of the principle component analysis (PCA) algorithm, the sensor operating in the temperature modulation mode demonstrates acceptable discrimination features. The satisfactory discrimination features disclose the future that it is possible to differentiate gas mixture efficiently through operating a single electrode sensor at temperature modulation mode.

Hydrogen gas sensing feature of polyaniline/titania (rutile) nanocomposite at environmental conditions

Science.gov (United States)

Milani Moghaddam, Hossain; Nasirian, Shahruz

2014-10-01

The resistance-based sensors of polyaniline/titania (rutile) nanocomposite (TPNC) were prepared by spin coating technique onto an epoxy glass substrate with Cu-interdigited electrodes to study their hydrogen (H2) gas sensing features. Our findings are that the change of the surface morphology, porosity and wt% of titania in TPNCs have a significant effect on H2 gas sensing of sensors. All of the sensors had a reproducibility response toward 0.8 vol% H2 gas at room temperature, air pressure and 50% relative humidity. A sensor with 40 wt% of titania nanoparticles had better response/recovery time and the response than other sensors. Moreover, H2 gas sensing mechanism of TPNC sensors based contact areas and the correlation of energy levels between PANI chains and the titania grains were studied.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

Science.gov (United States)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Hemispheric asymmetry of the brain as a psycho-physiological basis of individual and typological features of the formation of a sense of humour

Directory of Open Access Journals (Sweden)

Shportun O.N.

2016-05-01

Full Text Available The article describes the psycho-physiological peculiarities of hemispheric asymmetry of the brain as the basis of individual and typological features of the formation of a sense of humour. The analysis of the impact of the functional brain hemispheric asymmetry on emotional, intellectual and physiological features of development of sense of humour in ontogeny is conducted. Analysis of studies of inter-hemispheric asymmetry of the brain makes it possible to ascertain the impact of the functioning of each hemisphere on the formation of the perception of humour. Studies show that in the process of developing of sense of humour, two functional hemispheres of the brain are involved. As the emotion of humour – is an intellectual emotion, and in the development of intelligence a lot of mental processes are involved, in the formation of humour two hemispheres of the brain are functioned. The right hemisphere is responsible for the emotional nature of humour (intonation, sound level of language, speed of response to a joke ..., the left hemisphere – for processing verbal information (content of the joke, category, purpose, content analysis .... After analysing the research of hemispheric functional asymmetry of the human brain, its psycho-physiological and neurochemical characteristics, it can be assumed that people with more developed left hemisphere in perceiving humour are more prone to displays of gelotophilia and “right hemisphere” people – show signs of gelotophobia and katagelasticism. Examining gender differences of hemisphere asymmetry of the brain, it can be argued that diagnosing sense of humour is important to take into account gender-specific functioning of hemispheres, because men have more clearly functioning the left hemisphere, and women – the right one. This fact of sexual peculiarities of functioning of inter-hemispheric asymmetry of the brain allows diagnosing objectively sense of humour, as well as different variations

Introduction to remote sensing

CERN Document Server

Cracknell, Arthur P

2007-01-01

Addressing the need for updated information in remote sensing, Introduction to Remote Sensing, Second Edition provides a full and authoritative introduction for scientists who need to know the scope, potential, and limitations in the field. The authors discuss the physical principles of common remote sensing systems and examine the processing, interpretation, and applications of data. This new edition features updated and expanded material, including greater coverage of applications from across earth, environmental, atmospheric, and oceanographic sciences. Illustrated with remotely sensed colo

Sensing Features of Long Period Gratings in Hollow Core Fibers

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Agostino Iadicicco

2015-04-01

Full Text Available We report on the investigation of the sensing features of the Long-Period fiber Gratings (LPGs fabricated in hollow core photonic crystal fibers (HC-PCFs by the pressure assisted Electric Arc Discharge (EAD technique. In particular, the characterization of the LPG in terms of shift in resonant wavelengths and changes in attenuation band depth to the environmental parameters: strain, temperature, curvature, refractive index and pressure is presented. The achieved results show that LPGs in HC-PCFs represent a novel high performance sensing platform for measurements of different physical parameters including strain, temperature and, especially, for measurements of environmental pressure. The pressure sensitivity enhancement is about four times greater if we compare LPGs in HC and standard fibers. Moreover, differently from LPGs in standard fibers, these LPGs realized in innovative fibers, i.e., the HC-PCFs, are not sensitive to surrounding refractive index.

Hydrogen gas sensing feature of polyaniline/titania (rutile) nanocomposite at environmental conditions

Energy Technology Data Exchange (ETDEWEB)

Milani Moghaddam, Hossain, E-mail: hossainmilani@yahoo.com [Solid State Physics Department, University of Mazandaran, Babolsar (Iran, Islamic Republic of); Nasirian, Shahruz [Solid State Physics Department, University of Mazandaran, Babolsar (Iran, Islamic Republic of); Basic Sciences Department, Mazandaran University of Science and Technology, Babol (Iran, Islamic Republic of)

2014-10-30

Graphical abstract: - Highlights: • Polyaniline/titania (rutile) nanocomposite (TPNC) was synthesized by a chemical oxidative polymerization method. • Surface morphology and titania (rutile) wt% in TPNC sensors were significant factors for H{sub 2} gas sensing. • TPNC sensors could be used for H{sub 2} gas sensing at different R.H. humidity. • TPNC Sensors exhibited considerable sensitive, reversible and repeatable response to H{sub 2} gas at environmental conditions. - Abstract: The resistance-based sensors of polyaniline/titania (rutile) nanocomposite (TPNC) were prepared by spin coating technique onto an epoxy glass substrate with Cu-interdigited electrodes to study their hydrogen (H{sub 2}) gas sensing features. Our findings are that the change of the surface morphology, porosity and wt% of titania in TPNCs have a significant effect on H{sub 2} gas sensing of sensors. All of the sensors had a reproducibility response toward 0.8 vol% H{sub 2} gas at room temperature, air pressure and 50% relative humidity. A sensor with 40 wt% of titania nanoparticles had better response/recovery time and the response than other sensors. Moreover, H{sub 2} gas sensing mechanism of TPNC sensors based contact areas and the correlation of energy levels between PANI chains and the titania grains were studied.

Divided Attention and Processes Underlying Sense of Agency

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Wen eWen

2016-01-01

Full Text Available Sense of agency refers to the subjective feeling of controlling events through one’s behavior or will. Sense of agency results from matching predictions of one’s own actions with actual feedback regarding the action. Furthermore, when an action involves a cued goal, performance-based inference contributes to sense of agency. That is, if people achieve their goal, they would believe themselves to be in control. Previous studies have shown that both action-effect comparison and performance-based inference contribute to sense of agency; however, the dominance of one process over the other may shift based on task conditions such as the presence or absence of specific goals. In this study, we examined the influence of divided attention on these two processes underlying sense of agency in two conditions. In the experimental task, participants continuously controlled a moving dot for 10 s while maintaining a string of three or seven digits in working memory. We found that when there was no cued goal (no-cued-goal condition, sense of agency was impaired by high cognitive load. Contrastingly, when participants controlled the dot based on a cued goal (cued-goal-directed condition, their sense of agency was lower than in the no-cued-goal condition and was not affected by cognitive load. The results suggest that the action-effect comparison process underlying sense of agency requires attention. On the other hand, the weaker influence of divided attention in the cued-goal-directed condition could be attributed to the dominance of performance-based inference, which is probably automatic.

Sensing the gas metal arc welding process

Science.gov (United States)

Carlson, N. M.; Johnson, J. A.; Smartt, H. B.; Watkins, A. D.; Larsen, E. D.; Taylor, P. L.; Waddoups, M. A.

1994-01-01

Control of gas metal arc welding (GMAW) requires real-time sensing of the process. Three sensing techniques for GMAW are being developed at the Idaho National Engineering Laboratory (INEL). These are (1) noncontacting ultrasonic sensing using a laser/EMAT (electromagnetic acoustic transducer) to detect defects in the solidified weld on a pass-by-pass basis, (2) integrated optical sensing using a CCD camera and a laser stripe to obtain cooling rate and weld bead geometry information, and (3) monitoring fluctuations in digitized welding voltage data to detect the mode of metal droplet transfer and assure that the desired mass input is achieved.

DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM

Directory of Open Access Journals (Sweden)

H.-T. Gao

2016-06-01

Full Text Available Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.

AUTOMATIC GLOBAL REGISTRATION BETWEEN AIRBORNE LIDAR DATA AND REMOTE SENSING IMAGE BASED ON STRAIGHT LINE FEATURES

Directory of Open Access Journals (Sweden)

Z. Q. Liu

2018-04-01

Full Text Available An automatic global registration approach for point clouds and remote sensing image based on straight line features is proposed which is insensitive to rotational and scale transformation. First, the building ridge lines and contour lines in point clouds are automatically detected as registration primitives by integrating region growth and topology identification. Second, the collinear condition equation is selected as registration transformation function which is based on rotation matrix described by unit quaternion. The similarity measure is established according to the distance between the corresponding straight line features from point clouds and the image in the same reference coordinate system. Finally, an iterative Hough transform is adopted to simultaneously estimate the parameters and obtain correspondence between registration primitives. Experimental results prove the proposed method is valid and the spectral information is useful for the following classification processing.

Classification of line features from remote sensing data

OpenAIRE

Kolankiewiczová, Soňa

2009-01-01

This work deals with object-based classification of high resolution data. The aim of the thesis (paper, work) is to develope an acceptable classification process of linear features (roads and railways) from high-resolution satellite images. The first part shows different approaches of the linear feature classification and compares theoretic differences between an object-oriented and a pixel-based classification. Linear feature classification was created in the second part. The high-resolution...

Spectral characteristics and feature selection of satellite remote sensing data for climate and anthropogenic changes assessment in Bucharest area

Science.gov (United States)

Zoran, Maria; Savastru, Roxana; Savastru, Dan; Tautan, Marina; Miclos, Sorin; Cristescu, Luminita; Carstea, Elfrida; Baschir, Laurentiu

2010-05-01

was suggested. On the basis of analyzing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from remote sensing data was discussed. Spectral signatures of different terrain features have been used to extract structural patterns aiming to separate surface units and to classify the general categories. The synergetic analysis and interpretation of the different satellite images (LANDSAT: TM, ETM; MODIS, IKONOS) acquired over a period of more than 20 years reveals significant aspects regarding impacts of climate and anthropogenic changes on urban/periurban environment. It was delimited residential zones of industrial zones which are very often a source of pollution. An important role has urban green cover assessment. Have been emphasized the particularities of the functional zones from different points of view: architectural, streets and urban surface traffic, some components of urban infrastructure as well as habitat quality. The growth of Bucharest urban area in Romania has been a result of a rapid process of industrialization, and also of the increase of urban population. Information on the spatial pattern and temporal dynamics of land cover and land use of urban areas is critical to address a wide range of practical problems relating to urban regeneration, urban sustainability and rational planning policy.

Including product features in process redesign

DEFF Research Database (Denmark)

Hvam, Lars; Hauksdóttir, Dagný; Mortensen, Niels Henrik

2017-01-01

do not take into account how the product features are applied throughout the process, which makes it difficult to obtain a comprehensive understanding of the activities in the processes and to generate significant improvements. The suggested approach models the product family using the so......This article suggests a visual modelling method for integrating models of product features with business process models for redesigning the business processes involving specifications of customer-tailored products and services. The current methods for redesigning these types of business processes......-called product variant master and the business process modelling notation for modelling the process flow. The product model is combined with the process map by identifying features used in each step of the process flow. Additionally, based on the information absorbed from the integrated model, the value stream...

Fusion of shallow and deep features for classification of high-resolution remote sensing images

Science.gov (United States)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

Distributed Aerodynamic Sensing and Processing Toolbox

Science.gov (United States)

Brenner, Martin; Jutte, Christine; Mangalam, Arun

2011-01-01

A Distributed Aerodynamic Sensing and Processing (DASP) toolbox was designed and fabricated for flight test applications with an Aerostructures Test Wing (ATW) mounted under the fuselage of an F-15B on the Flight Test Fixture (FTF). DASP monitors and processes the aerodynamics with the structural dynamics using nonintrusive, surface-mounted, hot-film sensing. This aerodynamic measurement tool benefits programs devoted to static/dynamic load alleviation, body freedom flutter suppression, buffet control, improvement of aerodynamic efficiency through cruise control, supersonic wave drag reduction through shock control, etc. This DASP toolbox measures local and global unsteady aerodynamic load distribution with distributed sensing. It determines correlation between aerodynamic observables (aero forces) and structural dynamics, and allows control authority increase through aeroelastic shaping and active flow control. It offers improvements in flutter suppression and, in particular, body freedom flutter suppression, as well as aerodynamic performance of wings for increased range/endurance of manned/ unmanned flight vehicles. Other improvements include inlet performance with closed-loop active flow control, and development and validation of advanced analytical and computational tools for unsteady aerodynamics.

Broadband pH-Sensing Organic Transistors with Polymeric Sensing Layers Featuring Liquid Crystal Microdomains Encapsulated by Di-Block Copolymer Chains.

Science.gov (United States)

Seo, Jooyeok; Song, Myeonghun; Jeong, Jaehoon; Nam, Sungho; Heo, Inseok; Park, Soo-Young; Kang, Inn-Kyu; Lee, Joon-Hyung; Kim, Hwajeong; Kim, Youngkyoo

2016-09-14

We report broadband pH-sensing organic field-effect transistors (OFETs) with the polymer-dispersed liquid crystal (PDLC) sensing layers. The PDLC layers are prepared by spin-coating using ethanol solutions containing 4-cyano-4'-pentyl-biphenyl (5CB) and a diblock copolymer (PAA-b-PCBOA) that consists of LC-philic block [poly(4-cyano-biphenyl-4-oxyundecyl acrylate) (PCBOA)] and acrylic acid block [poly(acrylic acid) (PAA)]. The spin-coated sensing layers feature of 5CB microdomains (pH with only small amounts (10-40 μL) of analyte solutions in both static and dynamic perfusion modes. The positive drain current change is measured for acidic solutions (pH pH > 7) result in the negative change of drain current. The drain current trend in the present PDLC-i-OFET devices is explained by the shrinking-expanding mechanism of the PAA chains in the diblock copolymer layers.

Portable remote sensing image processing system; Kahangata remote sensing gazo shori system

Energy Technology Data Exchange (ETDEWEB)

Fujikawa, S; Uchida, K; Tanaka, S; Jingo, H [Dowa Engineering Co. Ltd., Tokyo (Japan); Hato, M [Earth Remote Sensing Data Analysis Center, Tokyo (Japan)

1997-10-22

Recently, geological analysis using remote sensing data has been put into practice due to data with high spectral resolution and high spatial resolution. There has been a remarkable increase in both software and hardware of personal computer. Software is independent of hardware due to Windows. It has become easy to develop softwares. Under such situation, a portable remote sensing image processing system coping with Window 95 has been developed. Using this system, basic image processing can be conducted, and present location can be displayed on the image in real time by linking with GPS. Accordingly, it is not required to bring printed images for the field works of image processing. This system can be used instead of topographic maps for overseas surveys. Microsoft Visual C++ ver. 2.0 is used for the software. 1 fig.

A Neutral-Network-Fusion Architecture for Automatic Extraction of Oceanographic Features from Satellite Remote Sensing Imagery

National Research Council Canada - National Science Library

Askari, Farid

1999-01-01

This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence...

Ionization current sensing; Jonstroem-maetning

Energy Technology Data Exchange (ETDEWEB)

Aengeby, Jakob; Goeras, Anders; Nytomt, Jan [Hoerbiger Control Systems AB, Aamaal, (Sweden)

2012-05-15

Ion current measurements give information on the combustion in the cylinders of an internal combustion engine in run time, cycle by cycle. Ion sense has been used in gasoline engines for many years for detection of knock and misfire, combustion stability and for air to fuel ratio estimation. However, the use of ion sense in industrial gas engines has been limited, despite the potential of ion sense. The objective with the project is to investigate which combustion process information that can be retrieved using ion sense applied to industrial lean burn engines using pre-chambers for the ignition in which case the spark plug is encapsulated in the pre-chamber. Experiments show that ion current measured in the pre-chamber can successfully be used to retrieve information from the in-cylinder combustion process. It is possible to detect misfire and to some extent knock. It is also possible to optimize the ignition and hence minimize emissions and optimize the performance by using the ion current measured in the pre- chamber. A statistical signal processing approach to use more than one ion current feature in the estimation of combustion parameters was evaluated on a heavy duty gas engine. By using more than one feature the performance of in cylinder air to fuel ratio estimation was improved.

Researching on the process of remote sensing video imagery

Science.gov (United States)

Wang, He-rao; Zheng, Xin-qi; Sun, Yi-bo; Jia, Zong-ren; Wang, He-zhan

Unmanned air vehicle remotely-sensed imagery on the low-altitude has the advantages of higher revolution, easy-shooting, real-time accessing, etc. It's been widely used in mapping , target identification, and other fields in recent years. However, because of conditional limitation, the video images are unstable, the targets move fast, and the shooting background is complex, etc., thus it is difficult to process the video images in this situation. In other fields, especially in the field of computer vision, the researches on video images are more extensive., which is very helpful for processing the remotely-sensed imagery on the low-altitude. Based on this, this paper analyzes and summarizes amounts of video image processing achievement in different fields, including research purposes, data sources, and the pros and cons of technology. Meantime, this paper explores the technology methods more suitable for low-altitude video image processing of remote sensing.

A Method of Road Extraction from High-resolution Remote Sensing Images Based on Shape Features

Directory of Open Access Journals (Sweden)

LEI Xiaoqi

2016-02-01

Full Text Available Road extraction from high-resolution remote sensing image is an important and difficult task.Since remote sensing images include complicated information,the methods that extract roads by spectral,texture and linear features have certain limitations.Also,many methods need human-intervention to get the road seeds(semi-automatic extraction,which have the great human-dependence and low efficiency.The road-extraction method,which uses the image segmentation based on principle of local gray consistency and integration shape features,is proposed in this paper.Firstly,the image is segmented,and then the linear and curve roads are obtained by using several object shape features,so the method that just only extract linear roads are rectified.Secondly,the step of road extraction is carried out based on the region growth,the road seeds are automatic selected and the road network is extracted.Finally,the extracted roads are regulated by combining the edge information.In experiments,the images that including the better gray uniform of road and the worse illuminated of road surface were chosen,and the results prove that the method of this study is promising.

Dry adhesives with sensing features

International Nuclear Information System (INIS)

Krahn, J; Menon, C

2013-01-01

Geckos are capable of detecting detachment of their feet. Inspired by this basic observation, a novel functional dry adhesive is proposed, which can be used to measure the instantaneous forces and torques acting on an adhesive pad. Such a novel sensing dry adhesive could potentially be used by climbing robots to quickly realize and respond appropriately to catastrophic detachment conditions. The proposed torque and force sensing dry adhesive was fabricated by mixing Carbon Black (CB) and Polydimethylsiloxane (PDMS) to form a functionalized adhesive with mushroom caps. The addition of CB to PDMS resulted in conductive PDMS which, when under compression, tension or torque, resulted in a change in the resistance across the adhesive patch terminals. The proposed design of the functionalized dry adhesive enables distinguishing an applied torque from a compressive force in a single adhesive pad. A model based on beam theory was used to predict the change in resistance across the terminals as either a torque or compressive force was applied to the adhesive patch. Under a compressive force, the sensing dry adhesive was capable of measuring compression stresses from 0.11 Pa to 20.9 kPa. The torque measured by the adhesive patch ranged from 2.6 to 10 mN m, at which point the dry adhesives became detached. The adhesive strength was 1.75 kPa under an applied preload of 1.65 kPa for an adhesive patch with an adhesive contact area of 7.07 cm 2 . (paper)

Dry adhesives with sensing features

Science.gov (United States)

Krahn, J.; Menon, C.

2013-08-01

Geckos are capable of detecting detachment of their feet. Inspired by this basic observation, a novel functional dry adhesive is proposed, which can be used to measure the instantaneous forces and torques acting on an adhesive pad. Such a novel sensing dry adhesive could potentially be used by climbing robots to quickly realize and respond appropriately to catastrophic detachment conditions. The proposed torque and force sensing dry adhesive was fabricated by mixing Carbon Black (CB) and Polydimethylsiloxane (PDMS) to form a functionalized adhesive with mushroom caps. The addition of CB to PDMS resulted in conductive PDMS which, when under compression, tension or torque, resulted in a change in the resistance across the adhesive patch terminals. The proposed design of the functionalized dry adhesive enables distinguishing an applied torque from a compressive force in a single adhesive pad. A model based on beam theory was used to predict the change in resistance across the terminals as either a torque or compressive force was applied to the adhesive patch. Under a compressive force, the sensing dry adhesive was capable of measuring compression stresses from 0.11 Pa to 20.9 kPa. The torque measured by the adhesive patch ranged from 2.6 to 10 mN m, at which point the dry adhesives became detached. The adhesive strength was 1.75 kPa under an applied preload of 1.65 kPa for an adhesive patch with an adhesive contact area of 7.07 cm2.

Online sensing and control of oil in process wastewater

Science.gov (United States)

Khomchenko, Irina B.; Soukhomlinoff, Alexander D.; Mitchell, T. F.; Selenow, Alexander E.

2002-02-01

Industrial processes, which eliminate high concentration of oil in their waste stream, find it extremely difficult to measure and control the water purification process. Most oil separation processes involve chemical separation using highly corrosive caustics, acids, surfactants, and emulsifiers. Included in the output of this chemical treatment process are highly adhesive tar-like globules, emulsified and surface oils, and other emulsified chemicals, in addition to suspended solids. The level of oil/hydrocarbons concentration in the wastewater process may fluctuate from 1 ppm to 10,000 ppm, depending upon the specifications of the industry and level of water quality control. The authors have developed a sensing technology, which provides the accuracy of scatter/absorption sensing in a contactless environment by combining these methodologies with reflective measurement. The sensitivity of the sensor may be modified by changing the fluid level control in the flow cell, allowing for a broad range of accurate measurement from 1 ppm to 10,000 ppm. Because this sensing system has been designed to work in a highly invasive environment, it can be placed close to the process source to allow for accurate real time measurement and control.

Enhancing the Teaching of Digital Processing of Remote Sensing Image Course through Geospatial Web Processing Services

Science.gov (United States)

di, L.; Deng, M.

2010-12-01

Remote sensing (RS) is an essential method to collect data for Earth science research. Huge amount of remote sensing data, most of them in the image form, have been acquired. Almost all geography departments in the world offer courses in digital processing of remote sensing images. Such courses place emphasis on how to digitally process large amount of multi-source images for solving real world problems. However, due to the diversity and complexity of RS images and the shortcomings of current data and processing infrastructure, obstacles for effectively teaching such courses still remain. The major obstacles include 1) difficulties in finding, accessing, integrating and using massive RS images by students and educators, and 2) inadequate processing functions and computing facilities for students to freely explore the massive data. Recent development in geospatial Web processing service systems, which make massive data, computing powers, and processing capabilities to average Internet users anywhere in the world, promises the removal of the obstacles. The GeoBrain system developed by CSISS is an example of such systems. All functions available in GRASS Open Source GIS have been implemented as Web services in GeoBrain. Petabytes of remote sensing images in NASA data centers, the USGS Landsat data archive, and NOAA CLASS are accessible transparently and processable through GeoBrain. The GeoBrain system is operated on a high performance cluster server with large disk storage and fast Internet connection. All GeoBrain capabilities can be accessed by any Internet-connected Web browser. Dozens of universities have used GeoBrain as an ideal platform to support data-intensive remote sensing education. This presentation gives a specific example of using GeoBrain geoprocessing services to enhance the teaching of GGS 588, Digital Remote Sensing taught at the Department of Geography and Geoinformation Science, George Mason University. The course uses the textbook "Introductory

Scale issues in remote sensing

CERN Document Server

Weng, Qihao

2014-01-01

This book provides up-to-date developments, methods, and techniques in the field of GIS and remote sensing and features articles from internationally renowned authorities on three interrelated perspectives of scaling issues: scale in land surface properties, land surface patterns, and land surface processes. The book is ideal as a professional reference for practicing geographic information scientists and remote sensing engineers as well as a supplemental reading for graduate level students.

Features of the Manufacturing Vision Development Process

DEFF Research Database (Denmark)

Dukovska-Popovska, Iskra; Riis, Jens Ove; Boer, Harry

2005-01-01

of action research. The methodology recommends wide participation of people from different hierarchical and functional positions, who engage in a relatively short, playful and creative process and come up with a vision (concept) for the future manufacturing system in the company. Based on three case studies......This paper discusses the key features of the process of Manufacturing Vision Development, a process that enables companies to develop their future manufacturing concept. The basis for the process is a generic five-phase methodology (Riis and Johansen, 2003) developed as a result of ten years...... of companies going through the initial phases of the methodology, this research identified the key features of the Manufacturing Vision Development process. The paper elaborates the key features by defining them, discussing how and when they can appear, and how they influence the process....

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

Science.gov (United States)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

The ship edge feature detection based on high and low threshold for remote sensing image

Science.gov (United States)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

Science.gov (United States)

Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

2018-02-01

The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

Automatic processing of unattended object features by functional connectivity

Directory of Open Access Journals (Sweden)

Katja Martina Mayer

2013-05-01

Full Text Available Observers can selectively attend to object features that are relevant for a task. However, unattended task-irrelevant features may still be processed and possibly integrated with the attended features. This study investigated the neural mechanisms for processing both task-relevant (attended and task-irrelevant (unattended object features. The Garner paradigm was adapted for functional magnetic resonance imaging (fMRI to test whether specific brain areas process the conjunction of features or whether multiple interacting areas are involved in this form of feature integration. Observers attended to shape, colour, or non-rigid motion of novel objects while unattended features changed from trial to trial (change blocks or remained constant (no-change blocks during a given block. This block manipulation allowed us to measure the extent to which unattended features affected neural responses which would reflect the extent to which multiple object features are automatically processed. We did not find Garner interference at the behavioural level. However, we designed the experiment to equate performance across block types so that any fMRI results could not be due solely to differences in task difficulty between change and no-change blocks. Attention to specific features localised several areas known to be involved in object processing. No area showed larger responses on change blocks compared to no-change blocks. However, psychophysiological interaction analyses revealed that several functionally-localised areas showed significant positive interactions with areas in occipito-temporal and frontal areas that depended on block type. Overall, these findings suggest that both regional responses and functional connectivity are crucial for processing multi-featured objects.

A Fresh Look at Spatio-Temporal Remote Sensing Data: Data Formats, Processing Flow, and Visualization

Science.gov (United States)

Gens, R.

2017-12-01

With increasing number of experimental and operational satellites in orbit, remote sensing based mapping and monitoring of the dynamic Earth has entered into the realm of `big data'. Just the Landsat series of satellites provide a near continuous archive of 45 years of data. The availability of such spatio-temporal datasets has created opportunities for long-term monitoring diverse features and processes operating on the Earth's terrestrial and aquatic systems. Processes such as erosion, deposition, subsidence, uplift, evapotranspiration, urbanization, land-cover regime shifts can not only be monitored and change can be quantified using time-series data analysis. This unique opportunity comes with new challenges in management, analysis, and visualization of spatio-temporal datasets. Data need to be stored in a user-friendly format, and relevant metadata needs to be recorded, to allow maximum flexibility for data exchange and use. Specific data processing workflows need to be defined to support time-series analysis for specific applications. Value-added data products need to be generated keeping in mind the needs of the end-users, and using best practices in complex data visualization. This presentation systematically highlights the various steps for preparing spatio-temporal remote sensing data for time series analysis. It showcases a prototype workflow for remote sensing based change detection that can be generically applied while preserving the application-specific fidelity of the datasets. The prototype includes strategies for visualizing change over time. This has been exemplified using a time-series of optical and SAR images for visualizing the changing glacial, coastal, and wetland landscapes in parts of Alaska.

Research on Remote Sensing Image Template Processing Based on Global Subdivision Theory

OpenAIRE

Xiong Delan; Du Genyuan

2013-01-01

Aiming at the questions of vast data, complex operation, and time consuming processing for remote sensing image, subdivision template was proposed based on global subdivision theory, which can set up high level of abstraction and generalization for remote sensing image. The paper emphatically discussed the model and structure of subdivision template, and put forward some new ideas for remote sensing image template processing, key technology and quickly applied demonstration. The research has ...

Distributed Sensing and Processing for Multi-Camera Networks

Science.gov (United States)

Sankaranarayanan, Aswin C.; Chellappa, Rama; Baraniuk, Richard G.

Sensor networks with large numbers of cameras are becoming increasingly prevalent in a wide range of applications, including video conferencing, motion capture, surveillance, and clinical diagnostics. In this chapter, we identify some of the fundamental challenges in designing such systems: robust statistical inference, computationally efficiency, and opportunistic and parsimonious sensing. We show that the geometric constraints induced by the imaging process are extremely useful for identifying and designing optimal estimators for object detection and tracking tasks. We also derive pipelined and parallelized implementations of popular tools used for statistical inference in non-linear systems, of which multi-camera systems are examples. Finally, we highlight the use of the emerging theory of compressive sensing in reducing the amount of data sensed and communicated by a camera network.

The perceptual processing capacity of summary statistics between and within feature dimensions

Science.gov (United States)

Attarha, Mouna; Moore, Cathleen M.

2015-01-01

The simultaneous–sequential method was used to test the processing capacity of statistical summary representations both within and between feature dimensions. Sixteen gratings varied with respect to their size and orientation. In Experiment 1, the gratings were equally divided into four separate smaller sets, one of which with a mean size that was larger or smaller than the other three sets, and one of which with a mean orientation that was tilted more leftward or rightward. The task was to report the mean size and orientation of the oddball sets. This therefore required four summary representations for size and another four for orientation. The sets were presented at the same time in the simultaneous condition or across two temporal frames in the sequential condition. Experiment 1 showed evidence of a sequential advantage, suggesting that the system may be limited with respect to establishing multiple within-feature summaries. Experiment 2 eliminates the possibility that some aspect of the task, other than averaging, was contributing to this observed limitation. In Experiment 3, the same 16 gratings appeared as one large superset, and therefore the task only required one summary representation for size and another one for orientation. Equal simultaneous–sequential performance indicated that between-feature summaries are capacity free. These findings challenge the view that within-feature summaries drive a global sense of visual continuity across areas of the peripheral visual field, and suggest a shift in focus to seeking an understanding of how between-feature summaries in one area of the environment control behavior. PMID:26360153

Hemispheric asymmetry of the brain as a psycho-physiological basis of individual and typological features of the formation of a sense of humour.

OpenAIRE

Shportun, O. N.

2016-01-01

The article describes the psycho-physiological peculiarities of hemispheric asymmetry of the brain as the basis of individual and typological features of the formation of a sense of humour. The analysis of the impact of the functional brain hemispheric asymmetry on emotional, intellectual and physiological features of development of sense of humour in ontogeny is conducted. Analysis of studies of inter-hemispheric asymmetry of the brain makes it possible to ascertain the impact of the functio...

EXTRACTING SPATIOTEMPORAL OBJECTS FROM RASTER DATA TO REPRESENT PHYSICAL FEATURES AND ANALYZE RELATED PROCESSES

Directory of Open Access Journals (Sweden)

J. A. Zollweg

2017-10-01

Full Text Available Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don’t see millions of cubes of atmosphere; we see a thunderstorm ‘object’. Temporally, we don’t see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain’s perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA’s High-Resolution Rapid Refresh v2 (HRRRv2 data stream.

Extracting Spatiotemporal Objects from Raster Data to Represent Physical Features and Analyze Related Processes

Science.gov (United States)

Zollweg, J. A.

2017-10-01

Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don't see millions of cubes of atmosphere; we see a thunderstorm `object'. Temporally, we don't see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain's perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA's High-Resolution Rapid Refresh v2 (HRRRv2) data stream.

Advances in waveform-agile sensing for tracking

CERN Document Server

Sira, Sandeep Prasad

2009-01-01

Recent advances in sensor technology and information processing afford a new flexibility in the design of waveforms for agile sensing. Sensors are now developed with the ability to dynamically choose their transmit or receive waveforms in order to optimize an objective cost function. This has exposed a new paradigm of significant performance improvements in active sensing: dynamic waveform adaptation to environment conditions, target structures, or information features. The manuscript provides a review of recent advances in waveform-agile sensing for target tracking applications. A dynamic wav

AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

Science.gov (United States)

Dasarathy, B. V.

1976-01-01

An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

Key Features of the Manufacturing Vision Development Process

DEFF Research Database (Denmark)

Dukovska-Popovska, Iskra; Riis, Jens Ove; Boer, Harry

2005-01-01

of action research. The methodology recommends wide participation of people from different hierarchical and functional positions, who engage in a relatively short, playful and creative process and come up with a vision (concept) for the future manufacturing system in the company. Based on three case studies......This paper discusses the key features of the process of Manufacturing Vision Development, a process that enables companies to develop their future manufacturing concept. The basis for the process is a generic five-phase methodology (Riis and Johansen 2003) developed as a result of ten years...... of companies going through the initial phases of the methodology, this research identified the key features of the Manufacturing Vision Development process. The paper elaborates the key features by defining them, discussing how and when they can appear, and how they influence the process....

Feature constrained compressed sensing CT image reconstruction from incomplete data via robust principal component analysis of the database

International Nuclear Information System (INIS)

Wu, Dufan; Li, Liang; Zhang, Li

2013-01-01

In computed tomography (CT), incomplete data problems such as limited angle projections often cause artifacts in the reconstruction results. Additional prior knowledge of the image has shown the potential for better results, such as a prior image constrained compressed sensing algorithm. While a pre-full-scan of the same patient is not always available, massive well-reconstructed images of different patients can be easily obtained from clinical multi-slice helical CTs. In this paper, a feature constrained compressed sensing (FCCS) image reconstruction algorithm was proposed to improve the image quality by using the prior knowledge extracted from the clinical database. The database consists of instances which are similar to the target image but not necessarily the same. Robust principal component analysis is employed to retrieve features of the training images to sparsify the target image. The features form a low-dimensional linear space and a constraint on the distance between the image and the space is used. A bi-criterion convex program which combines the feature constraint and total variation constraint is proposed for the reconstruction procedure and a flexible method is adopted for a good solution. Numerical simulations on both the phantom and real clinical patient images were taken to validate our algorithm. Promising results are shown for limited angle problems. (paper)

Geological remote sensing

Science.gov (United States)

Bishop, Charlotte; Rivard, Benoit; de Souza Filho, Carlos; van der Meer, Freek

2018-02-01

Geology is defined as the 'study of the planet Earth - the materials of which it is made, the processes that act on these materials, the products formed, and the history of the planet and its life forms since its origin' (Bates and Jackson, 1976). Remote sensing has seen a number of variable definitions such as those by Sabins and Lillesand and Kiefer in their respective textbooks (Sabins, 1996; Lillesand and Kiefer, 2000). Floyd Sabins (Sabins, 1996) defined it as 'the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter' while Lillesand and Kiefer (Lillesand and Kiefer, 2000) defined it as 'the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation'. Thus Geological Remote Sensing can be considered the study of, not just Earth given the breadth of work undertaken in planetary science, geological features and surfaces and their interaction with the electromagnetic spectrum using technology that is not in direct contact with the features of interest.

Atmospheric-water absorption features near 2.2 micrometers and their importance in high spectral resolution remote sensing

Science.gov (United States)

Kruse, F. A.; Clark, R. N.

1986-01-01

Selective absorption of electromagnetic radiation by atmospheric gases and water vapor is an accepted fact in terrestrial remote sensing. Until recently, only a general knowledge of atmospheric effects was required for analysis of remote sensing data; however, with the advent of high spectral resolution imaging devices, detailed knowledge of atmospheric absorption bands has become increasingly important for accurate analysis. Detailed study of high spectral resolution aircraft data at the U.S. Geological Survey has disclosed narrow absorption features centered at approximately 2.17 and 2.20 micrometers not caused by surface mineralogy. Published atmospheric transmission spectra and atmospheric spectra derived using the LOWTRAN-5 computer model indicate that these absorption features are probably water vapor. Spectral modeling indicates that the effects of atmospheric absorption in this region are most pronounced in spectrally flat materials with only weak absorption bands. Without correction and detailed knowledge of the atmospheric effects, accurate mapping of surface mineralogy (particularly at low mineral concentrations) is not possible.

Remote sensing models and methods for image processing

CERN Document Server

Schowengerdt, Robert A

2007-01-01

Remote sensing is a technology that engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Normally this is accomplished through the use of a satellite or aircraft. This book, in its 3rd edition, seamlessly connects the art and science of earth remote sensing with the latest interpretative tools and techniques of computer-aided image processing. Newly expanded and updated, this edition delivers more of the applied scientific theory and practical results that helped the previous editions earn wide acclaim and become classroom and industry standa

Compressed Sensing Methods in Radio Receivers Exposed to Noise and Interference

DEFF Research Database (Denmark)

Pierzchlewski, Jacek

, there is a problem of interference, which makes digitization of radio receivers even more dicult. High-order low-pass lters are needed to remove interfering signals and secure a high-quality reception. In the mid-2000s a new method of signal acquisition, called compressed sensing, emerged. Compressed sensing...... the downconverted baseband signal and interference, may be replaced by low-order lters. Additional digital signal processing is a price to pay for this feature. Hence, the signal processing is moved from the analog to the digital domain. Filtering compressed sensing, which is a new application of compressed sensing...

Real-Time and Post-Processed Georeferencing for Hyperpspectral Drone Remote Sensing

Science.gov (United States)

Oliveira, R. A.; Khoramshahi, E.; Suomalainen, J.; Hakala, T.; Viljanen, N.; Honkavaara, E.

2018-05-01

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites.

Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors

Directory of Open Access Journals (Sweden)

Jorge Fernández-Berni

2014-08-01

Full Text Available The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.

Focal-plane sensing-processing: a power-efficient approach for the implementation of privacy-aware networked visual sensors.

Science.gov (United States)

Fernández-Berni, Jorge; Carmona-Galán, Ricardo; del Río, Rocío; Kleihorst, Richard; Philips, Wilfried; Rodríguez-Vázquez, Ángel

2014-08-19

The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.

Wireless Sensor Networks Data Processing Summary Based on Compressive Sensing

Directory of Open Access Journals (Sweden)

Caiyun Huang

2014-07-01

Full Text Available As a newly proposed theory, compressive sensing (CS is commonly used in signal processing area. This paper investigates the applications of compressed sensing (CS in wireless sensor networks (WSNs. First, the development and research status of compressed sensing technology and wireless sensor networks are described, then a detailed investigation of WSNs research based on CS are conducted from aspects of data fusion, signal acquisition, signal routing transmission, and signal reconstruction. At the end of the paper, we conclude our survey and point out the possible future research directions.

Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

Directory of Open Access Journals (Sweden)

Yongguang Zhai

2018-03-01

Full Text Available Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method.

Soft-sensing Modeling Based on MLS-SVM Inversion for L-lysine Fermentation Processes

Directory of Open Access Journals (Sweden)

Bo Wang

2015-06-01

Full Text Available A modeling approach 63 based on multiple output variables least squares support vector machine (MLS-SVM inversion is presented by a combination of inverse system and support vector machine theory. Firstly, a dynamic system model is developed based on material balance relation of a fed-batch fermentation process, with which it is analyzed whether an inverse system exists or not, and into which characteristic information of a fermentation process is introduced to set up an extended inversion model. Secondly, an initial extended inversion model is developed off-line by the use of the fitting capacity of MLS-SVM; on-line correction is made by the use of a differential evolution (DE algorithm on the basis of deviation information. Finally, a combined pseudo-linear system is formed by means of a serial connection of a corrected extended inversion model behind the L-lysine fermentation processes; thereby crucial biochemical parameters of a fermentation process could be predicted on-line. The simulation experiment shows that this soft-sensing modeling method features very high prediction precision and can predict crucial biochemical parameters of L-lysine fermentation process very well.

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

Directory of Open Access Journals (Sweden)

Zhiyong Lv

2018-03-01

Full Text Available In recent decades, land cover change detection (LCCD using very high-spatial resolution (VHR remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution, respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type

Feature-based tolerancing for intelligent inspection process definition

International Nuclear Information System (INIS)

Brown, C.W.

1993-07-01

This paper describes a feature-based tolerancing capability that complements a geometric solid model with an explicit representation of conventional and geometric tolerances. This capability is focused on supporting an intelligent inspection process definition system. The feature-based tolerance model's benefits include advancing complete product definition initiatives (e.g., STEP -- Standard for Exchange of Product model dam), suppling computer-integrated manufacturing applications (e.g., generative process planning and automated part programming) with product definition information, and assisting in the solution of measurement performance issues. A feature-based tolerance information model was developed based upon the notion of a feature's toleranceable aspects and describes an object-oriented scheme for representing and relating tolerance features, tolerances, and datum reference frames. For easy incorporation, the tolerance feature entities are interconnected with STEP solid model entities. This schema will explicitly represent the tolerance specification for mechanical products, support advanced dimensional measurement applications, and assist in tolerance-related methods divergence issues

The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

Directory of Open Access Journals (Sweden)

Kun-Ching Wang

2014-09-01

Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing

Directory of Open Access Journals (Sweden)

B. Kuhlenkötter

2006-01-01

Full Text Available In production processes the use of image processing systems is widespread. Hardware solutions and cameras respectively are available for nearly every application. One important challenge of image processing systems is the development and selection of appropriate algorithms and software solutions in order to realise ambitious quality control for production processes. This article characterises the development of innovative software by combining features for an automatic defect classification on product surfaces. The artificial intelligent method Support Vector Machine (SVM is used to execute the classification task according to the combined features. This software is one crucial element for the automation of a manually operated production process. 

Study on algorithm of process neural network for soft sensing in sewage disposal system

Science.gov (United States)

Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

2006-11-01

A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

Development of multiple source data processing for structural analysis at a regional scale. [digital remote sensing in geology

Science.gov (United States)

Carrere, Veronique

1990-01-01

Various image processing techniques developed for enhancement and extraction of linear features, of interest to the structural geologist, from digital remote sensing, geologic, and gravity data, are presented. These techniques include: (1) automatic detection of linear features and construction of rose diagrams from Landsat MSS data; (2) enhancement of principal structural directions using selective filters on Landsat MSS, Spacelab panchromatic, and HCMM NIR data; (3) directional filtering of Spacelab panchromatic data using Fast Fourier Transform; (4) detection of linear/elongated zones of high thermal gradient from thermal infrared data; and (5) extraction of strong gravimetric gradients from digitized Bouguer anomaly maps. Processing results can be compared to each other through the use of a geocoded database to evaluate the structural importance of each lineament according to its depth: superficial structures in the sedimentary cover, or deeper ones affecting the basement. These image processing techniques were successfully applied to achieve a better understanding of the transition between Provence and the Pyrenees structural blocks, in southeastern France, for an improved structural interpretation of the Mediterranean region.

Integrated Gis-remote sensing processing applied to vegetation ...

African Journals Online (AJOL)

A remotely sensed digital image of SPOT by its linear enhancement on a large memory, high speed, and digital electronic computer revealed from false colour composite that vegetation is expressed as red. Further processing of SPOT digital image for arithmetic banding of Normalized Differential Vegetation Index (NDVI) ...

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Asymmetric cryptography based on wavefront sensing.

Science.gov (United States)

Peng, Xiang; Wei, Hengzheng; Zhang, Peng

2006-12-15

A system of asymmetric cryptography based on wavefront sensing (ACWS) is proposed for the first time to our knowledge. One of the most significant features of the asymmetric cryptography is that a trapdoor one-way function is required and constructed by analogy to wavefront sensing, in which the public key may be derived from optical parameters, such as the wavelength or the focal length, while the private key may be obtained from a kind of regular point array. The ciphertext is generated by the encoded wavefront and represented with an irregular array. In such an ACWS system, the encryption key is not identical to the decryption key, which is another important feature of an asymmetric cryptographic system. The processes of asymmetric encryption and decryption are formulized mathematically and demonstrated with a set of numerical experiments.

A Non-Intrusive GMA Welding Process Quality Monitoring System Using Acoustic Sensing.

Science.gov (United States)

Cayo, Eber Huanca; Alfaro, Sadek Crisostomo Absi

2009-01-01

Most of the inspection methods used for detection and localization of welding disturbances are based on the evaluation of some direct measurements of welding parameters. This direct measurement requires an insertion of sensors during the welding process which could somehow alter the behavior of the metallic transference. An inspection method that evaluates the GMA welding process evolution using a non-intrusive process sensing would allow not only the identification of disturbances during welding runs and thus reduce inspection time, but would also reduce the interference on the process caused by the direct sensing. In this paper a nonintrusive method for weld disturbance detection and localization for weld quality evaluation is demonstrated. The system is based on the acoustic sensing of the welding electrical arc. During repetitive tests in welds without disturbances, the stability acoustic parameters were calculated and used as comparison references for the detection and location of disturbances during the weld runs.

An improved optimum-path forest clustering algorithm for remote sensing image segmentation

Science.gov (United States)

Chen, Siya; Sun, Tieli; Yang, Fengqin; Sun, Hongguang; Guan, Yu

2018-03-01

Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.

Experimental Validation of Advanced Dispersed Fringe Sensing (ADFS) Algorithm Using Advanced Wavefront Sensing and Correction Testbed (AWCT)

Science.gov (United States)

Wang, Xu; Shi, Fang; Sigrist, Norbert; Seo, Byoung-Joon; Tang, Hong; Bikkannavar, Siddarayappa; Basinger, Scott; Lay, Oliver

2012-01-01

Large aperture telescope commonly features segment mirrors and a coarse phasing step is needed to bring these individual segments into the fine phasing capture range. Dispersed Fringe Sensing (DFS) is a powerful coarse phasing technique and its alteration is currently being used for JWST.An Advanced Dispersed Fringe Sensing (ADFS) algorithm is recently developed to improve the performance and robustness of previous DFS algorithms with better accuracy and unique solution. The first part of the paper introduces the basic ideas and the essential features of the ADFS algorithm and presents the some algorithm sensitivity study results. The second part of the paper describes the full details of algorithm validation process through the advanced wavefront sensing and correction testbed (AWCT): first, the optimization of the DFS hardware of AWCT to ensure the data accuracy and reliability is illustrated. Then, a few carefully designed algorithm validation experiments are implemented, and the corresponding data analysis results are shown. Finally the fiducial calibration using Range-Gate-Metrology technique is carried out and a <10nm or <1% algorithm accuracy is demonstrated.

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

Directory of Open Access Journals (Sweden)

Xue Yang

2018-01-01

Full Text Available Ship detection has been playing a significant role in the field of remote sensing for a long time, but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection, and the redundancy of the detection region. In order to solve these problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN which can effectively detect ships in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN, which is aimed at solving problems resulting from the narrow width of the ship. Compared with previous multiscale detectors such as Feature Pyramid Network (FPN, DFPN builds high-level semantic feature-maps for all scales by means of dense connections, through which feature propagation is enhanced and feature reuse is encouraged. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multiscale region of interest (ROI Align for the purpose of maintaining the completeness of the semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has state-of-the-art performance.

Remote sensing application for delineating coastal vegetation - A case study

Digital Repository Service at National Institute of Oceanography (India)

Kunte, P.D.; Wagle, B.G.

Remote sensing data has been used for mapping coastal vegetation along the Goa Coast, India. The study envisages the use of digital image processing techniques for delineating geomorphic features and associated vegetation, including mangrove, along...

Research on Remote Sensing recognition features of Yuan Yang Terraces in Yunnan Province (China)

Science.gov (United States)

Xiang, Jie; Chen, Jianping; Lai, ZiLi; Yang, Wei

2016-04-01

Yuan Yang terraces is one of the most famous terraces in China, and it was successfully listed in the world heritage list at the 37th world heritage convention. On the one hand, Yuan Yang terraces retain more soil and water, to reduce both hydrological connectivity and erosion, and to support irrigation. On the other hand, It has the important tourism value, bring the huge revenue to local residents. In order to protect and make use of Yuan Yang terraces better, This study analyzed the spatial distribution and spectral characteristics of terraces:(1) Through visual interpretation, the study recognized the terraces based on the spatial adjusted remote sensing image (2010 Geoeye-1 with resolution of 1m/pix), and extracted topographic feature (elevation, slope, aspect, etc.) based on the digital elevation model with resolution of 20m/pix. The terraces cover a total area of about 11.58Km2, accounted for 24.4% of the whole study area. The terraces appear at range from 1400m to 1800m in elevation, 10°to 20°in slope, northwest to northeast in aspect; (2) Using the method of weight of evidence, this study assessed the importance of different topographic feature. The results show that the sort of importance: elevation>slope>aspect; (3) The study counted the Normalized Difference Vegetation Index (NDVI) changes of terraces throughout the year, based on the landsat-5 image with resolution of 30m/pix. The results show that the changes of terraces' NDVI are bigger than other stuff (e.g. forest, road, house, etc.). Those work made a good preparations for establishing the dynamic remote sensing monitoring system of Yuan Yang terraces.

Reshaping an enduring sense of self: the process of recovery from a first episode of schizophrenia.

Science.gov (United States)

Romano, Donna M; McCay, Elizabeth; Goering, Paula; Boydell, Katherine; Zipursky, Robert

2010-08-01

Although advances in the treatment of schizophrenia have been made, little is known about the process of recovery from first episode of schizophrenia (FES). To date, the study of recovery in the field of mental health has focused on long-term mental illness. This qualitative study addresses ways in which individuals with FES describe their process of recovery and how identified individuals (e.g. family members) describe their perceptions of and roles in the participant's process of recovery. Charmaz's constructivist grounded theory methodology was used to interview 10 young adults twice who self-identified as recovering from FES. In addition, 10 individuals were identified who had influenced their recovery and were interviewed once, for a total of 30 interviews. Data collection sources included in-depth semi-structured interviews. Data analysis methods were consistent with Charmaz's methodology and included coding, and constant comparison of data. The results provide a substantive theory of the process of recovery from FES that is comprised of the following phases: 'Who they were prior to the illness', 'Lives interrupted: Encountering the illness', 'Engaging in services and supports', 'Re-engaging in life', 'Envisioning the future'; and the core category, 'Re-shaping an enduring sense of self', that occurred throughout all phases. A prominent feature of this model is that participants' enduring sense of self were reshaped rather than reconstructed throughout their recovery. This model of recovery from FES is unique, and as such, provides implications for clinical care, research and policy development for these young adults and their families.

Edge Detection from High Resolution Remote Sensing Images using Two-Dimensional log Gabor Filter in Frequency Domain

International Nuclear Information System (INIS)

Wang, K; Yu, T; Meng, Q Y; Wang, G K; Li, S P; Liu, S H

2014-01-01

Edges are vital features to describe the structural information of images, especially high spatial resolution remote sensing images. Edge features can be used to define the boundaries between different ground objects in high spatial resolution remote sensing images. Thus edge detection is important in the remote sensing image processing. Even though many different edge detection algorithms have been proposed, it is difficult to extract the edge features from high spatial resolution remote sensing image including complex ground objects. This paper introduces a novel method to detect edges from the high spatial resolution remote sensing image based on frequency domain. Firstly, the high spatial resolution remote sensing images are Fourier transformed to obtain the magnitude spectrum image (frequency image) by FFT. Then, the frequency spectrum is analyzed by using the radius and angle sampling. Finally, two-dimensional log Gabor filter with optimal parameters is designed according to the result of spectrum analysis. Finally, dot product between the result of Fourier transform and the log Gabor filter is inverse Fourier transformed to obtain the detections. The experimental result shows that the proposed algorithm can detect edge features from the high resolution remote sensing image commendably

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

Science.gov (United States)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

THE MORPHOLOGICAL PYRAMID AND ITS APPLICATIONS TO REMOTE SENSING: MULTIRESOLUTION DATA ANALYSIS AND FEATURES EXTRACTION

Directory of Open Access Journals (Sweden)

Laporterie Florence

2011-05-01

Full Text Available In remote sensing, sensors are more and more numerous, and their spatial resolution is higher and higher. Thus, the availability of a quick and accurate characterisation of the increasing amount of data is now a quite important issue. This paper deals with an approach combining a pyramidal algorithm and mathematical morphology to study the physiographic characteristics of terrestrial ecosystems. Our pyramidal strategy involves first morphological filters, then extraction at each level of resolution of well-known landscapes features. The approach is applied to a digitised aerial photograph representing an heterogeneous landscape of orchards and forests along the Garonne river (France. This example, simulating very high spatial resolution imagery, highlights the influence of the parameters of the pyramid according to the spatial properties of the studied patterns. It is shown that, the morphological pyramid approach is a promising attempt for multi-level features extraction by modelling geometrical relevant parameters.

Ontology patterns for complex topographic feature yypes

Science.gov (United States)

Varanka, Dalia E.

2011-01-01

Complex feature types are defined as integrated relations between basic features for a shared meaning or concept. The shared semantic concept is difficult to define in commonly used geographic information systems (GIS) and remote sensing technologies. The role of spatial relations between complex feature parts was recognized in early GIS literature, but had limited representation in the feature or coverage data models of GIS. Spatial relations are more explicitly specified in semantic technology. In this paper, semantics for topographic feature ontology design patterns (ODP) are developed as data models for the representation of complex features. In the context of topographic processes, component assemblages are supported by resource systems and are found on local landscapes. The topographic ontology is organized across six thematic modules that can account for basic feature types, resource systems, and landscape types. Types of complex feature attributes include location, generative processes and physical description. Node/edge networks model standard spatial relations and relations specific to topographic science to represent complex features. To demonstrate these concepts, data from The National Map of the U. S. Geological Survey was converted and assembled into ODP.

Waste Form Features, Events, and Processes

International Nuclear Information System (INIS)

R. Schreiner

2004-01-01

The purpose of this report is to evaluate and document the inclusion or exclusion of the waste form features, events and processes (FEPs) with respect to modeling used to support the Total System Performance Assessment for License Application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical bases for screening decisions. This information is required by the Nuclear Regulatory Commission (NRC) in 10 CFR 63.114 (d, e, and f) [DIRS 156605]. The FEPs addressed in this report deal with the issues related to the degradation and potential failure of the waste form and the migration of the waste form colloids. For included FEPs, this analysis summarizes the implementation of the FEP in TSPA-LA, (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical bases for exclusion from TSPA-LA (i.e., why the FEP is excluded). This revision addresses the TSPA-LA FEP list (DTN: MO0407SEPFEPLA.000 [DIRS 170760]). The primary purpose of this report is to identify and document the analyses and resolution of the features, events, and processes (FEPs) associated with the waste form performance in the repository. Forty FEPs were identified that are associated with the waste form performance. This report has been prepared to document the screening methodology used in the process of FEP inclusion and exclusion. The analyses documented in this report are for the license application (LA) base case design (BSC 2004 [DIRS 168489]). In this design, a drip shield is placed over the waste package and no backfill is placed over the drip shield (BSC 2004 [DIRS 168489]). Each FEP may include one or more specific issues that are collectively described by a FEP name and a FEP description. The FEP description may encompass a single feature, process or event, or a few closely related or coupled processes if the entire FEP can be addressed by a single specific screening argument or TSPA-LA disposition. The FEPs are

Waste Form Features, Events, and Processes

Energy Technology Data Exchange (ETDEWEB)

R. Schreiner

2004-10-27

The purpose of this report is to evaluate and document the inclusion or exclusion of the waste form features, events and processes (FEPs) with respect to modeling used to support the Total System Performance Assessment for License Application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical bases for screening decisions. This information is required by the Nuclear Regulatory Commission (NRC) in 10 CFR 63.114 (d, e, and f) [DIRS 156605]. The FEPs addressed in this report deal with the issues related to the degradation and potential failure of the waste form and the migration of the waste form colloids. For included FEPs, this analysis summarizes the implementation of the FEP in TSPA-LA, (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical bases for exclusion from TSPA-LA (i.e., why the FEP is excluded). This revision addresses the TSPA-LA FEP list (DTN: MO0407SEPFEPLA.000 [DIRS 170760]). The primary purpose of this report is to identify and document the analyses and resolution of the features, events, and processes (FEPs) associated with the waste form performance in the repository. Forty FEPs were identified that are associated with the waste form performance. This report has been prepared to document the screening methodology used in the process of FEP inclusion and exclusion. The analyses documented in this report are for the license application (LA) base case design (BSC 2004 [DIRS 168489]). In this design, a drip shield is placed over the waste package and no backfill is placed over the drip shield (BSC 2004 [DIRS 168489]). Each FEP may include one or more specific issues that are collectively described by a FEP name and a FEP description. The FEP description may encompass a single feature, process or event, or a few closely related or coupled processes if the entire FEP can be addressed by a single specific screening argument or TSPA-LA disposition. The FEPs are

Luminescent sensing and imaging of oxygen: fierce competition to the Clark electrode.

Science.gov (United States)

Wolfbeis, Otto S

2015-08-01

Luminescence-based sensing schemes for oxygen have experienced a fast growth and are in the process of replacing the Clark electrode in many fields. Unlike electrodes, sensing is not limited to point measurements via fiber optic microsensors, but includes additional features such as planar sensing, imaging, and intracellular assays using nanosized sensor particles. In this essay, I review and discuss the essentials of (i) common solid-state sensor approaches based on the use of luminescent indicator dyes and host polymers; (ii) fiber optic and planar sensing schemes; (iii) nanoparticle-based intracellular sensing; and (iv) common spectroscopies. Optical sensors are also capable of multiple simultaneous sensing (such as O2 and temperature). Sensors for O2 are produced nowadays in large quantities in industry. Fields of application include sensing of O2 in plant and animal physiology, in clinical chemistry, in marine sciences, in the chemical industry and in process biotechnology. © 2015 The Author. Bioessays published by WILEY Periodicals, Inc.

Feature extraction from multiple data sources using genetic programming.

Energy Technology Data Exchange (ETDEWEB)

Szymanski, J. J. (John J.); Brumby, Steven P.; Pope, P. A. (Paul A.); Eads, D. R. (Damian R.); Galassi, M. C. (Mark C.); Harvey, N. R. (Neal R.); Perkins, S. J. (Simon J.); Porter, R. B. (Reid B.); Theiler, J. P. (James P.); Young, A. C. (Aaron Cody); Bloch, J. J. (Jeffrey J.); David, N. A. (Nancy A.); Esch-Mosher, D. M. (Diana M.)

2002-01-01

Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Poisson point processes imaging, tracking, and sensing

CERN Document Server

Streit, Roy L

2010-01-01

This overview of non-homogeneous and multidimensional Poisson point processes and their applications features mathematical tools and applications from emission- and transmission-computed tomography to multiple target tracking and distributed sensor detection.

Estimation of metallurgical parameters of flotation process from froth visual features

Directory of Open Access Journals (Sweden)

Mohammad Massinaei

2015-06-01

Full Text Available The estimation of metallurgical parameters of flotation process from froth visual features is the ultimate goal of a machine vision based control system. In this study, a batch flotation system was operated under different process conditions and metallurgical parameters and froth image data were determined simultaneously. Algorithms have been developed for measuring textural and physical froth features from the captured images. The correlation between the froth features and metallurgical parameters was successfully modeled, using artificial neural networks. It has been shown that the performance parameters of flotation process can be accurately estimated from the extracted image features, which is of great importance for developing automatic control systems.

Real-time hypothesis driven feature extraction on parallel processing architectures

DEFF Research Database (Denmark)

Granmo, O.-C.; Jensen, Finn Verner

2002-01-01

the problem of higher-order feature-content/feature-feature correlation, causally complexly interacting features are identified through Bayesian network d-separation analysis and combined into joint features. When used on a moderately complex object-tracking case, the technique is able to select...... extraction, which selectively extract relevant features one-by-one, have in some cases achieved real-time performance on single processing element architectures. In this paperwe propose a novel technique which combines the above two approaches. Features are selectively extracted in parallelizable sets...

Very low drift and high sensitivity of nanocrystal-TiO2 sensing membrane on pH-ISFET fabricated by CMOS compatible process

International Nuclear Information System (INIS)

Bunjongpru, W.; Sungthong, A.; Porntheeraphat, S.; Rayanasukha, Y.; Pankiew, A.; Jeamsaksiri, W.; Srisuwan, A.; Chaisriratanakul, W.; Chaowicharat, E.; Klunngien, N.; Hruanun, C.; Poyai, A.; Nukeaw, J.

2013-01-01

High sensitivity and very low drift rate pH sensors are successfully prepared by using nanocrystal-TiO 2 as sensing membrane of ion sensitive field effect transistor (ISFET) device fabricated via CMOS process. This paper describes the physical properties and sensing characteristics of the TiO 2 membrane prepared by annealing Ti and TiN thin films that deposited on SiO 2 /p-Si substrates through reactive DC magnetron sputtering system. The X-ray diffraction, scanning electron microscopy and Auger electron spectroscopy were used to investigate the structural and morphological features of deposited films after they had been subjected to annealing at various temperatures. The experimental results are interpreted in terms of the effects of amorphous-to-crystalline phase transition and subsequent oxidation of the annealed films. The electrolyte–insulator–semiconductor (EIS) device incorporating Ti-O-N membrane that had been obtained by annealing of TiN thin film at 850 °C exhibited a higher sensitivity (57 mV/pH), a higher linearity (1), a lower hysteresis voltage (1 mV in the pH cycle of 7 → 4 → 7 → 10 → 7), and a smaller drift rate (0.246 mV/h) than did those devices prepared at the other annealing temperatures. Furthermore, this pH-sensing device fabrication process is fully compatible with CMOS fabrication process technology.

An image-processing methodology for extracting bloodstain pattern features.

Science.gov (United States)

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

Feature extraction for classification in the data mining process

NARCIS (Netherlands)

Pechenizkiy, M.; Puuronen, S.; Tsymbal, A.

2003-01-01

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". Three different eigenvector-based feature extraction approaches

Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

NARCIS (Netherlands)

Shahbahrami, A.; Pham, T.A.; Bertels, K.L.M.

2011-01-01

Texture features extraction algorithms are key functions in various image processing applications such as medical images, remote sensing, and content-based image retrieval. The most common way to extract texture features is the use of Gray Level Co-occurrence Matrices (GLCMs). The GLCM contains the

Pattern Recognition in Optical Remote Sensing Data Processing

Science.gov (United States)

Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir

Computational procedures of the land surface biophysical parameters retrieval imply that modeling techniques are available of the outgoing radiation description together with monitoring techniques of remote sensing data processing using registered radiances between the related optical sensors and the land surface objects called “patterns”. Pattern recognition techniques are a valuable approach to the processing of remote sensing data for images of the land surface - atmosphere system. Many simplified codes of the direct and inverse problems of atmospheric optics are considered applicable for the imagery processing of low and middle spatial resolution. Unless the authors are not interested in the accuracy of the final information products, they utilize these standard procedures. The emerging necessity of processing data of high spectral and spatial resolution given by imaging spectrometers puts forward the newly defined pattern recognition techniques. The proposed tools of using different types of classifiers combined with the parameter retrieval procedures for the forested environment are maintained to have much wider applications as compared with the image features and object shapes extraction, which relates to photometry and geometry in pixel-level reflectance representation of the forested land cover. The pixel fraction and reflectance of “end-members” (sunlit forest canopy, sunlit background and shaded background for a particular view and solar illumination angle) are only a part in the listed techniques. It is assumed that each pixel views collections of the individual forest trees and the pixel-level reflectance can thus be computed as a linear mixture of sunlit tree tops, sunlit background (or understory) and shadows. Instead of these photometry and geometry constraints, the improved models are developed of the functional description of outgoing spectral radiation, in which such parameters of the forest canopy like the vegetation biomass density for

Remote sensing and vegetation mapping in South Africa

Directory of Open Access Journals (Sweden)

M. L. Jarman

1983-12-01

Full Text Available The kinds of imagery, types of data and general relationships between scale of study, scale of mapping and scale of remote sensing products that are appropriate to the South African situation for visual and digital analysis are presented. The type of remote sensing product and processing, the type of field exercise appropriate to each, and the purpose of producing maps at each scale are discussed. Lack of repetitive imagery to date has not allowed for the full investigation of monitoring potential and careful planning at national level is needed to ensure availability of imagery for monitoring purposes. Map production processes which are rapid and accurate should be utilized. An integrated approach to vegetation mapping and surveying, which incorporates the best features of both visual and digital processing, is recommended for use.

Hierarchical Categorical Perception in Sensing and Cognitive Processes

DEFF Research Database (Denmark)

Bruni, Luis Emilio

2008-01-01

This article considers categorical perception (CP) as a crucial process involved in all sort of communication throughout the biological hierarchy, i.e. in all of biosemiosis. Until now, there has been consideration of CP exclusively within the functional cycle of perception-cognition-action and i...... of categorical sensing and perception with the equally hierarchical issues of the "binding problem", "triadic causality", the "emergent interpretant" and the increasing semiotic freedom observed in biological and cognitive systems....

The role of sports in making sense of the process of growing old.

Science.gov (United States)

Eman, Josefin

2012-12-01

Drawing on interviews with 22 athletically active old men and women, the study explores whether and how the practice of sports can affect old adults' processes of sense-making about old age and the process of growing old in ways that challenge dominant constructions about old age. Thereto, the study will explore the possible impact of gender in this process. The results show that men and women who continue to practice competitive sports into old age make sense of the process of growing old by focusing primarily on their physical abilities, at least in the context of sports. This focus on capability age allows them partly, although not completely, to challenge the usual thinking about old age and the process of growing old. Copyright © 2012 Elsevier Inc. All rights reserved.

Features, Events, and Processes: Disruptive Events

Energy Technology Data Exchange (ETDEWEB)

J. King

2004-03-31

The primary purpose of this analysis is to evaluate seismic- and igneous-related features, events, and processes (FEPs). These FEPs represent areas of natural system processes that have the potential to produce disruptive events (DE) that could impact repository performance and are related to the geologic processes of tectonism, structural deformation, seismicity, and igneous activity. Collectively, they are referred to as the DE FEPs. This evaluation determines which of the DE FEPs are excluded from modeling used to support the total system performance assessment for license application (TSPA-LA). The evaluation is based on the data and results presented in supporting analysis reports, model reports, technical information, or corroborative documents that are cited in the individual FEP discussions in Section 6.2 of this analysis report.

Features, Events, and Processes: Disruptive Events

International Nuclear Information System (INIS)

J. King

2004-01-01

The primary purpose of this analysis is to evaluate seismic- and igneous-related features, events, and processes (FEPs). These FEPs represent areas of natural system processes that have the potential to produce disruptive events (DE) that could impact repository performance and are related to the geologic processes of tectonism, structural deformation, seismicity, and igneous activity. Collectively, they are referred to as the DE FEPs. This evaluation determines which of the DE FEPs are excluded from modeling used to support the total system performance assessment for license application (TSPA-LA). The evaluation is based on the data and results presented in supporting analysis reports, model reports, technical information, or corroborative documents that are cited in the individual FEP discussions in Section 6.2 of this analysis report

Sense of Place, Fast and Slow: The Potential Contributions of Affordance Theory to Sense of Place.

Science.gov (United States)

Raymond, Christopher M; Kyttä, Marketta; Stedman, Richard

2017-01-01

Over the past 40 years, the sense of place concept has been well-established across a range of applications and settings; however, most theoretical developments have "privileged the slow." Evidence suggests that place attachments and place meanings are slow to evolve, sometimes not matching material or social reality (lag effects), and also tending to inhibit change. Here, we present some key blind spots in sense of place scholarship and then suggest how a reconsideration of sense of place as "fast" and "slow" could fill them. By this, we mean how direct and immediate perception-action processes presented in affordance theory (resulting in immediately perceived place meanings) can complement slower forms of social construction presented in sense of place scholarship. Key blind spots are that sense of place scholarship: (1) rarely accounts for sensory or immediately perceived meanings; (2) pays little attention to how place meanings are the joint product of attributes of environmental features and the attributes of the individual; and (3) assumes that the relationship between place attachment and behavior is linear and not constituted in dynamic relations among mind, culture, and environment. We show how these blind spots can begin to be addressed by reviewing key insights from affordance theory, and through the presentation of applied examples. We discuss future empirical research directions in terms of: (1) how sense of place is both perceived and socially constructed; (2) whether perceived and socially constructed dimensions of place can relate to one another when perceived meanings become unsituated; and (3) how place attachment may change over different stages of the life course based upon dynamic relationships between processes of perception-action and social construction. We conclude with insights into how processes of perception-action and social construction could be included in the design and management of urban landscapes.

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

Science.gov (United States)

Quattrochi, Dale A.; Luvall, Jeffrey C.

1998-01-01

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of

Investigating the Prospective Sense of Agency: Effects of Processing Fluency, Stimulus Ambiguity, and Response Conflict

Science.gov (United States)

Sidarus, Nura; Vuorre, Matti; Metcalfe, Janet; Haggard, Patrick

2017-01-01

How do we know how much control we have over our environment? The sense of agency refers to the feeling that we are in control of our actions, and that, through them, we can control our external environment. Thus, agency clearly involves matching intentions, actions, and outcomes. The present studies investigated the possibility that processes of action selection, i.e., choosing what action to make, contribute to the sense of agency. Since selection of action necessarily precedes execution of action, such effects must be prospective. In contrast, most literature on sense of agency has focussed on the retrospective computation whether an outcome fits the action performed or intended. This hypothesis was tested in an ecologically rich, dynamic task based on a computer game. Across three experiments, we manipulated three different aspects of action selection processing: visual processing fluency, categorization ambiguity, and response conflict. Additionally, we measured the relative contributions of prospective, action selection-based cues, and retrospective, outcome-based cues to the sense of agency. Manipulations of action selection were orthogonally combined with discrepancy of visual feedback of action. Fluency of action selection had a small but reliable effect on the sense of agency. Additionally, as expected, sense of agency was strongly reduced when visual feedback was discrepant with the action performed. The effects of discrepant feedback were larger than the effects of action selection fluency, and sometimes suppressed them. The sense of agency is highly sensitive to disruptions of action-outcome relations. However, when motor control is successful, and action-outcome relations are as predicted, fluency or dysfluency of action selection provides an important prospective cue to the sense of agency. PMID:28450839

Does dystonic muscle activity affect sense of effort in cervical dystonia?

OpenAIRE

Carment, Lo?c; Maier, Marc A.; Sangla, Sophie; Guiraud, Vincent; Mesure, Serge; Vidailhet, Marie; Lindberg, P?vel G; Bleton, Jean-Pierre

2017-01-01

International audience; BackgroundFocal dystonia has been associated with deficient processing of sense of effort cues. However, corresponding studies are lacking in cervical dystonia (CD). We hypothesized that dystonic muscle activity would perturb neck force control based on sense of effort cues.MethodsNeck extension force control was investigated in 18 CD patients with different clinical features (7 with and 11 without retrocollis) and in 19 control subjects. Subjects performed force-match...

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

Science.gov (United States)

Liu, Yan; Wang, Hai; Zhao, Wei; Qin, Hongbo; Xie, Yongqiang

2018-01-01

Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed. PMID:29470408

Information mining in remote sensing imagery

Science.gov (United States)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and

Right-hemispheric processing of non-linguistic word features

DEFF Research Database (Denmark)

Baumgaertner, Annette; Hartwigsen, Gesa; Roman Siebner, Hartwig

2013-01-01

-hemispheric homologues of classic left-hemispheric language areas may partly be due to processing nonlinguistic perceptual features of verbal stimuli. We used functional MRI (fMRI) to clarify the role of the right hemisphere in the perception of nonlinguistic word features in healthy individuals. Participants made...... perceptual, semantic, or phonological decisions on the same set of auditorily and visually presented word stimuli. Perceptual decisions required judgements about stimulus-inherent changes in font size (visual modality) or fundamental frequency contour (auditory modality). The semantic judgement required......, the right inferior frontal gyrus (IFG), an area previously suggested to support language recovery after left-hemispheric stroke, displayed modality-independent activation during perceptual processing of word stimuli. Our findings indicate that activation of the right hemisphere during language tasks may...

Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

Science.gov (United States)

Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

2018-04-01

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Remote sensing the plasmasphere, plasmapause, plumes and other features using ground-based magnetometers

Directory of Open Access Journals (Sweden)

Menk Frederick

2014-01-01

Full Text Available The plasmapause is a highly dynamic boundary between different magnetospheric particle populations and convection regimes. Some of the most important space weather processes involve wave-particle interactions in this region, but wave properties may also be used to remote sense the plasmasphere and plasmapause, contributing to plasmasphere models. This paper discusses the use of existing ground magnetometer arrays for such remote sensing. Using case studies we illustrate measurement of plasmapause location, shape and movement during storms; refilling of flux tubes within and outside the plasmasphere; storm-time increase in heavy ion concentration near the plasmapause; and detection and mapping of density irregularities near the plasmapause, including drainage plumes, biteouts and bulges. We also use a 2D MHD model of wave propagation through the magnetosphere, incorporating a realistic ionosphere boundary and Alfvén speed profile, to simulate ground array observations of power and cross-phase spectra, hence confirming the signatures of plumes and other density structures.

Progress in Analysis to Remote Sensed Thermal Abnormity with Fault Activity and Seismogenic Process

Directory of Open Access Journals (Sweden)

WU Lixin

2017-10-01

Full Text Available Research to the remote sensed thermal abnormity with fault activity and seismogenic process is a vital topic of the Earth observation and remote sensing application. It is presented that a systematic review on the international researches on the topic during the past 30 years, in the respects of remote sensing data applications, anomaly analysis methods, and mechanism understanding. Firstly, the outlines of remote sensing data applications are given including infrared brightness temperature, microwave brightness temperature, outgoing longwave radiation, and assimilated data from multiple earth observations. Secondly, three development phases are summarized as qualitative analysis based on visual interpretation, quantitative analysis based on image processing, and multi-parameter spatio-temporal correlation analysis. Thirdly, the theoretical hypotheses presented for the mechanism understanding are introduced including earth degassing, stress-induced heat, crustal rock battery conversion, latent heat release due to radon decay as well as multi-spheres coupling effect. Finally, three key directions of future research on this topic are proposed:anomaly recognizing by remote sensing monitoring and data analysis for typical tectonic activity areas; anomaly mechanism understanding based on earthquake-related earth system responses; spatio-temporal correlation analysis of air-based, space-based and ground-based stereoscopic observations.

ENGINEERED BARRIER SYSTEM FEATURES, EVENTS AND PROCESSES

International Nuclear Information System (INIS)

Jaros, W.

2005-01-01

The purpose of this report is to evaluate and document the inclusion or exclusion of engineered barrier system (EBS) features, events, and processes (FEPs) with respect to models and analyses used to support the total system performance assessment for the license application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical basis for exclusion screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.114 (d, e, and f) [DIRS 173273]. The FEPs addressed in this report deal with those features, events, and processes relevant to the EBS focusing mainly on those components and conditions exterior to the waste package and within the rock mass surrounding emplacement drifts. The components of the EBS are the drip shield, waste package, waste form, cladding, emplacement pallet, emplacement drift excavated opening (also referred to as drift opening in this report), and invert. FEPs specific to the waste package, cladding, and drip shield are addressed in separate FEP reports: for example, ''Screening of Features, Events, and Processes in Drip Shield and Waste Package Degradation'' (BSC 2005 [DIRS 174995]), ''Clad Degradation--FEPs Screening Arguments (BSC 2004 [DIRS 170019]), and Waste-Form Features, Events, and Processes'' (BSC 2004 [DIRS 170020]). For included FEPs, this report summarizes the implementation of the FEP in the TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from TSPA-LA (i.e., why the FEP is excluded). This report also documents changes to the EBS FEPs list that have occurred since the previous versions of this report. These changes have resulted due to a reevaluation of the FEPs for TSPA-LA as identified in Section 1.2 of this report and described in more detail in Section 6.1.1. This revision addresses updates in Yucca Mountain Project (YMP) administrative procedures as they

Shielding voices: The modulation of binding processes between voice features and response features by task representations.

Science.gov (United States)

Bogon, Johanna; Eisenbarth, Hedwig; Landgraf, Steffen; Dreisbach, Gesine

2017-09-01

Vocal events offer not only semantic-linguistic content but also information about the identity and the emotional-motivational state of the speaker. Furthermore, most vocal events have implications for our actions and therefore include action-related features. But the relevance and irrelevance of vocal features varies from task to task. The present study investigates binding processes for perceptual and action-related features of spoken words and their modulation by the task representation of the listener. Participants reacted with two response keys to eight different words spoken by a male or a female voice (Experiment 1) or spoken by an angry or neutral male voice (Experiment 2). There were two instruction conditions: half of participants learned eight stimulus-response mappings by rote (SR), and half of participants applied a binary task rule (TR). In both experiments, SR instructed participants showed clear evidence for binding processes between voice and response features indicated by an interaction between the irrelevant voice feature and the response. By contrast, as indicated by a three-way interaction with instruction, no such binding was found in the TR instructed group. These results are suggestive of binding and shielding as two adaptive mechanisms that ensure successful communication and action in a dynamic social environment.

Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

Xi Gong

2018-03-01

Full Text Available Remote sensing (RS scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises. This paper proposes a deep salient feature based anti-noise transfer network (DSFATN method that effectively enhances and explores the high-level features for RS scene classification in different scales and noise conditions. In DSFATN, a novel discriminative deep salient feature (DSF is introduced by saliency-guided DSF extraction, which conducts a patch-based visual saliency (PBVS algorithm using “visual attention” mechanisms to guide pre-trained CNNs for producing the discriminative high-level features. Then, an anti-noise network is proposed to learn and enhance the robust and anti-noise structure information of RS scene by directly propagating the label information to fully-connected layers. A joint loss is used to minimize the anti-noise network by integrating anti-noise constraint and a softmax classification loss. The proposed network architecture can be easily trained with a limited amount of training data. The experiments conducted on three different scale RS scene datasets show that the DSFATN method has achieved excellent performance and great robustness in different scales and noise conditions. It obtains classification accuracy of 98.25%, 98.46%, and 98.80%, respectively, on the UC Merced Land Use Dataset (UCM, the Google image dataset of SIRI-WHU, and the SAT-6 dataset, advancing the state-of-the-art substantially.

Proceedings of the Eleventh International Symposium on Remote Sensing of Environment, volume 2. [application and processing of remotely sensed data

Science.gov (United States)

1977-01-01

Application and processing of remotely sensed data are discussed. Areas of application include: pollution monitoring, water quality, land use, marine resources, ocean surface properties, and agriculture. Image processing and scene analysis are described along with automated photointerpretation and classification techniques. Data from infrared and multispectral band scanners onboard LANDSAT satellites are emphasized.

A Web GIS Framework for Participatory Sensing Service: An Open Source-Based Implementation

Directory of Open Access Journals (Sweden)

Yu Nakayama

2017-04-01

Full Text Available Participatory sensing is the process in which individuals or communities collect and analyze systematic data using mobile phones and cloud services. To efficiently develop participatory sensing services, some server-side technologies have been proposed. Although they provide a good platform for participatory sensing, they are not optimized for spatial data management and processing. For the purpose of spatial data collection and management, many web GIS approaches have been studied. However, they still have not focused on the optimal framework for participatory sensing services. This paper presents a web GIS framework for participatory sensing service (FPSS. The proposed FPSS enables an integrated deployment of spatial data capture, storage, and data management functions. In various types of participatory sensing experiments, users can collect and manage spatial data in a unified manner. This feature is realized by the optimized system architecture and use case based on the general requirements for participatory sensing. We developed an open source GIS-based implementation of the proposed framework, which can overcome financial difficulties that are one of the major problems of deploying sensing experiments. We confirmed with the prototype that participatory sensing experiments can be performed efficiently with the proposed FPSS.

Effect of processing on structural features of anodic aluminum oxides

Science.gov (United States)

Erdogan, Pembe; Birol, Yucel

2012-09-01

Morphological features of the anodic aluminum oxide (AAO) templates fabricated by electrochemical oxidation under different processing conditions were investigated. The selection of the polishing parameters does not appear to be critical as long as the aluminum substrate is polished adequately prior to the anodization process. AAO layers with a highly ordered pore distribution are obtained after anodizing in 0.6 M oxalic acid at 20 °C under 40 V for 5 minutes suggesting that the desired pore features are attained once an oxide layer develops on the surface. While the pore features are not affected much, the thickness of the AAO template increases with increasing anodization treatment time. Pore features are better and the AAO growth rate is higher at 20 °C than at 5 °C; higher under 45 V than under 40 V; higher with 0.6 M than with 0.3 M oxalic acid.

Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal

Science.gov (United States)

Sanches, Ieda Del´Arco; Souza Filho, Carlos Roberto de; Kokaly, Raymond F.

2014-01-01

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.

Wireless Sensing Based on RFID and Capacitive Technologies for Safety in Marble Industry Process Control

Directory of Open Access Journals (Sweden)

Fabrizio Iacopetti

2013-01-01

Full Text Available This paper presents wireless sensing systems to increase safety and robustness in industrial process control, particularly in industrial machines for marble slab working. The process is performed by abrasive or cutting heads activated independently by the machine controller when the slab, transported on a conveyer belt, is under them. Current slab detection systems are based on electromechanical or optical devices at the machine entrance stage, suffering from deterioration and from the harsh environment. Slab displacement or break inside the machine due to the working stress may result in safety issues and damages to the conveyer belt due to incorrect driving of the working tools. The experimented contactless sensing techniques are based on four RFID and two capacitive sensing technologies and on customized hardware/software. The proposed solutions aim at overcoming some limitations of current state-of-the-art detection systems, allowing for reliable slab detection, outside and/or inside the machine, while maintaining low complexity and at the same time robustness to industrial harsh conditions. The proposed sensing devices may implement a wireless or wired sensor network feeding detection data to the machine controller. Data integrity check and process control algorithms have to be implemented for the safety and reliability of the overall industrial process.

Remote Sensing Image Registration with Line Segments and Their Intersections

Directory of Open Access Journals (Sweden)

Chengjin Lyu

2017-05-01

Full Text Available Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster. In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remote sensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remote sensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes.

Some features of radiation processing in the plastics industry

International Nuclear Information System (INIS)

Dobo, J.

1985-01-01

In the last few years, the production of free radicals by radiation became competitive with chemical initiators. Nevertheless radiation processing got only a firm footing, where distinct advantages could be demonstrated as compared with conventional processes, either in the technology or the product quality. This paper is intended to direct attention to some of the special features of radiation processing. (author)

Some features of radiation processing in the plastics industry

Science.gov (United States)

D´, J.

In the last few years, the production of free radicals by radiation became competitive with chemical initiators. Nevertheless, radiation processing got only a firm footing, where distinct advantages could be demonstrated as compared with conventional processes, either in the technology or the product quality. This paper is intended to direct attention to some of the special features of radiation processing.

Building Children's Sense of Community in a Day Care Centre through Small Groups in Play

Science.gov (United States)

Koivula, Merja; Hännikäinen, Maritta

2017-01-01

This study examines the process through which children build a sense of community in small groups in a day care centre. The study asks the following: how does children's sense of community develop, and what are its key features? Data were collected by applying ethnographic methods in a group of three- to five-year-old children over eleven months.…

The perceptual flow of phonetic feature processing

DEFF Research Database (Denmark)

Greenberg, Steven; Christiansen, Thomas Ulrich

2008-01-01

How does the brain process spoken language? It is our thesis that word intelligibility and consonant identification are insufficient by themselves to model how the speech signal is decoded - a finer-grained approach is required. In this study, listeners identified 11 different Danish consonants....... This asymmetric pattern of feature decoding may provide extra-segmental information of utility for speech processing, particularly in adverse listening conditions....... spoken in a Consonant + Vowel + [l] environment. Each syllable was processed so that only a portion of the original audio spectrum was present. Three-quarter-octave bands of speech, centered at 750, 1500, and 3000 Hz, were presented individually and in combination with each other. The conditional...

ENGINEERED BARRIER SYSTEM FEATURES, EVENTS AND PROCESSES

Energy Technology Data Exchange (ETDEWEB)

Jaros, W.

2005-08-30

The purpose of this report is to evaluate and document the inclusion or exclusion of engineered barrier system (EBS) features, events, and processes (FEPs) with respect to models and analyses used to support the total system performance assessment for the license application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical basis for exclusion screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.114 (d, e, and f) [DIRS 173273]. The FEPs addressed in this report deal with those features, events, and processes relevant to the EBS focusing mainly on those components and conditions exterior to the waste package and within the rock mass surrounding emplacement drifts. The components of the EBS are the drip shield, waste package, waste form, cladding, emplacement pallet, emplacement drift excavated opening (also referred to as drift opening in this report), and invert. FEPs specific to the waste package, cladding, and drip shield are addressed in separate FEP reports: for example, ''Screening of Features, Events, and Processes in Drip Shield and Waste Package Degradation'' (BSC 2005 [DIRS 174995]), ''Clad Degradation--FEPs Screening Arguments (BSC 2004 [DIRS 170019]), and Waste-Form Features, Events, and Processes'' (BSC 2004 [DIRS 170020]). For included FEPs, this report summarizes the implementation of the FEP in the TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from TSPA-LA (i.e., why the FEP is excluded). This report also documents changes to the EBS FEPs list that have occurred since the previous versions of this report. These changes have resulted due to a reevaluation of the FEPs for TSPA-LA as identified in Section 1.2 of this report and described in more detail in Section 6.1.1. This revision addresses updates in Yucca Mountain Project

From Word Alignment to Word Senses, via Multilingual Wordnets

Directory of Open Access Journals (Sweden)

Dan Tufis

2006-05-01

Full Text Available Most of the successful commercial applications in language processing (text and/or speech dispense with any explicit concern on semantics, with the usual motivations stemming from the computational high costs required for dealing with semantics, in case of large volumes of data. With recent advances in corpus linguistics and statistical-based methods in NLP, revealing useful semantic features of linguistic data is becoming cheaper and cheaper and the accuracy of this process is steadily improving. Lately, there seems to be a growing acceptance of the idea that multilingual lexical ontologisms might be the key towards aligning different views on the semantic atomic units to be used in characterizing the general meaning of various and multilingual documents. Depending on the granularity at which semantic distinctions are necessary, the accuracy of the basic semantic processing (such as word sense disambiguation can be very high with relatively low complexity computing. The paper substantiates this statement by presenting a statistical/based system for word alignment and word sense disambiguation in parallel corpora. We describe a word alignment platform which ensures text pre-processing (tokenization, POS-tagging, lemmatization, chunking, sentence and word alignment as required by an accurate word sense disambiguation.

[Constructivism: a characteristic of the psychic process].

Science.gov (United States)

Ruiz, R H

1995-05-01

Constructivism, more than a posture or a school concept, is a specific feature of the psychic process. The term "specific" needs to be understood taking into account two senses. On the one hand, as a first sense, one must consider it as something inherent to the psychic process. On the other, as a second sense, it means that constructivism in the psychic process has distinctive characteristics: and this make it differ from the concept of constructivism applied in other fields. The fact of considering the psychism as a "construction" lets explain those mental productions such as art and science; as well as madness and crime: possibilities and risks of that construction.

Collaborative Educational Leadership: The Emergence of Human Interactional Sense-Making Process as a Complex System

Science.gov (United States)

Jäppinen, Aini-Kristiina

2014-01-01

The article aims at explicating the emergence of human interactional sense-making process within educational leadership as a complex system. The kind of leadership is understood as a holistic entity called collaborative leadership. There, sense-making emerges across interdependent domains, called attributes of collaborative leadership. The…

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

Science.gov (United States)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Features of financial support of reproduction processes in agriculture

Directory of Open Access Journals (Sweden)

Kudrina Valentina Aleksandrovna

2016-08-01

Full Text Available The article describes the features of the financing of reproduction processes in agriculture, arising from the specific production in the industry. Considered and analyzed the main sources of financial resources for the implementation of the reproduction processes in the agricultural sector, including bank lending, leasing, public financial support.

Software for Demonstration of Features of Chain Polymerization Processes

Science.gov (United States)

Sosnowski, Stanislaw

2013-01-01

Free software for the demonstration of the features of homo- and copolymerization processes (free radical, controlled radical, and living) is described. The software is based on the Monte Carlo algorithms and offers insight into the kinetics, molecular weight distribution, and microstructure of the macromolecules formed in those processes. It also…

Features, Events, and Processes: System Level

Energy Technology Data Exchange (ETDEWEB)

D. McGregor

2004-04-19

The primary purpose of this analysis is to evaluate System Level features, events, and processes (FEPs). The System Level FEPs typically are overarching in nature, rather than being focused on a particular process or subsystem. As a result, they are best dealt with at the system level rather than addressed within supporting process-level or subsystem level analyses and models reports. The System Level FEPs also tend to be directly addressed by regulations, guidance documents, or assumptions listed in the regulations; or are addressed in background information used in development of the regulations. This evaluation determines which of the System Level FEPs are excluded from modeling used to support the total system performance assessment for license application (TSPA-LA). The evaluation is based on the information presented in analysis reports, model reports, direct input, or corroborative documents that are cited in the individual FEP discussions in Section 6.2 of this analysis report.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

L. Ding

2018-04-01

Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

[INVITED] Evaluation of process observation features for laser metal welding

Science.gov (United States)

Tenner, Felix; Klämpfl, Florian; Nagulin, Konstantin Yu.; Schmidt, Michael

2016-06-01

In the present study we show how fast the fluid dynamics change when changing the laser power for different feed rates during laser metal welding. By the use of two high-speed cameras and a data acquisition system we conclude how fast we have to image the process to measure the fluid dynamics with a very high certainty. Our experiments show that not all process features which can be measured during laser welding do represent the process behavior similarly well. Despite the good visibility of the vapor plume the monitoring of its movement is less suitable as an input signal for a closed-loop control. The features measured inside the keyhole show a good correlation with changes of process parameters. Due to its low noise, the area of the keyhole opening is well suited as an input signal for a closed-loop control of the process.

Advanced social features in a recommendation system for process modeling

NARCIS (Netherlands)

Koschmider, A.; Song, M.S.; Reijers, H.A.; Abramowicz, W.

2009-01-01

Social software is known to stimulate the exchange and sharing of information among peers. This paper describes how an existing system that supports process builders in completing a business process can be enhanced with various social features. In that way, it is easier for process modeler to become

Passive and Active Sensing Technologies for Structural Health Monitoring

Science.gov (United States)

Do, Richard

A combination of passive and active sensing technologies is proposed as a structural health monitoring solution for several applications. Passive sensing is differentiated from active sensing in that with the former, no energy is intentionally imparted into the structure under test; sensors are deployed in a pure detection mode for collecting data mined for structural health monitoring purposes. In this thesis, passive sensing using embedded fiber Bragg grating optical strain gages was used to detect varying degrees of impact damage using two different classes of features drawn from traditional spectral analysis and auto-regressive time series modeling. The two feature classes were compared in detail through receiver operating curve performance analysis. The passive detection problem was then augmented with an active sensing system using ultrasonic guided waves (UGWs). This thesis considered two main challenges associated with UGW SHM including in-situ wave propagation property determination and thermal corruption of data. Regarding determination of wave propagation properties, of which dispersion characteristics are the most important, a new dispersion curve extraction method called sparse wavenumber analysis (SWA) was experimentally validated. Also, because UGWs are extremely sensitive to ambient temperature changes on the structure, it significantly affects the wave propagation properties by causing large errors in the residual error in the processing of the UGWs from an array. This thesis presented a novel method that compensates for uniform temperature change by considering the magnitude and phase of the signal separately and applying a scalable transformation.

Remote Sensing of shallow sea floor for digital earth environment

International Nuclear Information System (INIS)

Yahya, N N; Hashim, M; Ahmad, S

2014-01-01

Understanding the sea floor biodiversity requires spatial information that can be acquired from remote sensing satellite data. Species volume, spatial patterns and species coverage are some of the information that can be derived. Current approaches for mapping sea bottom type have evolved from field observation, visual interpretation from aerial photography, mapping from remote sensing satellite data along with field survey and hydrograhic chart. Remote sensing offers most versatile technique to map sea bottom type up to a certain scale. This paper reviews the technical characteristics of signal and light interference within marine features, space and remote sensing satellite. In addition, related image processing techniques that are applicable to remote sensing satellite data for sea bottom type digital mapping is also presented. The sea bottom type can be differentiated by classification method using appropriate spectral bands of satellite data. In order to verify the existence of particular sea bottom type, field observations need to be carried out with proper technique and equipment

Wuzzit Trouble: The Influence of a Digital Math Game on Student Number Sense

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Holly Pope

2015-12-01

Full Text Available This study sought to determine if playing a digital math game could increase student number sense (mathematical proficiency in numeracy. We used a pre- and post-assessment to measure the number sense of two groups of third grade students with the same mathematics teacher. One group played the game Wuzzit Trouble and the other did not. Overall, the group who played Wuzzit Trouble showed a significant increase in number sense between the pre- and post-assessment, compared to the other group who did not. A qualitative analysis of a novel problem revealed differences between the treatment and comparison groups from pre- to post-. A discussion of these findings and features of the game are addressed. Namely, two features inherent in Wuzzit Trouble are associated with the learners’ increased number sense. First, Wuzzit Trouble promoted mathematical proficiency by requiring learners to attend to several mathematical constraints at once. Second, the game engaged learners in an iterative process of decision-making by calling for students to try, check, and revise their strategy as they played.

Geospatial Image Stream Processing: Models, techniques, and applications in remote sensing change detection

Science.gov (United States)

Rueda-Velasquez, Carlos Alberto

Detection of changes in environmental phenomena using remotely sensed data is a major requirement in the Earth sciences, especially in natural disaster related scenarios where real-time detection plays a crucial role in the saving of human lives and the preservation of natural resources. Although various approaches formulated to model multidimensional data can in principle be applied to the inherent complexity of remotely sensed geospatial data, there are still challenging peculiarities that demand a precise characterization in the context of change detection, particularly in scenarios of fast changes. In the same vein, geospatial image streams do not fit appropriately in the standard Data Stream Management System (DSMS) approach because these systems mainly deal with tuple-based streams. Recognizing the necessity for a systematic effort to address the above issues, the work presented in this thesis is a concrete step toward the foundation and construction of an integrated Geospatial Image Stream Processing framework, GISP. First, we present a data and metadata model for remotely sensed image streams. We introduce a precise characterization of images and image streams in the context of remotely sensed geospatial data. On this foundation, we define spatially-aware temporal operators with a consistent semantics for change analysis tasks. We address the change detection problem in settings where multiple image stream sources are available, and thus we introduce an architectural design for the processing of geospatial image streams from multiple sources. With the aim of targeting collaborative scientific environments, we construct a realization of our architecture based on Kepler, a robust and widely used scientific workflow management system, as the underlying computational support; and open data and Web interface standards, as a means to facilitate the interoperability of GISP instances with other processing infrastructures and client applications. We demonstrate our

Residual Neural Processing of Musical Sound Features in Adult Cochlear Implant Users

Science.gov (United States)

Timm, Lydia; Vuust, Peter; Brattico, Elvira; Agrawal, Deepashri; Debener, Stefan; Büchner, Andreas; Dengler, Reinhard; Wittfoth, Matthias

2014-01-01

Auditory processing in general and music perception in particular are hampered in adult cochlear implant (CI) users. To examine the residual music perception skills and their underlying neural correlates in CI users implanted in adolescence or adulthood, we conducted an electrophysiological and behavioral study comparing adult CI users with normal-hearing age-matched controls (NH controls). We used a newly developed musical multi-feature paradigm, which makes it possible to test automatic auditory discrimination of six different types of sound feature changes inserted within a musical enriched setting lasting only 20 min. The presentation of stimuli did not require the participants’ attention, allowing the study of the early automatic stage of feature processing in the auditory cortex. For the CI users, we obtained mismatch negativity (MMN) brain responses to five feature changes but not to changes of rhythm, whereas we obtained MMNs for all the feature changes in the NH controls. Furthermore, the MMNs to deviants of pitch of CI users were reduced in amplitude and later than those of NH controls for changes of pitch and guitar timber. No other group differences in MMN parameters were found to changes in intensity and saxophone timber. Furthermore, the MMNs in CI users reflected the behavioral scores from a respective discrimination task and were correlated with patients’ age and speech intelligibility. Our results suggest that even though CI users are not performing at the same level as NH controls in neural discrimination of pitch-based features, they do possess potential neural abilities for music processing. However, CI users showed a disrupted ability to automatically discriminate rhythmic changes compared with controls. The current behavioral and MMN findings highlight the residual neural skills for music processing even in CI users who have been implanted in adolescence or adulthood. Highlights: -Automatic brain responses to musical feature changes

Image Processing and Features Extraction of Fingerprint Images ...

African Journals Online (AJOL)

To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of ...

REAL TIME DATA PROCESSING FOR OPTICAL REMOTE SENSING PAYLOADS

Directory of Open Access Journals (Sweden)

J. Wohlfeil

2012-07-01

Full Text Available The application of operational systems for remote sensing requires new approaches for data processing. It has to be the goal to derive user relevant information close the sensor itself and to downlink this information to a ground station or to provide them as input to an actuator of the space-borne platform. A complete automation of data processing is an essential first step for a thematic onboard data processing. In a second step, an appropriate onboard computer system has to be de-signed being able to fulfill the requirements. In this paper, standard data processing steps will be introduced correcting systematic errors during image capturing. A new hardware operating system, which is the interface between FPGA hardware and data processing algorithms, gives the opportunity to implement complex data processing modules in an effective way. As an example the derivation the camera's orientation based on data of an optical payload is described in detail. The thereby derived absolute or relative orientation is essential for high level data products. This will be illustrated by means of an onboard image matcher

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

Directory of Open Access Journals (Sweden)

Jie Wang

2017-03-01

Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

The active electric sense of weakly electric fish: from electric organ discharge to sensory processing and behaviour

Directory of Open Access Journals (Sweden)

Krahe Rüdiger

2016-01-01

Full Text Available Sensory systems have been shaped by evolution to extract information that is relevant for decision making. In order to understand the mechanisms used by sensory systems for filtering the incoming stream of sensory input, it is important to have a quantitative understanding of the natural sensory scenes that are to be processed. Weakly electric fish lead a rather cryptic nocturnal life in often turbid tropical rainforest streams. They produce electric discharges and sense perturbations of their selfgenerated electric field for prey detection and navigation, and also use their active sense for communication in the context of courtship and aggression. The fact that they produce their electric signals throughout day and night permits the use of electrode arrays to track the movements of multiple individual fish and monitor their communication interactions, thus offering a window into their electrosensory world. This approach yields unprecedented access to information on the biology of these fishes and also on the statistical properties of the sensory scenes that are to be processed by their electrosensory system. The electrosensory system shares many organizational features with other sensory systems, in particular, the use of multiple topographic maps. In fact, the sensory surface (the skin is represented in three parallel maps in the hindbrain, with each map covering the receptor organ array with six different cell types that project to the next higher level of processing. Thus, the electroreceptive body surface is represented a total of 18 times in the hindbrain, with each representation having its specific filter properties and degree of response plasticity. Thus, the access to the sensory world of these fish as well as the manifold filtering of the sensory input makes these fish an excellent model system for exploring the cell-intrinsic and network characteristics underlying the extraction of behaviourally relevant sensory information.

Anthropogenic features and hillslope processes interaction

Science.gov (United States)

Tarolli, Paolo; Sofia, Giulia

2016-04-01

Topography emerges as a result of natural driving forces, but some human activities (such as mining, agricultural practices and the construction of road networks) directly or indirectly move large quantities of soil, which leave clear topographic signatures embedded on the Earth's morphology. These signatures can cause drastic changes to the geomorphological organization of the landscape, with direct consequences on Earth surface processes (Tarolli and Sofia, 2016). To this point, the present research investigates few case studies highlighting the influences of anthropogenic topographic signatures on hillslope processes, and it shows the effectiveness of High-Resolution Topography (HRT) derived from the recent remote sensing technologies (e.g. lidar, satellite, structure from motion photogrammetry), to better understand this interaction. The first example is related to agricultural terraces. In recent times, terraced areas acquired a new relevance to modern concerns about erosion and land instability, being the agricultural land mostly threatened by abandonment or intensification and specialization of agriculture, resulting in more landslide-prone bench terraces, or heavy land levelling with increased erosion. The second case study discusses about the role of agricultural and forest roads on surface erosion and landslides. The third case study investigates geomorphic processes in an open pit mine. In all case studies, HRT served as the basis for the development of new methodologies able to recognize and analyze changes on Earth surface processes along hillslopes. The results show how anthropogenic elements have crucial effects on sediment production and sediment delivery, also influencing the landscape connectivity. The availability of HRT can improve our ability to actually model anthropogenic morphologies, quantify them, and analyse the links between anthropogenic elements and geomorphic processes. The results presented here, and the creation and dissemination of

Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model

Directory of Open Access Journals (Sweden)

TAO Feixiang

2015-08-01

Full Text Available Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpretability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform, the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects.

New security features and their impact on low-cost note readers

Science.gov (United States)

Bernardini, Ronald R.

2004-06-01

Banknote security features are evolving and changing. New features are constantly being developed and slowly being incorporated into banknotes. The assumption is that these features make the notes more secure for everyone; but do they? This paper looks at some of the features incorporated in today's banknotes and how (or if) they add security to banknotes processed by low cost banknote readers. The sensing technology used in low cost note readers has changed somewhat in the last few years but the industry is still faced by the cost constraints of a very competitive market. Some of the new note features require high-resolution image capture, complex optical measurements or expensive emission/detection devices. Paper watermarks, digital watermarks, OVI, Holograms, Stokes conversion, IR and magnetic features are examined, as well as the technologies used and the relative cost/benefit developed for these note features.

The Catchment Feature Model: A Device for Multimodal Fusion and a Bridge between Signal and Sense

Science.gov (United States)

Quek, Francis

2004-12-01

The catchment feature model addresses two questions in the field of multimodal interaction: how we bridge video and audio processing with the realities of human multimodal communication, and how information from the different modes may be fused. We argue from a detailed literature review that gestural research has clustered around manipulative and semaphoric use of the hands, motivate the catchment feature model psycholinguistic research, and present the model. In contrast to "whole gesture" recognition, the catchment feature model applies a feature decomposition approach that facilitates cross-modal fusion at the level of discourse planning and conceptualization. We present our experimental framework for catchment feature-based research, cite three concrete examples of catchment features, and propose new directions of multimodal research based on the model.

Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager

Science.gov (United States)

Duong, Tuan A. (Inventor)

2015-01-01

A CMOS imager integrated circuit using compressive sensing and bio-inspired detection is presented which integrates novel functions and algorithms within a novel hardware architecture enabling efficient on-chip implementation.

Optimization of an individual re-identification modeling process using biometric features

Energy Technology Data Exchange (ETDEWEB)

Heredia-Langner, Alejandro; Amidan, Brett G.; Matzner, Shari; Jarman, Kristin H.

2014-09-24

We present results from the optimization of a re-identification process using two sets of biometric data obtained from the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database. The datasets contain real measurements of features for 2378 individuals in a standing (43 features) and seated (16 features) position. A genetic algorithm (GA) was used to search a large combinatorial space where different features are available between the probe (seated) and gallery (standing) datasets. Results show that optimized model predictions obtained using less than half of the 43 gallery features and data from roughly 16% of the individuals available produce better re-identification rates than two other approaches that use all the information available.

Impact of load-related neural processes on feature binding in visuospatial working memory.

Directory of Open Access Journals (Sweden)

Nicole A Kochan

Full Text Available BACKGROUND: The capacity of visual working memory (WM is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood. OBJECTIVE: To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval. METHODS AND FINDINGS: 18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network. CONCLUSIONS AND SIGNIFICANCE: The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be 'automatic' but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this

The Catchment Feature Model: A Device for Multimodal Fusion and a Bridge between Signal and Sense

Directory of Open Access Journals (Sweden)

Francis Quek

2004-09-01

Full Text Available The catchment feature model addresses two questions in the field of multimodal interaction: how we bridge video and audio processing with the realities of human multimodal communication, and how information from the different modes may be fused. We argue from a detailed literature review that gestural research has clustered around manipulative and semaphoric use of the hands, motivate the catchment feature model psycholinguistic research, and present the model. In contrast to “whole gesture” recognition, the catchment feature model applies a feature decomposition approach that facilitates cross-modal fusion at the level of discourse planning and conceptualization. We present our experimental framework for catchment feature-based research, cite three concrete examples of catchment features, and propose new directions of multimodal research based on the model.

Remote Sensing

CERN Document Server

Khorram, Siamak; Koch, Frank H; van der Wiele, Cynthia F

2012-01-01

Remote Sensing provides information on how remote sensing relates to the natural resources inventory, management, and monitoring, as well as environmental concerns. It explains the role of this new technology in current global challenges. "Remote Sensing" will discuss remotely sensed data application payloads and platforms, along with the methodologies involving image processing techniques as applied to remotely sensed data. This title provides information on image classification techniques and image registration, data integration, and data fusion techniques. How this technology applies to natural resources and environmental concerns will also be discussed.

Biological features produced by additive manufacturing processes using vat photopolymerization method

DEFF Research Database (Denmark)

Davoudinejad, Ali; Mendez Ribo, Macarena; Pedersen, David Bue

2017-01-01

of micro biological features by Additive Manufacturing (AM) processes. The study characterizes the additive manufacturing processes for polymeric micro part productions using the vat photopolymerization method. A specifically designed vat photopolymerization AM machine suitable for precision printing...

Small numbers are sensed directly, high numbers constructed from size and density.

Science.gov (United States)

Zimmermann, Eckart

2018-04-01

Two theories compete to explain how we estimate the numerosity of visual object sets. The first suggests that the apparent numerosity is derived from an analysis of more low-level features like size and density of the set. The second theory suggests that numbers are sensed directly. Consistent with the latter claim is the existence of neurons in parietal cortex which are specialized for processing the numerosity of elements in the visual scene. However, recent evidence suggests that only low numbers can be sensed directly whereas the perception of high numbers is supported by the analysis of low-level features. Processing of low and high numbers, being located at different levels of the neural hierarchy should involve different receptive field sizes. Here, I tested this idea with visual adaptation. I measured the spatial spread of number adaptation for low and high numerosities. A focused adaptation spread of high numerosities suggested the involvement of early neural levels where receptive fields are comparably small and the broad spread for low numerosities was consistent with processing of number neurons which have larger receptive fields. These results provide evidence for the claim that different mechanism exist generating the perception of visual numerosity. Whereas low numbers are sensed directly as a primary visual attribute, the estimation of high numbers however likely depends on the area size over which the objects are spread. Copyright © 2017 Elsevier B.V. All rights reserved.

Low-temperature solution processing of palladium/palladium oxide films and their pH sensing performance.

Science.gov (United States)

Qin, Yiheng; Alam, Arif U; Pan, Si; Howlader, Matiar M R; Ghosh, Raja; Selvaganapathy, P Ravi; Wu, Yiliang; Deen, M Jamal

2016-01-01

Highly sensitive, easy-to-fabricate, and low-cost pH sensors with small dimensions are required to monitor human bodily fluids, drinking water quality and chemical/biological processes. In this study, a low-temperature, solution-based process is developed to prepare palladium/palladium oxide (Pd/PdO) thin films for pH sensing. A precursor solution for Pd is spin coated onto pre-cleaned glass substrates and annealed at low temperature to generate Pd and PdO. The percentages of PdO at the surface and in the bulk of the electrodes are correlated to their sensing performance, which was studied by using the X-ray photoelectron spectroscope. Large amounts of PdO introduced by prolonged annealing improve the electrode's sensitivity and long-term stability. Atomic force microscopy study showed that the low-temperature annealing results in a smooth electrode surface, which contributes to a fast response. Nano-voids at the electrode surfaces were observed by scanning electron microscope, indicating a reason for the long-term degradation of the pH sensitivity. Using the optimized annealing parameters of 200°C for 48 h, a linear pH response with sensitivity of 64.71±0.56 mV/pH is obtained for pH between 2 and 12. These electrodes show a response time shorter than 18 s, hysteresis less than 8 mV and stability over 60 days. High reproducibility in the sensing performance is achieved. This low-temperature solution-processed sensing electrode shows the potential for the development of pH sensing systems on flexible substrates over a large area at low cost without using vacuum equipment. Copyright © 2015 Elsevier B.V. All rights reserved.

Hybrid Arrays for Chemical Sensing

Science.gov (United States)

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

Attention in the processing of complex visual displays: detecting features and their combinations.

Science.gov (United States)

Farell, B

1984-02-01

The distinction between operations in visual processing that are parallel and preattentive and those that are serial and attentional receives both theoretical and empirical support. According to Treisman's feature-integration theory, independent features are available preattentively, but attention is required to veridically combine features into objects. Certain evidence supporting this theory is consistent with a different interpretation, which was tested in four experiments. The first experiment compared the detection of features and feature combinations while eliminating a factor that confounded earlier comparisons. The resulting priority of access to combinatorial information suggests that features and nonlocal combinations of features are not connected solely by a bottom-up hierarchical convergence. Causes of the disparity between the results of Experiment 1 and the results of previous research were investigated in three subsequent experiments. The results showed that of the two confounded factors, it was the difference in the mapping of alternatives onto responses, not the differing attentional demands of features and objects, that underlaid the results of the previous research. The present results are thus counterexamples to the feature-integration theory. Aspects of this theory are shown to be subsumed by more general principles, which are discussed in terms of attentional processes in the detection of features, objects, and stimulus alternatives.

Building a sense of virtual community: the role of the features of social networking sites.

Science.gov (United States)

Chen, Chi-Wen; Lin, Chiun-Sin

2014-07-01

In recent years, social networking sites have received increased attention because of the potential of this medium to transform business by building virtual communities. However, theoretical and empirical studies investigating how specific features of social networking sites contribute to building a sense of virtual community (SOVC)-an important dimension of a successful virtual community-are rare. Furthermore, SOVC scales have been developed, and research on this issue has been called for, but few studies have heeded this call. On the basis of prior literature, this study proposes that perceptions of the three most salient features of social networking sites-system quality (SQ), information quality (IQ), and social information exchange (SIE)-play a key role in fostering SOVC. In particular, SQ is proposed to increase IQ and SIE, and SIE is proposed to enhance IQ, both of which thereafter build SOVC. The research model was examined in the context of Facebook, one of the most popular social networking sites in the world. We adopted Blanchard's scales to measure SOVC. Data gathered using a Web-based questionnaire, and analyzed with partial least squares, were utilized to test the model. The results demonstrate that SIE, SQ, and IQ are the factors that form SOVC. The findings also suggest that SQ plays a fundamental role in supporting SIE and IQ in social networking sites. Implications for theory, practice, and future research directions are discussed.

Feature extraction & image processing for computer vision

CERN Document Server

Nixon, Mark

2012-01-01

This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, ""The main strength of the proposed book is the exemplar code of the algorithms."" Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filt

New SCALE-4 features related to cross-section processing

International Nuclear Information System (INIS)

Petrie, L.M.; Landers, N.F.; Greene, N.M.; Parks, C.V.

1991-01-01

The SCALE code system has a standardized scheme for processing problem-dependent cross section from problem-independent waste libraries. Some improvements and new capabilities in the processing scheme have been incorporated into the new Version 4 release of the SCALE system. The new features include the capability to consider annular cylindrical and spherical unit cells, and improved Dancoff factor formulation, and changes to the NITAWL-II module to perform resonance self-shielding with reference to infinite dilute values. A review of these major changes in the cross-section processing scheme for SCALE-4 is presented in this paper

Application of remote sensing methods and GIS in erosive process investigations

Directory of Open Access Journals (Sweden)

Mustafić Sanja

2007-01-01

Full Text Available Modern geomorphologic investigations of condition and change of the intensity of erosive process should be based on application of remote sensing methods which are based on processing of aerial and satellite photographs. Using of these methods is very important because it enables good possibilities for realizing regional relations of the investigated phenomenon, as well as the estimate of spatial and temporal variability of all physical-geographical and anthropogenic factors influencing given process. Realizing process of land erosion, on the whole, is only possible by creating universal data base, as well as by using of appropriate software, more exactly by establishing uniform information system. Geographical information system, as the most effective one, the most complex and the most integral system of information about the space enables unification as well as analytical and synthetically processing of all data.

Basic Remote Sensing Investigations for Beach Reconnaissance.

Science.gov (United States)

Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

FEATURES, EVENTS, AND PROCESSES: SYSTEM-LEVEL AND CRITICALITY

Energy Technology Data Exchange (ETDEWEB)

D.L. McGregor

2000-12-20

The primary purpose of this Analysis/Model Report (AMR) is to identify and document the screening analyses for the features, events, and processes (FEPs) that do not easily fit into the existing Process Model Report (PMR) structure. These FEPs include the 3 1 FEPs designated as System-Level Primary FEPs and the 22 FEPs designated as Criticality Primary FEPs. A list of these FEPs is provided in Section 1.1. This AMR (AN-WIS-MD-000019) documents the Screening Decision and Regulatory Basis, Screening Argument, and Total System Performance Assessment (TSPA) Disposition for each of the subject Primary FEPs. This AMR provides screening information and decisions for the TSPA-SR report and provides the same information for incorporation into a project-specific FEPs database. This AMR may also assist reviewers during the licensing-review process.

FEATURES, EVENTS, AND PROCESSES: SYSTEM-LEVEL AND CRITICALITY

International Nuclear Information System (INIS)

D.L. McGregor

2000-01-01

The primary purpose of this Analysis/Model Report (AMR) is to identify and document the screening analyses for the features, events, and processes (FEPs) that do not easily fit into the existing Process Model Report (PMR) structure. These FEPs include the 3 1 FEPs designated as System-Level Primary FEPs and the 22 FEPs designated as Criticality Primary FEPs. A list of these FEPs is provided in Section 1.1. This AMR (AN-WIS-MD-000019) documents the Screening Decision and Regulatory Basis, Screening Argument, and Total System Performance Assessment (TSPA) Disposition for each of the subject Primary FEPs. This AMR provides screening information and decisions for the TSPA-SR report and provides the same information for incorporation into a project-specific FEPs database. This AMR may also assist reviewers during the licensing-review process

THE INFLUENCE OF AUTONOMOUS DIVING ON SENSES AND MENTAL PROCESSES

Directory of Open Access Journals (Sweden)

Dragan Krivokapić

2010-09-01

Full Text Available Diving is classified within a group of sports accompanied with an increased risk, yet it is a sport of full biological significance. Diving implies change of immediate human environment. Water, as the natural ambient for diving issues specific demands to the organism, which in turn influence decrease in psychophysical abilities when underwater, and in some instances, immediately after emerging from it. The most important factors influencing decrease in psychophysical abilities are: immersion, increased ambient pressure, characteristics of diving equipment and atmosphere separation. The senses and the mental processes of the diver are significantly altered during the autonomous diving. Loss of self-weight perception and pressure put on joints cause disorders in function of kinesthetic senses and vestibular apparatus, which in turn becomes reflected on proprioception. Coldness of water, especially at grater depths, induces decline in pain sensation as well as in aptness and mobility of fingers. Sight remains normal, but the image received is slightly changed due to refraction of light on boundary surfaces. Visual field is narrowed down to fit the limited diving mask field of view. At the same time, diffusion of light and color absorption brings about the loss of both ability to perceive things and contrasts when at depths .Objects tend to appear bigger and closer underwater. Hearing is changed owing to the fact that the sound is not carried through the air but through the water, yet the speed of transmission causes only slight difference of left and right ear stimulation. Mental processes, informationassessment, creation of clear mental images of the actual moment, abstract thinking, decision making, etc. are not effective and precise. This state can be partly ascribed to the above mentioned problems with senses, partly to the greater influence of emotional as opposed to rational, but also to the narcotic effect of nitrogen that is produced while

Exploring potentials of sense-making theory for understanding social processes in public hearing

DEFF Research Database (Denmark)

Lyhne, Ivar

authorities and the public in such planning often characterised by conflict. A sense-making framework is developed based on Karl Weick's theory to investigate how participants at the meeting change their understanding aspects like other actors' opinions and the infrastructure project. Through interviews...... and observations it is shown that participants' senses do not change except from a few aspects. The participants at the meeting thus seem stuck in their positions without interest in being open for other interpretations or arguments. The investigation leads to considerations about the benefit and role...... of such a public meeting and the importance of trust and openness in the social processes in a public hearing....

Research on distributed optical fiber sensing data processing method based on LabVIEW

Science.gov (United States)

Li, Zhonghu; Yang, Meifang; Wang, Luling; Wang, Jinming; Yan, Junhong; Zuo, Jing

2018-01-01

The pipeline leak detection and leak location problem have gotten extensive attention in the industry. In this paper, the distributed optical fiber sensing system is designed based on the heat supply pipeline. The data processing method of distributed optical fiber sensing based on LabVIEW is studied emphatically. The hardware system includes laser, sensing optical fiber, wavelength division multiplexer, photoelectric detector, data acquisition card and computer etc. The software system is developed using LabVIEW. The software system adopts wavelet denoising method to deal with the temperature information, which improved the SNR. By extracting the characteristic value of the fiber temperature information, the system can realize the functions of temperature measurement, leak location and measurement signal storage and inquiry etc. Compared with traditional negative pressure wave method or acoustic signal method, the distributed optical fiber temperature measuring system can measure several temperatures in one measurement and locate the leak point accurately. It has a broad application prospect.

Optical fibre multi-parameter sensing with secure cloud based signal capture and processing

Science.gov (United States)

Newe, Thomas; O'Connell, Eoin; Meere, Damien; Yuan, Hongwei; Leen, Gabriel; O'Keeffe, Sinead; Lewis, Elfed

2016-05-01

Recent advancements in cloud computing technologies in the context of optical and optical fibre based systems are reported. The proliferation of real time and multi-channel based sensor systems represents significant growth in data volume. This coupled with a growing need for security presents many challenges and presents a huge opportunity for an evolutionary step in the widespread application of these sensing technologies. A tiered infrastructural system approach is adopted that is designed to facilitate the delivery of Optical Fibre-based "SENsing as a Service- SENaaS". Within this infrastructure, novel optical sensing platforms, deployed within different environments, are interfaced with a Cloud-based backbone infrastructure which facilitates the secure collection, storage and analysis of real-time data. Feedback systems, which harness this data to affect a change within the monitored location/environment/condition, are also discussed. The cloud based system presented here can also be used with chemical and physical sensors that require real-time data analysis, processing and feedback.

Fiber optic sensing for telecommunication satellites

Science.gov (United States)

Reutlinger, Arnd; Glier, Markus; Zuknik, Karl-Heinz; Hoffmann, Lars; Müller, Mathias; Rapp, Stephan; Kurvin, Charles; Ernst, Thomas; McKenzie, Iain; Karafolas, Nikos

2017-11-01

Modern telecommunication satellites can benefit from the features of fiber optic sensing wrt to mass savings, improved performance and lower costs. Within the course of a technology study, launched by the European Space Agency, a fiber optic sensing system has been designed and is to be tested on representative mockups of satellite sectors and environment.

Losing Control in Social Situations: How the Presence of Others Affects Neural Processes Related to Sense of Agency.

Science.gov (United States)

Beyer, Frederike; Sidarus, Nura; Fleming, Stephen; Haggard, Patrick

2018-01-01

Social contexts substantially influence individual behavior, but little is known about how they affect cognitive processes related to voluntary action. Previously, it has been shown that social context reduces participants' sense of agency over the outcomes of their actions and outcome monitoring. In this fMRI study on human volunteers, we investigated the neural mechanisms by which social context alters sense of agency. Participants made costly actions to stop inflating a balloon before it burst. On "social" trials, another player could act in their stead, but we analyzed only trials in which the other player remained passive. We hypothesized that mentalizing processes during social trials would affect decision-making fluency and lead to a decreased sense of agency. In line with this hypothesis, we found increased activity in the bilateral temporo-parietal junction (TPJ), precuneus, and middle frontal gyrus during social trials compared with nonsocial trials. Activity in the precuneus was, in turn, negatively related to sense of agency at a single-trial level. We further found a double dissociation between TPJ and angular gyrus (AG): activity in the left AG was not sensitive to social context but was negatively related to sense of agency. In contrast, activity in the TPJ was modulated by social context but was not sensitive to sense of agency.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Science.gov (United States)

Warren, Mark A.; Taylor, Benjamin H.; Grant, Michael G.; Shutler, Jamie D.

2014-03-01

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Remote sensing for nuclear power plant siting

International Nuclear Information System (INIS)

Siegal, B.S.; Welby, C.W.

1981-01-01

Remote sensing techniques enhance the selection and evaluation process for nuclear power plant siting. The principal advantage is the synoptic view which improves recognition of linear features, possibly indicative of faults. The interpretation of such images, in conjunction with seismological studies, also permits delineation of seismo-tectonic provinces. In volcanic terrains, geomorphic-age boundaries can be delineated and volcanic centers identified, providing necessary guidance for field sampling and regional model derivation. The use of such techniques is considered for studies in the Philippines, Mexico, and Greece. 5 refs

Soft sensor design by multivariate fusion of image features and process measurements

DEFF Research Database (Denmark)

Lin, Bao; Jørgensen, Sten Bay

2011-01-01

This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors. A key issue of fusing multiple sensor data, i.e. to determine...... with a multivariate analysis technique from RGB pictures. The color information is also transformed to hue, saturation and intensity components. Both sets of image features are combined with traditional process measurements to obtain an inferential model by partial least squares (PLS) regression. A dynamic PLS model...... oxides (NOx) emission of cement kilns. On-site tests demonstrate improved performance over soft sensors based on conventional process measurements only....

Simulation on scattering features of biological tissue based on generated refractive-index model

International Nuclear Information System (INIS)

Wang Baoyong; Ding Zhihua

2011-01-01

Important information on morphology of biological tissue can be deduced from elastic scattering spectra, and their analyses are based on the known refractive-index model of tissue. In this paper, a new numerical refractive-index model is put forward, and its scattering properties are intensively studied. Spectral decomposition [1] is a widely used method to generate random medium in geology, but it is never used in biology. Biological tissue is different from geology in the sense of random medium. Autocorrelation function describe almost all of features in geology, but biological tissue is not as random as geology, its structure is regular in the sense of fractal geometry [2] , and fractal dimension can be used to describe its regularity under random. Firstly scattering theories of this fractal media are reviewed. Secondly the detailed generation process of refractive-index is presented. Finally the scattering features are simulated in FDTD (Finite Difference Time Domain) Solutions software. From the simulation results, we find that autocorrelation length and fractal dimension controls scattering feature of biological tissue.

Water column correction for coral reef studies by remote sensing.

Science.gov (United States)

Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

2014-09-11

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

ASPIE: A Framework for Active Sensing and Processing of Complex Events in the Internet of Manufacturing Things

Directory of Open Access Journals (Sweden)

Shaobo Li

2018-03-01

Full Text Available Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing.

Science.gov (United States)

Delis, Ioannis; Dmochowski, Jacek P; Sajda, Paul; Wang, Qi

2018-03-23

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making. Copyright © 2018 Elsevier Inc. All rights reserved.

Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring

Directory of Open Access Journals (Sweden)

Charles R. Farrar

2006-01-01

Full Text Available The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM. The authors' approach is to address the SHM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1 Operational Evaluation, (2 Data Acquisition and Cleansing, (3 Feature Extraction and Data Compression, and (4 Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discuss each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring with a modular wireless sensing and processing platform. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.

Occupant traffic estimation through structural vibration sensing

Science.gov (United States)

Pan, Shijia; Mirshekari, Mostafa; Zhang, Pei; Noh, Hae Young

2016-04-01

The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of +/-0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

Directory of Open Access Journals (Sweden)

Weixun Zhou

2017-05-01

Full Text Available Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs for high-resolution remote sensing image retrieval (HRRSIR. To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

Science.gov (United States)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

Processing of word stress related acoustic information: A multi-feature MMN study.

Science.gov (United States)

Honbolygó, Ferenc; Kolozsvári, Orsolya; Csépe, Valéria

2017-08-01

In the present study, we investigated the processing of word stress related acoustic features in a word context. In a passive oddball multi-feature MMN experiment, we presented a disyllabic pseudo-word with two acoustically similar syllables as standard stimulus, and five contrasting deviants that differed from the standard in that they were either stressed on the first syllable or contained a vowel change. Stress was realized by an increase of f0, intensity, vowel duration or consonant duration. The vowel change was used to investigate if phonemic and prosodic changes elicit different MMN components. As a control condition, we presented non-speech counterparts of the speech stimuli. Results showed all but one feature (non-speech intensity deviant) eliciting the MMN component, which was larger for speech compared to non-speech stimuli. Two other components showed stimulus related effects: the N350 and the LDN (Late Discriminative Negativity). The N350 appeared to the vowel duration and consonant duration deviants, specifically to features related to the temporal characteristics of stimuli, while the LDN was present for all features, and it was larger for speech than for non-speech stimuli. We also found that the f0 and consonant duration features elicited a larger MMN than other features. These results suggest that stress as a phonological feature is processed based on long-term representations, and listeners show a specific sensitivity to segmental and suprasegmental cues signaling the prosodic boundaries of words. These findings support a two-stage model in the perception of stress and phoneme related acoustical information. Copyright © 2017 Elsevier B.V. All rights reserved.

In-database processing of a large collection of remote sensing data: applications and implementation

Science.gov (United States)

Kikhtenko, Vladimir; Mamash, Elena; Chubarov, Dmitri; Voronina, Polina

2016-04-01

Large archives of remote sensing data are now available to scientists, yet the need to work with individual satellite scenes or product files constrains studies that span a wide temporal range or spatial extent. The resources (storage capacity, computing power and network bandwidth) required for such studies are often beyond the capabilities of individual geoscientists. This problem has been tackled before in remote sensing research and inspired several information systems. Some of them such as NASA Giovanni [1] and Google Earth Engine have already proved their utility for science. Analysis tasks involving large volumes of numerical data are not unique to Earth Sciences. Recent advances in data science are enabled by the development of in-database processing engines that bring processing closer to storage, use declarative query languages to facilitate parallel scalability and provide high-level abstraction of the whole dataset. We build on the idea of bridging the gap between file archives containing remote sensing data and databases by integrating files into relational database as foreign data sources and performing analytical processing inside the database engine. Thereby higher level query language can efficiently address problems of arbitrary size: from accessing the data associated with a specific pixel or a grid cell to complex aggregation over spatial or temporal extents over a large number of individual data files. This approach was implemented using PostgreSQL for a Siberian regional archive of satellite data products holding hundreds of terabytes of measurements from multiple sensors and missions taken over a decade-long span. While preserving the original storage layout and therefore compatibility with existing applications the in-database processing engine provides a toolkit for provisioning remote sensing data in scientific workflows and applications. The use of SQL - a widely used higher level declarative query language - simplifies interoperability

FEATURES OF USING AUGMENTED REALITY TECHNOLOGY TO SUPPORT EDUCATIONAL PROCESSES

Directory of Open Access Journals (Sweden)

Yury A. Kravchenko

2014-01-01

Full Text Available The paper discusses the concept and technology of augmented reality, the rationale given the relevance and timeliness of its use to support educational processes. Paper is a survey and study of the possibility of using augmented reality technology in education. Architecture is proposed and constructed algorithms of the software system management QR-codes media objects. An overview of the features and uses of augmented reality technology to support educational processes is displayed, as an option of a new form of visual demonstration of complex objects, models and processes. 

Losing Control in Social Situations: How the Presence of Others Affects Neural Processes Related to Sense of Agency

Science.gov (United States)

Fleming, Stephen

2018-01-01

Social contexts substantially influence individual behavior, but little is known about how they affect cognitive processes related to voluntary action. Previously, it has been shown that social context reduces participants’ sense of agency over the outcomes of their actions and outcome monitoring. In this fMRI study on human volunteers, we investigated the neural mechanisms by which social context alters sense of agency. Participants made costly actions to stop inflating a balloon before it burst. On “social” trials, another player could act in their stead, but we analyzed only trials in which the other player remained passive. We hypothesized that mentalizing processes during social trials would affect decision-making fluency and lead to a decreased sense of agency. In line with this hypothesis, we found increased activity in the bilateral temporo-parietal junction (TPJ), precuneus, and middle frontal gyrus during social trials compared with nonsocial trials. Activity in the precuneus was, in turn, negatively related to sense of agency at a single-trial level. We further found a double dissociation between TPJ and angular gyrus (AG): activity in the left AG was not sensitive to social context but was negatively related to sense of agency. In contrast, activity in the TPJ was modulated by social context but was not sensitive to sense of agency. PMID:29527568

Yucca Mountain Feature, Event, and Process (FEP) Analysis

International Nuclear Information System (INIS)

Freeze, G.

2005-01-01

A Total System Performance Assessment (TSPA) model was developed for the U.S. Department of Energy (DOE) Yucca Mountain Project (YMP) to help demonstrate compliance with applicable postclosure regulatory standards and support the License Application (LA). Two important precursors to the development of the TSPA model were (1) the identification and screening of features, events, and processes (FEPs) that might affect the Yucca Mountain disposal system (i.e., FEP analysis), and (2) the formation of scenarios from screened in (included) FEPs to be evaluated in the TSPA model (i.e., scenario development). YMP FEP analysis and scenario development followed a five-step process: (1) Identify a comprehensive list of FEPs potentially relevant to the long-term performance of the disposal system. (2) Screen the FEPs using specified criteria to identify those FEPs that should be included in the TSPA analysis and those that can be excluded from the analysis. (3) Form scenarios from the screened in (included) FEPs. (4) Screen the scenarios using the same criteria applied to the FEPs to identify any scenarios that can be excluded from the TSPA, as appropriate. (5) Specify the implementation of the scenarios in the computational modeling for the TSPA, and document the treatment of included FEPs. This paper describes the FEP analysis approach (Steps 1 and 2) for YMP, with a brief discussion of scenario formation (Step 3). Details of YMP scenario development (Steps 3 and 4) and TSPA modeling (Step 5) are beyond scope of this paper. The identification and screening of the YMP FEPs was an iterative process based on site-specific information, design, and regulations. The process was iterative in the sense that there were multiple evaluation and feedback steps (e.g., separate preliminary, interim, and final analyses). The initial YMP FEP list was compiled from an existing international list of FEPs from other radioactive waste disposal programs and was augmented by YMP site- and design

Annotated bibliography of remote sensing methods for monitoring desertification

Science.gov (United States)

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

Science.gov (United States)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Synergistic linkage between remote sensing and biophysical models for estimating plant ecophysiological and ecosystem processes

International Nuclear Information System (INIS)

Inoue, Y.; Olioso, A.

2004-01-01

Abstract Information on the ecological and physiological status of crops is essential for growth diagnostics and yield prediction. Within-field or between-field spatial information is required, especially with the recent trend toward precision agriculture, which seeks the efficient use of agrochemicals, water, and energy. The study of carbon and nitrogen cycles as well as environmental management on local and regional scales requires assessment of the spatial variability of biophysical and ecophysiological variables, scaling up of which is also needed for scientific and decision-making purposes. Remote sensing has great potential for these applications because it enables wide-area non-destructive, and real-time acquisition of information about ecophysiological conditions of vegetation. With recent advances in sensor technology, a variety of electromagnetic signatures, such as hyperspectral reflectance, thermal-infrared temperature, and microwave backscattering coefficients, can be acquired for both plants and ecosystems using ground-based, airborne, and satellite platforms. Their spatial and temporal resolutions have both recently been improved. This article reviews the state of the art in the remote sensing of plant ecophysiological data, with special emphasis on the synergy between remote sensing signatures and biophysical and ecophysiological process models. Several case studies for the optical, thermal, and microwave domains have demonstrated the potential of this synergistic linkage. Remote sensing and process modeling methods complement each other when combined synergistically. Further research on this approach is needed f or a wide range of ecophysiological and ecosystem studies, as well as for practical crop management

Vision-aided Monitoring and Control of Thermal Spray, Spray Forming, and Welding Processes

Science.gov (United States)

Agapakis, John E.; Bolstad, Jon

1993-01-01

Vision is one of the most powerful forms of non-contact sensing for monitoring and control of manufacturing processes. However, processes involving an arc plasma or flame such as welding or thermal spraying pose particularly challenging problems to conventional vision sensing and processing techniques. The arc or plasma is not typically limited to a single spectral region and thus cannot be easily filtered out optically. This paper presents an innovative vision sensing system that uses intense stroboscopic illumination to overpower the arc light and produce a video image that is free of arc light or glare and dedicated image processing and analysis schemes that can enhance the video images or extract features of interest and produce quantitative process measures which can be used for process monitoring and control. Results of two SBIR programs sponsored by NASA and DOE and focusing on the application of this innovative vision sensing and processing technology to thermal spraying and welding process monitoring and control are discussed.

A bistable mechanism for directional sensing

International Nuclear Information System (INIS)

Beta, C; Amselem, G; Bodenschatz, E

2008-01-01

We present a generic mechanism for directional sensing in eukaryotic cells that is based on bistable dynamics. As the key feature of this modeling approach, the velocity of trigger waves in the bistable sensing system changes its sign across cells that are exposed to an external chemoattractant gradient. This is achieved by combining a two-component activator/inhibitor system with a bistable switch that induces an identical symmetry breaking for arbitrary gradient input signals. A simple kinetic example is designed to illustrate the dynamics of a bistable directional sensing mechanism in numerical simulations

FEATURES OF THE SOCIO-POLITICAL PROCESS IN THE UNITED STATES

Directory of Open Access Journals (Sweden)

Tatyana Evgenevna Beydina

2017-06-01

Full Text Available The subject of this article is the study of political and social developments of the USA at the present stage. There are four stages of the American tradition of studying political processes. The first stage is connected with substantiation of the Executive, Legislative and Judicial branches of political system (works of F. Pollack and R. Sili. The second one includes behavioral studies of politics. Besides studying political processes Charles Merriam has studied their similarities and differences. The third stage is characterized by political system studies – the works of T. Parsons, D. Easton, R. Aron, G. Almond and K. Deutsch. The fourth stage is characterized by superpower and the systems democratization problem (S. Huntington, Zb. Bzhezinsky. American social processes were qualified by R. Park, P. Sorokin, E. Giddens. The work is concentrated on the divided explanation of social and political processes of the us and the reflection of unity of American social-political reality. Academic novelty is composed of substantiation of the US social-political process concept and characterization of its features. The US social-political process is characterized by two channels: soft power and aggression. Soft power appears in the US economy dominancy. The main results of the research are features of the socio-political process in the United States. Purpose: the main goal of the research is to systematize the definition of social-political process of the USA and estimate the line of its study within American political tradition. Methodology: in this article have used methods: such as system, comparison and historical analysis, structural-functional analysis. Results: during the research the analysis of the dynamics of social and political processes of the United States had been made. Practical implications it is expedient to apply the received results in the international relation theory and practice.

Features, Events, and Processes: system Level

Energy Technology Data Exchange (ETDEWEB)

D. McGregor

2004-10-15

The purpose of this analysis report is to evaluate and document the inclusion or exclusion of the system-level features, events, and processes (FEPs) with respect to modeling used to support the total system performance assessment for the license application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical basis for screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.113 (d, e, and f) (DIRS 156605). The system-level FEPs addressed in this report typically are overarching in nature, rather than being focused on a particular process or subsystem. As a result, they are best dealt with at the system level rather than addressed within supporting process-level or subsystem-level analyses and models reports. The system-level FEPs also tend to be directly addressed by regulations, guidance documents, or assumptions listed in the regulations; or are addressed in background information used in development of the regulations. For included FEPs, this analysis summarizes the implementation of the FEP in the TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from the TSPA-LA (i.e., why the FEP is excluded). The initial version of this report (Revision 00) was developed to support the total system performance assessment for site recommendation (TSPA-SR). This revision addresses the license application (LA) FEP List (DIRS 170760).

Features, Events, and Processes: system Level

International Nuclear Information System (INIS)

D. McGregor

2004-01-01

The purpose of this analysis report is to evaluate and document the inclusion or exclusion of the system-level features, events, and processes (FEPs) with respect to modeling used to support the total system performance assessment for the license application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical basis for screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.113 (d, e, and f) (DIRS 156605). The system-level FEPs addressed in this report typically are overarching in nature, rather than being focused on a particular process or subsystem. As a result, they are best dealt with at the system level rather than addressed within supporting process-level or subsystem-level analyses and models reports. The system-level FEPs also tend to be directly addressed by regulations, guidance documents, or assumptions listed in the regulations; or are addressed in background information used in development of the regulations. For included FEPs, this analysis summarizes the implementation of the FEP in the TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from the TSPA-LA (i.e., why the FEP is excluded). The initial version of this report (Revision 00) was developed to support the total system performance assessment for site recommendation (TSPA-SR). This revision addresses the license application (LA) FEP List (DIRS 170760)

Bubble feature extracting based on image processing of coal flotation froth

Energy Technology Data Exchange (ETDEWEB)

Wang, F.; Wang, Y.; Lu, M.; Liu, W. [China University of Mining and Technology, Beijing (China). Dept of Chemical Engineering and Environment

2001-11-01

Using image processing the contrast ratio between the bubble on the surface of flotation froth and the image background was enhanced, and the edges of bubble were extracted. Thus a model about the relation between the statistic feature of the bubbles in the image and the cleaned coal can be established. It is feasible to extract the bubble by processing the froth image of coal flotation on the basis of analysing the shape of the bubble. By means of processing the 51 group images sampled from laboratory column, it is thought that the use of the histogram equalization of image gradation and the medium filtering can obviously improve the dynamic contrast range and the brightness of bubbles. Finally, the method of threshold value cut and the bubble edge detecting for extracting the bubble were also discussed to describe the bubble feature, such as size and shape, in the froth image and to distinguish the froth image of coal flotation. 6 refs., 3 figs.

A single theoretical framework for circular features processing in humans: orientation and direction of motion compared

Directory of Open Access Journals (Sweden)

Tzvetomir eTzvetanov

2012-05-01

Full Text Available Common computational principles underly processing of various visual features in the cortex. They are considered to create similar patterns of contextual modulations in behavioral studies for different features as orientation and direction of motion. Here, I studied the possibility that a single theoretical framework, implemented in different visual areas, of circular feature coding and processing could explain these similarities in observations. Stimuli were created that allowed direct comparison of the contextual effects on orientation and motion direction with two different psychophysical probes: changes in weak and strong signal perception. One unique simplified theoretical model of circular feature coding including only inhibitory interactions, and decoding through standard vector average, successfully predicted the similarities in the two domains, while different feature population characteristics explained well the differences in modulation on both experimental probes. These results demonstrate how a single computational principle underlies processing of various features across the cortices.

Crop status sensing system by multi-spectral imaging sensor, 1: Image processing and paddy field sensing

International Nuclear Information System (INIS)

Ishii, K.; Sugiura, R.; Fukagawa, T.; Noguchi, N.; Shibata, Y.

2006-01-01

The objective of the study is to construct a sensing system for precision farming. A Multi-Spectral Imaging Sensor (MSIS), which can obtain three images (G. R and NIR) simultaneously, was used for detecting growth status of plants. The sensor was mounted on an unmanned helicopter. An image processing method for acquiring information of crop status with high accuracy was developed. Crop parameters that were measured include SPAD, leaf height, and stems number. Both direct seeding variety and transplant variety of paddy rice were adopted in the research. The result of a field test showed that crop status of both varieties could be detected with sufficient accuracy to apply to precision farming

An integrated condition-monitoring method for a milling process using reduced decomposition features

International Nuclear Information System (INIS)

Liu, Jie; Wu, Bo; Hu, Youmin; Wang, Yan

2017-01-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification. (paper)

Accuracy Dimensions in Remote Sensing

Science.gov (United States)

Barsi, Á.; Kugler, Zs.; László, I.; Szabó, Gy.; Abdulmutalib, H. M.

2018-04-01

The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice

ACCURACY DIMENSIONS IN REMOTE SENSING

Directory of Open Access Journals (Sweden)

Á. Barsi

2018-04-01

Full Text Available The technological developments in remote sensing (RS during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS, which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs. The present paper gives the theoretic overview of the issue, besides

Sensors Technology and Advanced Signal Processing Concepts for Layered Warfare/Layered Sensing

Science.gov (United States)

2010-04-01

accurate model of the earth is a geoid defined as the shape of the gravitational equipotential of the earth’s surface . However, geoid models are often...processing framework is inadequate when confronted with complex surface target engagement applications being addressed by layered sensing. The...United States’ combat advantage in surface target engagement. Over the past 20+ years, the US has gathered a large quantity of information on the

On the Convergence of Implicit Iterative Processes for Asymptotically Pseudocontractive Mappings in the Intermediate Sense

Directory of Open Access Journals (Sweden)

Xiaolong Qin

2011-01-01

Full Text Available An implicit iterative process is considered. Strong and weak convergence theorems of common fixed points of a finite family of asymptotically pseudocontractive mappings in the intermediate sense are established in a real Hilbert space.

Water Column Correction for Coral Reef Studies by Remote Sensing

Directory of Open Access Journals (Sweden)

Maria Laura Zoffoli

2014-09-01

Full Text Available Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Water Column Correction for Coral Reef Studies by Remote Sensing

Science.gov (United States)

Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

2014-01-01

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application. PMID:25215941

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

Fan Hu

2016-06-01

Full Text Available Scene classification of high-resolution remote sensing (HRRS imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

Rational Design of a Green-Light-Mediated Unimolecular Platform for Fast Switchable Acidic Sensing.

Science.gov (United States)

Zhou, Yunyun; Zou, Qi; Qiu, Jing; Wang, Linjun; Zhu, Liangliang

2018-02-01

A controllable sensing ability strongly connects to complex and precise events in diagnosis and treatment. However, imposing visible light into the molecular-scale mediation of sensing processes is restricted by the lack of structural relevance. To address this critical challenge, we present the rational design, synthesis, and in vitro studies of a novel cyanostyryl-modified azulene system for green-light-mediated fast switchable acidic sensing. The advantageous features of the design include a highly efficient green-light-driven Z/E-isomerization (a quantum yield up to 61.3%) for fast erasing chromatic and luminescent expressions and a superior compatibility with control of ratiometric protonation. Significantly, these merits of the design enable the development of a microfluidic system to perform a green-light-mediated reusable sensing function toward a gastric acid analyte in a miniaturized platform. The results may provide new insights for building future integrated green materials.

INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES

Directory of Open Access Journals (Sweden)

H. Ru

2016-06-01

Full Text Available Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.

[Object-oriented stand type classification based on the combination of multi-source remote sen-sing data].

Science.gov (United States)

Mao, Xue Gang; Wei, Jing Yu

2017-11-01

The recognition of forest type is one of the key problems in forest resource monitoring. The Radarsat-2 data and QuickBird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multi-source remote sensing data. In the process of object-based classification, three segmentation schemes (segmentation with QuickBird remote sensing image only, segmentation with Radarsat-2 data only, segmentation with combination of QuickBird and Radarsat-2) were adopted. For the three segmentation schemes, ten segmentation scale parameters were adopted (25-250, step 25), and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result, three forest types of Chinese fir, Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine (SVM) classifier with Radial Basis Foundation (RBF) kernel according to different feature combinations of topography, height, spectrum and common features. The results showed that the combination of Radarsat-2 data and QuickBird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or QuickBird remote sensing image only. The optimal scale parameter for QuickBirdRadarsat-2 segmentation was 100, and at the optimal scale, the accuracy of object-based forest type classification was the highest (OA=86%, Kappa=0.86), when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data, but also had a practical significance for forest resource investigation and monitoring.

Fiber sensing based on new structures and post-processing enhancement

Science.gov (United States)

Ferreira, Marta Sofia dos Anjos

The work described in this PhD Thesis focuses on the post-processing of optical fibers and their enhancement as sensing element. Since the majority of sensors presented are based in Fabry-Perot interferometers, an historical overview of this category of optical fiber sensors is firstly presented. This review considers the works published since the early years, in the beginning of the 1980s, until the middle of 2015. The incorporation of microcavities at the tip of a single mode fiber was extensively studied, particularly for the measurement of nitrogen and methane gas pressure. These cavities were fabricated using hollow core silica tubes and a hollow core photonic crystal fiber. Following a different approach, the microcavities were incorporated between two sections of single mode fiber. In this case, the low sensitivity to temperature makes these microcavities highly desirable for the measurement of strain at high temperatures. Competences in post-processing techniques such as the chemical etching and the writing of periodical structures in the fiber core by means of an excimer or a femtosecond laser were also acquired in the course of the PhD programme. One of the works consisted in the design and manufacturing of a double clad optical fiber. The refractive index of the inner cladding was higher than the one of the outer cladding and the core. Thus, light was guided in the inner cladding instead of propagating in the core. This situation was overcome by applying chemical etching, thus removing the inner cladding. The core, surrounded by air, was then able to guide light. Two different applications were found for this fiber, as a temperature sensor and as an optical refractometer. In the last, the optical phase changes with the liquid refractive index. Two different types of fiber Bragg gratings were characterized in strain and temperature. Sensing structures obtained through the phase mask technique at the tip of an optical fiber were subjected to chemical

Multi-source remote sensing data management system

International Nuclear Information System (INIS)

Qin Kai; Zhao Yingjun; Lu Donghua; Zhang Donghui; Wu Wenhuan

2014-01-01

In this thesis, the author explored multi-source management problems of remote sensing data. The main idea is to use the mosaic dataset model, and the ways of an integreted display of image and its interpretation. Based on ArcGIS and IMINT feature knowledge platform, the author used the C# and other programming tools for development work, so as to design and implement multi-source remote sensing data management system function module which is able to simply, conveniently and efficiently manage multi-source remote sensing data. (authors)

A method of vehicle license plate recognition based on PCANet and compressive sensing

Science.gov (United States)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

The Temporal Dynamics of Visual Search: Evidence for Parallel Processing in Feature and Conjunction Searches

Science.gov (United States)

McElree, Brian; Carrasco, Marisa

2012-01-01

Feature and conjunction searches have been argued to delineate parallel and serial operations in visual processing. The authors evaluated this claim by examining the temporal dynamics of the detection of features and conjunctions. The 1st experiment used a reaction time (RT) task to replicate standard mean RT patterns and to examine the shapes of the RT distributions. The 2nd experiment used the response-signal speed–accuracy trade-off (SAT) procedure to measure discrimination (asymptotic detection accuracy) and detection speed (processing dynamics). Set size affected discrimination in both feature and conjunction searches but affected detection speed only in the latter. Fits of models to the SAT data that included a serial component overpredicted the magnitude of the observed dynamics differences. The authors concluded that both features and conjunctions are detected in parallel. Implications for the role of attention in visual processing are discussed. PMID:10641310

Extremely sensitive multiple sensing ring PCF sensor for lower indexed chemical detection

Directory of Open Access Journals (Sweden)

Veerpal Kaur

2017-09-01

Full Text Available In this article, we have designed and analysed a photonic crystal fiber with multiple sensing ring in core for chemical and biochemical sensing applications. In this proposed design, three and four sensing ring describe in core which offers remarkable high sensitivity and spiral cladding pattern confines large fraction of power in core region and thus reduce the overall confinement loss. This novel proposed model exhibits simultaneously ultra high relative sensitivity 95.40%, 93.13% and minimum confinement loss 7.108×10−08, 2.47×10−08dB/km for four and three ring pattern. These sensing rings are filled with different sensing liquid. Multiple sensing rings as compared to multiple air holes are desirable feature from fabrication point of view. This proposed PCF design overcomes some experimental challenge such as PCF probe needs some displacement after filling the sensing liquid. These uniform circular sensing rings around the solid core overcome the losses and support better evanescent field matter interaction for sensing application. Multiple sensing rings as compared to multiple tiny air holes are desirable feature from fabrication point of view.

The Simple Concurrent Online Processing System (SCOPS) - An open-source interface for remotely sensed data processing

Science.gov (United States)

Warren, M. A.; Goult, S.; Clewley, D.

2018-06-01

Advances in technology allow remotely sensed data to be acquired with increasingly higher spatial and spectral resolutions. These data may then be used to influence government decision making and solve a number of research and application driven questions. However, such large volumes of data can be difficult to handle on a single personal computer or on older machines with slower components. Often the software required to process data is varied and can be highly technical and too advanced for the novice user to fully understand. This paper describes an open-source tool, the Simple Concurrent Online Processing System (SCOPS), which forms part of an airborne hyperspectral data processing chain that allows users accessing the tool over a web interface to submit jobs and process data remotely. It is demonstrated using Natural Environment Research Council Airborne Research Facility (NERC-ARF) instruments together with other free- and open-source tools to take radiometrically corrected data from sensor geometry into geocorrected form and to generate simple or complex band ratio products. The final processed data products are acquired via an HTTP download. SCOPS can cut data processing times and introduce complex processing software to novice users by distributing jobs across a network using a simple to use web interface.

Multiscale deep features learning for land-use scene recognition

Science.gov (United States)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Quality as Sense-Making

Science.gov (United States)

Marshall, Stephen

2016-01-01

Sense-making is a process of engaging with complex and dynamic environments that provides organisations and their leaders with a flexible and agile model of the world. The seven key properties of sense-making describe a process that is social and that respects the range of different stakeholders in an organisation. It also addresses the need to…

Making Sense of Conceptual Tools in Student-Generated Cases: Student Teachers' Problem-Solving Processes

Science.gov (United States)

Jahreie, Cecilie Flo

2010-01-01

This article examines the way student teachers make sense of conceptual tools when writing cases. In order to understand the problem-solving process, an analysis of the interactions is conducted. The findings show that transforming practical experiences into theoretical reflection is not a straightforward matter. To be able to elaborate on the…

Influence of Fabricating Process on Gas Sensing Properties of ZnO Nanofiber-Based Sensors

International Nuclear Information System (INIS)

Xu Lei; Wang Rui; Liu Yong; Dong Liang

2011-01-01

ZnO nanofibers are synthesized by an electrospinning method and characterized by x-ray diffraction (XRD) and scanning electron microscopy (SEM). Two types of gas sensors are fabricated by loading these nanofibers as the sensing materials and their performances are investigated in detail. Compared with the sensors based on traditional ceramic tubes with Au electrodes (traditional sensors), the sensors fabricated by spinning ZnO nanofibers on ceramic planes with Ag-Pd electrodes (plane sensors) exhibit much higher sensing properties. The sensitivity for the plane sensors is about 30 to 100 ppm ethanol at 300°C, while the value is only 13 for the traditional sensors. The response and recovery times are about 2 and 3s for the plane sensors and are 3 and 6s for the traditional sensors, respectively. Lower minimum-detection-limit is also found for the plane sensors. These improvements are explained by considering the morphological damage in the fabricating process for traditional sensors. The results suggest that the plane sensors are more suitable to sensing investigation for higher veracity. (general)

Remote sensing in meteorology, oceanography and hydrology

Energy Technology Data Exchange (ETDEWEB)

Cracknell, A P [ed.

1981-01-01

Various aspects of remote sensing are discussed. Topics include: the EARTHNET data acquisition, processing, and distribution facility the design and implementation of a digital interactive image processing system geometrical aspects of remote sensing and space cartography remote sensing of a complex surface legal aspects of remote sensing remote sensing of pollution, dust storms, ice masses, and ocean waves and currents use of satellite images for weather forecasting. Notes on field trips and work-sheets for laboratory exercises are included.

Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

Directory of Open Access Journals (Sweden)

Till F. Sonnemann

2017-12-01

Full Text Available Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, detectable by analyzing remote sensing imagery. A variety of pre-processed, very diverse data sets went through a process of image registration, with the intention to combine multispectral bands to feed two different semi-automatic direct detection algorithms: a posterior probability, and a frequentist approach. Two 5 × 5 km2 areas in the northwestern Dominican Republic with diverse environments, having sufficient imagery coverage, and a representative number of known indigenous site locations, served each for one approach. Buffers around the locations of known sites, as well as areas with no likely archaeological evidence were used as samples. The resulting maps offer quantifiable statistical outcomes of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. These still very experimental and rather unvalidated trials, as they have not been subsequently groundtruthed, show variable potential of this method in diverse environments.

Sensing using rare-earth-doped upconversion nanoparticles.

Science.gov (United States)

Hao, Shuwei; Chen, Guanying; Yang, Chunhui

2013-01-01

Optical sensing plays an important role in theranostics due to its capability to detect hint biochemical entities or molecular targets as well as to precisely monitor specific fundamental psychological processes. Rare-earth (RE) doped upconversion nanoparticles (UCNPs) are promising for these endeavors due to their unique frequency converting capability; they emit efficient and sharp visible or ultraviolet (UV) luminescence via use of ladder-like energy levels of RE ions when excited at near infrared (NIR) light that are silent to tissues. These features allow not only a high penetration depth in biological tissues but also a high detection sensitivity. Indeed, the energy transfer between UCNPs and biomolecular or chemical indicators provide opportunities for high-sensitive bio- and chemical-sensing. A temperature-sensitive change of the intensity ratio between two close UC bands promises them for use in temperature mapping of a single living cell. In this work, we review recent investigations on using UCNPs for the detection of biomolecules (avidin, ATP, etc.), ions (cyanide, mecury, etc.), small gas molecules (oxygen, carbon dioxide, ammonia, etc.), as well as for in vitro temperature sensing. We also briefly summarize chemical methods in synthesizing UCNPs of high efficiency that are important for the detection limit.

Hyperspectral remote sensing

CERN Document Server

Eismann, Michael

2012-01-01

Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. This book provides a holistic treatment that captures its multidisciplinary nature, emphasizing the physical principles of hyperspectral remote sensing.

Current perspective on remote sensing

International Nuclear Information System (INIS)

Goodman, R.H.

1992-01-01

Surveillance and tracking of oil spills has been a feature of most spill response situations for many years. The simplest and most direct method uses visual observations from an aircraft and hand-plotting of the data on a map. This technique has proven adequate for most small spills and for responses in fair weather. As the size of the spill increases or the weather deteriorates, there is a need to augment visual aerial observations with remote sensing methods. Remote sensing and its associated systems are one of the most technically complex and sophisticated elements of an oil spill response. During the past few years, a number of initiatives have been undertaken to use contemporary electronic and computing systems to develop new and improved remote sensing systems

Remote Sensing and Reflectance Profiling in Entomology.

Science.gov (United States)

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

An approach to eliminate stepped features in multistage incremental sheet forming process: Experimental and FEA analysis

Energy Technology Data Exchange (ETDEWEB)

Nirala, Harish Kumar; Jain, Prashant K.; Tandon, Puneet [PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur Jabalpur-482005, Madhya Pradesh (India); Roy, J. J.; Samal, M. K. [Bhabha Atomic Research Centre, Mumbai (India)

2017-02-15

Incremental sheet forming (ISF) is a recently developed manufacturing technique. In ISF, forming is done by applying deformation force through the motion of Numerically controlled (NC) single point forming tool on the clamped sheet metal blank. Single Point Incremental sheet forming (SPISF) is also known as a die-less forming process because no die is required to fabricate any component by using this process. Now a day it is widely accepted for rapid manufacturing of sheet metal components. The formability of SPISF process improves by adding some intermediate stages into it, which is known as Multi-stage SPISF (MSPISF) process. However during forming in MSPISF process because of intermediate stages stepped features are generated. This paper investigates the generation of stepped features with simulation and experimental results. An effective MSPISF strategy is proposed to remove or eliminate this generated undesirable stepped features.

Process Features in Writing: Internal Structure and Incremental Value over Product Features. Research Report. ETS RR-15-27

Science.gov (United States)

Zhang, Mo; Deane, Paul

2015-01-01

In educational measurement contexts, essays have been evaluated and formative feedback has been given based on the end product. In this study, we used a large sample collected from middle school students in the United States to investigate the factor structure of the writing process features gathered from keystroke logs and the association of that…

Highly Sensitive and Selective Sensing of Free Bilirubin Using Metal-Organic Frameworks-Based Energy Transfer Process.

Science.gov (United States)

Du, Yaran; Li, Xiqian; Lv, Xueju; Jia, Qiong

2017-09-13

Free bilirubin, a key biomarker for jaundice, was detected with a newly designed fluorescent postsynthetically modified metal organic framework (MOF) (UIO-66-PSM) sensor. UiO-66-PSM was prepared based on the aldimine condensation reaction of UiO-66-NH 2 with 2,3,4-trihydroxybenzaldehyde. The fluorescence of UIO-66-PSM could be effectively quenched by free bilirubin via a fluorescent resonant energy transfer process, thus achieving its recognition of free bilirubin. It was the first attempt to design a MOF-based fluorescent probe for sensing free bilirubin. The probe exhibited fast response time, low detection limit, wide linear range, and high selectivity toward free bilirubin. The sensing system enabled the monitor of free bilirubin in real human serum. Hence, the reported free bilirubin sensing platform has potential applications for clinical diagnosis of jaundice.

Digitise this! A quick and easy remote sensing method to monitor the daily extent of dredge plumes.

Directory of Open Access Journals (Sweden)

Richard D Evans

Full Text Available Technological advancements in remote sensing and GIS have improved natural resource managers' abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS imagery and associated algorithms to predict the total suspended solids (TSS concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remote sensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L(-1. However, remote sensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L(-1, and began providing false-negatives (excluding actual plume at a threshold as low as 4 mg L(-1. Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training.

Intelligent environmental sensing

CERN Document Server

Mukhopadhyay, Subhas

2015-01-01

Developing environmental sensing and monitoring technologies become essential especially for industries that may cause severe contamination. Intelligent environmental sensing uses novel sensor techniques, intelligent signal and data processing algorithms, and wireless sensor networks to enhance environmental sensing and monitoring. It finds applications in many environmental problems such as oil and gas, water quality, and agriculture. This book addresses issues related to three main approaches to intelligent environmental sensing and discusses their latest technological developments. Key contents of the book include:   Agricultural monitoring Classification, detection, and estimation Data fusion Geological monitoring Motor monitoring Multi-sensor systems Oil reservoirs monitoring Sensor motes Water quality monitoring Wireless sensor network protocol  

Optical remote sensing

CERN Document Server

Prasad, Saurabh; Chanussot, Jocelyn

2011-01-01

Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data: challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, patter

Features, Events, and Processes in UZ and Transport

Energy Technology Data Exchange (ETDEWEB)

P. Persoff

2004-11-06

The purpose of this report is to evaluate and document the inclusion or exclusion of the unsaturated zone (UZ) features, events, and processes (FEPs) with respect to modeling that supports the total system performance assessment (TSPA) for license application (LA) for a nuclear waste repository at Yucca Mountain, Nevada. A screening decision, either ''Included'' or ''Excluded'', is given for each FEP, along with the technical basis for the screening decision. This information is required by the U.S. Nuclear Regulatory Commission (NRC) in 10 CFR 63.114 (d, e, and f) [DIRS 156605]. The FEPs deal with UZ flow and radionuclide transport, including climate, surface water infiltration, percolation, drift seepage, and thermally coupled processes. This analysis summarizes the implementation of each FEP in TSPA-LA (that is, how the FEP is included) and also provides the technical basis for exclusion from TSPA-LA (that is, why the FEP is excluded). This report supports TSPA-LA.

Geological remote sensing signatures of terrestrial impact craters

International Nuclear Information System (INIS)

Garvin, J.B.; Schnetzler, C.; Grieve, R.A.F.

1988-01-01

Geological remote sensing techniques can be used to investigate structural, depositional, and shock metamorphic effects associated with hypervelocity impact structures, some of which may be linked to global Earth system catastrophies. Although detailed laboratory and field investigations are necessary to establish conclusive evidence of an impact origin for suspected crater landforms, the synoptic perspective provided by various remote sensing systems can often serve as a pathfinder to key deposits which can then be targetted for intensive field study. In addition, remote sensing imagery can be used as a tool in the search for impact and other catastrophic explosion landforms on the basis of localized disruption and anomaly patterns. In order to reconstruct original dimensions of large, complex impact features in isolated, inaccessible regions, remote sensing imagery can be used to make preliminary estimates in the absence of field geophysical surveys. The experienced gained from two decades of planetary remote sensing of impact craters on the terrestrial planets, as well as the techniques developed for recognizing stages of degradation and initial crater morphology, can now be applied to the problem of discovering and studying eroded impact landforms on Earth. Preliminary results of remote sensing analyses of a set of terrestrial impact features in various states of degradation, geologic settings, and for a broad range of diameters and hence energies of formation are summarized. The intention is to develop a database of remote sensing signatures for catastrophic impact landforms which can then be used in EOS-era global surveys as the basis for locating the possibly hundreds of missing impact structures

Opening the Learning Process: The Potential Role of Feature Film in Teaching Employment Relations

Science.gov (United States)

Lafferty, George

2016-01-01

This paper explores the potential of feature film to encourage more inclusive, participatory and open learning in the area of employment relations. Evaluations of student responses in a single postgraduate course over a five-year period revealed how feature film could encourage participatory learning processes in which students reexamined their…

Spatial Attention Effects during Conscious and Nonconscious Processing of Visual Features and Objects

Science.gov (United States)

Tapia, Evelina; Breitmeyer, Bruno G.; Jacob, Jane; Broyles, Elizabeth C.

2013-01-01

Flanker congruency effects were measured in a masked flanker task to assess the properties of spatial attention during conscious and nonconscious processing of form, color, and conjunctions of these features. We found that (1) consciously and nonconsciously processed colored shape distractors (i.e., flankers) produce flanker congruency effects;…

Enhanced damping for bridge cables using a self-sensing MR damper

Science.gov (United States)

Chen, Z. H.; Lam, K. H.; Ni, Y. Q.

2016-08-01

This paper investigates enhanced damping for protecting bridge stay cables from excessive vibration using a newly developed self-sensing magnetorheological (MR) damper. The semi-active control strategy for effectively operating the self-sensing MR damper is formulated based on the linear-quadratic-Gaussian (LQG) control by further considering a collocated control configuration, limited measurements and nonlinear damper dynamics. Due to its attractive feature of sensing-while-damping, the self-sensing MR damper facilitates the collocated control. On the other hand, only the sensor measurements from the self-sensing device are employed in the feedback control. The nonlinear dynamics of the self-sensing MR damper, represented by a validated Bayesian NARX network technique, are further accommodated in the control formulation to compensate for its nonlinearities. Numerical and experimental investigations are conducted on stay cables equipped with the self-sensing MR damper operated in passive and semi-active control modes. The results verify that the collocated self-sensing MR damper facilitates smart damping for inclined cables employing energy-dissipative LQG control with only force and displacement measurements at the damper. It is also demonstrated that the synthesis of nonlinear damper dynamics in the LQG control enhances damping force tracking efficiently, explores the features of the self-sensing MR damper, and achieves better control performance over the passive MR damping control and the Heaviside step function-based LQG control that ignores the damper dynamics.

Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

Science.gov (United States)

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

Machine learning in geosciences and remote sensing

Directory of Open Access Journals (Sweden)

David J. Lary

2016-01-01

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Study on cooperative active sensing system

International Nuclear Information System (INIS)

Tsukune, Hideo; Kita, Nobuyuki; Hirai, Shigeoki; Kuniyoshi, Yasuo; Hara, Isao; Matsui, Toshihiro

1999-01-01

In order to realize autonomous type nuclear plant, three-dimensional geometrical modelling method, and a basic technology on information collection and processing system preparation in some nuclear basic technology developments such as 'study on system evaluation of nuclear facility furnished with artificial intelligence for nuclear power' and 'study on adaptability evaluation of information collection and processing system into autonomous type plant' had already been developed. In this study, a study on sensing system required for constructing robot groups capable of conducting autonomously traveling inspection and maintenance in large scale, complicated and diverse plant has been processed by aiming at establishment of dispersed cooperative intelligent system technology. In 1997 fiscal year, integration of cooperative visual sensing technique was attempted. And, at the same time, upgrading of individual element technology and transportation method essential to the integrated system were investigated. As a result, an operative active sensing prototype system due to transportation robot groups furnished with real time processing capacity on diverse informations by integration of cooperative active sensing technique and real time active sensing technique developed independently plural transportation robot. (G.K.)

Ambipolar Organic Phototransistors with p-Type/n-Type Conjugated Polymer Bulk Heterojunction Light-Sensing Layers

KAUST Repository

Nam, Sungho; Han, Hyemi; Seo, Jooyeok; Song, Myeonghun; Kim, Hwajeong; Anthopoulos, Thomas D.; McCulloch, Iain; Bradley, Donal D C; Kim, Youngkyoo

2016-01-01

Ambipolar organic phototransistors with sensing channel layers, featuring p-type and n-type conjugated polymer bulk heterojunctions, exhibit outstanding light-sensing characteristics in both p-channel and n-channel sensing operation modes.

Ambipolar Organic Phototransistors with p-Type/n-Type Conjugated Polymer Bulk Heterojunction Light-Sensing Layers

KAUST Repository

Nam, Sungho

2016-11-18

Ambipolar organic phototransistors with sensing channel layers, featuring p-type and n-type conjugated polymer bulk heterojunctions, exhibit outstanding light-sensing characteristics in both p-channel and n-channel sensing operation modes.

Making Sense of Natural Selection

Science.gov (United States)

Passmore, Cynthia; Coleman, Elizabeth; Horton, Jennifer; Parker, Heather

2013-01-01

At its core, science is about making sense of the world around us. Therefore, science education should engage students in that sense-making process. Helping students make sense of disciplinary core ideas and crosscutting concepts by engaging in scientific practices is the key innovation of the "Next Generation Science Standards"…

Sense of social support in chonic pain patients

Directory of Open Access Journals (Sweden)

Ancane G.

2012-10-01

Full Text Available Statistical data show that one in five adults of the European citizen suffer from some type of chronic pain. One of the most common types of chronic pain is chronic low back and neck pain. Emotional factors are currently viewed as important determinants in pain perception and behaviour. The perceived social and emotional support have impact to the individual’s adaptation to chronic disease (Cohen, Wills, 1985. The material: 110 chronic low back pain (CLBP patients (48 male and 62 female; in age from 24 to 60 years, mean: 44.2±8, 0 and pilot study of 23 chronic neck pain (CNP patients (19 female and 4 male; in age from 35 to 60 years, mean: 48, 1 ±6. The assessment methods: structured interview; Hospital Anxiety and Depression Scale (HADS. SF-36 ® Health Survey: assessment of emotional and social support. Results and conclusions: CLBP patients in presence of symptoms of depression and elevated level of anxiety matched for socio-demographic features had less sense of social support and marked pain impact to daily activities, lower self rating health relating quality of life. In CLBP patients the sense of social and emotional support had relevant interaction with level of participation in daily activities both in patients with and without mental health problems. This fact has to be considered in process of rehabilitation and in managing of health care of CLBP patients. The results of CNP patients pilot study revealed interesting trend that chronic back and neck pain patients seems to be quite different according to sense of social and emotional support, therefore sense of social and emotional support in different chronic pain patients need further research to improve the process and results of rehabilitation in these patients.

Sense-making and Impact Assessment

DEFF Research Database (Denmark)

Lyhne, Ivar

The poster integrates knowledge about how we make sense of situations into SEA methodology to strengthen the staging of impact assessments and the process of scoping impacts.......The poster integrates knowledge about how we make sense of situations into SEA methodology to strengthen the staging of impact assessments and the process of scoping impacts....

AnonySense: Opportunistic and Privacy-Preserving Context Collection

DEFF Research Database (Denmark)

Triandopoulos, Nikolaos; Kapadia, Apu; Cornelius, Cory

2008-01-01

on tessellation and clustering to protect users' privacy against the system while reporting context, and k-anonymous report aggregation to improve the users' privacy against applications receiving the context. We outline the architecture and security properties of AnonySense, and focus on evaluating our....... We propose AnonySense, a general-purpose architecture for leveraging users' mobile devices for measuring context, while maintaining the privacy of the users.AnonySense features multiple layers of privacy protection-a framework for nodes to receive tasks anonymously, a novel blurring mechanism based...

Terahertz wave reflective sensing and imaging

Science.gov (United States)

Zhong, Hua

Sensing and imaging technologies using terahertz (THz) radiation have found diverse applications as they approach maturity. Since the burgeoning of this technique in the 1990's, many THz sensing and imaging investigations have been designed and conducted in transmission geometry, which provides sufficient phase and amplitude contrast for the study of the spectral properties of targets in the THz domain. Driven by rising expectations that THz technology will be a potential candidate in the next generation of security screening, remote sensing, biomedical imaging and non-destructive testing (NDT), most THz sensing and imaging modalities are being extended to reflection geometry, which offers unique and adaptive solutions, and multi-dimensional information in many real scenarios. This thesis takes an application-focused approach to the advancement of THz wave reflective sensing and imaging systems: The absorption signature of the explosive material hexahydro-1,3,5-trinitro-1,3,5triazine (RDX) is measured at 30 m---the longest standoff distance so far attained by THz time-domain spectroscopy (THz-TDS). The standoff distance sensing ability of THz-TDS is investigated along with discussions specifying the influences of a variety of factors such as propagation distance, water vapor absorption and collection efficiency. Highly directional THz radiation from four-wave mixing in laser-induced air plasmas is first observed and measured, which provides a potential solution for the atmospheric absorption effect in standoff THz sensing. The simulations of the beam profiles also illuminate the underlying physics behind the interaction of the optical beam with the plasma. THz wave reflective spectroscopic focal-plane imaging is realized the first time. Absorption features of some explosives and related compounds (ERCs) and biochemical materials are identified by using adaptive feature extraction method. Good classification results using multiple pattern recognition methods are

Screening Analysis of Criticality Features, Events, and Processes for License Application

International Nuclear Information System (INIS)

J.A. McClure

2004-01-01

This report documents the screening analysis of postclosure criticality features, events, and processes. It addresses the probability of criticality events resulting from degradation processes as well as disruptive events (i.e., seismic, rock fall, and igneous). Probability evaluations are performed utilizing the configuration generator described in ''Configuration Generator Model'', a component of the methodology from ''Disposal Criticality Analysis Methodology Topical Report''. The total probability per package of criticality is compared against the regulatory probability criterion for inclusion of events established in 10 CFR 63.114(d) (consider only events that have at least one chance in 10,000 of occurring over 10,000 years). The total probability of criticality accounts for the evaluation of identified potential critical configurations of all baselined commercial and U.S. Department of Energy spent nuclear fuel waste form and waste package combinations, both internal and external to the waste packages. This criticality screening analysis utilizes available information for the 21-Pressurized Water Reactor Absorber Plate, 12-Pressurized Water Reactor Absorber Plate, 44-Boiling Water Reactor Absorber Plate, 24-Boiling Water Reactor Absorber Plate, and the 5-Defense High-Level Radioactive Waste/U.S. Department of Energy Short waste package types. Where defensible, assumptions have been made for the evaluation of the following waste package types in order to perform a complete criticality screening analysis: 21-Pressurized Water Reactor Control Rod, 5-Defense High-Level Radioactive Waste/U.S. Department of Energy Long, and 2-Multi-Canister Overpack/2-Defense High-Level Radioactive Waste package types. The inputs used to establish probabilities for this analysis report are based on information and data generated for the Total System Performance Assessment for the License Application, where available. This analysis report determines whether criticality is to be

A data fusion framework for floodplain analysis using GIS and remotely sensed data

Science.gov (United States)

Necsoiu, Dorel Marius

Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse/landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery). Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by

Time-sensitive remote sensing

CERN Document Server

Lippitt, Christopher; Coulter, Lloyd

2015-01-01

This book documents the state of the art in the use of remote sensing to address time-sensitive information requirements. Specifically, it brings together a group of authors who are both researchers and practitioners, who work toward or are currently using remote sensing to address time-sensitive information requirements with the goal of advancing the effective use of remote sensing to supply time-sensitive information. The book addresses the theoretical implications of time-sensitivity on the remote sensing process, assessments or descriptions of methods for expediting the delivery and improving the quality of information derived from remote sensing, and describes and analyzes time-sensitive remote sensing applications, with an emphasis on lessons learned. This book is intended for remote sensing scientists, practitioners (e.g., emergency responders or administrators of emergency response agencies), and students, but will also be of use to those seeking to understand the potential of remote sensing to addres...

Connotations of pixel-based scale effect in remote sensing and the modified fractal-based analysis method

Science.gov (United States)

Feng, Guixiang; Ming, Dongping; Wang, Min; Yang, Jianyu

2017-06-01

Scale problems are a major source of concern in the field of remote sensing. Since the remote sensing is a complex technology system, there is a lack of enough cognition on the connotation of scale and scale effect in remote sensing. Thus, this paper first introduces the connotations of pixel-based scale and summarizes the general understanding of pixel-based scale effect. Pixel-based scale effect analysis is essentially important for choosing the appropriate remote sensing data and the proper processing parameters. Fractal dimension is a useful measurement to analysis pixel-based scale. However in traditional fractal dimension calculation, the impact of spatial resolution is not considered, which leads that the scale effect change with spatial resolution can't be clearly reflected. Therefore, this paper proposes to use spatial resolution as the modified scale parameter of two fractal methods to further analyze the pixel-based scale effect. To verify the results of two modified methods (MFBM (Modified Windowed Fractal Brownian Motion Based on the Surface Area) and MDBM (Modified Windowed Double Blanket Method)); the existing scale effect analysis method (information entropy method) is used to evaluate. And six sub-regions of building areas and farmland areas were cut out from QuickBird images to be used as the experimental data. The results of the experiment show that both the fractal dimension and information entropy present the same trend with the decrease of spatial resolution, and some inflection points appear at the same feature scales. Further analysis shows that these feature scales (corresponding to the inflection points) are related to the actual sizes of the geo-object, which results in fewer mixed pixels in the image, and these inflection points are significantly indicative of the observed features. Therefore, the experiment results indicate that the modified fractal methods are effective to reflect the pixel-based scale effect existing in remote sensing

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

Science.gov (United States)

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

Science.gov (United States)

Elmasri, B.; Rahman, A. F.

2010-12-01

Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation

A New Tool for Intelligent Parallel Processing of Radar/SAR Remotely Sensed Imagery

Directory of Open Access Journals (Sweden)

A. Castillo Atoche

2013-01-01

Full Text Available A novel parallel tool for large-scale image enhancement/reconstruction and postprocessing of radar/SAR sensor systems is addressed. The proposed parallel tool performs the following intelligent processing steps: image formation, for the application of different system-level effects of image degradation with a particular remote sensing (RS system and simulation of random noising effects, enhancement/reconstruction by employing nonparametric robust high-resolution techniques, and image postprocessing using the fuzzy anisotropic diffusion technique which incorporates a better edge-preserving noise removal effect and faster diffusion process. This innovative tool allows the processing of high-resolution images provided with different radar/SAR sensor systems as required by RS endusers for environmental monitoring, risk prevention, and resource management. To verify the performance implementation of the proposed parallel framework, the processing steps are developed and specifically tested on graphic processing units (GPU, achieving considerable speedups compared to the serial version of the same techniques implemented in C language.

The contributions of numerical acuity and non-numerical stimulus features to the development of the number sense and symbolic math achievement.

Science.gov (United States)

Starr, Ariel; DeWind, Nicholas K; Brannon, Elizabeth M

2017-11-01

Numerical acuity, frequently measured by a Weber fraction derived from nonsymbolic numerical comparison judgments, has been shown to be predictive of mathematical ability. However, recent findings suggest that stimulus controls in these tasks are often insufficiently implemented, and the proposal has been made that alternative visual features or inhibitory control capacities may actually explain this relation. Here, we use a novel mathematical algorithm to parse the relative influence of numerosity from other visual features in nonsymbolic numerical discrimination and to examine the strength of the relations between each of these variables, including inhibitory control, and mathematical ability. We examined these questions developmentally by testing 4-year-old children, 6-year-old children, and adults with a nonsymbolic numerical comparison task, a symbolic math assessment, and a test of inhibitory control. We found that the influence of non-numerical features decreased significantly over development but that numerosity was a primary determinate of decision making at all ages. In addition, numerical acuity was a stronger predictor of math achievement than either non-numerical bias or inhibitory control in children. These results suggest that the ability to selectively attend to number contributes to the maturation of the number sense and that numerical acuity, independent of inhibitory control, contributes to math achievement in early childhood. Copyright © 2017 Elsevier B.V. All rights reserved.

The complete genome sequence of Trueperella pyogenes UFV1 reveals a processing system involved in the quorumsensing signal response

DEFF Research Database (Denmark)

Duarte, Vinicius da Silva; Treu, Laura; Campanaro, Stefano

2017-01-01

We present here the complete genome sequence of Trueperella pyogenes UFV1. The 2.3-Mbp genome contains an extremely interesting AI-2 transporter and processing system related to the quorum-sensing signal response. This specific feature is described in this species for the first time and might be ...... be responsible for a new pathogenic behavior.......We present here the complete genome sequence of Trueperella pyogenes UFV1. The 2.3-Mbp genome contains an extremely interesting AI-2 transporter and processing system related to the quorum-sensing signal response. This specific feature is described in this species for the first time and might...

High efficient optical remote sensing images acquisition for nano-satellite: reconstruction algorithms

Science.gov (United States)

Liu, Yang; Li, Feng; Xin, Lei; Fu, Jie; Huang, Puming

2017-10-01

Large amount of data is one of the most obvious features in satellite based remote sensing systems, which is also a burden for data processing and transmission. The theory of compressive sensing(CS) has been proposed for almost a decade, and massive experiments show that CS has favorable performance in data compression and recovery, so we apply CS theory to remote sensing images acquisition. In CS, the construction of classical sensing matrix for all sparse signals has to satisfy the Restricted Isometry Property (RIP) strictly, which limits applying CS in practical in image compression. While for remote sensing images, we know some inherent characteristics such as non-negative, smoothness and etc.. Therefore, the goal of this paper is to present a novel measurement matrix that breaks RIP. The new sensing matrix consists of two parts: the standard Nyquist sampling matrix for thumbnails and the conventional CS sampling matrix. Since most of sun-synchronous based satellites fly around the earth 90 minutes and the revisit cycle is also short, lots of previously captured remote sensing images of the same place are available in advance. This drives us to reconstruct remote sensing images through a deep learning approach with those measurements from the new framework. Therefore, we propose a novel deep convolutional neural network (CNN) architecture which takes in undersampsing measurements as input and outputs an intermediate reconstruction image. It is well known that the training procedure to the network costs long time, luckily, the training step can be done only once, which makes the approach attractive for a host of sparse recovery problems.

Symmetric and asymmetric hybrid cryptosystem based on compressive sensing and computer generated holography

Science.gov (United States)

Ma, Lihong; Jin, Weimin

2018-01-01

A novel symmetric and asymmetric hybrid optical cryptosystem is proposed based on compressive sensing combined with computer generated holography. In this method there are six encryption keys, among which two decryption phase masks are different from the two random phase masks used in the encryption process. Therefore, the encryption system has the feature of both symmetric and asymmetric cryptography. On the other hand, because computer generated holography can flexibly digitalize the encrypted information and compressive sensing can significantly reduce data volume, what is more, the final encryption image is real function by phase truncation, the method favors the storage and transmission of the encryption data. The experimental results demonstrate that the proposed encryption scheme boosts the security and has high robustness against noise and occlusion attacks.

WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches

Directory of Open Access Journals (Sweden)

Amir Mohammad Amiri

2017-02-01

Full Text Available Autism is a complex developmental disorder that affects approximately 1 in 68 children (according to the recent survey conducted by the Centers for Disease Control and Prevention—CDC in the U.S., and has become the fastest growing category of special education. Each student with autism comes with her or his own unique needs and an array of behaviors and habits that can be severe and which interfere with everyday tasks. Autism is associated with intellectual disability, impairments in social skills, and physical health issues such as sleep and abdominal disturbances. We have designed an Internet-of-Things (IoT framework named WearSense that leverages the sensing capabilities of modern smartwatches to detect stereotypic behaviors in children with autism. In this work, we present a study that used the inbuilt accelerometer of a smartwatch to detect three behaviors, including hand flapping, painting, and sibbing that are commonly observed in children with autism. In this feasibility study, we recruited 14 subjects to record the accelerometer data from the smartwatch worn on the wrist. The processing part extracts 34 different features in each dimension of the three-axis accelerometer, resulting in 102 features. Using and comparing various classification techniques revealed that an ensemble of 40 decision trees has the best accuracy of around 94.6%. This accuracy shows the quality of the data collected from the smartwatch and feature extraction methods used in this study. The recognition of these behaviors by using a smartwatch would be helpful in monitoring individuals with autistic behaviors, since the smartwatch can send the data to the cloud for comprehensive analysis and also to help parents, caregivers, and clinicians make informed decisions.

Electroactive polymers for sensing

Science.gov (United States)

2016-01-01

Electromechanical coupling in electroactive polymers (EAPs) has been widely applied for actuation and is also being increasingly investigated for sensing chemical and mechanical stimuli. EAPs are a unique class of materials, with low-moduli high-strain capabilities and the ability to conform to surfaces of different shapes. These features make them attractive for applications such as wearable sensors and interfacing with soft tissues. Here, we review the major types of EAPs and their sensing mechanisms. These are divided into two classes depending on the main type of charge carrier: ionic EAPs (such as conducting polymers and ionic polymer–metal composites) and electronic EAPs (such as dielectric elastomers, liquid-crystal polymers and piezoelectric polymers). This review is intended to serve as an introduction to the mechanisms of these materials and as a first step in material selection for both researchers and designers of flexible/bendable devices, biocompatible sensors or even robotic tactile sensing units. PMID:27499846

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments.

Science.gov (United States)

Tigges, Jan; Lakes, Tobia

2017-10-04

Urban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany. Recent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified

Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

Science.gov (United States)

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

Humidity sensing in insects-from ecology to neural processing.

Science.gov (United States)

Enjin, Anders

2017-12-01

Humidity is an omnipresent climatic factor that influences the fitness, reproductive behavior and geographic distribution of animals. Insects in particular use humidity cues to navigate the environment. Although the sensory neurons of this elusive sense were first described more than fifty years ago, the transduction mechanism of humidity sensing (hygrosensation) remains unknown. Recent work has uncovered some of the key molecules involved, opening up for novel approaches to study hygrosensory transduction. In this review, I will discuss this progress made toward understanding hygrosensation in insects. Copyright © 2017 Elsevier Inc. All rights reserved.

Aging and the number sense: preserved basic non-symbolic numerical processing and enhanced basic symbolic processing

Directory of Open Access Journals (Sweden)

Jade Eloise eNorris

2015-07-01

Full Text Available Aging often leads to general cognitive decline in domains such as memory and attention. The effect of aging on numerical cognition, particularly on foundational numerical skills known as the Number Sense, is not well known. Early research focused on the effect of aging on arithmetic. Recent studies have begun to investigate the impact of healthy aging on basic numerical skills, but focused on non-symbolic quantity discrimination alone. Moreover, contradictory findings have emerged. The current study aimed to further investigate the impact of aging on basic non-symbolic and symbolic numerical skills. A group of 25 younger (18-25 and 25 older adults (60-77 participated in non-symbolic and symbolic numerical comparison tasks. Mathematical and spelling abilities were also measured. Results showed that aging had no effect on foundational non-symbolic numerical skills, as both groups performed similarly (RTs, accuracy and Weber fractions (w. All participants showed decreased non-symbolic acuity (accuracy and w in trials requiring inhibition. However, aging appears to be associated with a greater decline in discrimination speed in such trials. Furthermore, aging seems to have a positive impact on mathematical ability and basic symbolic numerical processing, as older participants attained significantly higher mathematical achievement scores, and performed significantly better on the symbolic comparison task than younger participants. The findings suggest that aging and its lifetime exposure to numbers may lead to better mathematical achievement and stronger basic symbolic numerical skills. Our results further support the observation that basic non-symbolic numerical skills are resilient to aging, but that aging may exacerbate poorer performance on trials requiring inhibitory processes. These findings lend further support to the notion that preserved basic numerical skills in aging may reflect the preservation of an innate, primitive and embedded Number

WPS-based technology for client-side remote sensing data processing

Science.gov (United States)

Kazakov, E.; Terekhov, A.; Kapralov, E.; Panidi, E.

2015-04-01

Server-side processing is principal for most of the current Web-based geospatial data processing tools. However, in some cases the client-side geoprocessing may be more convenient and acceptable. This study is dedicated to the development of methodology and techniques of Web services elaboration, which allow the client-side geoprocessing also. The practical objectives of the research are focused on the remote sensing data processing, which are one of the most resource-intensive data types. The idea underlying the study is to propose such geoprocessing Web service schema that will be compatible with the current serveroriented Open Geospatial Consortium standard (OGC WPS standard), and additionally will allow to run the processing on the client, transmitting processing tool (executable code) over the network instead of the data. At the same time, the unity of executable code must be preserved, and the transmitted code should be the same to that is used for server-side processing. This unity should provide unconditional identity of the processing results that performed using of any schema. The appropriate services are pointed by the authors as a Hybrid Geoprocessing Web Services (HGWSs). The common approaches to architecture and structure of the HGWSs are proposed at the current stage as like as a number of service prototypes. For the testing of selected approaches, the geoportal prototype was implemented, which provides access to created HGWS. Further works are conducted on the formalization of platform independent HGWSs implementation techniques, and on the approaches to conceptualization of theirs safe use and chaining possibilities. The proposed schema of HGWSs implementation could become one of the possible solutions for the distributed systems, assuming that the processing servers could play the role of the clients connecting to the service supply server. The study was partially supported by Russian Foundation for Basic Research (RFBR), research project No. 13

NASA programs in technology transfer and their relation to remote sensing education

Science.gov (United States)

Weinstein, R. H.

1980-01-01

Technology transfer to users is a central feature of NASA programs. In each major area of responsibility, a variety of mechanisms was established to provide for this transfer of operational capability to the proper end user, be it a Federal agency, industry, or other public sector users. In addition, the Technology Utilization program was established to cut across all program areas and to make available a wealth of 'spinoff' technology (i.e., secondary applications of space technology to ground-based use). The transfer of remote sensing technology, particularly to state and local users, presents some real challenges in application and education for NASA and the university community. The agency's approach to the transfer of remote sensing technology and the current and potential role of universities in the process are considered.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

Science.gov (United States)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

Taiwan's second remote sensing satellite

Science.gov (United States)

Chern, Jeng-Shing; Ling, Jer; Weng, Shui-Lin

2008-12-01

FORMOSAT-2 is Taiwan's first remote sensing satellite (RSS). It was launched on 20 May 2004 with five-year mission life and a very unique mission orbit at 891 km altitude. This orbit gives FORMOSAT-2 the daily revisit feature and the capability of imaging the Arctic and Antarctic regions due to the high enough altitude. For more than three years, FORMOSAT-2 has performed outstanding jobs and its global effectiveness is evidenced in many fields such as public education in Taiwan, Earth science and ecological niche research, preservation of the world heritages, contribution to the International Charter: space and major disasters, observation of suspected North Korea and Iranian nuclear facilities, and scientific observation of the atmospheric transient luminous events (TLEs). In order to continue the provision of earth observation images from space, the National Space Organization (NSPO) of Taiwan started to work on the second RSS from 2005. This second RSS will also be Taiwan's first indigenous satellite. Both the bus platform and remote sensing instrument (RSI) shall be designed and manufactured by NSPO and the Instrument Technology Research Center (ITRC) under the supervision of the National Applied Research Laboratories (NARL). Its onboard computer (OBC) shall use Taiwan's indigenous LEON-3 central processing unit (CPU). In order to achieve cost effective design, the commercial off the shelf (COTS) components shall be widely used. NSPO shall impose the up-screening/qualification and validation/verification processes to ensure their normal functions for proper operations in the severe space environments.

Features, Events, and Processes: Disruptive Events

International Nuclear Information System (INIS)

P. Sanchez

2004-01-01

The purpose of this analysis report is to evaluate and document the inclusion or exclusion of the disruptive events features, events, and processes (FEPs) with respect to modeling used to support the total system performance assessment for license application (TSPA-LA). A screening decision, either ''Included'' or ''Excluded,'' is given for each FEP, along with the technical basis for screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.114 (d), (e), and (f) [DIRS 156605]. The FEPs addressed in this report deal with both seismic and igneous disruptive events, such as fault displacements through the repository and an igneous intrusion into the repository. For included FEPs, this analysis summarizes the implementation of the FEP in TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from TSPA-LA (i.e., why the FEP is excluded). Previous versions of this report were developed to support the total system performance assessments (TSPA) for various prior repository designs. This revision addresses the repository design for the license application (LA)

Features, Events, and Processes: Disruptive Events

Energy Technology Data Exchange (ETDEWEB)

P. Sanchez

2004-11-08

The purpose of this analysis report is to evaluate and document the inclusion or exclusion of the disruptive events features, events, and processes (FEPs) with respect to modeling used to support the total system performance assessment for license application (TSPA-LA). A screening decision, either ''Included'' or ''Excluded,'' is given for each FEP, along with the technical basis for screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.114 (d), (e), and (f) [DIRS 156605]. The FEPs addressed in this report deal with both seismic and igneous disruptive events, such as fault displacements through the repository and an igneous intrusion into the repository. For included FEPs, this analysis summarizes the implementation of the FEP in TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from TSPA-LA (i.e., why the FEP is excluded). Previous versions of this report were developed to support the total system performance assessments (TSPA) for various prior repository designs. This revision addresses the repository design for the license application (LA).

Sensing in tissue bioreactors

Science.gov (United States)

Rolfe, P.

2006-03-01

Specialized sensing and measurement instruments are under development to aid the controlled culture of cells in bioreactors for the fabrication of biological tissues. Precisely defined physical and chemical conditions are needed for the correct culture of the many cell-tissue types now being studied, including chondrocytes (cartilage), vascular endothelial cells and smooth muscle cells (blood vessels), fibroblasts, hepatocytes (liver) and receptor neurones. Cell and tissue culture processes are dynamic and therefore, optimal control requires monitoring of the key process variables. Chemical and physical sensing is approached in this paper with the aim of enabling automatic optimal control, based on classical cell growth models, to be achieved. Non-invasive sensing is performed via the bioreactor wall, invasive sensing with probes placed inside the cell culture chamber and indirect monitoring using analysis within a shunt or a sampling chamber. Electroanalytical and photonics-based systems are described. Chemical sensing for gases, ions, metabolites, certain hormones and proteins, is under development. Spectroscopic analysis of the culture medium is used for measurement of glucose and for proteins that are markers of cell biosynthetic behaviour. Optical interrogation of cells and tissues is also investigated for structural analysis based on scatter.

Image Relaxation Matching Based on Feature Points for DSM Generation

Institute of Scientific and Technical Information of China (English)

ZHENG Shunyi; ZHANG Zuxun; ZHANG Jianqing

2004-01-01

In photogrammetry and remote sensing, image matching is a basic and crucial process for automatic DEM generation. In this paper we presented a image relaxation matching method based on feature points. This method can be considered as an extention of regular grid point based matching. It avoids the shortcome of grid point based matching. For example, with this method, we can avoid low or even no texture area where errors frequently appear in cross correlaton matching. In the mean while, it makes full use of some mature techniques such as probability relaxation, image pyramid and the like which have already been successfully used in grid point matching process. Application of the technique to DEM generaton in different regions proved that it is more reasonable and reliable.

Robust Sensing of Approaching Vehicles Relying on Acoustic Cues

Directory of Open Access Journals (Sweden)

Mitsunori Mizumachi

2014-05-01

Full Text Available The latest developments in automobile design have allowed them to be equipped with various sensing devices. Multiple sensors such as cameras and radar systems can be simultaneously used for active safety systems in order to overcome blind spots of individual sensors. This paper proposes a novel sensing technique for catching up and tracking an approaching vehicle relying on an acoustic cue. First, it is necessary to extract a robust spatial feature from noisy acoustical observations. In this paper, the spatio-temporal gradient method is employed for the feature extraction. Then, the spatial feature is filtered out through sequential state estimation. A particle filter is employed to cope with a highly non-linear problem. Feasibility of the proposed method has been confirmed with real acoustical observations, which are obtained by microphones outside a cruising vehicle.

Information Processing Features Can Detect Behavioral Regimes of Dynamical Systems

Directory of Open Access Journals (Sweden)

Rick Quax

2018-01-01

Full Text Available In dynamical systems, local interactions between dynamical units generate correlations which are stored and transmitted throughout the system, generating the macroscopic behavior. However a framework to quantify exactly how these correlations are stored, transmitted, and combined at the microscopic scale is missing. Here we propose to characterize the notion of “information processing” based on all possible Shannon mutual information quantities between a future state and all possible sets of initial states. We apply it to the 256 elementary cellular automata (ECA, which are the simplest possible dynamical systems exhibiting behaviors ranging from simple to complex. Our main finding is that only a few information features are needed for full predictability of the systemic behavior and that the “information synergy” feature is always most predictive. Finally we apply the idea to foreign exchange (FX and interest-rate swap (IRS time-series data. We find an effective “slowing down” leading indicator in all three markets for the 2008 financial crisis when applied to the information features, as opposed to using the data itself directly. Our work suggests that the proposed characterization of the local information processing of units may be a promising direction for predicting emergent systemic behaviors.

Combining machine learning and remotely sensed bandratios to investigate chlorophyll content and photosynthetic processes

Science.gov (United States)

Gholizadeh, Hamed

Photosynthesis in aquatic and terrestrial ecosystems is the key component of the food chain and the most important driver of the global carbon cycle. Therefore, estimation of photosynthesis at large spatial scales is of great scientific importance and can only practically be achieved by remote sensing data and techniques. In this dissertation, remotely sensed information and techniques, as well as field measurements, are used to improve current approaches of assessing photosynthetic processes. More specifically, three topics are the focus here: (1) investigating the application of spectral vegetation indices as proxies for terrestrial chlorophyll in a mangrove ecosystem, (2) evaluating and improving one of the most common empirical ocean-color algorithms (OC4), and (3) developing an improved approach based on sunlit-to-shaded scaled photochemical reflectance index (sPRI) ratios for detecting drought signals in a deciduous forest at eastern United States. The results indicated that although the green normalized difference vegetation index (GNDVI) is an efficient proxy for terrestrial chlorophyll content, there are opportunities to improve the performance of vegetation indices by optimizing the band weights. In regards to the second topic, we concluded that the parameters of the OC4 algorithm and similar empirical models should be tuned regionally and the addition of sea-surface temperature makes the global ocean-color approaches more valid. Results obtained from the third topic showed that considering shaded and sunlit portions of the canopy (i.e., two-leaf models instead of single big leaf models) and taking into account the divergent stomatal behavior of the species (i.e. isohydric and anisohydric) can improve the capability of sPRI in detecting drought. In addition to investigating the photosynthetic processes, the other common theme of the three research topics is the evaluation of "off- the-shelf" solutions to remote-sensing problems. Although widely used

Sensing sense and mobility at the end of the life course

DEFF Research Database (Denmark)

Blaakilde, Anne Leonora

2015-01-01

is struggling with memory loss, which is impeding his life as a retired migrant. The method of embodied interaction is used in order to sense and understand his sensing of the process of mental decline. This is exemplified by three analytic perspectives: touch, embodied map, and materialised mind....... The methodology presented contributes with a focus on understanding based on sensouos theory which implies embodied interaction and an active co-construction of meaning by ethnographer as well as by reader. This chapter's discussion of a methodology that values the senses adds richness to research on the life...

Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification

International Nuclear Information System (INIS)

Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo

2009-01-01

Myoelectrical pattern classification is a crucial part in multi-functional prosthesis control. This paper investigates a discriminant Fourier-derived cepstrum (DFC) and feature-level post-processing (FLPP) to discriminate hand and wrist motions using the surface electromyographic signal. The Fourier-derived cepstrum takes advantage of the Fourier magnitude or sub-band power energy of signals directly and provides flexible use of spectral information changing with different motions. Appropriate cepstral coefficients are selected by a proposed separability criterion to construct DFC features. For the post-processing, FLPP which combines features from several analysis windows is used to improve the feature performance further. In this work, two classifiers (a linear discriminant classifier and quadratic discriminant classifier) without hyper-parameter optimization are employed to simplify the training procedure and avoid the possible bias of feature evaluation. Experimental results of the 11-motion problem show that the proposed DFC feature outperforms traditional features such as time-domain statistics and autoregressive-derived cepstrum in terms of the classification accuracy, and it is a promising method for the multi-functionality and high-accuracy control of myoelectric prostheses

Feature Scaling via Second-Order Cone Programming

Directory of Open Access Journals (Sweden)

Zhizheng Liang

2016-01-01

Full Text Available Feature scaling has attracted considerable attention during the past several decades because of its important role in feature selection. In this paper, a novel algorithm for learning scaling factors of features is proposed. It first assigns a nonnegative scaling factor to each feature of data and then adopts a generalized performance measure to learn the optimal scaling factors. It is of interest to note that the proposed model can be transformed into a convex optimization problem: second-order cone programming (SOCP. Thus the scaling factors of features in our method are globally optimal in some sense. Several experiments on simulated data, UCI data sets, and the gene data set are conducted to demonstrate that the proposed method is more effective than previous methods.

Quantification of upland thermokarst features with high resolution remote sensing

International Nuclear Information System (INIS)

Belshe, E F; Schuur, E A G; Grosse, G

2013-01-01

Climate-induced changes to permafrost are altering high latitude landscapes in ways that could increase the vulnerability of the vast soil carbon pools of the region. Permafrost thaw is temporally dynamic and spatially heterogeneous because, in addition to the thickening of the active layer, localized thermokarst features form when ice-rich permafrost thaws and the ground subsides. Thermokarst produces a diversity of landforms and alters the physical environment in dynamic ways. To estimate potential changes to the carbon cycle it is imperative to quantify the size and distribution of thermokarst landforms. By performing a supervised classification on a high resolution IKONOS image, we detected and mapped small, irregular thermokarst features occurring within an upland watershed in discontinuous permafrost of Interior Alaska. We found that 12% of the Eight Mile Lake (EML) watershed has undergone thermokarst, predominantly in valleys where tussock tundra resides. About 35% of the 3.7 km 2 tussock tundra class has likely transitioned to thermokarst. These landscape level changes created by permafrost thaw at EML have important implications for ecosystem carbon cycling because thermokarst features are forming in carbon-rich areas and are altering the hydrology in ways that increase seasonal thawing of the soil. (letter)

Significance of Joint Features Derived from the Modified Group Delay Function in Speech Processing

Directory of Open Access Journals (Sweden)

Murthy Hema A

2007-01-01

Full Text Available This paper investigates the significance of combining cepstral features derived from the modified group delay function and from the short-time spectral magnitude like the MFCC. The conventional group delay function fails to capture the resonant structure and the dynamic range of the speech spectrum primarily due to pitch periodicity effects. The group delay function is modified to suppress these spikes and to restore the dynamic range of the speech spectrum. Cepstral features are derived from the modified group delay function, which are called the modified group delay feature (MODGDF. The complementarity and robustness of the MODGDF when compared to the MFCC are also analyzed using spectral reconstruction techniques. Combination of several spectral magnitude-based features and the MODGDF using feature fusion and likelihood combination is described. These features are then used for three speech processing tasks, namely, syllable, speaker, and language recognition. Results indicate that combining MODGDF with MFCC at the feature level gives significant improvements for speech recognition tasks in noise. Combining the MODGDF and the spectral magnitude-based features gives a significant increase in recognition performance of 11% at best, while combining any two features derived from the spectral magnitude does not give any significant improvement.

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Directory of Open Access Journals (Sweden)

Xin Wang

2018-02-01

Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Indium oxide octahedrons based on sol–gel process enhance room temperature gas sensing performance

Energy Technology Data Exchange (ETDEWEB)

Mu, Xiaohui [Key Laboratory of Chemical Sensing & Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250022, Shandong (China); Chen, Changlong, E-mail: chem.chencl@hotmail.com [Key Laboratory of Chemical Sensing & Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250022, Shandong (China); Han, Liuyuan [Key Laboratory of Chemical Sensing & Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250022, Shandong (China); Shao, Baiqi [State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 (China); Graduate School of the Chinese Academy of Sciences, Beijing 100049 (China); Wei, Yuling [Instrumental Analysis Center, Qilu University of Technology, Jinan 250353, Shandong (China); Liu, Qinglong; Zhu, Peihua [Key Laboratory of Chemical Sensing & Analysis in Universities of Shandong, School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250022, Shandong (China)

2015-07-15

Highlights: • In{sub 2}O{sub 3} octahedron films are prepared based on sol–gel technique for the first time. • The preparation possesses merits of low temperature, catalyst-free and large production. • It was found that the spin-coating process in film fabrication was key to achieve the octahedrons. • The In{sub 2}O{sub 3} octahedrons could significantly enhance room temperature NO{sub 2} gas sensing performance. - Abstract: Indium oxide octahedrons were prepared on glass substrates through a mild route based on sol–gel technique. The preparation possesses characteristics including low temperature, catalyst-free and large production, which is much distinguished from the chemical-vapor-deposition based methods that usually applied to prepare indium oxide octahedrons. Detailed characterization revealed that the indium oxide octahedrons were single crystalline, with {1 1 1} crystal facets exposed. It was found that the spin-coating technique was key for achieving the indium oxide crystals with octahedron morphology. The probable formation mechanism of the indium oxide octahedrons was proposed based on the experiment results. Room temperature NO{sub 2} gas sensing measurements exhibited that the indium oxide octahedrons could significantly enhance the sensing performance in comparison with the plate-like indium oxide particles that prepared from the dip-coated gel films, which was attributed to the abundant sharp edges and tips as well as the special {1 1 1} crystal facets exposed that the former possessed. Such a simple wet-chemical based method to prepare indium oxide octahedrons with large-scale production is promising to provide the advanced materials that can be applied in wide fields like gas sensing, solar energy conversion, field emission, and so on.

Indium oxide octahedrons based on sol–gel process enhance room temperature gas sensing performance

International Nuclear Information System (INIS)

Mu, Xiaohui; Chen, Changlong; Han, Liuyuan; Shao, Baiqi; Wei, Yuling; Liu, Qinglong; Zhu, Peihua

2015-01-01

Highlights: • In 2 O 3 octahedron films are prepared based on sol–gel technique for the first time. • The preparation possesses merits of low temperature, catalyst-free and large production. • It was found that the spin-coating process in film fabrication was key to achieve the octahedrons. • The In 2 O 3 octahedrons could significantly enhance room temperature NO 2 gas sensing performance. - Abstract: Indium oxide octahedrons were prepared on glass substrates through a mild route based on sol–gel technique. The preparation possesses characteristics including low temperature, catalyst-free and large production, which is much distinguished from the chemical-vapor-deposition based methods that usually applied to prepare indium oxide octahedrons. Detailed characterization revealed that the indium oxide octahedrons were single crystalline, with {1 1 1} crystal facets exposed. It was found that the spin-coating technique was key for achieving the indium oxide crystals with octahedron morphology. The probable formation mechanism of the indium oxide octahedrons was proposed based on the experiment results. Room temperature NO 2 gas sensing measurements exhibited that the indium oxide octahedrons could significantly enhance the sensing performance in comparison with the plate-like indium oxide particles that prepared from the dip-coated gel films, which was attributed to the abundant sharp edges and tips as well as the special {1 1 1} crystal facets exposed that the former possessed. Such a simple wet-chemical based method to prepare indium oxide octahedrons with large-scale production is promising to provide the advanced materials that can be applied in wide fields like gas sensing, solar energy conversion, field emission, and so on

A concept for extraction of habitat features from laser scanning and hypersprectral imaging for evaluation of Natura 2000 sites - the ChangeHabitats2 project approach

Science.gov (United States)

Székely, B.; Kania, A.; Pfeifer, N.; Heilmeier, H.; Tamás, J.; Szöllősi, N.; Mücke, W.

2012-04-01

The goal of the ChangeHabitats2 project is the development of cost- and time-efficient habitat assessment strategies by employing effective field work techniques supported by modern airborne remote sensing methods, i.e. hyperspectral imagery and laser scanning (LiDAR). An essential task of the project is the design of a novel field work technique that on the one hand fulfills the reporting requirements of the Flora-Fauna-Habitat (FFH-) directive and on the other hand serves as a reference for the aerial data analysis. Correlations between parameters derived from remotely sensed data and terrestrial field measurements shall be exploited in order to create half- or fully-automated methods for the extraction of relevant Natura2000 habitat parameters. As a result of these efforts a comprehensive conceptual model has been developed for extraction and integration of Natura 2000 relevant geospatial data. This scheme is an attempt to integrate various activities within ChangeHabitats2 project defining pathways of development, as well as encompassing existing data processing chains, theoretical approaches and field work. The conceptual model includes definition of processing levels (similar to those existing in remote sensing), whereas these levels cover the range from the raw data to the extracted habitat feature. For instance, the amount of dead wood (standing or lying on the surface) is an important evaluation criterion for the habitat. The tree trunks lying on the ground surface typically can be extracted from the LiDAR point cloud, and the amount of wood can be estimated accordingly. The final result will be considered as a habitat feature derived from laser scanning data. Furthermore, we are also interested not only in the determination of the specific habitat feature, but also in the detection of its variations (especially in deterioration). In this approach the variation of this important habitat feature is considered to be a differential habitat feature, that can

Remote sensing of coastal sea-surface features off northern British Columbia

International Nuclear Information System (INIS)

Jardine, I.D.; Thomson, K.A.; LeBlond, P.H.; Foreman, M.G.

1993-01-01

This article presents an overview of surface oceanographic features identified by AVHRR imagery in Hecate Strait and adjacent waters surrounding the Queen Charlotte Islands, Canada, an area still poor in in situ observations. The observed features and their temporal variability are interpreted in terms of meteorological and hydrological forcing. The effects of tidal mixing are discussed through the application of a finite element numerical model

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

Science.gov (United States)

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter

KAUST Repository

Sana, Furrukh

2015-07-26

Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.

Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter

KAUST Repository

Sana, Furrukh; Katterbauer, Klemens; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim

2015-01-01

Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.

Deep learning decision fusion for the classification of urban remote sensing data

Science.gov (United States)

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.

System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads

Science.gov (United States)

Lu, Bin; Yang, Yi; Sharma, Santosh K; Zambare, Prachi; Madane, Mayura A

2014-12-23

A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.

Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash

Directory of Open Access Journals (Sweden)

Mathew G. Pelletier

2008-02-01

Full Text Available One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU as an alternative to thePC’s traditional use of the central processing unit (CPU. The use of the GPU, as analternative computation platform, allowed for the machine vision system to gain asignificant improvement in processing time. By improving the processing time, thisresearch seeks to address the lack of availability of rapid trash sensing systems and thusalleviate a situation in which the current systems view the cotton lint either well before, orafter, the cotton is cleaned. This extended lag/lead time that is currently imposed on thecotton trash cleaning control systems, is what is responsible for system operators utilizing avery large dead-band safety buffer in order to ensure that the cotton lint is not undercleaned.Unfortunately, the utilization of a large dead-band buffer results in the majority ofthe cotton lint being over-cleaned which in turn causes lint fiber-damage as well assignificant losses of the valuable lint due to the excessive use of cleaning machinery. Thisresearch estimates that upwards of a 30% reduction in lint loss could be gained through theuse of a tightly coupled trash sensor to the cleaning machinery control systems. Thisresearch seeks to improve processing times through the development of a new algorithm forcotton trash sensing that allows for implementation on a highly parallel architecture.Additionally, by moving the new parallel algorithm onto an alternative computing platform,the graphic processing unit “GPU”, for processing of the cotton trash images, a speed up ofover 6.5 times, over optimized code running on the PC’s central processing

Remote Sensing of Surficial Process Responses to Extreme Meteorological Events

Science.gov (United States)

Brakenridge, G. Robert

1997-01-01

Changes in the frequency and magnitude of extreme meteorological events are associated with changing environmental means. Such events are important in human affairs, and can also be investigated by orbital remote sensing. During the course of this project, we applied ERS-1, ERS-2, Radarsat, and an airborne sensor (AIRSAR-TOPSAR) to measure flood extents, flood water surface profiles, and flood depths. We established a World Wide Web site (the Dartmouth Flood Observatory) for publishing remote sensing-based maps of contemporary floods worldwide; this is also an online "active archive" that presently constitutes the only global compilation of extreme flood events. We prepared an article for EOS concerning SAR imaging of the Mississippi Valley flood; an article for the International Journal of Remote Sensing on measurement of a river flood wave using ERS-2, began work on an article (since completed and published) on the Flood Observatory for a Geoscience Information Society Proceedings volume, and presented lectures at several Geol. Soc. of America Natl. Meetings, an Assoc. of Amer. Geographers Natl. Meeting, and a Binghamton Geomorphology Symposium (all on SAR remote sensing of the Mississippi Valley flood). We expanded in-house modeling capabilities by installing the latest version of the Army Corps of Engineers RMA two-dimensional hydraulics software and BYU Engineering Graphics Lab's Surface Water Modeling System (finite elements based pre- and post-processors for RMA work) and also added watershed modeling software. We are presently comparing the results of the 2-d flow models with SAR image data. The grant also supported several important upgrades of pc-based remote sensing infrastructure at Dartmouth. During work on this grant, we collaborated with several workers at the U.S. Army Corps of Engineers, Remote Sensing/GIS laboratory (for flood inundation mapping and modeling; particularly of the Illinois River using the AIRSAR/TOPSAR/ERS-2 combined data), with Dr

Features, Events and Processes in UZ Flow and Transport

Energy Technology Data Exchange (ETDEWEB)

P. Persoff

2005-08-04

The purpose of this report is to evaluate and document the inclusion or exclusion of the unsaturated zone (UZ) features, events, and processes (FEPs) with respect to modeling that supports the total system performance assessment (TSPA) for license application (LA) for a nuclear waste repository at Yucca Mountain, Nevada. A screening decision, either Included or Excluded, is given for each FEP, along with the technical basis for the screening decision. This information is required by the U.S. Nuclear Regulatory Commission (NRC) in 10 CFR 63.114 (d, e, and f) [DIRS 173273]. The FEPs deal with UZ flow and radionuclide transport, including climate, surface water infiltration, percolation, drift seepage, and thermally coupled processes. This analysis summarizes the implementation of each FEP in TSPA-LA (that is, how the FEP is included) and also provides the technical basis for exclusion from TSPA-LA (that is, why the FEP is excluded). This report supports TSPA-LA.

Features, Events and Processes in UZ Flow and Transport

International Nuclear Information System (INIS)

P. Persoff

2005-01-01

The purpose of this report is to evaluate and document the inclusion or exclusion of the unsaturated zone (UZ) features, events, and processes (FEPs) with respect to modeling that supports the total system performance assessment (TSPA) for license application (LA) for a nuclear waste repository at Yucca Mountain, Nevada. A screening decision, either Included or Excluded, is given for each FEP, along with the technical basis for the screening decision. This information is required by the U.S. Nuclear Regulatory Commission (NRC) in 10 CFR 63.114 (d, e, and f) [DIRS 173273]. The FEPs deal with UZ flow and radionuclide transport, including climate, surface water infiltration, percolation, drift seepage, and thermally coupled processes. This analysis summarizes the implementation of each FEP in TSPA-LA (that is, how the FEP is included) and also provides the technical basis for exclusion from TSPA-LA (that is, why the FEP is excluded). This report supports TSPA-LA

Features, Events, and Processes in UZ Flow and Transport

International Nuclear Information System (INIS)

Persoff, P.

2004-01-01

The purpose of this report is to evaluate and document the inclusion or exclusion of the unsaturated zone (UZ) features, events, and processes (FEPs) with respect to modeling that supports the total system performance assessment (TSPA) for license application (LA) for a nuclear waste repository at Yucca Mountain, Nevada. A screening decision, either ''Included'' or ''Excluded'', is given for each FEP, along with the technical basis for the screening decision. This information is required by the U.S. Nuclear Regulatory Commission (NRC) in 10 CFR 63.114 (d, e, and f) [DIRS 156605]. The FEPs deal with UZ flow and radionuclide transport, including climate, surface water infiltration, percolation, drift seepage, and thermally coupled processes. This analysis summarizes the implementation of each FEP in TSPA-LA (that is, how the FEP is included) and also provides the technical basis for exclusion from TSPA-LA (that is, why the FEP is excluded). This report supports TSPA-LA

Features, events and processes evaluation catalogue for argillaceous media

International Nuclear Information System (INIS)

Mazurek, M.; Pearson, F.J.; Volckaert, G.; Bock, H.

2003-01-01

The OECD/NEA Working Group on the Characterisation, the Understanding and the Performance of Argillaceous Rocks as Repository Host Formations for the disposal of radioactive waste (known as the 'Clay Club') launched a project called FEPCAT (Features, Events and Processes Catalogue for argillaceous media) in late 1998. The present report provides the results of work performed by an expert group to develop a FEPs database related to argillaceous formations, whether soft or indurated. It describes the methodology used for the work performed, provides a list of relevant FEPs and summarises the knowledge on each of them. It also provides general conclusions and identifies priorities for future work. (authors)

Automatic feature extraction in large fusion databases by using deep learning approach

Energy Technology Data Exchange (ETDEWEB)

Farias, Gonzalo, E-mail: gonzalo.farias@ucv.cl [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile); Dormido-Canto, Sebastián [Departamento de Informática y Automática, UNED, Madrid (Spain); Vega, Jesús; Rattá, Giuseppe [Asociación EURATOM/CIEMAT Para Fusión, CIEMAT, Madrid (Spain); Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile)

2016-11-15

Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

Automatic feature extraction in large fusion databases by using deep learning approach

International Nuclear Information System (INIS)

Farias, Gonzalo; Dormido-Canto, Sebastián; Vega, Jesús; Rattá, Giuseppe; Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín

2016-01-01

Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

Assessing Climate-Induced Change in River Flow Using Satellite Remote Sensing and Process Modeling in High Mountain Asia

Science.gov (United States)

McDonald, K. C.

2017-12-01

Snow- and glacier-fed river systems originating from High Mountain Asia (HMA) support diverse ecosystems and provide the basis for food and energy production for more than a billion people living downstream. Climate-driven changes in the melting of snow and glaciers and in precipitation patterns are expected to significantly alter the flow of the rivers in the HMA region at various temporal scales, which in turn could heavily affect the socioeconomics of the region. Hence, climate change effects on seasonal and long-term hydrological conditions may have far reaching economic impact annually and over the century. We are developing a decision support tool utilizing integrated microwave remote sensing datasets, process modeling and economic models to inform water resource management decisions and ecosystem sustainability as related to the High Mountain Asia (HMA) region's response to climate change. The availability of consistent time-series microwave remote sensing datasets from Earth-orbiting scatterometers, radiometers and synthetic aperture radar (SAR) imagery provides the basis for the observational framework of this monitoring system. We discuss the assembly, processing and application of scatterometer and SAR data sets from the Advanced Scatterometer (ASCAT) and Sentinal-1 SARs, and the enlistment of these data to monitor seasonal melt and thaw status of glacier-dominated and surrounding regions. We present current status and future plans for this effort. Our team's study emphasizes processes and economic modeling within the Trishuli basin; our remote sensing analysis supports analyses across the HiMAT domain.

Non-retinotopic feature processing in the absence of retinotopic spatial layout and the construction of perceptual space from motion.

Science.gov (United States)

Ağaoğlu, Mehmet N; Herzog, Michael H; Oğmen, Haluk

2012-10-15

The spatial representation of a visual scene in the early visual system is well known. The optics of the eye map the three-dimensional environment onto two-dimensional images on the retina. These retinotopic representations are preserved in the early visual system. Retinotopic representations and processing are among the most prevalent concepts in visual neuroscience. However, it has long been known that a retinotopic representation of the stimulus is neither sufficient nor necessary for perception. Saccadic Stimulus Presentation Paradigm and the Ternus-Pikler displays have been used to investigate non-retinotopic processes with and without eye movements, respectively. However, neither of these paradigms eliminates the retinotopic representation of the spatial layout of the stimulus. Here, we investigated how stimulus features are processed in the absence of a retinotopic layout and in the presence of retinotopic conflict. We used anorthoscopic viewing (slit viewing) and pitted a retinotopic feature-processing hypothesis against a non-retinotopic feature-processing hypothesis. Our results support the predictions of the non-retinotopic feature-processing hypothesis and demonstrate the ability of the visual system to operate non-retinotopically at a fine feature processing level in the absence of a retinotopic spatial layout. Our results suggest that perceptual space is actively constructed from the perceptual dimension of motion. The implications of these findings for normal ecological viewing conditions are discussed. 2012 Elsevier Ltd. All rights reserved

High-Temperature Piezoelectric Sensing

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Xiaoning Jiang

2013-12-01

Full Text Available Piezoelectric sensing is of increasing interest for high-temperature applications in aerospace, automotive, power plants and material processing due to its low cost, compact sensor size and simple signal conditioning, in comparison with other high-temperature sensing techniques. This paper presented an overview of high-temperature piezoelectric sensing techniques. Firstly, different types of high-temperature piezoelectric single crystals, electrode materials, and their pros and cons are discussed. Secondly, recent work on high-temperature piezoelectric sensors including accelerometer, surface acoustic wave sensor, ultrasound transducer, acoustic emission sensor, gas sensor, and pressure sensor for temperatures up to 1,250 °C were reviewed. Finally, discussions of existing challenges and future work for high-temperature piezoelectric sensing are presented.

Compressed sensing & sparse filtering

CERN Document Server

Carmi, Avishy Y; Godsill, Simon J

2013-01-01

This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related app

Geospatial-temporal semantic graph representations of trajectories from remote sensing and geolocation data

Science.gov (United States)

Perkins, David Nikolaus; Brost, Randolph; Ray, Lawrence P.

2017-08-08

Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasets relative to moving objects such as vehicles.

Remote sensing application research of mineralization prospect of uranium-polymetal deposits in west side of Daxinganling

International Nuclear Information System (INIS)

Luo Fusheng; Cui Zhenkui; Fang Maolong; Wang Guojuan; Yao Hua

1998-12-01

The key of mineral exploration by remote sensing method is the extraction and identification of mineralization-related geologic information from remote sensing data under the guidance of mineralization theory. Remote sensing research of deposits is combined with the analysis of regional geology setting, so as to give full play to the advantage of remote sensing technology. According to the geologic features of the covered area, different kinds of satellite data are, at first, selected and processed with different methods and therefore mineralization-related geologic information is effectively extracted. Then regional geologic setting is discussed and main mineralization-controlled factors, such as uranium-occurred volcanic basins, mineralization-controlled faults and granite bodies, Mesozoic volcanic rock series, volcanic framework, are identified. On the basis of the former study, the remote sensing image models of different kinds of deposits have been established. Finally, multi-source information integration technique has been applied to the assessment of favorable mineralization areas. This research shows that it is feasible to extract and identify mineralization-related information from remote sensing images in complicated and covered areas, and that the study area is favorable for uranium and polymetal deposit explorations because of its favorable geologic setting and mineralization conditions

Figurativeness in the Sense of Distraction (Studies by Lithuanian Authors

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Laimutė Monginaitė

2017-04-01

Full Text Available The phenomenon of the sense of distraction and the feature of figurativeness in it are analysed with the help of phenomenological description, the concept of sense of Juozas Mureika and the conception of imagination of Kristupas Sabolius. The position is followed that the acts of sense and the being of those existing found in them cannot be known in a purely rational way. Knowing is reached with intuitive insights. The experiencing of distraction is approached as one of the norms or intentions of consciousness. The sense of distraction is acknowledged to be a basic value becoming more and more important in a modern stressful life. The article indicates that the intentional beings of the sense of distraction are expressed in really various human activities and are distinguished with mono-subjectivity and unrepeatable feeling. Figurativeness is perceived as the result of imaginary, creative activity of the imagination and aesthetical quality. The peculiarities of the formation of figurativeness are revealed through the phenomenological description of imagination by Sabolius. Four features of the act of visualisation, determining the quality of figurativeness, are emphasized: intentionality, power of transformation, relation with emotions and the symbolism of the image. The conclusion is made that figurativeness, being the result of the creative act (visualisation of imagination, appears as aesthetical quality or the ensemble of qualities. Figurativeness sharpens the sense of distraction and calls the wave of new experiences.

Minimum Mean-Square Error Estimation of Mel-Frequency Cepstral Features

DEFF Research Database (Denmark)

Jensen, Jesper; Tan, Zheng-Hua

2015-01-01

In this work we consider the problem of feature enhancement for noise-robust automatic speech recognition (ASR). We propose a method for minimum mean-square error (MMSE) estimation of mel-frequency cepstral features, which is based on a minimum number of well-established, theoretically consistent......-of-the-art MFCC feature enhancement algorithms within this class of algorithms, while theoretically suboptimal or based on theoretically inconsistent assumptions, perform close to optimally in the MMSE sense....

Fault detection of Tennessee Eastman process based on topological features and SVM

Science.gov (United States)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

An image-processing method to detect sub-optical features based on understanding noise in intensity measurements.

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Bhatia, Tripta

2018-02-01

Accurate quantitative analysis of image data requires that we distinguish between fluorescence intensity (true signal) and the noise inherent to its measurements to the extent possible. We image multilamellar membrane tubes and beads that grow from defects in the fluid lamellar phase of the lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine dissolved in water and water-glycerol mixtures by using fluorescence confocal polarizing microscope. We quantify image noise and determine the noise statistics. Understanding the nature of image noise also helps in optimizing image processing to detect sub-optical features, which would otherwise remain hidden. We use an image-processing technique "optimum smoothening" to improve the signal-to-noise ratio of features of interest without smearing their structural details. A high SNR renders desired positional accuracy with which it is possible to resolve features of interest with width below optical resolution. Using optimum smoothening, the smallest and the largest core diameter detected is of width [Formula: see text] and [Formula: see text] nm, respectively, discussed in this paper. The image-processing and analysis techniques and the noise modeling discussed in this paper can be used for detailed morphological analysis of features down to sub-optical length scales that are obtained by any kind of fluorescence intensity imaging in the raster mode.

Object-based vegetation classification with high resolution remote sensing imagery

Science.gov (United States)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions

Research on an uplink carrier sense multiple access algorithm of large indoor visible light communication networks based on an optical hard core point process.

Science.gov (United States)

Nan, Zhufen; Chi, Xuefen

2016-12-20

The IEEE 802.15.7 protocol suggests that it could coordinate the channel access process based on the competitive method of carrier sensing. However, the directionality of light and randomness of diffuse reflection would give rise to a serious imperfect carrier sense (ICS) problem [e.g., hidden node (HN) problem and exposed node (EN) problem], which brings great challenges in realizing the optical carrier sense multiple access (CSMA) mechanism. In this paper, the carrier sense process implemented by diffuse reflection light is modeled as the choice of independent sets. We establish an ICS model with the presence of ENs and HNs for the multi-point to multi-point visible light communication (VLC) uplink communications system. Considering the severe optical ICS problem, an optical hard core point process (OHCPP) is developed, which characterizes the optical CSMA for the indoor VLC uplink communications system. Due to the limited coverage of the transmitted optical signal, in our OHCPP, the ENs within the transmitters' carrier sense region could be retained provided that they could not corrupt the ongoing communications. Moreover, because of the directionality of both light emitting diode (LED) transmitters and receivers, theoretical analysis of the HN problem becomes difficult. In this paper, we derive the closed-form expression for approximating the outage probability and transmission capacity of VLC networks with the presence of HNs and ENs. Simulation results validate the analysis and also show the existence of an optimal physical carrier-sensing threshold that maximizes the transmission capacity for a given emission angle of LED.

Remote Sensing Digital Image Analysis An Introduction

CERN Document Server

Richards, John A

2013-01-01

Remote Sensing Digital Image Analysis provides the non-specialist with a treatment of the quantitative analysis of satellite and aircraft derived remotely sensed data. Since the first edition of the book there have been significant developments in the algorithms used for the processing and analysis of remote sensing imagery; nevertheless many of the fundamentals have substantially remained the same.  This new edition presents material that has retained value since those early days, along with new techniques that can be incorporated into an operational framework for the analysis of remote sensing data. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image processing in remote sensing.  The presentation level is for the mathematical non-specialist.  Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a leve...

Spatial analysis of vector-borne infectious diseases and ecological indicators using GIS and remote sensing

Science.gov (United States)

Anh, N. K.; Liou, Y. A.

2017-12-01

Ecological and climate indicators play a vital role in defining patterns of human activities and behaviors, such as seasonal features, migration, winter-summer lifestyles, which in turn might be associated with vector-borne disease habitats and transmission risks. Remote sensing has been instrumental in deriving environmental variables and indicators. GIS is shown to be a powerful tool in spatiotemporal visualization and distribution of vector-borne diseases and for analysis of associations between environmental conditions and characteristics of vector-borne habitats. Vietnam is in the sub-tropical climate zone with high humidity and abundant precipitation, while the distribution of precipitation is uneven leading to frequently annual occurrence of drought and flood disasters. Moreover, urban heat island effect is significantly enhanced in urbanized areas in recent years. The increase in the frequency and magnitude of severity of weather extremes that are potentially linked to climate change and anthropogenic processes have highlighted the demand of research into health risk assessment and adaptive capacity. This research focuses on the analysis of physical features of environmental indicators and its association with vector-borne diseases as well as adaptive capacity. The study illustrates how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, promising possibilities of allowing disease early-warning systems with citizen participation platform will be proposed. Keywords: Vector-borne diseases; environmental indicators; remote sensing; GIS; Vietnam.

Feature relevance assessment for the semantic interpretation of 3D point cloud data

Directory of Open Access Journals (Sweden)

M. Weinmann

2013-10-01

Full Text Available The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

Universitality features in (pp), (e+e-), and deep inelastic scattering process

International Nuclear Information System (INIS)

Zichichi, A.

1985-01-01

This paper discusses the use of the correct variables in (pp), (e + e - ), and deep-inelastic scattering (DIS) processes which allow universality features to be established different ways of producing mulihadronic states. The experimental data where (pp) interactions are compared with (e + e - ) are shown. Extrapolations of collider physics are discussed and it is found that the leading effects must be subtracted and the correct variables have to be used if one wants to compare purely hadronic interactions with (e + e - ) and DIS. The new method of studying (pp) and (pp) collisions allows one to put on equal footing the multiparticle systems that are produced in purely hadronic interactions, in (e + e - ) annihilation, and in DIS processes

The meaning of sense of place: The community of Vredefort Dome and Parys, Free State

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T. Erasmus

2015-12-01

Full Text Available The Vredefort Dome was declared a World Heritage Site by the United Nations Educational, Scientific and Cultural Organization (UNESCO in 2005. This status has led to an increase in tourism to the adjacent town of Parys and the Dome as well as an increase in developers that could change the character of the area. Therefore, the sense of place of the residents of, and tourists to the area requires consideration in order to find a balance when development takes place. Sense of place refers to the unique features that gives the area its character and the manner in which people relate to these features. This study investigated the participants’ understanding of sense of place of the area under study. The participants consisted of residents and tourists from Parys and the Dome. The non-probability sampling technique, amongst other, was used. The qualitative research method was utilised to gather data which was analysed through content analysis. Thereupon, themes were identified and interpreted. The connotation the participants attached to sense of place was categorised into three broad themes: affective, anthropogenic and the physical environment. The participants showed a special awareness towards protecting the character, nature and history of the area. Sense of place is rarely considered when development is considered. The findings of this study could be used as a guideline for developers in the area. A better understanding of the relationship between place and the attributes individuals assign to Parys and the Vredefort Dome World Heritage Site may help the stakeholders to acquire enhanced approaches to address, identify and engage the community (both residents and tourists in conservation and future planning processes to ensure the well-being of all concerned.

DEVELOPING AN IMAGE PROCESSING APPLICATION THAT SUPPORTS NEW FEATURES OF JPEG2000 STANDARD

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Evgin GÖÇERİ

2007-03-01

Full Text Available In recent years, developing technologies in multimedia brought the importance of image processing and compression. Images that are reduced in size using lossless and lossy compression techniques without degrading the quality of the image to an unacceptable level take up much less space in memory. This enables them to be sent and received over the Internet or mobile devices in much shorter time. The wavelet-based image compression standard JPEG2000 has been created by the Joint Photographic Experts Group (JPEG committee to superseding the former JPEG standard. Works on various additions to this standard are still under development. In this study, an Application has been developed in Visual C# 2005 which implies important image processing techniques such as edge detection and noise reduction. The important feature of this Application is to support JPEG2000 standard as well as supporting other image types, and the implementation does not only apply to two-dimensional images, but also to multi-dimensional images. Modern software development platforms that support image processing have also been compared and several features of the developed software have been identified.

CYBERNETIC BASIS AND SYSTEM PRACTICE OF REMOTE SENSING AND SPATIAL INFORMATION SCIENCE

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X. Tan

2017-09-01

Full Text Available Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

Science.gov (United States)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

Science.gov (United States)

Fan, Lei

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc

Audio-visual synchrony and feature-selective attention co-amplify early visual processing.

Science.gov (United States)

Keitel, Christian; Müller, Matthias M

2016-05-01

Our brain relies on neural mechanisms of selective attention and converging sensory processing to efficiently cope with rich and unceasing multisensory inputs. One prominent assumption holds that audio-visual synchrony can act as a strong attractor for spatial attention. Here, we tested for a similar effect of audio-visual synchrony on feature-selective attention. We presented two superimposed Gabor patches that differed in colour and orientation. On each trial, participants were cued to selectively attend to one of the two patches. Over time, spatial frequencies of both patches varied sinusoidally at distinct rates (3.14 and 3.63 Hz), giving rise to pulse-like percepts. A simultaneously presented pure tone carried a frequency modulation at the pulse rate of one of the two visual stimuli to introduce audio-visual synchrony. Pulsed stimulation elicited distinct time-locked oscillatory electrophysiological brain responses. These steady-state responses were quantified in the spectral domain to examine individual stimulus processing under conditions of synchronous versus asynchronous tone presentation and when respective stimuli were attended versus unattended. We found that both, attending to the colour of a stimulus and its synchrony with the tone, enhanced its processing. Moreover, both gain effects combined linearly for attended in-sync stimuli. Our results suggest that audio-visual synchrony can attract attention to specific stimulus features when stimuli overlap in space.

Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors.

Science.gov (United States)

Murguía, José S; Vergara, Alexander; Vargas-Olmos, Cecilia; Wong, Travis J; Fonollosa, Jordi; Huerta, Ramón

2013-06-27

Designing reliable, fast responding, highly sensitive, and low-power consuming chemo-sensory systems has long been a major goal in chemo-sensing. This goal, however, presents a difficult challenge because having a set of chemo-sensory detectors exhibiting all these aforementioned ideal conditions are still largely un-realizable to-date. This paper presents a unique perspective on capturing more in-depth insights into the physicochemical interactions of two distinct, selectively chemically modified porous silicon (pSi) film-based optical gas sensors by implementing an innovative, based on signal processing methodology, namely the two-dimensional discrete wavelet transform. Specifically, the method consists of using the two-dimensional discrete wavelet transform as a feature extraction method to capture the non-stationary behavior from the bi-dimensional pSi rugate sensor response. Utilizing a comprehensive set of measurements collected from each of the aforementioned optically based chemical sensors, we evaluate the significance of our approach on a complex, six-dimensional chemical analyte discrimination/quantification task problem. Due to the bi-dimensional aspects naturally governing the optical sensor response to chemical analytes, our findings provide evidence that the proposed feature extractor strategy may be a valuable tool to deepen our understanding of the performance of optically based chemical sensors as well as an important step toward attaining their implementation in more realistic chemo-sensing applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review

Directory of Open Access Journals (Sweden)

Lise Safatly

2014-01-01

reconfigurable radio frequency (RF parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL algorithm that has been proposed to perform decision-making in CR networks.

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Science.gov (United States)

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

Wavelength calibration of imaging spectrometer using atmospheric absorption features

Science.gov (United States)

Zhou, Jiankang; Chen, Yuheng; Chen, Xinhua; Ji, Yiqun; Shen, Weimin

2012-11-01

Imaging spectrometer is a promising remote sensing instrument widely used in many filed, such as hazard forecasting, environmental monitoring and so on. The reliability of the spectral data is the determination to the scientific communities. The wavelength position at the focal plane of the imaging spectrometer will change as the pressure and temperature vary, or the mechanical vibration. It is difficult for the onboard calibration instrument itself to keep the spectrum reference accuracy and it also occupies weight and the volume of the remote sensing platform. Because the spectral images suffer from the atmospheric effects, the carbon oxide, water vapor, oxygen and solar Fraunhofer line, the onboard wavelength calibration can be processed by the spectral images themselves. In this paper, wavelength calibration is based on the modeled and measured atmospheric absorption spectra. The modeled spectra constructed by the atmospheric radiative transfer code. The spectral angle is used to determine the best spectral similarity between the modeled spectra and measured spectra and estimates the wavelength position. The smile shape can be obtained when the matching process across all columns of the data. The present method is successful applied on the Hyperion data. The value of the wavelength shift is obtained by shape matching of oxygen absorption feature and the characteristics are comparable to that of the prelaunch measurements.

A Computational model for compressed sensing RNAi cellular screening

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Tan Hua

2012-12-01

Full Text Available Abstract Background RNA interference (RNAi becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. Results In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi, which employs a unique combination of group of small interfering RNAs (siRNAs to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear, which is ill-posed in general. However, the recently developed compressed sensing (CS theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially. Conclusions This csRNAi system is very promising in saving both time and cost for large-scale RNAi

Optimization of pH sensing using silicon nanowire field effect transistors with HfO2 as the sensing surface

International Nuclear Information System (INIS)

Zafar, Sufi; D'Emic, Christopher; Afzali, Ali; Fletcher, Benjamin; Zhu, Y; Ning, Tak

2011-01-01

Silicon nanowire field effect transistor sensors with SiO 2 /HfO 2 as the gate dielectric sensing surface are fabricated using a top down approach. These sensors are optimized for pH sensing with two key characteristics. First, the pH sensitivity is shown to be independent of buffer concentration. Second, the observed pH sensitivity is enhanced and is equal to the Nernst maximum sensitivity limit of 59 mV/pH with a corresponding subthreshold drain current change of ∼ 650%/pH. These two enhanced pH sensing characteristics are attributed to the use of HfO 2 as the sensing surface and an optimized fabrication process compatible with silicon processing technology.

Optimization of pH sensing using silicon nanowire field effect transistors with HfO2 as the sensing surface.

Science.gov (United States)

Zafar, Sufi; D'Emic, Christopher; Afzali, Ali; Fletcher, Benjamin; Zhu, Y; Ning, Tak

2011-10-07

Silicon nanowire field effect transistor sensors with SiO(2)/HfO(2) as the gate dielectric sensing surface are fabricated using a top down approach. These sensors are optimized for pH sensing with two key characteristics. First, the pH sensitivity is shown to be independent of buffer concentration. Second, the observed pH sensitivity is enhanced and is equal to the Nernst maximum sensitivity limit of 59 mV/pH with a corresponding subthreshold drain current change of ∼ 650%/pH. These two enhanced pH sensing characteristics are attributed to the use of HfO(2) as the sensing surface and an optimized fabrication process compatible with silicon processing technology.

Soil Moisture Retrieval Using Convolutional Neural Networks: Application to Passive Microwave Remote Sensing

Science.gov (United States)

Hu, Z.; Xu, L.; Yu, B.

2018-04-01

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

Directory of Open Access Journals (Sweden)

Z. Hu

2018-04-01

Full Text Available A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN. Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR for soil moisture retrieval.

Efficient Lossy Compression for Compressive Sensing Acquisition of Images in Compressive Sensing Imaging Systems

Directory of Open Access Journals (Sweden)

Xiangwei Li

2014-12-01

Full Text Available Compressive Sensing Imaging (CSI is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. Finally, we apply these two methods in the CSI system for efficient lossy compression of CS acquisition. Simulation results demonstrate that the proposed solution improves the rate-distortion performance by 0.4~2 dB comparing with current state-of-the-art, while maintaining a low computational complexity.

Quorum sensing: a quantum perspective.

Science.gov (United States)

Majumdar, Sarangam; Pal, Sukla

2016-09-01

Quorum sensing is the efficient mode of communication in the bacterial world. After a lot of advancements in the classical theory of quorum sensing few basic questions of quorum sensing still remain unanswered. The sufficient progresses in quantum biology demands to explain these questions from the quantum perspective as non trivial quantum effects already have manifested in various biological processes like photosynthesis, magneto-reception etc. Therefore, it's the time to review the bacterial communications from the quantum view point. In this article we carefully accumulate the latest results and arguments to strengthen quantum biology through the addition of quorum sensing mechanism in the light of quantum mechanics.

Remote sensing image fusion in the context of Digital Earth

International Nuclear Information System (INIS)

Pohl, C

2014-01-01

The increase in the number of operational Earth observation satellites gives remote sensing image fusion a new boost. As a powerful tool to integrate images from different sensors it enables multi-scale, multi-temporal and multi-source information extraction. Image fusion aims at providing results that cannot be obtained from a single data source alone. Instead it enables feature and information mining of higher reliability and availability. The process required to prepare remote sensing images for image fusion comprises most of the necessary steps to feed the database of Digital Earth. The virtual representation of the planet uses data and information that is referenced and corrected to suit interpretation and decision-making. The same pre-requisite is valid for image fusion, the outcome of which can directly flow into a geographical information system. The assessment and description of the quality of the results remains critical. Depending on the application and information to be extracted from multi-source images different approaches are necessary. This paper describes the process of image fusion based on a fusion and classification experiment, explains the necessary quality measures involved and shows with this example which criteria have to be considered if the results of image fusion are going to be used in Digital Earth

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

Science.gov (United States)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Coding of visual object features and feature conjunctions in the human brain.

Science.gov (United States)

Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

2008-01-01

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

Photogrammetry and remote sensing for visualization of spatial data in a virtual reality environment

Science.gov (United States)

Bhagawati, Dwipen

2001-07-01

Researchers in many disciplines have started using the tool of Virtual Reality (VR) to gain new insights into problems in their respective disciplines. Recent advances in computer graphics, software and hardware technologies have created many opportunities for VR systems, advanced scientific and engineering applications being among them. In Geometronics, generally photogrammetry and remote sensing are used for management of spatial data inventory. VR technology can be suitably used for management of spatial data inventory. This research demonstrates usefulness of VR technology for inventory management by taking the roadside features as a case study. Management of roadside feature inventory involves positioning and visualization of the features. This research has developed a methodology to demonstrate how photogrammetric principles can be used to position the features using the video-logging images and GPS camera positioning and how image analysis can help produce appropriate texture for building the VR, which then can be visualized in a Cave Augmented Virtual Environment (CAVE). VR modeling was implemented in two stages to demonstrate the different approaches for modeling the VR scene. A simulated highway scene was implemented with the brute force approach, while modeling software was used to model the real world scene using feature positions produced in this research. The first approach demonstrates an implementation of the scene by writing C++ codes to include a multi-level wand menu for interaction with the scene that enables the user to interact with the scene. The interactions include editing the features inside the CAVE display, navigating inside the scene, and performing limited geographic analysis. The second approach demonstrates creation of a VR scene for a real roadway environment using feature positions determined in this research. The scene looks realistic with textures from the real site mapped on to the geometry of the scene. Remote sensing and

Enhancing facial features by using clear facial features

Science.gov (United States)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Remote sensing of the Canadian Arctic: Modelling biophysical variables

Science.gov (United States)

Liu, Nanfeng

It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic

Hyperspectral remote sensing

National Research Council Canada - National Science Library

Eismann, Michael Theodore

2012-01-01

..., and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment...

An Observation Task Chain Representation Model for Disaster Process-Oriented Remote Sensing Satellite Sensor Planning: A Flood Water Monitoring Application

Directory of Open Access Journals (Sweden)

Chao Yang

2018-03-01

Full Text Available An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme.

Sound & The Senses

DEFF Research Database (Denmark)

Schulze, Holger

2012-01-01

How are those sounds you hear right now technically generated and post-produced, how are they aesthetically conceptualized and how culturally dependant are they really? How is your ability to hear intertwined with all the other senses and their cultural, biographical and technological constructio...... over time? And how is listening and sounding a deeply social activity – constructing our way of living together in cities as well as in apartment houses? A radio feature with Jonathan Sterne, AGF a.k.a Antye Greie, Jens Gerrit Papenburg & Holger Schulze....

SENSOR CORRECTION AND RADIOMETRIC CALIBRATION OF A 6-BAND MULTISPECTRAL IMAGING SENSOR FOR UAV REMOTE SENSING

Directory of Open Access Journals (Sweden)

J. Kelcey

2012-07-01

Full Text Available The increased availability of unmanned aerial vehicles (UAVs has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping. The development and utilisation of UAV platforms requires broad technical skills covering the three major facets of remote sensing: data acquisition, data post-processing, and image analysis. In this study, UAV image data acquired by a miniature 6-band multispectral imaging sensor was corrected and calibrated using practical image-based data post-processing techniques. Data correction techniques included dark offset subtraction to reduce sensor noise, flat-field derived per-pixel look-up-tables to correct vignetting, and implementation of the Brown- Conrady model to correct lens distortion. Radiometric calibration was conducted with an image-based empirical line model using pseudo-invariant features (PIFs. Sensor corrections and radiometric calibration improve the quality of the data, aiding quantitative analysis and generating consistency with other calibrated datasets.

Sensing Athletes: Sensory Dimensions of Recreational Endurance Sports

Directory of Open Access Journals (Sweden)

Stefan Groth

2017-12-01

Full Text Available Sport has become increasingly popular with recreational athletes over the last couple of decades. This has only gained minimal attention so far from scholars interested in the relations between recreational sports and everyday culture. With this paper, we seek to contribute to this field by scrutinising the sensory dimensions of recreational sport. Rather than probing into or highlighting isolated senses, we look at sensory dimensions understood as a combination of different, non-separable sensory experiences featured in recreational endurance sports. We are interested in how senses play a role for recreational endurance athletes in running, triathlon and cycling both in training and competition. We start by examining how cultural and social dimensions are inextricably linked to doing sports. Secondly, we show how different configurations of the senses and their communicative mediation are contingent on sport disciplines, specific settings, technology, development and change as sensory careers over time. Thirdly, we discuss the kinaesthetic dimensions of doing sports in relation to the senses and the role of atmospheres. We conclude by arguing that highlighting specific senses by athletes is a cultural practice that calls for a holistic analysis of senses in sport, and outline some methodological implications for research on the senses.

THE FEATURES OF PROCESSES OF SKILLS (SPECIAL COMPETENCIES FORMATION

Directory of Open Access Journals (Sweden)

A. N. Pechnikov

2018-01-01

problem of the students’ skills formation process. The conclusion is drawn that these particular options have to make an initial set of alternatives for the educational process organization.Practical significance. This work has revealed basic features of variable processes for formation of skills (special competencies of students to create correct, exact and reliable tools for measurement of learning outcomes.

Identification of High Potential Bays for HABs Occurrence in Peninsular Malysia Using Palsar Remote Sensing Data

Science.gov (United States)

Pour, A. B.; Hashim, M.

2016-09-01

Increasing frequency, intensity, and geographic distribution of Harmful algal blooms (HABs) poses a serious threat to the coastal fish/shellfish aquaculture and fisheries in Malaysian bays. Rising in sea level, shoreline erosion, stresses on fisheries, population pressure, interference of land-use and lack of institutional capabilities for integrated management make major challenges. Recent investigations and satellite observations indicate HABs originated from specific coast that have favourable geographic, geomorphic and coastal geology conditions to bring the green macro algae from the coast offshore. Therefore, the identification of high HABs frequented bays using remote sensing and geology investigations in Malaysian waters is required to reduce future challenges in this unique case. This research implemented comprehensive geomorphic and coastal geology investigations combined with remote sensing digital image processing approach to identify Malaysian bays frequented with HABs occurrence in Malaysian waters territory. The landscape and geomorphological features of the Malaysian bays were constructed from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) remote sensing satellite data combined with field observations and surveying. The samples for laboratory analysis were collected from the sediment stations with different distance across shorelines features and watersheds of the Johor Bahru estuary. This research identified that semi-enclosed bays such as Kuala Lumpur and Johor Bahru bays with connection to estuaries have high potential to be frequented with HABs occurrence.

IDENTIFICATION OF HIGH POTENTIAL BAYS FOR HABs OCCURRENCE IN PENINSULAR MALYSIA USING PALSAR REMOTE SENSING DATA

Directory of Open Access Journals (Sweden)

A. B. Pour

2016-09-01

Full Text Available Increasing frequency, intensity, and geographic distribution of Harmful algal blooms (HABs poses a serious threat to the coastal fish/shellfish aquaculture and fisheries in Malaysian bays. Rising in sea level, shoreline erosion, stresses on fisheries, population pressure, interference of land-use and lack of institutional capabilities for integrated management make major challenges. Recent investigations and satellite observations indicate HABs originated from specific coast that have favourable geographic, geomorphic and coastal geology conditions to bring the green macro algae from the coast offshore. Therefore, the identification of high HABs frequented bays using remote sensing and geology investigations in Malaysian waters is required to reduce future challenges in this unique case. This research implemented comprehensive geomorphic and coastal geology investigations combined with remote sensing digital image processing approach to identify Malaysian bays frequented with HABs occurrence in Malaysian waters territory. The landscape and geomorphological features of the Malaysian bays were constructed from the Phased Array type L-band Synthetic Aperture Radar (PALSAR remote sensing satellite data combined with field observations and surveying. The samples for laboratory analysis were collected from the sediment stations with different distance across shorelines features and watersheds of the Johor Bahru estuary. This research identified that semi-enclosed bays such as Kuala Lumpur and Johor Bahru bays with connection to estuaries have high potential to be frequented with HABs occurrence.

Supporting Craft Sense in Early Education

Directory of Open Access Journals (Sweden)

Kalle Virta

2013-10-01

Full Text Available The research task was to describe and construct theoretical background for Craft Sense in early education. Craft Sense represents a learner’s skill for obtaining Sloyd (Craft, Design & Technology related knowledge, skills and understanding. The development of Craft Sense is based on producing artefacts and evaluating the production process. In this research, the concept of Craft Sense is based on the integration of Sloyd and meta-cognitive regulation of learning activities. Based on theoretical information, an empirical research question was formulated: “What kind of Craft Sense do children have in early education Sloyd?” The method of study was assessing picture supported learning on a Sloyd course for young children. The data was analyzed by qualitative content analysis and Child Behaviour Rating Scale (CBRS. Findings indicate that the development of children’s Craft Sense can be supported with pictures. Furthermore, the CBRS can be used to evaluate and understand children’s Craft Sense. Keywords: Craft Sense, Sloyd, Sloyd Education, Meta-cognition

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

Science.gov (United States)

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Earthquake Building Damage Mapping Based on Feature Analyzing Method from Synthetic Aperture Radar Data

Science.gov (United States)

An, L.; Zhang, J.; Gong, L.

2018-04-01

Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.

Remote RemoteRemoteRemote sensing potential for sensing ...

African Journals Online (AJOL)

Remote RemoteRemoteRemote sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing p. A Ngie, F Ahmed, K Abutaleb ...

Remote Sensing Image in the Application of Agricultural Tourism Planning

Directory of Open Access Journals (Sweden)

Guojing Fan

2013-06-01

Full Text Available This paper introduces the processing technology of high resolution remote sensing image, the specific making process of tourism map and different remote sensing data in the key application of tourism planning and so on. Remote sensing extracts agricultural tourism planning information, improving the scientificalness and operability of agricultural tourism planning. Therefore remote sensing image in the application of agricultural tourism planning will be the inevitable trend of tourism development.

System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

Science.gov (United States)

Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.

2014-06-17

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Voltage Sensing in Membranes: From Macroscopic Currents to Molecular Motions.

Science.gov (United States)

Freites, J Alfredo; Tobias, Douglas J

2015-06-01

Voltage-sensing domains (VSDs) are integral membrane protein units that sense changes in membrane electric potential, and through the resulting conformational changes, regulate a specific function. VSDs confer voltage-sensitivity to a large superfamily of membrane proteins that includes voltage-gated Na[Formula: see text], K[Formula: see text], Ca[Formula: see text] ,and H[Formula: see text] selective channels, hyperpolarization-activated cyclic nucleotide-gated channels, and voltage-sensing phosphatases. VSDs consist of four transmembrane segments (termed S1 through S4). Their most salient structural feature is the highly conserved positions for charged residues in their sequences. S4 exhibits at least three conserved triplet repeats composed of one basic residue (mostly arginine) followed by two hydrophobic residues. These S4 basic side chains participate in a state-dependent internal salt-bridge network with at least four acidic residues in S1-S3. The signature of voltage-dependent activation in electrophysiology experiments is a transient current (termed gating or sensing current) upon a change in applied membrane potential as the basic side chains in S4 move across the membrane electric field. Thus, the unique structural features of the VSD architecture allow for competing requirements: maintaining a series of stable transmembrane conformations, while allowing charge motion, as briefly reviewed here.

Advanced sensing techniques for cognitive radio

CERN Document Server

Zhao, Guodong; Li, Shaoqian

2017-01-01

This SpringerBrief investigates advanced sensing techniques to detect and estimate the primary receiver for cognitive radio systems. Along with a comprehensive overview of existing spectrum sensing techniques, this brief focuses on the design of new signal processing techniques, including the region-based sensing, jamming-based probing, and relay-based probing. The proposed sensing techniques aim to detect the nearby primary receiver and estimate the cross-channel gain between the cognitive transmitter and primary receiver. The performance of the proposed algorithms is evaluated by simulations in terms of several performance parameters, including detection probability, interference probability, and estimation error. The results show that the proposed sensing techniques can effectively sense the primary receiver and improve the cognitive transmission throughput. Researchers and postgraduate students in electrical engineering will find this an exceptional resource.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

Science.gov (United States)

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Remote sensing science - new concepts and applications

Energy Technology Data Exchange (ETDEWEB)

Gerstl, S.A.; Cooke, B.J.; Henderson, B.G.; Love, S.P.; Zardecki, A.

1996-10-01

This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The science and technology of satellite remote sensing is an emerging interdisciplinary field that is growing rapidly with many global and regional applications requiring quantitative sensing of earth`s surface features as well as its atmosphere from space. It is possible today to resolve structures on the earth`s surface as small as one meter from space. If this high spatial resolution is coupled with high spectral resolution, instant object identification can also be achieved. To interpret these spectral signatures correctly, it is necessary to perform a computational correction on the satellite imagery that removes the distorting effects of the atmosphere. This project studied such new concepts and applied innovative new approaches in remote sensing science.

Generalization of the Wide-Sense Markov Concept to a Widely Linear Processing

International Nuclear Information System (INIS)

Espinosa-Pulido, Juan Antonio; Navarro-Moreno, Jesús; Fernández-Alcalá, Rosa María; Ruiz-Molina, Juan Carlos; Oya-Lechuga, Antonia; Ruiz-Fuentes, Nuria

2014-01-01

In this paper we show that the classical definition and the associated characterizations of wide-sense Markov (WSM) signals are not valid for improper complex signals. For that, we propose an extension of the concept of WSM to a widely linear (WL) setting and the study of new characterizations. Specifically, we introduce a new class of signals, called widely linear Markov (WLM) signals, and we analyze some of their properties based either on second-order properties or on state-space models from a WL processing standpoint. The study is performed in both the forwards and backwards directions of time. Thus, we provide two forwards and backwards Markovian representations for WLM signals. Finally, different estimation recursive algorithms are obtained for these models

Visual perception of complex shape-transforming processes.

Science.gov (United States)

Schmidt, Filipp; Fleming, Roland W

2016-11-01

Morphogenesis-or the origin of complex natural form-has long fascinated researchers from practically every branch of science. However, we know practically nothing about how we perceive and understand such processes. Here, we measured how observers visually infer shape-transforming processes. Participants viewed pairs of objects ('before' and 'after' a transformation) and identified points that corresponded across the transformation. This allowed us to map out in spatial detail how perceived shape and space were affected by the transformations. Participants' responses were strikingly accurate and mutually consistent for a wide range of non-rigid transformations including complex growth-like processes. A zero-free-parameter model based on matching and interpolating/extrapolating the positions of high-salience contour features predicts the data surprisingly well, suggesting observers infer spatial correspondences relative to key landmarks. Together, our findings reveal the operation of specific perceptual organization processes that make us remarkably adept at identifying correspondences across complex shape-transforming processes by using salient object features. We suggest that these abilities, which allow us to parse and interpret the causally significant features of shapes, are invaluable for many tasks that involve 'making sense' of shape. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Viewshed and sense of place as conservation features: A case study and research agenda for South Africa's national parks

Directory of Open Access Journals (Sweden)

Jaco Barendse

2016-08-01

Full Text Available Sense of place (SoP refers to the meanings and values that people attach to places. The concept can be used to frame how people engage or form a connection with the natural environment. At a sensory level, SoP is influenced by people’s visual experiences, which in turn can be linked to the concept of viewsheds. Viewsheds can be transformed, either abruptly (e.g. by infrastructure development such as wind turbines or more gradually (e.g. by non-native trees invading a landscape. In this study, we focus on the Garden Route National Park to explore the potential importance of viewsheds as a conservation feature, specifically in the context of non-native (especially invasive tree species. Using mixed information sources, we explore the potential role of invasive trees on experiences of visitors to this protected area and speculate on how viewsheds may shape SoP associations and how such associations may inform protected area management. Our investigation shows that people’s experiences regarding natural and modified viewsheds are varied and intricate. Both SoP and viewsheds have the potential to inform conservation action, and these concepts should form an integral part of objective hierarchies and management plans for national parks. However, while legislation and park management plans make provision for the use of these concepts, associated research in South Africa is virtually non-existent. We conclude by proposing a conceptual model and research agenda to promote the use of viewsheds and SoP in the management of national parks in South Africa. Conservation implications: Viewshed and sense of place can be used as boundary concepts to (1 facilitate interdisciplinary research between social and natural scientists, (2 help understand the connectedness and feedbacks between people and nature and (3 promote communication between science, management and stakeholders regarding desired conditions of landscapes in and around parks.

Application of remote sensing in aquatic ecosystems

Science.gov (United States)

Yousef, Foad

I utilized state the art remote sensing and GIS (Geographical Information System) techniques to study large scale biological, physical and ecological processes of coastal, nearshore, and offshore waters of Lake Michigan and Lake Superior. These processes ranged from chlorophyll alpha and primary production time series analysies in Lake Michigan to coastal stamp sand threats on Buffalo Reef in Lake Superior. I used SeaWiFS (Sea-viewing Wide Field-of-view Sensor) satellite imagery to trace various biological, chemical and optical water properties of Lake Michigan during the past decade and to investigate the collapse of early spring primary production. Using spatial analysis techniques, I was able to connect these changes to some important biological processes of the lake (quagga mussels filtration). In a separate study on Lake Superior, using LiDAR (Light Detection and Ranging) and aerial photos, we examined natural coastal erosion in Grand Traverse Bay, Michigan, and discussed a variety of geological features that influence general sediment accumulation patterns and interactions with migrating tailings from legacy mining. These sediments are moving southwesterly towards Buffalo Reef, creating a threat to the lake trout and lake whitefish breeding ground.

Different processing phases for features, figures, and selective attention in the primary visual cortex

NARCIS (Netherlands)

Roelfsema, P.R.; Tolboom, M.; Khayat, P.S.

2007-01-01

Our visual system imposes structure onto images that usually contain a diversity of surfaces, contours, and colors. Psychological theories propose that there are multiple steps in this process that occur in hierarchically organized regions of the cortex: early visual areas register basic features,

Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward

Science.gov (United States)

Näsi, R.; Viljanen, N.; Oliveira, R.; Kaivosoja, J.; Niemeläinen, O.; Hakala, T.; Markelin, L.; Nezami, S.; Suomalainen, J.; Honkavaara, E.

2018-04-01

Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10 % better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40 % compared to approaches where only spectral features were applied. The best estimation RMSE of 239 kg/ha (6.0 %) was obtained with multiview anisotropy corrected data set and the 3D features.

Use cases and DEMO: aligning functional features of ICT-infrastructure to business processes.

Science.gov (United States)

Maij, E; Toussaint, P J; Kalshoven, M; Poerschke, M; Zwetsloot-Schonk, J H M

2002-11-12

The proper alignment of functional features of the ICT-infrastructure to business processes is a major challenge in health care organisations. This alignment takes into account that the organisational structure not only shapes the ICT-infrastructure, but that the inverse also holds. To solve the alignment problem, relevant features of the ICT-infrastructure should be derived from the organisational structure and the influence of this envisaged ICT to the work practices should be pointed out. The objective of our study was to develop a method to solve this alignment problem. In a previous study we demonstrated the appropriateness of the business process modelling methodology Dynamic Essential Modelling of Organizations (DEMO). A proven and widely used modelling language for expressing functional features is Unified Modelling Language (UML). In the context of a specific case study at the University Medical Centre Utrecht in the Netherlands we investigated if the combined use of DEMO and UML could solve the alignment problem. The study demonstrated that the DEMO models were suited as a starting point in deriving system functionality by using the use case concept of UML. Further, the case study demonstrated that in using this approach for the alignment problem, insight is gained into the mutual influence of ICT-infrastructure and organisation structure: (a) specification of independent, re-usable components-as a set of related functionalities-is realised, and (b) a helpful representation of the current and future work practice is provided for in relation to the envisaged ICT support.

Pervasive sensing

Science.gov (United States)

Nagel, David J.

2000-11-01

The coordinated exploitation of modern communication, micro- sensor and computer technologies makes it possible to give global reach to our senses. Web-cameras for vision, web- microphones for hearing and web-'noses' for smelling, plus the abilities to sense many factors we cannot ordinarily perceive, are either available or will be soon. Applications include (1) determination of weather and environmental conditions on dense grids or over large areas, (2) monitoring of energy usage in buildings, (3) sensing the condition of hardware in electrical power distribution and information systems, (4) improving process control and other manufacturing, (5) development of intelligent terrestrial, marine, aeronautical and space transportation systems, (6) managing the continuum of routine security monitoring, diverse crises and military actions, and (7) medicine, notably the monitoring of the physiology and living conditions of individuals. Some of the emerging capabilities, such as the ability to measure remotely the conditions inside of people in real time, raise interesting social concerns centered on privacy issues. Methods for sensor data fusion and designs for human-computer interfaces are both crucial for the full realization of the potential of pervasive sensing. Computer-generated virtual reality, augmented with real-time sensor data, should be an effective means for presenting information from distributed sensors.

PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT

Directory of Open Access Journals (Sweden)

A. Olasz

2017-07-01

Full Text Available The Earth observation (EO missions of the space agencies and space industry (ESA, NASA, national and commercial companies are evolving as never before. These missions aim to develop and launch next-generation series of satellites and sensors and often provide huge amounts of data, even free of charge, to enable novel monitoring services. The wide geospatial sector is targeted to handle new challenges to store, process and visualize these geospatial data, reaching the level of Big Data by their volume, variety, velocity, along with the need of multi-source spatio-temporal geospatial data processing. Handling and analysis of remote sensing data has always been a cumbersome task due to the ever-increasing size and frequency of collected information. This paper presents the achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of geospatial big data in the context of flood and waterlogging detection.

Evidence for unlimited capacity processing of simple features in visual cortex.

Science.gov (United States)

White, Alex L; Runeson, Erik; Palmer, John; Ernst, Zachary R; Boynton, Geoffrey M

2017-06-01

Performance in many visual tasks is impaired when observers attempt to divide spatial attention across multiple visual field locations. Correspondingly, neuronal response magnitudes in visual cortex are often reduced during divided compared with focused spatial attention. This suggests that early visual cortex is the site of capacity limits, where finite processing resources must be divided among attended stimuli. However, behavioral research demonstrates that not all visual tasks suffer such capacity limits: The costs of divided attention are minimal when the task and stimulus are simple, such as when searching for a target defined by orientation or contrast. To date, however, every neuroimaging study of divided attention has used more complex tasks and found large reductions in response magnitude. We bridged that gap by using functional magnetic resonance imaging to measure responses in the human visual cortex during simple feature detection. The first experiment used a visual search task: Observers detected a low-contrast Gabor patch within one or four potentially relevant locations. The second experiment used a dual-task design, in which observers made independent judgments of Gabor presence in patches of dynamic noise at two locations. In both experiments, blood-oxygen level-dependent (BOLD) signals in the retinotopic cortex were significantly lower for ignored than attended stimuli. However, when observers divided attention between multiple stimuli, BOLD signals were not reliably reduced and behavioral performance was unimpaired. These results suggest that processing of simple features in early visual cortex has unlimited capacity.

Data compressive paradigm for multispectral sensing using tunable DWELL mid-infrared detectors.

Science.gov (United States)

Jang, Woo-Yong; Hayat, Majeed M; Godoy, Sebastián E; Bender, Steven C; Zarkesh-Ha, Payman; Krishna, Sanjay

2011-09-26

While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELL's bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELL's non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (>30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated. © 2011 Optical Society of America

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

Science.gov (United States)

Lu, Xiaoman; Zheng, Guang; Miller, Colton; Alvarado, Ernesto

2017-09-08

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% ( n = 35, p forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

A tool for NDVI time series extraction from wide-swath remotely sensed images

Science.gov (United States)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

The influence of visual and phonological features on the hemispheric processing of hierarchical Navon letters.

Science.gov (United States)

Aiello, Marilena; Merola, Sheila; Lasaponara, Stefano; Pinto, Mario; Tomaiuolo, Francesco; Doricchi, Fabrizio

2018-01-31

The possibility of allocating attentional resources to the "global" shape or to the "local" details of pictorial stimuli helps visual processing. Investigations with hierarchical Navon letters, that are large "global" letters made up of small "local" ones, consistently demonstrate a right hemisphere advantage for global processing and a left hemisphere advantage for local processing. Here we investigated how the visual and phonological features of the global and local components of Navon letters influence these hemispheric advantages. In a first study in healthy participants, we contrasted the hemispheric processing of hierarchical letters with global and local items competing for response selection, to the processing of hierarchical letters in which a letter, a false-letter conveying no phonological information or a geometrical shape presented at the unattended level did not compete for response selection. In a second study, we investigated the hemispheric processing of hierarchical stimuli in which global and local letters were both visually and phonologically congruent (e.g. large uppercase G made of smaller uppercase G), visually incongruent and phonologically congruent (e.g. large uppercase G made of small lowercase g) or visually incongruent and phonologically incongruent (e.g. large uppercase G made of small lowercase or uppercase M). In a third study, we administered the same tasks to a right brain damaged patient with a lesion involving pre-striate areas engaged by global processing. The results of the first two experiments showed that the global abilities of the left hemisphere are limited because of its strong susceptibility to interference from local letters even when these are irrelevant to the task. Phonological features played a crucial role in this interference because the interference was entirely maintained also when letters at the global and local level were presented in different uppercase vs. lowercase formats. In contrast, when local features

What sense can the sense-making perspective make for economics?

Science.gov (United States)

Cortés, Mauricio; Londoño, Sandra

2009-06-01

A landscape of interdisciplinary decision theories is sketched with a tentative place for the sense-making approach presented by Salvatore et al (2009). Uncertainty and ambiguity are highlighted as key concepts in both, economics and sense-making perspectives aligning possible and useful conceptual coincidences among post Keynesian economics (Shackle 1974; Davidson 2005) cultural psychology (Salvatore et al. 2009) and organization theory (March 1978, 1994; Weick 1995). Few ideas for the construction of possible research agenda aimed to build a complex model of decision making are suggested. Finally a brief reflection on the study of underground economy is presented. The challenge for the link between psychology and economics is to figure out a descriptive general model of the process of decision making, perhaps by combining weighted elements of the different ways in which human rationality emerges--calculation, rule following, sense-making--for explaining singular and specific decisions.

LAND COVER CHANGE DETECTION BASED ON GENETICALLY FEATURE AELECTION AND IMAGE ALGEBRA USING HYPERION HYPERSPECTRAL IMAGERY

Directory of Open Access Journals (Sweden)

S. T. Seydi

2015-12-01

Full Text Available The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral images. Hyperspectral images due to high spectral resolution created special placed in many of field. Nevertheless, selecting suitable and adequate features/bands from this data is crucial for any analysis and especially for the change detection algorithms. This research aims to automatically feature selection for detect land use changes are introduced. In this study, the optimal band images using hyperspectral sensor using Hyperion hyperspectral images by using genetic algorithms and Ratio bands, we select the optimal band. In addition, the results reveal the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 79% using multi temporal hyperspectral imagery.

Ethylene Gas Sensing Properties of Tin Oxide Nanowires Synthesized via CVD Method

Science.gov (United States)

Akhir, Maisara A. M.; Mohamed, Khairudin; Rezan, Sheikh A.; Arafat, M. M.; Haseeb, A. S. M. A.; Uda, M. N. A.; Nuradibah, M. A.

2018-03-01

This paper studies ethylene gas sensing performance of tin oxide (SnO2) nanowires (NWs) as sensing material synthesized using chemical vapor deposition (CVD) technique. The effect of NWs diameter on ethylene gas sensing characteristics were investigated. SnO2 NWs with diameter of ∼40 and ∼240 nm were deposited onto the alumina substrate with printed gold electrodes and tested for sensing characteristic toward ethylene gas. From the finding, the smallest diameter of NWs (42 nm) exhibit fast response and recovery time and higher sensitivity compared to largest diameter of NWs (∼240 nm). Both sensor show good reversibility features for ethylene gas sensor.

Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain

Directory of Open Access Journals (Sweden)

Xiangzeng Liu

2011-12-01

Full Text Available This paper proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT features in Steerable-Domain, which can deal with the large variations of scale, rotation and illumination between images. First, a new robust SIFT descriptor is presented, which is invariant under affine transformation. Then, an adaptive similarity measure is developed according to the robust SIFT descriptor and the adaptive normalized cross correlation of feature point’s neighborhood. Finally, the corresponding feature points can be determined by the adaptive similarity measure in Steerable-Domain of the two input images, and the final refined transformation parameters determined by using gradual optimization are adopted to achieve the registration results. Quantitative comparisons of our algorithm with the related methods show a significant improvement in the presence of large scale, rotation changes, and illumination contrast. The effectiveness of the proposed method is demonstrated by the experimental results.

Real-time skin feature identification in a time-sequential video stream

Science.gov (United States)

Kramberger, Iztok

2005-04-01

Skin color can be an important feature when tracking skin-colored objects. Particularly this is the case for computer-vision-based human-computer interfaces (HCI). Humans have a highly developed feeling of space and, therefore, it is reasonable to support this within intelligent HCI, where the importance of augmented reality can be foreseen. Joining human-like interaction techniques within multimodal HCI could, or will, gain a feature for modern mobile telecommunication devices. On the other hand, real-time processing plays an important role in achieving more natural and physically intuitive ways of human-machine interaction. The main scope of this work is the development of a stereoscopic computer-vision hardware-accelerated framework for real-time skin feature identification in the sense of a single-pass image segmentation process. The hardware-accelerated preprocessing stage is presented with the purpose of color and spatial filtering, where the skin color model within the hue-saturation-value (HSV) color space is given with a polyhedron of threshold values representing the basis of the filter model. An adaptive filter management unit is suggested to achieve better segmentation results. This enables the adoption of filter parameters to the current scene conditions in an adaptive way. Implementation of the suggested hardware structure is given at the level of filed programmable system level integrated circuit (FPSLIC) devices using an embedded microcontroller as their main feature. A stereoscopic clue is achieved using a time-sequential video stream, but this shows no difference for real-time processing requirements in terms of hardware complexity. The experimental results for the hardware-accelerated preprocessing stage are given by efficiency estimation of the presented hardware structure using a simple motion-detection algorithm based on a binary function.

Making sense with ePortfolios

DEFF Research Database (Denmark)

Poulsen, Bo Klindt; Dimsits, Miriam

2017-01-01

of the statements from the students concerning their understanding of ePortfolio processes are fundamentally questions of how to make sense of the ePortfolio tool, both in their professional and personal lives. This calls for a didactical stance with the teachers who use ePortfolios, based on empowerment through......This article discusses the question of making sense out of working with ePortfolio in adult education. The article presents the results of a small-scale survey among adults in continuing education who have worked with ePortfolio as the central didactic principle. It is argued that many...... meaning-making, in order for ePortfolios to make sense. It is suggested that two relevant didactic perspectives for making sense of the world can be found in theories of biographicity and metaphor work. Moreover, a strong didactic stance that supports sense-making must be based on a strong teacher role...

Making sense of food risk information: the case of organic food

NARCIS (Netherlands)

Hilverda, Marie-Susanne Dieudonnée

2017-01-01

When individuals encounter new information about food issues, such as organic food risks, they have to make sense of this information. Sense-making is the process by which individuals give meaning to the world around them. How the process of sense-making is influenced by the online social

Making sense of project management

DEFF Research Database (Denmark)

Kjærgaard, Annemette; Kautz, Karl; Nielsen, Peter Axel

2007-01-01

How can a software company make sense of project management when it becomes involved in software process improvement? In software development most research has an instrumental view of knowledge management thus neglecting what is probably the most important part of knowledge management namely making...... sense of practice by developers and project managers. Through an action case, we study the knowledge management processes in a Danish software company. We analyse the case through the lens of a theoretical framework. The theoretical framework focuses in particular on sensemaking, collective construed...... substantial insight which could not have been achieved through an instrumental perspective on knowledge management....

A dual-mode proximity sensor with integrated capacitive and temperature sensing units

International Nuclear Information System (INIS)

Qiu, Shihua; Huang, Ying; He, Xiaoyue; Sun, Zhiguang; Liu, Ping; Liu, Caixia

2015-01-01

The proximity sensor is one of the most important devices in the field of robot application. It can accurately provide the proximity information to assistant robots to interact with human beings and the external environment safely. In this paper, we have proposed and demonstrated a dual-mode proximity sensor composed of capacitive and resistive sensing units. We defined the capacitive type proximity sensor perceiving the proximity information as C-mode and the resistive type proximity sensor detecting as R-mode. Graphene nanoplatelets (GNPs) were chosen as the R-mode sensing material because of its high performance. The dual-mode proximity sensor presents the following features: (1) the sensing distance of the dual-mode proximity sensor has been enlarged compared with the single capacitive proximity sensor in the same geometrical pattern; (2) experiments have verified that the proposed sensor can sense the proximity information of different materials; (3) the proximity sensing capability of the sensor has been improved by two modes perceive collaboratively, for a plastic block at a temperature of 60 °C: the R-mode will perceive the proximity information when the distance d between the sensor and object is 6.0–17.0 mm and the C-mode will do that when their interval is 0–2.0 mm; additionally two modes will work together when the distance is 2.0–6.0 mm. These features indicate our transducer is very valuable in skin-like sensing applications. (paper)

Interplay of a multiplicity of security features

Science.gov (United States)

Moser, Jean-Frederic

2000-04-01

The great variety of existing security features can cause difficulty in choosing the adequate set for a particular security document. Considering the cost/benefit aspects with respect to the overall protection performance requested, a choice has to be made, for example, between either few features of high-security value or numerous many, less- resistant features. Another choice is the high versus low complexity of one particular features. A study aimed at providing a decision basis is a challenging matter because it involves human factors. Attention, perception, physiology of seeing and habits - to name some of the factors - are intangibles and are subject to evaluations involving normally a great number of experiments, if they are to be representative. The opportunity was given for a case study with the introduction of new Swiss banknotes between 1995 and 1998, because the new banknotes represent a novelty in the sense of the multiplicity and interplay of its optical security features. We have analyzed 652 articles which appeared in the press media concerning the new banknotes, seeking especially for peoples' reaction towards the security features.

Refractive index sensing and surface-enhanced Raman spectroscopy using silver–gold layered bimetallic plasmonic crystals

Directory of Open Access Journals (Sweden)

Somi Kang

2017-11-01

Full Text Available Herein we describe the fabrication and characterization of Ag and Au bimetallic plasmonic crystals as a system that exhibits improved capabilities for quantitative, bulk refractive index (RI sensing and surface-enhanced Raman spectroscopy (SERS as compared to monometallic plasmonic crystals of similar form. The sensing optics, which are bimetallic plasmonic crystals consisting of sequential nanoscale layers of Ag coated by Au, are chemically stable and useful for quantitative, multispectral, refractive index and spectroscopic chemical sensing. Compared to previously reported homometallic devices, the results presented herein illustrate improvements in performance that stem from the distinctive plasmonic features and strong localized electric fields produced by the Ag and Au layers, which are optimized in terms of metal thickness and geometric features. Finite-difference time-domain (FDTD simulations theoretically verify the nature of the multimode plasmonic resonances generated by the devices and allow for a better understanding of the enhancements in multispectral refractive index and SERS-based sensing. Taken together, these results demonstrate a robust and potentially useful new platform for chemical/spectroscopic sensing.

Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

Directory of Open Access Journals (Sweden)

Yi-Hung Liu

2014-01-01

Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

Proposal and Implementation of a Robust Sensing Method for DVB-T Signal

Science.gov (United States)

Song, Chunyi; Rahman, Mohammad Azizur; Harada, Hiroshi

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120dBm over an 8MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120dBm over an 8MHz channel with the sensing time equals to 0.1 second.

Study on a cooperative active sensing

International Nuclear Information System (INIS)

Tsukune, Hideo; Kita, Nobuyuki; Kuniyoshi, Yasuo; Hara, Isao; Matsui, Toshihiro; Matsushita, Toshio; Nagata, Kazuyuki; Nagakubo, Akihiko

1997-01-01

This study was made as a part of the research project ''Study on the evaluation of applicability of information collection·processing system to autonomous plant''. Previously, the basic techniques for 3-dimensional geometric modeling of working environments and for systemizing of information collection and processing have been developed. Thus, this study aimed to establish the techniques for a decentralized and cooperatively intellectualized system which allows to automatically perform patrol for inspection and maintenance in complicated plants. First, developments of cooperative active sensing for functioning in a multi-robot system and real-time active visual sensing were attempted and then the both were integrated to produce a prototype system for cooperative active sensing. The outcomes of the project in this year were as follows; a mobile platform with expanded functions, acoustic information processing, parallel EusLisp, a simulator for moving robot's behaviors, a visual monitoring system for moving objects, etc. All of these were usable for general purpose. (M.N.)

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

Science.gov (United States)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

FRACTAL DIMENSION OF URBAN EXPANSION BASED ON REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

IACOB I. CIPRIAN

2012-11-01

Full Text Available Fractal Dimension of Urban Expansion Based on Remote Sensing Images: In Cluj-Napoca city the process of urbanization has been accelerated during the years and implication of local authorities reflects a relevant planning policy. A good urban planning framework should take into account the society demands and also it should satisfy the natural conditions of local environment. The expansion of antropic areas it can be approached by implication of 5D variables (time as a sequence of stages, space: with x, y, z and magnitude of phenomena into the process, which will allow us to analyse and extract the roughness of city shape. Thus, to improve the decision factor we take a different approach in this paper, looking at geometry and scale composition. Using the remote sensing (RS and GIS techniques we manage to extract a sequence of built-up areas (from 1980 to 2012 and used the result as an input for modelling the spatialtemporal changes of urban expansion and fractal theory to analysed the geometric features. Taking the time as a parameter we can observe behaviour and changes in urban landscape, this condition have been known as self-organized – a condition which in first stage the system was without any turbulence (before the antropic factor and during the time tend to approach chaotic behaviour (entropy state without causing an disequilibrium in the main system.

Sensing our Environment: Remote sensing in a physics classroom

Science.gov (United States)

Isaacson, Sivan; Schüttler, Tobias; Cohen-Zada, Aviv L.; Blumberg, Dan G.; Girwidz, Raimund; Maman, Shimrit

2017-04-01

. The teams then processed their data and presented it to their foreign partners for evaluation in a video conference call. Alongside exciting insights about their respective environments and living conditions, the young scientists had daily access to live satellite sensors and remote sensing through the DLR_School_Lab in Germany and the Earth and Planetary Image Facility in Israel. This paper provides an overview regarding the project, the techniques used and the evaluation results following a pre-past-questionnaire design, and above all demonstrates the use of remote sensing as an application for physics teaching in a significant learning environment.

Multiscale and Multitemporal Urban Remote Sensing

Science.gov (United States)

Mesev, V.

2012-07-01

The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

A component-based system for agricultural drought monitoring by remote sensing.

Science.gov (United States)

Dong, Heng; Li, Jun; Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

Feature scale modeling for etching and deposition processes in semiconductor manufacturing

International Nuclear Information System (INIS)

Pyka, W.

2000-04-01

modeling of ballistic transport determined low-pressure processes, the equations for the calculation of local etching and deposition rates have been revised. New extensions like the full relation between angular and radial target emission characteristics and particle distributions resulting at different positions on the wafer have been added, and results from reactor scale simulations have been linked to the feature scale profile evolution. Moreover, a fitting model has been implemented, which reduces the number of parameters for particle distributions, scattering mechanisms, and angular dependent surface interactions. Concerning diffusion determined high-pressure CVD processes, a continuum transport and reaction model for the first time has been implemented in three dimensions. It comprises a flexible interface for the formulation of the involved process chemistry and derives the local deposition rate from a finite element diffusion calculation carried out on the three-dimensional mesh of the gas domain above the feature. For each time-step of the deposition simulation the mesh is automatically generated as counterpart to the surface of the three-dimensional structure evolving with time. The CVD model has also been coupled with equipment simulations. (author)

Specific features of NDT data and processing algorithms: new remedies to old ills

International Nuclear Information System (INIS)

Georgel, B.

1994-01-01

Non destructive testing data from in-service inspections have specific features that require the most sophisticated techniques of signal and image processing. Each step in the overall information extraction process must be optimized by using recent approaches such like data decomposition and modelization, compression, sensor fusion and knowledge based systems. This can be achieved by means of wavelet transform, inverse problems formulation, standard compression algorithms, combined detection and estimation, neural networks and expert systems. These techniques are briefly presented through a number of Electricite de France applications or through recent literature results. (author). 1 fig., 20 refs

Engaging All the Senses

DEFF Research Database (Denmark)

Schleicher, Marianne

2017-01-01

Based on an analysis of the process of making and inaugurating a Torah scroll, this article describes what is likely to trigger sensory responses in the participants in each phase of the process and the function of activating the five senses of touch, hearing, vision, smell, and taste. By disting...

Carbon for sensing devices

CERN Document Server

Tagliaferro, Alberto

2015-01-01

This book reveals why carbon is playing such an increasingly prominent role as a sensing material. The various steps that transform a raw material in a sensing device are thoroughly presented and critically discussed.  The authors deal with all aspects of carbon-based sensors, starting from the various hybridization and allotropes of carbon, with specific focus on micro and nanosized carbons (e.g., carbon nanotubes, graphene) and their growth processes. The discussion then moves to the role of functionalization and the different routes to achieve it. Finally, a number of sensing applications in various fields are presented, highlighting the connection with the basic properties of the various carbon allotropes.  Readers will benefit from this book’s bottom-up approach, which starts from the local bonding in carbon solids and ends with sensing applications, linking the local hybridization of carbon atoms and its modification by functionalization to specific device performance. This book is a must-have in th...

Sparse representations and compressive sensing for imaging and vision

CERN Document Server

Patel, Vishal M

2013-01-01

Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.

Displacement sensing system and method

Science.gov (United States)

VunKannon, Jr., Robert S

2006-08-08

A displacement sensing system and method addresses demanding requirements for high precision sensing of displacement of a shaft, for use typically in a linear electro-dynamic machine, having low failure rates over multi-year unattended operation in hostile environments. Applications include outer space travel by spacecraft having high-temperature, sealed environments without opportunity for servicing over many years of operation. The displacement sensing system uses a three coil sensor configuration, including a reference and sense coils, to provide a pair of ratio-metric signals, which are inputted into a synchronous comparison circuit, which is synchronously processed for a resultant displacement determination. The pair of ratio-metric signals are similarly affected by environmental conditions so that the comparison circuit is able to subtract or nullify environmental conditions that would otherwise cause changes in accuracy to occur.

Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.

Science.gov (United States)

Wilson, J Adam; Williams, Justin C

2009-01-01

The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

Cognitive networked sensing and big data

CERN Document Server

Qiu, Robert

2013-01-01

Wireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The author will discuss the integration of software defined radio implementation and testbed development. The book will also bridge new research results and contextual reviews. Also the author provides an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed

Features extraction from image based on processes of human vision; Ningen no shikaku shori tejun ni naratta gazo no tokucho chushutsu hoho

Energy Technology Data Exchange (ETDEWEB)

Nakajima, C.; Ishino, R. [Central Research Institute of Electric Power Industry, Tokyo (Japan)

1997-03-01

Described herein is a feature extraction processing method necessary for measurement of an object on a stationary image. It imitates the structures and functions of the visual area in the human brain to automatically extract features, such as edges, lines and apexes, from a stationary image or drawing. Information transmitted from the retina to the primary visual cortex area 1 (V1 area) is processed to extract feature candidates from brightness changes on the shading-treated image. The V1 area has the cells which react with long lines and the structure which controls the other cells. This structure is used to remove noise, where a portion which is not controlled is extracted from feature candidates, and is regarded as a line feature. This procedure, involving shading, is not suited for process of images containing an out-of-focus portion, but stably extracts features from clear images or drawings. 9 refs., 28 figs.

Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

Directory of Open Access Journals (Sweden)

Xicheng Tan

2015-10-01

Full Text Available Since the Open Geospatial Consortium (OGC proposed the geospatial Web Processing Service (WPS, standard OGC Web Service (OWS-based geospatial processing has become the major type of distributed geospatial application. However, improving the performance and sustainability of the distributed geospatial applications has become the dominant challenge for OWSs. This paper presents the construction of an elastic parallel OGC WPS service on a cloud-based cluster and the designs of a high-performance, cloud-based WPS service architecture, the scalability scheme of the cloud, and the algorithm of the elastic parallel geoprocessing. Experiments of the remote sensing data processing service demonstrate that our proposed method can provide a higher-performance WPS service that uses less computing resources. Our proposed method can also help institutions reduce hardware costs, raise the rate of hardware usage, and conserve energy, which is important in building green and sustainable geospatial services or applications.

Advanced Power Management of a Telehandler using Electronic Load Sensing

DEFF Research Database (Denmark)

Hansen, Rico Hjerm

2009-01-01

New possibilities within electronic control of mobile hydraulic systems are becoming available as hydraulic components are implemented with more electrical sensors and actuators. This paper presents how the traditional hydro-mechanical load sensing (HLS) control of a specific mobile hydraulic...... application, a telehandler, can be replaced with electronic control, i.e. Electronic Load Sensing (ELS). The motivation for ELS is the potentials of better dynamic performance and system utilization, along with reduced mechanical complexity by transferring features as pump pressure control, flow...

Developing status of satellite remote sensing and its application

International Nuclear Information System (INIS)

Zhang Wanliang; Liu Dechang

2005-01-01

This paper has discussed the latest development of satellite remote sensing in sensor resolutions, satellite motion models, load forms, data processing and its application. The authors consider that sensor resolutions of satellite remote sensing have increased largely. Valid integration of multisensors is a new idea and technology of satellite remote sensing in the 21st century, and post-remote sensing application technology is the important part of deeply applying remote sensing information and has great practical significance. (authors)

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

OpenAIRE

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history si...

Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation.

Science.gov (United States)

Jimeno Yepes, Antonio

2017-09-01

Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods, thus affect disambiguation performance. In this work, we have evaluated several types of features derived from the context of the ambiguous word and we have explored as well more global features derived from MEDLINE using word embeddings. Results show that word embeddings improve the performance of more traditional features and allow as well using recurrent neural network classifiers based on Long-Short Term Memory (LSTM) nodes. The combination of unigrams and word embeddings with an SVM sets a new state of the art performance with a macro accuracy of 95.97 in the MSH WSD data set. Copyright © 2017 Elsevier Inc. All rights reserved.

Contesting nonfiction: Fourth graders making sense of words and images in science information book discussions

Science.gov (United States)

Belfatti, Monica A.

Recently developed common core standards echo calls by educators for ensuring that upper elementary students become proficient readers of informational texts. Informational texts have been theorized as causing difficulty for students because they contain linguistic and visual features different from more familiar narrative genres (Lemke, 2004). It has been argued that learning to read informational texts, particularly those with science subject matter, requires making sense of words, images, and the relationships among them (Pappas, 2006). Yet, conspicuously absent in the research are empirical studies documenting ways students make use of textual resources to build textual and conceptual understandings during classroom literacy instruction. This 10-month practitioner research study was designed to investigate the ways a group of ethnically and linguistically diverse fourth graders in one metropolitan school made sense of science information books during dialogically organized literature discussions. In this nontraditional instructional context, I wondered whether and how young students might make use of science informational text features, both words and images, in the midst of collaborative textual and conceptual inquiry. Drawing on methods of constructivist grounded theory and classroom discourse analysis, I analyzed student and teacher talk in 25 discussions of earth and life science books. Digital voice recordings and transcriptions served as the main data sources for this study. I found that, without teacher prompts or mandates to do so, fourth graders raised a wide range of textual and conceptual inquiries about words, images, scientific figures, and phenomena. In addition, my analysis yielded a typology of ways students constructed relationships between words and images within and across page openings of the information books read for their sense-making endeavors. The diversity of constructed word-image relationships aided students in raising, exploring

Educational activities of remote sensing archaeology (Conference Presentation)

Science.gov (United States)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-10-01

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

Remote sensing image fusion

CERN Document Server

Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea

2015-01-01

A synthesis of more than ten years of experience, Remote Sensing Image Fusion covers methods specifically designed for remote sensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and panchromatic images, and fusion of data from heterogeneous sensors such as optical and synthetic aperture radar (SAR) images and integration of thermal and visible/near-infrared images. They also explore new trends of signal/image processing, such as