A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification

USDA-ARS?s Scientific Manuscript database

With the rapid development of small imaging sensors and unmanned aerial vehicles (UAVs), remote sensing is undergoing a revolution with greatly increased spatial and temporal resolutions. While more relevant detail becomes available, it is a challenge to analyze the large number of images to extract...

Efficient Wideband Spectrum Sensing with Maximal Spectral Efficiency for LEO Mobile Satellite Systems

PubMed Central

Li, Feilong; Li, Zhiqiang; Li, Guangxia; Dong, Feihong; Zhang, Wei

2017-01-01

The usable satellite spectrum is becoming scarce due to static spectrum allocation policies. Cognitive radio approaches have already demonstrated their potential towards spectral efficiency for providing more spectrum access opportunities to secondary user (SU) with sufficient protection to licensed primary user (PU). Hence, recent scientific literature has been focused on the tradeoff between spectrum reuse and PU protection within narrowband spectrum sensing (SS) in terrestrial wireless sensing networks. However, those narrowband SS techniques investigated in the context of terrestrial CR may not be applicable for detecting wideband satellite signals. In this paper, we mainly investigate the problem of joint designing sensing time and hard fusion scheme to maximize SU spectral efficiency in the scenario of low earth orbit (LEO) mobile satellite services based on wideband spectrum sensing. Compressed detection model is established to prove that there indeed exists one optimal sensing time achieving maximal spectral efficiency. Moreover, we propose novel wideband cooperative spectrum sensing (CSS) framework where each SU reporting duration can be utilized for its following SU sensing. The sensing performance benefits from the novel CSS framework because the equivalent sensing time is extended by making full use of reporting slot. Furthermore, in respect of time-varying channel, the spatiotemporal CSS (ST-CSS) is presented to attain space and time diversity gain simultaneously under hard decision fusion rule. Computer simulations show that the optimal sensing settings algorithm of joint optimization of sensing time, hard fusion rule and scheduling strategy achieves significant improvement in spectral efficiency. Additionally, the novel ST-CSS scheme performs much higher spectral efficiency than that of general CSS framework. PMID:28117712

Metal-organic frameworks as biosensors for luminescence-based detection and imaging

PubMed Central

Miller, Sophie E.; Teplensky, Michelle H.; Moghadam, Peyman Z.; Fairen-Jimenez, David

2016-01-01

Metal-organic frameworks (MOFs), formed by the self-assembly of metal centres or clusters and organic linkers, possess many key structural and chemical features that have enabled them to be used in sensing platforms for a variety of environmentally, chemically and biomedically relevant compounds. In particular, their high porosity, large surface area, tuneable chemical composition, high degree of crystallinity, and potential for post-synthetic modification for molecular recognition make MOFs promising candidates for biosensing applications. In this review, we separate our discussion of MOF biosensors into two categories: quantitative sensing, focusing specifically on luminescence-based sensors for the direct measurement of a specific analyte, and qualitative sensing, where we describe MOFs used for fluorescence microscopy and as magnetic resonance imaging contrast agents. We highlight several key publications in each of these areas, concluding that MOFs present an exciting, versatile new platform for biosensing applications and imaging, and we expect to see their usage grow as the field progresses. PMID:27499847

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

PubMed

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.

Active Thermal Extraction and Temperature Sensing of Near-field Thermal Radiation

DOE PAGES

Ding, D.; Kim, T.; Minnich, A. J.

2016-09-06

Recently, we proposed an active thermal extraction (ATX) scheme that enables thermally populated surface phonon polaritons to escape into the far-field. The concept is based on a fluorescence upconversion process that also occurs in laser cooling of solids (LCS). Here, we present a generalized analysis of our scheme using the theoretical framework for LCS. We show that both LCS and ATX can be described with the same mathematical formalism by replacing the electron-phonon coupling parameter in LCS with the electron-photon coupling parameter in ATX. Using this framework, we compare the ideal efficiency and power extracted for the two schemes andmore » examine the parasitic loss mechanisms. As a result, this work advances the application of ATX to manipulate near-field thermal radiation for applications such as temperature sensing and active radiative cooling.« less

Software-as-a-Service Vendors: Are They Ready to Successfully Deliver?

NASA Astrophysics Data System (ADS)

Heart, Tsipi; Tsur, Noa Shamir; Pliskin, Nava

Software as a service (SaaS) is a software sourcing option that allows organizations to remotely access enterprise applications, without having to install the application in-house. In this work we study vendors' readiness to deliver SaaS, a topic scarcely studied before. The innovation classification (evolutionary vs. revolutionary) and a new, Seven Fundamental Organizational Capabilities (FOCs) Model, are used as the theoretical frameworks. The Seven FOCs model suggests generic yet comprehensive set of capabilities that are required for organizational success: 1) sensing the stakeholders, 2) sensing the business environment, 3) sensing the knowledge environment, 4) process control, 5) process improvement, 6) new process development, and 7) appropriate resolution.

An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

PubMed Central

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-01-01

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

PubMed

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-02-11

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.

A compressive sensing based secure watermark detection and privacy preserving storage framework.

PubMed

Qia Wang; Wenjun Zeng; Jun Tian

2014-03-01

Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.

On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment.

PubMed

Cicirelli, Franco; Fortino, Giancarlo; Giordano, Andrea; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea

2016-09-01

A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.

Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning.

PubMed

Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D

2013-02-01

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

Photogrammetry and remote sensing education subjects

NASA Astrophysics Data System (ADS)

Lazaridou, Maria A.; Karagianni, Aikaterini Ch.

2017-09-01

The rapid technologic advances in the scientific areas of photogrammetry and remote sensing require continuous readjustments at the educational programs and their implementation. The teaching teamwork should deal with the challenge to offer the volume of the knowledge without preventing the understanding of principles and methods and also to introduce "new" knowledge (advances, trends) followed by evaluation and presentation of relevant applications. This is of particular importance for a Civil Engineering Faculty as this in Aristotle University of Thessaloniki, as the framework of Photogrammetry and Remote Sensing is closely connected with applications in the four educational Divisions of the Faculty. This paper refers to the above and includes subjects of organizing the courses in photogrammetry and remote sensing in the Civil Engineering Faculty of Aristotle University of Thessaloniki. A scheme of the general curriculum as well the teaching aims and methods are also presented.

Sparse aperture 3D passive image sensing and recognition

NASA Astrophysics Data System (ADS)

Daneshpanah, Mehdi

The way we perceive, capture, store, communicate and visualize the world has greatly changed in the past century Novel three dimensional (3D) imaging and display systems are being pursued both in academic and industrial settings. In many cases, these systems have revolutionized traditional approaches and/or enabled new technologies in other disciplines including medical imaging and diagnostics, industrial metrology, entertainment, robotics as well as defense and security. In this dissertation, we focus on novel aspects of sparse aperture multi-view imaging systems and their application in quantum-limited object recognition in two separate parts. In the first part, two concepts are proposed. First a solution is presented that involves a generalized framework for 3D imaging using randomly distributed sparse apertures. Second, a method is suggested to extract the profile of objects in the scene through statistical properties of the reconstructed light field. In both cases, experimental results are presented that demonstrate the feasibility of the techniques. In the second part, the application of 3D imaging systems in sensing and recognition of objects is addressed. In particular, we focus on the scenario in which only 10s of photons reach the sensor from the object of interest, as opposed to hundreds of billions of photons in normal imaging conditions. At this level, the quantum limited behavior of light will dominate and traditional object recognition practices may fail. We suggest a likelihood based object recognition framework that incorporates the physics of sensing at quantum-limited conditions. Sensor dark noise has been modeled and taken into account. This framework is applied to 3D sensing of thermal objects using visible spectrum detectors. Thermal objects as cold as 250K are shown to provide enough signature photons to be sensed and recognized within background and dark noise with mature, visible band, image forming optics and detector arrays. The results suggest that one might not need to venture into exotic and expensive detector arrays and associated optics for sensing room-temperature thermal objects in complete darkness.

Enhanced vapour sensing using silicon nanowire devices coated with Pt nanoparticle functionalized porous organic frameworks.

PubMed

Cao, Anping; Shan, Meixia; Paltrinieri, Laura; Evers, Wiel H; Chu, Liangyong; Poltorak, Lukasz; Klootwijk, Johan H; Seoane, Beatriz; Gascon, Jorge; Sudhölter, Ernst J R; de Smet, Louis C P M

2018-04-19

Recently various porous organic frameworks (POFs, crystalline or amorphous materials) have been discovered, and used for a wide range of applications, including molecular separations and catalysis. Silicon nanowires (SiNWs) have been extensively studied for diverse applications, including as transistors, solar cells, lithium ion batteries and sensors. Here we demonstrate the functionalization of SiNW surfaces with POFs and explore its effect on the electrical sensing properties of SiNW-based devices. The surface modification by POFs was easily achieved by polycondensation on amine-modified SiNWs. Platinum nanoparticles were formed in these POFs by impregnation with chloroplatinic acid followed by chemical reduction. The final hybrid system showed highly enhanced sensitivity for methanol vapour detection. We envisage that the integration of SiNWs with POF selector layers, loaded with different metal nanoparticles will open up new avenues, not only in chemical and biosensing, but also in separations and catalysis.

Complete Transmetalation in a Metal-Organic Framework by Metal Ion Metathesis in a Single Crystal for Selective Sensing of Phosphate Ions in Aqueous Media.

PubMed

Asha, K S; Bhattacharjee, Rameswar; Mandal, Sukhendu

2016-09-12

A complete transmetalation has been achieved on a barium metal-organic framework (MOF), leading to the isolation of a new Tb-MOF in a single-crystal (SC) to single-crystal (SC) fashion. It leads to the transformation of an anionic framework with cations in the pore to one that is neutral. The mechanistic studies proposed a core-shell metal exchange through dissociation of metal-ligand bonds. This Tb-MOF exhibits enhanced photoluminescence and acts as a selective sensor for phosphate anion in aqueous medium. Thus, this work not only provides a method to functionalize a MOF that can have potential application in sensing but also elucidates the formation mechanism of the resulting MOF. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

User-centric incentive design for participatory mobile phone sensing

NASA Astrophysics Data System (ADS)

Gao, Wei; Lu, Haoyang

2014-05-01

Mobile phone sensing is a critical underpinning of pervasive mobile computing, and is one of the key factors for improving people's quality of life in modern society via collective utilization of the on-board sensing capabilities of people's smartphones. The increasing demands for sensing services and ambient awareness in mobile environments highlight the necessity of active participation of individual mobile users in sensing tasks. User incentives for such participation have been continuously offered from an application-centric perspective, i.e., as payments from the sensing server, to compensate users' sensing costs. These payments, however, are manipulated to maximize the benefits of the sensing server, ignoring the runtime flexibility and benefits of participating users. This paper presents a novel framework of user-centric incentive design, and develops a universal sensing platform which translates heterogenous sensing tasks to a generic sensing plan specifying the task-independent requirements of sensing performance. We use this sensing plan as input to reduce three categories of sensing costs, which together cover the possible sources hindering users' participation in sensing.

Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application

NASA Astrophysics Data System (ADS)

Ouyang, Bing; Hou, Weilin; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.; Gong, Cuiling

2017-07-01

The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.

LORAKS Makes Better SENSE: Phase-Constrained Partial Fourier SENSE Reconstruction without Phase Calibration

PubMed Central

Kim, Tae Hyung; Setsompop, Kawin; Haldar, Justin P.

2016-01-01

Purpose Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information. Theory and Methods The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly-accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely-used calibrationless uniformly-undersampled trajectories. Results Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches. Conclusion The SENSE-LORAKS framework provides promising new opportunities for highly-accelerated MRI. PMID:27037836

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges.

PubMed

Chen, Yuanfang; Lee, Gyu Myoung; Shu, Lei; Crespi, Noel

2016-02-06

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges

PubMed Central

Chen, Yuanfang; Lee, Gyu Myoung; Shu, Lei; Crespi, Noel

2016-01-01

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well. PMID:26861345

Chemical principles underpinning the performance of the metal-organic framework HKUST-1.

PubMed

Hendon, Christopher H; Walsh, Aron

2015-07-15

A common feature of multi-functional metal-organic frameworks is a metal dimer in the form of a paddlewheel, as found in the structure of Cu 3 ( btc ) 2 (HKUST-1). The HKUST-1 framework demonstrates exceptional gas storage, sensing and separation, catalytic activity and, in recent studies, unprecedented ionic and electrical conductivity. These results are a promising step towards the real-world application of metal-organic materials. In this perspective, we discuss progress in the understanding of the electronic, magnetic and physical properties of HKUST-1, representative of the larger family of Cu···Cu containing metal-organic frameworks. We highlight the chemical interactions that give rise to its favourable properties, and which make this material well suited to a range of technological applications. From this analysis, we postulate key design principles for tailoring novel high-performance hybrid frameworks.

Chemical principles underpinning the performance of the metal–organic framework HKUST-1

PubMed Central

Hendon, Christopher H.

2015-01-01

A common feature of multi-functional metal–organic frameworks is a metal dimer in the form of a paddlewheel, as found in the structure of Cu3(btc)2 (HKUST-1). The HKUST-1 framework demonstrates exceptional gas storage, sensing and separation, catalytic activity and, in recent studies, unprecedented ionic and electrical conductivity. These results are a promising step towards the real-world application of metal–organic materials. In this perspective, we discuss progress in the understanding of the electronic, magnetic and physical properties of HKUST-1, representative of the larger family of Cu···Cu containing metal–organic frameworks. We highlight the chemical interactions that give rise to its favourable properties, and which make this material well suited to a range of technological applications. From this analysis, we postulate key design principles for tailoring novel high-performance hybrid frameworks. PMID:28706713

A Framework for Sharing and Integrating Remote Sensing and GIS Models Based on Web Service

PubMed Central

Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin

2014-01-01

Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a “black box” and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users. PMID:24901016

A framework for sharing and integrating remote sensing and GIS models based on Web service.

PubMed

Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin

2014-01-01

Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a "black box" and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users.

Information surfing with the JHU/APL coherent imager

NASA Astrophysics Data System (ADS)

Ratto, Christopher R.; Shipley, Kara R.; Beagley, Nathaniel; Wolfe, Kevin C.

2015-05-01

The ability to perform remote forensics in situ is an important application of autonomous undersea vehicles (AUVs). Forensics objectives may include remediation of mines and/or unexploded ordnance, as well as monitoring of seafloor infrastructure. At JHU/APL, digital holography is being explored for the potential application to underwater imaging and integration with an AUV. In previous work, a feature-based approach was developed for processing the holographic imagery and performing object recognition. In this work, the results of the image processing method were incorporated into a Bayesian framework for autonomous path planning referred to as information surfing. The framework was derived assuming that the location of the object of interest is known a priori, but the type of object and its pose are unknown. The path-planning algorithm adaptively modifies the trajectory of the sensing platform based on historical performance of object and pose classification. The algorithm is called information surfing because the direction of motion is governed by the local information gradient. Simulation experiments were carried out using holographic imagery collected from submerged objects. The autonomous sensing algorithm was compared to a deterministic sensing CONOPS, and demonstrated improved accuracy and faster convergence in several cases.

Active Wireless System for Structural Health Monitoring Applications.

PubMed

Perera, Ricardo; Pérez, Alberto; García-Diéguez, Marta; Zapico-Valle, José Luis

2017-12-11

The use of wireless sensors in Structural Health Monitoring (SHM) has increased significantly in the last years. Piezoelectric-based lead zirconium titanate (PZT) sensors have been on the rise in SHM due to their superior sensing abilities. They are applicable in different technologies such as electromechanical impedance (EMI)-based SHM. This work develops a flexible wireless smart sensor (WSS) framework based on the EMI method using active sensors for full-scale and autonomous SHM. In contrast to passive sensors, the self-sensing properties of the PZTs allow interrogating with or exciting a structure when desired. The system integrates the necessary software and hardware within a service-oriented architecture approach able to provide in a modular way the services suitable to satisfy the key requirements of a WSS. The framework developed in this work has been validated on different experimental applications. Initially, the reliability of the EMI method when carried out with the proposed wireless sensor system is evaluated by comparison with the wireless counterpart. Afterwards, the performance of the system is evaluated in terms of software stability and reliability of functioning.

Preparation of Iridescent 2D Photonic Crystals by Using a Mussel-Inspired Spatial Patterning of ZIF-8 with Potential Applications in Optical Switch and Chemical Sensor.

PubMed

Razmjou, Amir; Asadnia, Mohsen; Ghaebi, Omid; Yang, Hao-Cheng; Ebrahimi Warkiani, Majid; Hou, Jingwei; Chen, Vicki

2017-11-01

In this work, spatial patterning of a thin, dense, zeolitic imidazolate framework (ZIF-8) pattern was generated using photolithography and nanoscale (60 nm) dopamine coating. A bioinspired, unique, reversible, two-color iridescent pattern can be easily obtained for potential applications in sensing and photonics.

Process theology's relevance for older survivors of domestic violence.

PubMed

Bowland, Sharon

2011-01-01

Pastoral work with survivors of domestic violence may reveal theological struggles. Understandings of scripture that reinforce a sense of powerlessness and alienation from God may contribute to an impaired relationship and limit resources for healing. One framework for re-imaging a relationship with God is process theology. This framework was applied to a case study for one survivor. The application resulted in a line of inquiry that may assist survivors in their healing process.

Chemiresistive Sensor Arrays from Conductive 2D Metal–Organic Frameworks

DOE PAGES

Campbell, Michael G.; Liu, Sophie F.; Swager, Timothy M.; ...

2015-10-11

Applications of porous metal–organic frameworks (MOFs) in electronic devices are rare, owing in large part to a lack of MOFs that display electrical conductivity. Here, we describe the use of conductive two-dimensional (2D) MOFs as a new class of materials for chemiresistive sensing of volatile organic compounds (VOCs). We demonstrate that a family of structurally analogous 2D MOFs can be used to construct a cross-reactive sensor array that allows for clear discrimination between different categories of VOCs. Lastly, experimental data show that multiple sensing mechanisms are operative with high degrees of orthogonality, establishing that the 2D MOFs used here aremore » mechanistically unique and offer advantages relative to other known chemiresistor materials.« less

A Semantic Lexicon-Based Approach for Sense Disambiguation and Its WWW Application

NASA Astrophysics Data System (ADS)

di Lecce, Vincenzo; Calabrese, Marco; Soldo, Domenico

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts.

LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration.

PubMed

Kim, Tae Hyung; Setsompop, Kawin; Haldar, Justin P

2017-03-01

Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information. The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely used calibrationless uniformly undersampled trajectories. Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches. The SENSE-LORAKS framework provides promising new opportunities for highly accelerated MRI. Magn Reson Med 77:1021-1035, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Visual tracking strategies for intelligent vehicle highway systems

NASA Astrophysics Data System (ADS)

Smith, Christopher E.; Papanikolopoulos, Nikolaos P.; Brandt, Scott A.; Richards, Charles

1995-01-01

The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations. Of the sensors available, vision sensors provide information that is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system. In this paper we present robust techniques for intelligent vehicle-highway applications where computer vision plays a crucial role. In particular, we demonstrate that the controlled active vision framework can be utilized to provide a visual sensing modality to a traffic advisory system in order to increase the overall safety margin in a variety of common traffic situations. We have selected two application examples, vehicle tracking and pedestrian tracking, to demonstrate that the framework can provide precisely the type of information required to effectively manage the given situation.

Nanomaterials derived from metal-organic frameworks

NASA Astrophysics Data System (ADS)

Dang, Song; Zhu, Qi-Long; Xu, Qiang

2018-01-01

The thermal transformation of metal-organic frameworks (MOFs) generates a variety of nanostructured materials, including carbon-based materials, metal oxides, metal chalcogenides, metal phosphides and metal carbides. These derivatives of MOFs have characteristics such as high surface areas, permanent porosities and controllable functionalities that enable their good performance in sensing, gas storage, catalysis and energy-related applications. Although progress has been made to tune the morphologies of MOF-derived structures at the nanometre scale, it remains crucial to further our knowledge of the relationship between morphology and performance. In this Review, we summarize the synthetic strategies and optimized methods that enable control over the size, morphology, composition and structure of the derived nanomaterials. In addition, we compare the performance of materials prepared by the MOF-templated strategy and other synthetic methods. Our aim is to reveal the relationship between the morphology and the physico-chemical properties of MOF-derived nanostructures to optimize their performance for applications such as sensing, catalysis, and energy storage and conversion.

Enzyme-MOF (metal-organic framework) composites.

PubMed

Lian, Xizhen; Fang, Yu; Joseph, Elizabeth; Wang, Qi; Li, Jialuo; Banerjee, Sayan; Lollar, Christina; Wang, Xuan; Zhou, Hong-Cai

2017-06-06

The ex vivo application of enzymes in various processes, especially via enzyme immobilization techniques, has been extensively studied in recent years in order to enhance the recyclability of enzymes, to minimize enzyme contamination in the product, and to explore novel horizons for enzymes in biomedical applications. Possessing remarkable amenability in structural design of the frameworks as well as almost unparalelled surface tunability, Metal-Organic Frameworks (MOFs) have been gaining popularity as candidates for enzyme immobilization platforms. Many MOF-enzyme composites have achieved unprecedented results, far outperforming free enzymes in many aspects. This review summarizes recent developments of MOF-enzyme composites with special emphasis on preparative techniques and the synergistic effects of enzymes and MOFs. The applications of MOF-enzyme composites, primarily in transferation, catalysis and sensing, are presented as well. The enhancement of enzymatic activity of the composites over free enzymes in biologically incompatible conditions is emphasized in many cases.

Towards an IMU Evaluation Framework for Human Body Tracking.

PubMed

Venek, Verena; Kremser, Wolfgang; Schneider, Cornelia

2018-01-01

Existing full-body tracking systems, which use Inertial Measurement Units (IMUs) as sensing unit, require expert knowledge for setup and data collection. Thus, the daily application for human body tracking is difficult. In particular, in the field of active and assisted living (AAL), tracking human movements would enable novel insights not only into the quantity but also into the quality of human movement, for example by monitoring functional training. While the current market offers a wide range of products with vastly different properties, literature lacks guidelines for choosing IMUs for body tracking applications. Therefore, this paper introduces developments towards an IMU evaluation framework for human body tracking which compares IMUs against five requirement areas that consider device features and data quality. The data quality is assessed by conducting a static and a dynamic error analysis. In a first application to four IMUs of different component consumption, the IMU evaluation framework convinced as promising tool for IMU selection.

Semi-autonomous remote sensing time series generation tool

NASA Astrophysics Data System (ADS)

Babu, Dinesh Kumar; Kaufmann, Christof; Schmidt, Marco; Dhams, Thorsten; Conrad, Christopher

2017-10-01

High spatial and temporal resolution data is vital for crop monitoring and phenology change detection. Due to the lack of satellite architecture and frequent cloud cover issues, availability of daily high spatial data is still far from reality. Remote sensing time series generation of high spatial and temporal data by data fusion seems to be a practical alternative. However, it is not an easy process, since it involves multiple steps and also requires multiple tools. In this paper, a framework of Geo Information System (GIS) based tool is presented for semi-autonomous time series generation. This tool will eliminate the difficulties by automating all the steps and enable the users to generate synthetic time series data with ease. Firstly, all the steps required for the time series generation process are identified and grouped into blocks based on their functionalities. Later two main frameworks are created, one to perform all the pre-processing steps on various satellite data and the other one to perform data fusion to generate time series. The two frameworks can be used individually to perform specific tasks or they could be combined to perform both the processes in one go. This tool can handle most of the known geo data formats currently available which makes it a generic tool for time series generation of various remote sensing satellite data. This tool is developed as a common platform with good interface which provides lot of functionalities to enable further development of more remote sensing applications. A detailed description on the capabilities and the advantages of the frameworks are given in this paper.

Teaming to Teach the Information Problem-Solving Process.

ERIC Educational Resources Information Center

Sine, Lynn; Murphy, Becky

1992-01-01

Explains a problem-solving format developed by a school media specialist and first grade teacher that used the framework of Eisenberg and Berkowitz's "Big Six Skills" for library media programs. The application of the format to a science unit on the senses is described. (two references) (MES)

New Information Dispersal Techniques for Trustworthy Computing

ERIC Educational Resources Information Center

Parakh, Abhishek

2011-01-01

Information dispersal algorithms (IDA) are used for distributed data storage because they simultaneously provide security, reliability and space efficiency, constituting a trustworthy computing framework for many critical applications, such as cloud computing, in the information society. In the most general sense, this is achieved by dividing data…

Contact sensing from force measurements

NASA Technical Reports Server (NTRS)

Bicchi, Antonio; Salisbury, J. K.; Brock, David L.

1993-01-01

This article addresses contact sensing (i.e., the problem of resolving the location of a contact, the force at the interface, and the moment about the contact normals). Called 'intrinsic' contact sensing for the use of internal force and torque measurements, this method allows for practical devices that provide simple, relevant contact information in practical robotic applications. Such sensors have been used in conjunction with robot hands to identify objects, determine surface friction, detect slip, augment grasp stability, measure object mass, probe surfaces, and control collision and for a variety of other useful tasks. This article describes the theoretical basis for their operation and provides a framework for future device design.

Developing and Validating Practical Eye Metrics for the Sense-Assess-Augment Framework

DTIC Science & Technology

2015-09-29

Sense-Assess-Augment ( SAA ) Framework. To better close the loop between the human and machine teammates AFRL’s Human Performance Wing and Human...Sense-Assess-Augment ( SAA ) framework, which is designed to sense a suite of physiological signals from the operator, use these signals to assess the...to use psychophysiological measures to improve human-machine teamwork (such as Biocybernetics or Augmented Cognition) the AFRL- SAA research program

Rapid, sensitive, and selective fluorescent DNA detection using iron-based metal-organic framework nanorods: Synergies of the metal center and organic linker.

PubMed

Tian, Jingqi; Liu, Qian; Shi, Jinle; Hu, Jianming; Asiri, Abdullah M; Sun, Xuping; He, Yuquan

2015-09-15

Considerable recent attention has been paid to homogeneous fluorescent DNA detection with the use of nanostructures as a universal "quencher", but it still remains a great challenge to develop such nanosensor with the benefits of low cost, high speed, sensitivity, and selectivity. In this work, we report the use of iron-based metal-organic framework nanorods as a high-efficient sensing platform for fluorescent DNA detection. It only takes about 4 min to complete the whole "mix-and-detect" process with a low detection limit of 10 pM and a strong discrimination of single point mutation. Control experiments reveal the remarkable sensing behavior is a consequence of the synergies of the metal center and organic linker. This work elucidates how composition control of nanostructures can significantly impact their sensing properties, enabling new opportunities for the rational design of functional materials for analytical applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Magnetic porous carbon nanocomposites derived from metal-organic frameworks as a sensing platform for DNA fluorescent detection.

PubMed

Tan, Hongliang; Tang, Gonge; Wang, Zhixiong; Li, Qian; Gao, Jie; Wu, Shimeng

2016-10-12

Metal-organic frameworks (MOFs) have emerged as very fascinating functional materials due to their tunable nature and diverse applications. In this work, we prepared a magnetic porous carbon (MPC) nanocomposite by employing iron-containing MOFs (MIL-88A) as precursors through a one-pot thermolysis method. It was found that the MPC can absorb selectively single-stranded DNA (ssDNA) probe to form MPC/ssDNA complex and subsequently quench the labelled fluorescent dye of the ssDNA probe, which is resulted from the synergetic effect of magnetic nanoparticles and carbon matrix. Upon the addition of complementary target DNA, however, the absorbed ssDNA probe could be released from MPC surface by forming double-stranded DNA with target DNA, and accompanied by the recovery of the fluorescence of ssDNA probe. Based on these findings, a sensing platform with low background signal for DNA fluorescent detection was developed. The proposed sensing platform exhibits high sensitivity with detection limit of 1 nM and excellent selectivity to specific target DNA, even single-base mismatched nucleotide can be distinguished. We envision that the presented study would provide a new perspective on the potential applications of MOF-derived nanocomposites in biomedical fields. Copyright © 2016 Elsevier B.V. All rights reserved.

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Quattrochi, Dale A.; Ridd, Merrill K.; Lam, Nina S.-N.; Walsh, Stephen J.

1991-01-01

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

Pervasive Sound Sensing: A Weakly Supervised Training Approach.

PubMed

Kelly, Daniel; Caulfield, Brian

2016-01-01

Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.

Technical design and system implementation of region-line primitive association framework

NASA Astrophysics Data System (ADS)

Wang, Min; Xing, Jinjin; Wang, Jie; Lv, Guonian

2017-08-01

Apart from regions, image edge lines are an important information source, and they deserve more attention in object-based image analysis (OBIA) than they currently receive. In the region-line primitive association framework (RLPAF), we promote straight-edge lines as line primitives to achieve powerful OBIAs. Along with regions, straight lines become basic units for subsequent extraction and analysis of OBIA features. This study develops a new software system called remote-sensing knowledge finder (RSFinder) to implement RLPAF for engineering application purposes. This paper introduces the extended technical framework, a comprehensively designed feature set, key technology, and software implementation. To our knowledge, RSFinder is the world's first OBIA system based on two types of primitives, namely, regions and lines. It is fundamentally different from other well-known region-only-based OBIA systems, such as eCogntion and ENVI feature extraction module. This paper has important reference values for the development of similarly structured OBIA systems and line-involved extraction algorithms of remote sensing information.

Software to Facilitate Remote Sensing Data Access for Disease Early Warning Systems

PubMed Central

Liu, Yi; Hu, Jiameng; Snell-Feikema, Isaiah; VanBemmel, Michael S.; Lamsal, Aashis; Wimberly, Michael C.

2015-01-01

Satellite remote sensing produces an abundance of environmental data that can be used in the study of human health. To support the development of early warning systems for mosquito-borne diseases, we developed an open-source, client based software application to enable the Epidemiological Applications of Spatial Technologies (EASTWeb). Two major design decisions were full automation of the discovery, retrieval and processing of remote sensing data from multiple sources, and making the system easily modifiable in response to changes in data availability and user needs. Key innovations that helped to achieve these goals were the implementation of a software framework for data downloading and the design of a scheduler that tracks the complex dependencies among multiple data processing tasks and makes the system resilient to external errors. EASTWeb has been successfully applied to support forecasting of West Nile virus outbreaks in the United States and malaria epidemics in the Ethiopian highlands. PMID:26644779

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE.

PubMed

Adibi, Sasan

2014-01-01

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

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

Yu, Zongchao; Wang, Fengqin, E-mail: wangfengqin@tjpu.edu.cn; Lin, Xiangyi

Metal-organic frameworks (MOFs) are porous crystalline materials with high potential for applications in fluorescence sensors. In this work, two solvent-induced Zn(II)–based metal-organic frameworks, Zn{sub 3}L{sub 3}(DMF){sub 2} (1) and Zn{sub 3}L{sub 3}(DMA){sub 2}(H{sub 2}O){sub 3} (2) (L=4,4′-stilbenedicarboxylic acid), were investigated as selective sensing materials for detection of nitroaromatic compounds and metal ions. The sensing experiments show that 1 and 2 both exhibit selective fluorescence quenching toward nitroaniline with a low detection limit. In addition, 1 exhibits high selectivity for detection of Fe{sup 3+} and Al{sup 3+} by significant fluorescence quenching or enhancement effect. While for 2, it only exhibits significantmore » fluorescence quenching effect for Fe{sup 3+}. The results indicate that 1 and 2 are both promising fluorescence sensors for detecting and recognizing nitroaniline and metal ions with high sensitivity and selectivity. - Graphical abstract: Two MOFs have been selected as the fluorescence sensing materials for selectively sensing mitroaromatic compounds and metal ions. The high selectivity makes them promising fluorescence sensors for detecting and recognizing nitroaniline and Fe{sup 3+} or Al{sup 3+}.« less

The shifting sands of self: a framework for the experience of self in addiction.

PubMed

Gray, Mary Tod

2005-04-01

The self is a common yet unclear theme in addiction studies. William James's model of self provides a framework to explore the experience of self. His model details the subjective and objective constituents, the sense of self-continuity through time, and the ephemeral and plural nature of the changing self. This exploration yields insights into the self that can be usefully applied to subjective experiences with psychoactive drugs of addiction. Results of this application add depth to the common understanding of self in addiction, acknowledge the importance of feelings and choice in the sense of self created in addiction experiences, and affirm the values salient to these interior experiences in addiction. These results suggest meaning derived from those values, and provide important background knowledge for the nurse interacting with these clients.

Remote sensing data assimilation for a prognostic phenology model

Treesearch

R. Stockli; T. Rutishauser; D. Dragoni; J. O' Keefe; P. E. Thornton; M. Jolly; L. Lu; A. S. Denning

2008-01-01

Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active...

Theoretical Implementations of Various Mobile Applications Used in English Language Learning

ERIC Educational Resources Information Center

Small, Melissa

2014-01-01

This review of the theoretical framework for Mastery Learning Theory and Sense of Community theories is provided in conjunction with a review of the literature for mobile technology in relation to language learning. Although empirical research is minimal for mobile phone technology as an aid for language learning, the empirical research that…

A fluorescent paramagnetic Mn metal–organic framework based on semi-rigid pyrene tetra­carboxylic acid: sensing of solvent polarity and explosive nitroaromatics

PubMed Central

Bajpai, Alankriti; Mukhopadhyay, Arindam; Krishna, Manchugondanahalli Shivakumar; Govardhan, Savitha; Moorthy, Jarugu Narasimha

2015-01-01

An Mn metal–organic framework (Mn-MOF), Mn-L, based on a pyrene-tetraacid linker (H4 L), displays a respectable fluorescence quantum yield of 8.3% in spite of the presence of the paramagnetic metal ions, due presumably to fixation of the metal ions in geometries that do not allow complete energy/charge-transfer quenching. Remarkably, the porous Mn-L MOF with ∼25% solvent-accessible volume exhibits a heretofore unprecedented solvent-dependent fluorescence emission maximum, permitting its use as a probe of solvent polarity; the emission maxima in different solvents correlate excellently with Reichardt’s solvent polarity parameter (E T N). Further, the applicability of Mn-L to the sensing of nitroaromatics via fluorescence quenching is demonstrated; the detection limit for TNT is shown to be 125 p.p.m. The results bring out the fact that MOFs based on paramagnetic metal ions can indeed find application when the quenching mechanisms are attenuated by certain geometries of the organic linkers of the MOF. PMID:26306197

A fluorescent paramagnetic Mn metal-organic framework based on semi-rigid pyrene tetra-carboxylic acid: sensing of solvent polarity and explosive nitroaromatics.

PubMed

Bajpai, Alankriti; Mukhopadhyay, Arindam; Krishna, Manchugondanahalli Shivakumar; Govardhan, Savitha; Moorthy, Jarugu Narasimha

2015-09-01

An Mn metal-organic framework (Mn-MOF), Mn-L, based on a pyrene-tetraacid linker (H4 L), displays a respectable fluorescence quantum yield of 8.3% in spite of the presence of the paramagnetic metal ions, due presumably to fixation of the metal ions in geometries that do not allow complete energy/charge-transfer quenching. Remarkably, the porous Mn-L MOF with ∼25% solvent-accessible volume exhibits a heretofore unprecedented solvent-dependent fluorescence emission maximum, permitting its use as a probe of solvent polarity; the emission maxima in different solvents correlate excellently with Reichardt's solvent polarity parameter (E T (N)). Further, the applicability of Mn-L to the sensing of nitroaromatics via fluorescence quenching is demonstrated; the detection limit for TNT is shown to be 125 p.p.m. The results bring out the fact that MOFs based on paramagnetic metal ions can indeed find application when the quenching mechanisms are attenuated by certain geometries of the organic linkers of the MOF.

A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhao, Shaoshuai; Ni, Chen; Cao, Jing; Li, Zhengqiang; Chen, Xingfeng; Ma, Yan; Yang, Leiku; Hou, Weizhen; Qie, Lili; Ge, Bangyu; Liu, Li; Xing, Jin

2018-03-01

The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework's flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.

Simultaneous determination of environmental estrogens: Diethylstilbestrol and estradiol using Cu-BTC frameworks-sensitized electrode.

PubMed

Ji, Liudi; Wang, Yanying; Wu, Kangbing; Zhang, Weikang

2016-10-01

It is quite important to monitor environmental estrogens in a rapid, sensitive, simple and cost-effective manner due to their wide existence and high toxicity. Using 1,3,5-Benzenetricarboxylic acid (H3BTC) as the ligand and copper ions as the center, Cu-BTC frameworks with surface area of 654.6m(2)/g were prepared, and then used to construct a novel electrochemical sensing platform for diethylstilbestrol (DES) and estradiol (E2). On the surface of Cu-BTC frameworks, two oxidation waves at 0.26V and 0.45V are observed for DES and E2, and the oxidation signals are improved greatly. The prepared Cu-BTC frameworks not only enhance the accumulation efficiency of DES and E2, but also improve their electron transfer ability. The influences of pH value, modification amount of Cu-BTC and accumulation time were examined. As a result, a highly-sensitive, rapid and convenient electrochemical method was developed for the simultaneous determination of DES and E2, with detection limit of 2.7nM and 1.1nM. The practical applications manifest this new sensing system is accurate and feasible. Copyright © 2016 Elsevier B.V. All rights reserved.

Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

NASA Astrophysics Data System (ADS)

Shahtahmassebi, Amir Reza; Song, Jie; Zheng, Qing; Blackburn, George Alan; Wang, Ke; Huang, Ling Yan; Pan, Yi; Moore, Nathan; Shahtahmassebi, Golnaz; Sadrabadi Haghighi, Reza; Deng, Jing Song

2016-04-01

A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms.

BioNet Digital Communications Framework

NASA Technical Reports Server (NTRS)

Gifford, Kevin; Kuzminsky, Sebastian; Williams, Shea

2010-01-01

BioNet v2 is a peer-to-peer middleware that enables digital communication devices to talk to each other. It provides a software development framework, standardized application, network-transparent device integration services, a flexible messaging model, and network communications for distributed applications. BioNet is an implementation of the Constellation Program Command, Control, Communications and Information (C3I) Interoperability specification, given in CxP 70022-01. The system architecture provides the necessary infrastructure for the integration of heterogeneous wired and wireless sensing and control devices into a unified data system with a standardized application interface, providing plug-and-play operation for hardware and software systems. BioNet v2 features a naming schema for mobility and coarse-grained localization information, data normalization within a network-transparent device driver framework, enabling of network communications to non-IP devices, and fine-grained application control of data subscription band width usage. BioNet directly integrates Disruption Tolerant Networking (DTN) as a communications technology, enabling networked communications with assets that are only intermittently connected including orbiting relay satellites and planetary rover vehicles.

A motion sensing-based framework for robotic manipulation.

PubMed

Deng, Hao; Xia, Zeyang; Weng, Shaokui; Gan, Yangzhou; Fang, Peng; Xiong, Jing

2016-01-01

To data, outside of the controlled environments, robots normally perform manipulation tasks operating with human. This pattern requires the robot operators with high technical skills training for varied teach-pendant operating system. Motion sensing technology, which enables human-machine interaction in a novel and natural interface using gestures, has crucially inspired us to adopt this user-friendly and straightforward operation mode on robotic manipulation. Thus, in this paper, we presented a motion sensing-based framework for robotic manipulation, which recognizes gesture commands captured from motion sensing input device and drives the action of robots. For compatibility, a general hardware interface layer was also developed in the framework. Simulation and physical experiments have been conducted for preliminary validation. The results have shown that the proposed framework is an effective approach for general robotic manipulation with motion sensing control.

Rapid Change Detection Algorithm for Disaster Management

NASA Astrophysics Data System (ADS)

Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

2012-07-01

This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

Significant results from the HCMM program

NASA Technical Reports Server (NTRS)

1982-01-01

The major objectives of the HCMM program for applications in geology, agriculture, water studies, and the effects of climate on metropolitan areas are summarized. Results obtained by Principal Investigators in each of these disciplines are presented, discussed, and supported with figures and tables. In order to compare the types of observations and applications that can be derived from HCMM data with some remote sensing standard or framework for each discipline, the principal results already achieved with the LANDSAT system are included.

Full-wave Characterization of Rough Terrain Surface Effects for Forward-looking Radar Applications: A Scattering and Imaging Study from the Electromagnetic Perspective

DTIC Science & Technology

2011-09-01

and Imaging Framework First, the parallelized 3-D FDTD algorithm is applied to simulate composite scattering from targets in a rough ground...solver as pertinent to forward-looking radar sensing , the effects of surface clutter on multistatic target imaging are illustrated with large-scale...Full-wave Characterization of Rough Terrain Surface Effects for Forward-looking Radar Applications: A Scattering and Imaging Study from the

Metal-Organic Frameworks for Resonant-Gravimetric Detection of Trace-Level Xylene Molecules.

PubMed

Xu, Tao; Xu, Pengcheng; Zheng, Dan; Yu, Haitao; Li, Xinxin

2016-12-20

As one of typical VOCs, xylene is seriously harmful to human health. Nowadays, however, there is really lack of portable sensing method to directly detect environmental xylene that has chemical inertness. Especially when the concentration of xylene is lower than the human olfactory threshold of 470 ppb, people are indeed hard to be aware of and avoid this harmful vapor. Herein the metal-organic framework (MOF) of HKUST-1 is first explored for sensing to the nonpolar molecule of p-xylene. And the sensing mechanism is identified that is via host-guest interaction of MOF with xylene molecule. By loading MOFs on mass-gravimetric resonant-cantilevers, sensing experiments for four MOFs of MOF-5, HKUST-1, ZIF-8, and MOF-177 approve that HKUST-1 has the highest sensitivity to p-xylene. The resonant-gravimetric sensing experiments with our HKUST-1 based sensors have demonstrated that trace-level p-xylene of 400 ppb can be detected that is lower than the human olfactory threshold of 470 ppb. We analyze that the specificity of HKUST-1 to xylene comes from Cu 2+ -induced moderate Lewis acidity and the "like dissolves like" interaction of the benzene ring. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) is used to elucidate the adsorbing/sensing mechanism of HKUST-1 to p-xylene, where p-xylene adsorbing induced blue-shift phenomenon is observed that confirms the sensing mechanism. Our study also indicates that the sensor shows good selectivity to various kinds of common interfering gases. And the long-term repeatability and stability of the sensing material are also approved for the usage/storage period of two months. This research approves that the MOF materials exhibit potential usages for high performance chemical sensors applications.

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements.

PubMed

Da Poian, Giulia; Rozell, Christopher J; Bernardini, Riccardo; Rinaldo, Roberto; Clifford, Gari D

2017-09-14

Compressive Sensing (CS) has recently been applied as a low complexity compression framework for long-term monitoring of electrocardiogram signals using Wireless Body Sensor Networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat to beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in case of compressed sensed data, signal reconstruction can be performed with relatively complex optimisation algorithms, which may require significant energy consumption. This article addresses the problem of hearth rate estimation from compressive sensing electrocardiogram (ECG) recordings, avoiding the reconstruction of the entire signal. We consider a framework where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time, low-power applications.

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

PubMed Central

Santello, Marco; Bianchi, Matteo; Gabiccini, Marco; Ricciardi, Emiliano; Salvietti, Gionata; Prattichizzo, Domenico; Ernst, Marc; Moscatelli, Alessandro; Jörntell, Henrik; Kappers, Astrid M.L.; Kyriakopoulos, Kostas; Albu-Schäffer, Alin; Castellini, Claudio; Bicchi, Antonio

2017-01-01

The term ‘synergy’ – from the Greek synergia – means ‘working together’. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project “The Hand Embodied” (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies. PMID:26923030

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands.

PubMed

Santello, Marco; Bianchi, Matteo; Gabiccini, Marco; Ricciardi, Emiliano; Salvietti, Gionata; Prattichizzo, Domenico; Ernst, Marc; Moscatelli, Alessandro; Jörntell, Henrik; Kappers, Astrid M L; Kyriakopoulos, Kostas; Albu-Schäffer, Alin; Castellini, Claudio; Bicchi, Antonio

2016-07-01

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies. Copyright © 2016 Elsevier B.V. All rights reserved.

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

NASA Astrophysics Data System (ADS)

Santello, Marco; Bianchi, Matteo; Gabiccini, Marco; Ricciardi, Emiliano; Salvietti, Gionata; Prattichizzo, Domenico; Ernst, Marc; Moscatelli, Alessandro; Jörntell, Henrik; Kappers, Astrid M. L.; Kyriakopoulos, Kostas; Albu-Schäffer, Alin; Castellini, Claudio; Bicchi, Antonio

2016-07-01

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project ;The Hand Embodied; (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Nano-Enriched and Autonomous Sensing Framework for Dissolved Oxygen.

PubMed

Shehata, Nader; Azab, Mohammed; Kandas, Ishac; Meehan, Kathleen

2015-08-14

This paper investigates a nano-enhanced wireless sensing framework for dissolved oxygen (DO). The system integrates a nanosensor that employs cerium oxide (ceria) nanoparticles to monitor the concentration of DO in aqueous media via optical fluorescence quenching. We propose a comprehensive sensing framework with the nanosensor equipped with a digital interface where the sensor output is digitized and dispatched wirelessly to a trustworthy data collection and analysis framework for consolidation and information extraction. The proposed system collects and processes the sensor readings to provide clear indications about the current or the anticipated dissolved oxygen levels in the aqueous media.

Engineered embodiment: Comment on "The embodiment of assistive devices-from wheelchair to exoskeleton" by M. Pazzaglia and M. Molinari

NASA Astrophysics Data System (ADS)

Kannape, Oliver Alan; Lenggenhager, Bigna

2016-03-01

From brain-computer interfaces to wearable robotics and bionic prostheses - intelligent assistive devices have already become indispensable in the therapy of people living with reduced sensorimotor functioning of their physical body, be it due to spinal cord injury, amputation or brain lesions [1]. Rapid technological advances will continue to fuel this field for years to come. As Pazzaglia and Molinari [2] rightly point out, progress in this domain should not solely be driven by engineering prowess, but utilize the increasing psychological and neuroscientific understanding of cortical body-representations and their plasticity [3]. We argue that a core concept for such an integrated embodiment framework was introduced with the formalization of the forward model for sensorimotor control [4]. The application of engineering concepts to human movement control paved the way for rigorous computational and neuroscientific analysis. The forward model has successfully been adapted to investigate principles underlying aspects of bodily awareness such as the sense of agency in the comparator framework [5]. At the example of recent advances in lower limb prostheses, we propose a cross-disciplinary, integrated embodiment framework to investigate the sense of agency and the related sense of body ownership for such devices. The main onus now is on the engineers and cognitive scientists to embed such an approach into the design of assistive technology and its evaluation battery.

Capturing, Harmonizing and Delivering Data and Quality Provenance

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory; Lynnes, Christopher

2011-01-01

Satellite remote sensing data have proven to be vital for various scientific and applications needs. However, the usability of these data depends not only on the data values but also on the ability of data users to assess and understand the quality of these data for various applications and for comparison or inter-usage of data from different sensors and models. In this paper, we describe some aspects of capturing, harmonizing and delivering this information to users in the framework of distributed web-based data tools.

Flexible Plasmonic Sensors

PubMed Central

Shir, Daniel; Ballard, Zachary S.; Ozcan, Aydogan

2016-01-01

Mechanical flexibility and the advent of scalable, low-cost, and high-throughput fabrication techniques have enabled numerous potential applications for plasmonic sensors. Sensitive and sophisticated biochemical measurements can now be performed through the use of flexible plasmonic sensors integrated into existing medical and industrial devices or sample collection units. More robust sensing schemes and practical techniques must be further investigated to fully realize the potentials of flexible plasmonics as a framework for designing low-cost, embedded and integrated sensors for medical, environmental, and industrial applications. PMID:27547023

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

NASA Astrophysics Data System (ADS)

Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

2016-06-01

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

Continuum Reconfigurable Parallel Robots for Surgery: Shape Sensing and State Estimation with Uncertainty.

PubMed

Anderson, Patrick L; Mahoney, Arthur W; Webster, Robert J

2017-07-01

This paper examines shape sensing for a new class of surgical robot that consists of parallel flexible structures that can be reconfigured inside the human body. Known as CRISP robots, these devices provide access to the human body through needle-sized entry points, yet can be configured into truss-like structures capable of dexterous movement and large force application. They can also be reconfigured as needed during a surgical procedure. Since CRISP robots are elastic, they will deform when subjected to external forces or other perturbations. In this paper, we explore how to combine sensor information with mechanics-based models for CRISP robots to estimate their shapes under applied loads. The end result is a shape sensing framework for CRISP robots that will enable future research on control under applied loads, autonomous motion, force sensing, and other robot behaviors.

Securing While Sampling in Wireless Body Area Networks With Application to Electrocardiography.

PubMed

Dautov, Ruslan; Tsouri, Gill R

2016-01-01

Stringent resource constraints and broadcast transmission in wireless body area network raise serious security concerns when employed in biomedical applications. Protecting data transmission where any minor alteration is potentially harmful is of significant importance in healthcare. Traditional security methods based on public or private key infrastructure require considerable memory and computational resources, and present an implementation obstacle in compact sensor nodes. This paper proposes a lightweight encryption framework augmenting compressed sensing with wireless physical layer security. Augmenting compressed sensing to secure information is based on the use of the measurement matrix as an encryption key, and allows for incorporating security in addition to compression at the time of sampling an analog signal. The proposed approach eliminates the need for a separate encryption algorithm, as well as the predeployment of a key thereby conserving sensor node's limited resources. The proposed framework is evaluated using analysis, simulation, and experimentation applied to a wireless electrocardiogram setup consisting of a sensor node, an access point, and an eavesdropper performing a proximity attack. Results show that legitimate communication is reliable and secure given that the eavesdropper is located at a reasonable distance from the sensor node and the access point.

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

Newman, Jennifer; Clifton, Andrew; Bonin, Timothy

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing considermore » uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict errors in lidar-measured wind speed. The results show how uncertainty varies over time and can be used to help select data with different levels of uncertainty for different applications, for example, low uncertainty data for power performance testing versus all data for plant performance monitoring.« less

Ultrahigh-sensitive sensing platform based on p-type dumbbell-like Co3O4 network

NASA Astrophysics Data System (ADS)

Zhou, Tingting; Zhang, Tong; Zhang, Rui; Lou, Zheng; Deng, Jianan; Wang, Lili

2017-12-01

Development of high performance room temperature sensors remains a grand challenge for high demand of practical application. Metal oxide semiconductors (MOSs) have many advantages over others due to their easy functionalization, high surface area, and low cost. However, they typically need a high work temperature during sensing process. Here, p-type sensing layer is reported, consisting of pore-rich dumbbell-like Co3O4 particles (DP-Co3O4) with intrinsic high catalytic activity. The gas sensor (GS) based DP-Co3O4 catalyst exhibits ultrahigh NH3 sensing activity along with excellent stability over other structure based NH3 GSs in room temperature work environment. In addition, the unique structure of DP-Co3O4 with pore-rich and high catalytic activity endows fast gas diffusion rate and high sensitivity at room temperature. Taken together, the findings in this work highlight the merit of integrating highly active materials in p-type materials, offering a framework to develop high-sensitivity room temperature sensing platforms.

Linking Satellite Remote Sensing Based Environmental Predictors to Disease: AN Application to the Spatiotemporal Modelling of Schistosomiasis in Ghana

NASA Astrophysics Data System (ADS)

Wrable, M.; Liss, A.; Kulinkina, A.; Koch, M.; Biritwum, N. K.; Ofosu, A.; Kosinski, K. C.; Gute, D. M.; Naumova, E. N.

2016-06-01

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

Using hyperspectral remote sensing for land cover classification

NASA Astrophysics Data System (ADS)

Zhang, Wendy W.; Sriharan, Shobha

2005-01-01

This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earth-sensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. These sensors are designed to cover a wide range of the electromagnetic spectrum and are generating enormous amounts of data that must be processed, stored, and made available to the user community. The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40 and 2.45 mm. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty Fellowship Program at Stennis Space Center. Research Systems Inc.'s (RSI) ENVI software package was used in this application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data endmembers.

Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization

PubMed Central

González, Rocío Ballesteros; Leinster, Paul; Wright, Ros

2017-01-01

The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability of existing methods, with remote sensing based approaches being proposed as a potential means of harmonizing hydromorphological characterization protocols. However, the resolution achieved by remote sensing products may not be sufficient to assess some of the key hydromorphological features that are required to allow an accurate characterization. Methodologies based on high resolution aerial photography taken from Unmanned Aerial Vehicles (UAVs) have been proposed by several authors as potential approaches to overcome these limitations. Here, we explore the applicability of an existing UAV based framework for hydromorphological characterization to three different fluvial settings representing some of the distinct ecoregions defined by the WFD geographical intercalibration groups (GIGs). The framework is based on the automated recognition of hydromorphological features via tested and validated Artificial Neural Networks (ANNs). Results show that the framework is transferable to the Central-Baltic and Mediterranean GIGs with accuracies in feature identification above 70%. Accuracies of 50% are achieved when the framework is implemented in the Very Large Rivers GIG. The framework successfully identified vegetation, deep water, shallow water, riffles, side bars and shadows for the majority of the reaches. However, further algorithm development is required to ensure a wider range of features (e.g., chutes, structures and erosion) are accurately identified. This study also highlights the need to develop an objective and fit for purpose hydromorphological characterization framework to be adopted within all EU member states to facilitate comparison of results. PMID:28954434

Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization.

PubMed

Rivas Casado, Mónica; González, Rocío Ballesteros; Ortega, José Fernando; Leinster, Paul; Wright, Ros

2017-09-26

The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability of existing methods, with remote sensing based approaches being proposed as a potential means of harmonizing hydromorphological characterization protocols. However, the resolution achieved by remote sensing products may not be sufficient to assess some of the key hydromorphological features that are required to allow an accurate characterization. Methodologies based on high resolution aerial photography taken from Unmanned Aerial Vehicles (UAVs) have been proposed by several authors as potential approaches to overcome these limitations. Here, we explore the applicability of an existing UAV based framework for hydromorphological characterization to three different fluvial settings representing some of the distinct ecoregions defined by the WFD geographical intercalibration groups (GIGs). The framework is based on the automated recognition of hydromorphological features via tested and validated Artificial Neural Networks (ANNs). Results show that the framework is transferable to the Central-Baltic and Mediterranean GIGs with accuracies in feature identification above 70%. Accuracies of 50% are achieved when the framework is implemented in the Very Large Rivers GIG. The framework successfully identified vegetation, deep water, shallow water, riffles, side bars and shadows for the majority of the reaches. However, further algorithm development is required to ensure a wider range of features (e.g., chutes, structures and erosion) are accurately identified. This study also highlights the need to develop an objective and fit for purpose hydromorphological characterization framework to be adopted within all EU member states to facilitate comparison of results.

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery.

PubMed

Miga, Michael I

2016-01-01

With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

Photovoltaic system criteria documents. Volume 2: Quality assurance criteria for photovoltaic applications

NASA Technical Reports Server (NTRS)

Koenig, John C.; Billitti, Joseph W.; Tallon, John M.

1979-01-01

Quality assurance criteria are described for manufacturers and installers of solar photovoltaic tests and applications. Quality oriented activities are outlined to be pursued by the contractor/subcontractor to assure the physical and operational quality of equipment produced is included. In the broad sense, guidelines are provided for establishing a QA organization if none exists. Mainly, criteria is provided to be considered in any PV quality assurance plan selected as appropriate by the responsible Field Center. A framework is established for a systematic approach to ensure that photovoltaic tests and applications are constructed in a timely and cost effective manner.

The Relationships between Students' Use of Instant Messaging and Their Psychological Sense of Community

ERIC Educational Resources Information Center

Thomas, Amanda Garland

2009-01-01

The purpose of this study was to understand the extent to which students' psychological sense of community was influenced by IM use using the psychological sense of community theoretical framework created by McMillan and Chavis (1986), and the student development theoretical frameworks created by Schlossberg (1989) and Astin (1984). Thus, this…

Fluid flow sensing with ionic polymer-metal composites

NASA Astrophysics Data System (ADS)

Stalbaum, Tyler; Trabia, Sarah; Shen, Qi; Kim, Kwang J.

2016-04-01

Ionic polymer-metal composite (IPMC) actuators and sensors have been developed and modeled over the last two decades for use as soft-robotic deformable actuators and sensors. IPMC devices have been suggested for application as underwater actuators, energy harvesting devices, and medical devices such as in guided catheter insertion. Another interesting application of IPMCs in flow sensing is presented in this study. IPMC interaction with fluid flow is of interest to investigate the use of IPMC actuators as flow control devices and IPMC sensors as flow sensing devices. An organized array of IPMCs acting as interchanging sensors and actuators could potentially be designed for both flow measurement and control, providing an unparalleled tool in maritime operations. The underlying physics for this system include the IPMC ion transport and charge fundamental framework along with fluid dynamics to describe the flow around IPMCs. An experimental setup for an individual rectangular IPMC sensor with an externally controlled fluid flow has been developed to investigate this phenomenon and provide further insight into the design and application of this type of device. The results from this portion of the study include recommendations for IPMC device designs in flow control.

A New Computational Framework for Atmospheric and Surface Remote Sensing

NASA Technical Reports Server (NTRS)

Timucin, Dogan A.

2004-01-01

A Bayesian data-analysis framework is described for atmospheric and surface retrievals from remotely-sensed hyper-spectral data. Some computational techniques are high- lighted for improved accuracy in the forward physics model.

The South Dakota cooperative land use effort: A state level remote sensing demonstration project

NASA Technical Reports Server (NTRS)

Tessar, P. A.; Hood, D. R.; Todd, W. J.

1975-01-01

Remote sensing technology can satisfy or make significant contributions toward satisfying many of the information needs of governmental natural resource planners and policy makers. Recognizing this potential, the South Dakota State Planning Bureau and the EROS Data Center together formulated the framework for an ongoing Land Use and Natural Resource Inventory and Information System Program. Statewide land use/land cover information is generated from LANDSAT digital data and high altitude photography. Many applications of the system are anticipated as it evolves and data are added from more conventional sources. The conceptualization, design, and implementation of the program are discussed.

Reflective ghost imaging through turbulence

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

Hardy, Nicholas D.; Shapiro, Jeffrey H.

2011-12-15

Recent work has indicated that ghost imaging may have applications in standoff sensing. However, most theoretical work has addressed transmission-based ghost imaging. To be a viable remote-sensing system, the ghost imager needs to image rough-surfaced targets in reflection through long, turbulent optical paths. We develop, within a Gaussian-state framework, expressions for the spatial resolution, image contrast, and signal-to-noise ratio of such a system. We consider rough-surfaced targets that create fully developed speckle in their returns and Kolmogorov-spectrum turbulence that is uniformly distributed along all propagation paths. We address both classical and nonclassical optical sources, as well as a computational ghostmore » imager.« less

CAreDroid: Adaptation Framework for Android Context-Aware Applications

PubMed Central

Elmalaki, Salma; Wanner, Lucas; Srivastava, Mani

2015-01-01

Context-awareness is the ability of software systems to sense and adapt to their physical environment. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. In this framework, developers are required— only—to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context. CAreDroid is implemented as part of the Android runtime system. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. In particular, case study applications implemented using CAre-Droid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs. PMID:26834512

CAreDroid: Adaptation Framework for Android Context-Aware Applications.

PubMed

Elmalaki, Salma; Wanner, Lucas; Srivastava, Mani

2015-09-01

Context-awareness is the ability of software systems to sense and adapt to their physical environment. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. In this framework, developers are required- only-to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context. CAreDroid is implemented as part of the Android runtime system. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. In particular, case study applications implemented using CAre-Droid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs.

Exploring the potential of a capability framework as a vision and "sensemaking" tool for leaders of interprofessional education.

PubMed

Brewer, Margo

2016-09-01

Creating a vision (visioning) and sensemaking have been described as key leadership practices in the leadership literature. A vision provides clarity, motivation, and direction for staff, and is essential particularly in times of significant change. Closely related to visioning is sensemaking (the organisation of stimuli into a framework allowing people to understand, explain, attribute, extrapolate, and predict). The application of these strategies to leadership within the interprofessional field is yet to be scrutinised. This study examines an interprofessional capability framework as a visioning and sensemaking tool for use by leaders within a university health science curriculum. Interviews with 11 faculty members revealed that the framework had been embedded across multiple years and contexts within the curriculum. Furthermore, a range of responses to the framework were evoked in relation to its use to make sense of interprofessional practice and to provide a vision, guide, and focus for faculty. Overall the findings indicate that the framework can function as both a visioning and sensemaking tool.

Feasibility study for application of the compressed-sensing framework to interior computed tomography (ICT) for low-dose, high-accurate dental x-ray imaging

NASA Astrophysics Data System (ADS)

Je, U. K.; Cho, H. M.; Cho, H. S.; Park, Y. O.; Park, C. K.; Lim, H. W.; Kim, K. S.; Kim, G. A.; Park, S. Y.; Woo, T. H.; Choi, S. I.

2016-02-01

In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.

a Novel Framework for Remote Sensing Image Scene Classification

NASA Astrophysics Data System (ADS)

Jiang, S.; Zhao, H.; Wu, W.; Tan, Q.

2018-04-01

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

The application of data mining and cloud computing techniques in data-driven models for structural health monitoring

NASA Astrophysics Data System (ADS)

Khazaeli, S.; Ravandi, A. G.; Banerji, S.; Bagchi, A.

2016-04-01

Recently, data-driven models for Structural Health Monitoring (SHM) have been of great interest among many researchers. In data-driven models, the sensed data are processed to determine the structural performance and evaluate the damages of an instrumented structure without necessitating the mathematical modeling of the structure. A framework of data-driven models for online assessment of the condition of a structure has been developed here. The developed framework is intended for automated evaluation of the monitoring data and structural performance by the Internet technology and resources. The main challenges in developing such framework include: (a) utilizing the sensor measurements to estimate and localize the induced damage in a structure by means of signal processing and data mining techniques, and (b) optimizing the computing and storage resources with the aid of cloud services. The main focus in this paper is to demonstrate the efficiency of the proposed framework for real-time damage detection of a multi-story shear-building structure in two damage scenarios (change in mass and stiffness) in various locations. Several features are extracted from the sensed data by signal processing techniques and statistical methods. Machine learning algorithms are deployed to select damage-sensitive features as well as classifying the data to trace the anomaly in the response of the structure. Here, the cloud computing resources from Amazon Web Services (AWS) have been used to implement the proposed framework.

Bioconjugation of luminescent Eu-BDC-NH2 MOFs for highly efficient sensing of BSA

NASA Astrophysics Data System (ADS)

Kukkar, Preeti; Sammi, Heena; Rawat, Mohit; Singh, Pritpal; Basu, Soumen; Kukkar, Deepak

2018-05-01

Luminescent metal organic frameworks (MOFs) have emerged as an exciting prospect for molecular sensing applications owing to their tunable porosity and optical properties. In this study, we have reported the synthesis of luminescent Europium-amino terephthalic acid (Eu-BDC-NH2) MOFs through solvothermal approach subsequently followed by their bioconjugation with anti-Bovine serum albumin (BSA) antibody using standard carbodiimide linkage chemistry. Subsequently nanocomposite of the bioconjugate and Zeolotic Imidazole Frameworks -8(ZIF-8) nanoparticles was prepared by adding varying volumes of ZIF-8 NPs to check the variation in photoluminescence (PL) intensity. Finally, optimized nanocomposites with increased PL intensity were treated with different concentrations of BSA to show a turn on effect on the PL intensity. The prepared nanocomposites were able to screen 0.1 ppm concentration of the BSA thus showing their high efficiency as a molecular sensor. This fluorescent platform would be further utilized for sensitive detection of pesticides in solution.

Self-assembly of polyhedral metal-organic framework particles into three-dimensional ordered superstructures

NASA Astrophysics Data System (ADS)

Avci, Civan; Imaz, Inhar; Carné-Sánchez, Arnau; Pariente, Jose Angel; Tasios, Nikos; Pérez-Carvajal, Javier; Alonso, Maria Isabel; Blanco, Alvaro; Dijkstra, Marjolein; López, Cefe; Maspoch, Daniel

2018-01-01

Self-assembly of particles into long-range, three-dimensional, ordered superstructures is crucial for the design of a variety of materials, including plasmonic sensing materials, energy or gas storage systems, catalysts and photonic crystals. Here, we have combined experimental and simulation data to show that truncated rhombic dodecahedral particles of the metal-organic framework (MOF) ZIF-8 can self-assemble into millimetre-sized superstructures with an underlying three-dimensional rhombohedral lattice that behave as photonic crystals. Those superstructures feature a photonic bandgap that can be tuned by controlling the size of the ZIF-8 particles and is also responsive to the adsorption of guest substances in the micropores of the ZIF-8 particles. In addition, superstructures with different lattices can also be assembled by tuning the truncation of ZIF-8 particles, or by using octahedral UiO-66 MOF particles instead. These well-ordered, sub-micrometre-sized superstructures might ultimately facilitate the design of three-dimensional photonic materials for applications in sensing.

Applications of Satellite Remote Sensing Products to Enhance and Evaluate the AIRPACT Regional Air Quality Modeling System

NASA Astrophysics Data System (ADS)

Herron-Thorpe, F. L.; Mount, G. H.; Emmons, L. K.; Lamb, B. K.; Jaffe, D. A.; Wigder, N. L.; Chung, S. H.; Zhang, R.; Woelfle, M.; Vaughan, J. K.; Leung, F. T.

2013-12-01

The WSU AIRPACT air quality modeling system for the Pacific Northwest forecasts hourly levels of aerosols and atmospheric trace gases for use in determining potential health and ecosystem impacts by air quality managers. AIRPACT uses the WRF/SMOKE/CMAQ modeling framework, derives dynamic boundary conditions from MOZART-4 forecast simulations with assimilated MOPITT CO, and uses the BlueSky framework to derive fire emissions. A suite of surface measurements and satellite-based remote sensing data products across the AIRPACT domain are used to evaluate and improve model performance. Specific investigations include anthropogenic emissions, wildfire simulations, and the effects of long-range transport on surface ozone. In this work we synthesize results for multiple comparisons of AIRPACT with satellite products such as IASI ammonia, AIRS carbon monoxide, MODIS AOD, OMI tropospheric ozone and nitrogen dioxide, and MISR plume height. Features and benefits of the newest version of AIRPACT's web-interface are also presented.

Research on dynamic performance design of mobile phone application based on context awareness

NASA Astrophysics Data System (ADS)

Bo, Zhang

2018-05-01

It aims to explore the dynamic performance of different mobile phone applications and the user's cognitive differences, reduce the cognitive burden, and enhance the sense of experience. By analyzing the dynamic design performance in four different interactive contexts, and constructing the framework of information service process in the interactive context perception and the two perception principles of the cognitive consensus between designer and user, and the two kinds of knowledge in accordance with the perception principles. The analysis of the context will help users sense the dynamic performance more intuitively, so that the details of interaction will be performed more vividly and smoothly, thus enhance user's experience in the interactive process. The common perception experience enables designers and users to produce emotional resonance in different interactive contexts, and help them achieve rapid understanding of interactive content and perceive the logic and hierarchy of the content and the structure, therefore the effectiveness of mobile applications will be improved.

a Hadoop-Based Distributed Framework for Efficient Managing and Processing Big Remote Sensing Images

NASA Astrophysics Data System (ADS)

Wang, C.; Hu, F.; Hu, X.; Zhao, S.; Wen, W.; Yang, C.

2015-07-01

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Peters-Lidard, Christa

2013-01-01

The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.

Making sense of complexity in context and implementation: the Context and Implementation of Complex Interventions (CICI) framework.

PubMed

Pfadenhauer, Lisa M; Gerhardus, Ansgar; Mozygemba, Kati; Lysdahl, Kristin Bakke; Booth, Andrew; Hofmann, Bjørn; Wahlster, Philip; Polus, Stephanie; Burns, Jacob; Brereton, Louise; Rehfuess, Eva

2017-02-15

The effectiveness of complex interventions, as well as their success in reaching relevant populations, is critically influenced by their implementation in a given context. Current conceptual frameworks often fail to address context and implementation in an integrated way and, where addressed, they tend to focus on organisational context and are mostly concerned with specific health fields. Our objective was to develop a framework to facilitate the structured and comprehensive conceptualisation and assessment of context and implementation of complex interventions. The Context and Implementation of Complex Interventions (CICI) framework was developed in an iterative manner and underwent extensive application. An initial framework based on a scoping review was tested in rapid assessments, revealing inconsistencies with respect to the underlying concepts. Thus, pragmatic utility concept analysis was undertaken to advance the concepts of context and implementation. Based on these findings, the framework was revised and applied in several systematic reviews, one health technology assessment (HTA) and one applicability assessment of very different complex interventions. Lessons learnt from these applications and from peer review were incorporated, resulting in the CICI framework. The CICI framework comprises three dimensions-context, implementation and setting-which interact with one another and with the intervention dimension. Context comprises seven domains (i.e., geographical, epidemiological, socio-cultural, socio-economic, ethical, legal, political); implementation consists of five domains (i.e., implementation theory, process, strategies, agents and outcomes); setting refers to the specific physical location, in which the intervention is put into practise. The intervention and the way it is implemented in a given setting and context can occur on a micro, meso and macro level. Tools to operationalise the framework comprise a checklist, data extraction tools for qualitative and quantitative reviews and a consultation guide for applicability assessments. The CICI framework addresses and graphically presents context, implementation and setting in an integrated way. It aims at simplifying and structuring complexity in order to advance our understanding of whether and how interventions work. The framework can be applied in systematic reviews and HTA as well as primary research and facilitate communication among teams of researchers and with various stakeholders.

Linking remote sensing, land cover and disease.

PubMed

Curran, P J; Atkinson, P M; Foody, G M; Milton, E J

2000-01-01

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Essential Biodiversity Variables: A framework for communication between the biodiversity community and space agencies

NASA Astrophysics Data System (ADS)

Leidner, A. K.; Skidmore, A. K.; Turner, W. W.; Geller, G. N.

2017-12-01

The biodiversity community is working towards developing a consensus on a set of Essential Biodiversity Variables (EBVs) that can be used to measure and monitor biodiversity change over time. These EBVs will inform research, modeling, policy, and assessment efforts. The synoptic coverage provided by satellite data make remote sensing a particularly important observation tool to inform many EBVs. Biodiversity is a relatively new subject matter for space agencies, and thus the definition, description, and requirements of EBVs with a significant remote sensing component can foster ways for the biodiversity community to clearly and concisely communicate observational needs to space agencies and the Committee on Earth Observing Satellites (CEOS, the international coordinating body for civilian space agencies). Here, we present an overview of EBVs with a particular emphasis on those for which remote sensing will play a significant role and also report on the results of recent workshops to prioritize and refine EBVs. Our goal is to provide a framework for the biodiversity community to coalesce around a set of observational needs to convey to space agencies. Compared to many physical science disciplines, the biodiversity community represents a wide range of sub-disciplines and organizations (academia, non-governmental organizations, research institutes, national and local natural resource management agencies, etc.), which creates additional challenges when communicating needs to space agencies unfamiliar with the topic. EBVs thus offer a communication pathway that could increase awareness within space agencies of the uses of remote sensing for biodiversity research and applications, which in turn could foster greater use of remote sensing in the broader biodiversity community.

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks

PubMed Central

Ye, Dayon; Zhang, Minji; Yang, Yu

2015-01-01

Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage. PMID:25928063

The application of geologic remote sensing to vertebrate biostratigraphy - General results from the Wind River Basin, Wyoming

NASA Technical Reports Server (NTRS)

Stucky, Richard K.; Krishtalka, Leonard

1991-01-01

Since 1986, remote sensing images derived from satellite and aircraft-borne sensor data have been used to study the stratigraphy and sedimentology of the vertebrate-bearing Wind River and Wagon Bed formations in the Wind River Basin (Wyoming). Landsat 5 TM and aircraft Thermal Infrared Multispectral Scanner data were combined with conventional geologic analyses. The remote sensing data have contributed significantly to: (1) geologic mapping at the formation, member, and bed levels; (2) stratigraphic correlation; (3) reconstruction of ancient depositional environments; and (4) identification of structural complexity. This information is critical to vertebrate paleontology in providing the stratigraphic, sedimentologic, and structural framework required for evolutionary and paleoecologic studies. Of primary importance is the ability to map at minimal cost the geology of large areas (20,000 sq km or greater) at a high level of precision. Remote sensing data can be especially useful in geologically and paleontologically unexplored or poorly understood regions.

Ultrathin Two-Dimensional Covalent Organic Framework Nanosheets: Preparation and Application in Highly Sensitive and Selective DNA Detection.

PubMed

Peng, Yongwu; Huang, Ying; Zhu, Yihan; Chen, Bo; Wang, Liying; Lai, Zhuangchai; Zhang, Zhicheng; Zhao, Meiting; Tan, Chaoliang; Yang, Nailiang; Shao, Fangwei; Han, Yu; Zhang, Hua

2017-06-28

The ability to prepare ultrathin two-dimensional (2D) covalent organic framework (COF) nanosheets (NSs) in high yield is of great importance for the further exploration of their unique properties and potential applications. Herein, by elaborately designing and choosing two flexible molecules with C 3v molecular symmetry as building units, a novel imine-linked COF, namely, TPA-COF, with a hexagonal layered structure and sheet-like morphology, is synthesized. Since the flexible building units are integrated into the COF skeletons, the interlayer stacking becomes weak, resulting in the easy exfoliation of TPA-COF into ultrathin 2D NSs. Impressively, for the first time, the detailed structural information, i.e., the pore channels and individual building units in the NSs, is clearly visualized by using the recently developed low-dose imaging technique of transmission electron microscopy (TEM). As a proof-of-concept application, the obtained ultrathin COF NSs are used as a novel fluorescence sensing platform for the highly sensitive and selective detection of DNA.

Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-Modal Wireless Sensor Networks

PubMed Central

Hammoudeh, Mohammad; Newman, Robert; Dennett, Christopher; Mount, Sarah; Aldabbas, Omar

2015-01-01

This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service. PMID:26378539

Identity and the Military Profession: An Eriksonian Perspective

DTIC Science & Technology

1986-04-01

psychosocial development , Erlk Erikson ascribes great importance to achieving a sense of identity. First, he believes that "a firm sense of inner identity marks...nent psychoanalyst, Erik Erikson , as a conceptual Framework for analyzing how an Army officer’s individual and profes- sional identity develops . It...noted psychoanalyst Erik Erikson provide an ideal framework For studying the growth of identity and the problems in developing a coherent sense of self

Sensor devices comprising a metal-organic framework material and methods of making and using the same

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

Wang, Alan X.; Chang, Chih-hung; Kim, Ki-Joong

Disclosed herein are embodiments of sensor devices comprising a sensing component able to determine the presence of, detect, and/or quantify detectable species in a variety of environments and applications. The sensing components disclosed herein can comprise MOF materials, plasmonic nanomaterials, or combinations thereof. In an exemplary embodiment, light guides can be coupled with the sensing components described herein to provide sensor devices capable of increased NIR detection sensitivity in determining the presence of detectable species, such as gases and volatile organic compounds. In another exemplary embodiment, optical properties of the plasmonic nanomaterials combined with MOF materials can be monitored directlymore » to detect analyte species through their impact on external conditions surrounding the particle or as a result of charge transfer to and from the plasmonic material as a result of interactions with the plasmonic material and/or the MOF material.« less

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

PubMed

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-07-16

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

The Design and Development of an Intelligent Planning Aid

DTIC Science & Technology

1986-07-01

reasons why widening the scope of TACPLAK’s applicability make sense. First# plan execution and monitoring (and the re-planning that then occurs) are...Orsssnu, contracting officer’s representative I», KKY voees o Decision Making Tactical Planning Taxonomy Problem Solving ii M ifrntitr *r MM* I...planning aid. It documents the development of a decision- making , planning, and decision-aiding analytical framework comprising a set of models, s generic

Exploiting pressure to induce a "guest-blocked" spin transition in a framework material

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

Sciortino, Natasha F.; Ragon, Florence; Zenere, Katrina A.

A new functionalized 1,2,4-trizole ligand 4-[(E)-2-(5-methyl-2-thienyl)vinyl]-1,2,4-triazole (thiome) was prepared to assess the structural and magnetic consequence of ligand steric bulk in the resultant framework material [FeIIPd(CN)4(thiome)2]·2(H2O) (A·2(H2O)). Structural studies reveal that the pore size is smaller than realted 2-D Hofmann-type materials and that the water molecules can be reversibly removed with retention of the porous host framework. Magnetic measurements show ‘on-off’ sensing to the presence of water. The hydrated phase is spin crossover (SCO) inactive whereas the dehydrated phase undergoes an abrupt and hysteretic one-step spin transition. Partial dehydration (A·n(H2O), 0 ≤ n ≤ 2) leads to systematically varying spinmore » transition temperatures further demonstrating qualitative sensing. These studies suggest that the SCO properties are governed by internal lattice pressure effects. Variable pressure structure and magnetic studies on the hydrated phase, A·2(H2O), reveal that such internal guest pressure effects can be overcome with moderate external pressure application (0 – 0.68 GPa) resulting in a two-step spin transition at ambient temperatures at 0.68 GPa.« less

A malonitrile-functionalized metal-organic framework for hydrogen sulfide detection and selective amino acid molecular recognition

NASA Astrophysics Data System (ADS)

Li, Haiwei; Feng, Xiao; Guo, Yuexin; Chen, Didi; Li, Rui; Ren, Xiaoqian; Jiang, Xin; Dong, Yuping; Wang, Bo

2014-03-01

A novel porous polymeric fluorescence probe, MN-ZIF-90, has been designed and synthesized for quantitative hydrogen sulfide (H2S) fluorescent detection and highly selective amino acid recognition. This distinct crystalline structure, derived from rational design and malonitrile functionalization, can trigger significant enhancement of its fluorescent intensity when exposed to H2S or cysteine molecules. Indeed this new metal-organic framework (MOF) structure shows high selectivity of biothiols over other amino acids and exhibits favorable stability. Moreover, in vitro viability assays on HeLa cells show low cytotoxicity of MN-ZIF-90 and its imaging contrast efficiency is further demonstrated by fluorescence microscopy studies. This facile yet powerful strategy also offers great potential of using open-framework materials (i.e. MOFs) as the novel platform for sensing and other biological applications.

An Integrated Tone Mapping for High Dynamic Range Image Visualization

NASA Astrophysics Data System (ADS)

Liang, Lei; Pan, Jeng-Shyang; Zhuang, Yongjun

2018-01-01

There are two type tone mapping operators for high dynamic range (HDR) image visualization. HDR image mapped by perceptual operators have strong sense of reality, but will lose local details. Empirical operators can maximize local detail information of HDR image, but realism is not strong. A common tone mapping operator suitable for all applications is not available. This paper proposes a novel integrated tone mapping framework which can achieve conversion between empirical operators and perceptual operators. In this framework, the empirical operator is rendered based on improved saliency map, which simulates the visual attention mechanism of the human eye to the natural scene. The results of objective evaluation prove the effectiveness of the proposed solution.

Implementation of a compressive sampling scheme for wireless sensors to achieve energy efficiency in a structural health monitoring system

NASA Astrophysics Data System (ADS)

O'Connor, Sean M.; Lynch, Jerome P.; Gilbert, Anna C.

2013-04-01

Wireless sensors have emerged to offer low-cost sensors with impressive functionality (e.g., data acquisition, computing, and communication) and modular installations. Such advantages enable higher nodal densities than tethered systems resulting in increased spatial resolution of the monitoring system. However, high nodal density comes at a cost as huge amounts of data are generated, weighing heavy on power sources, transmission bandwidth, and data management requirements, often making data compression necessary. The traditional compression paradigm consists of high rate (>Nyquist) uniform sampling and storage of the entire target signal followed by some desired compression scheme prior to transmission. The recently proposed compressed sensing (CS) framework combines the acquisition and compression stage together, thus removing the need for storage and operation of the full target signal prior to transmission. The effectiveness of the CS approach hinges on the presence of a sparse representation of the target signal in a known basis, similarly exploited by several traditional compressive sensing applications today (e.g., imaging, MRI). Field implementations of CS schemes in wireless SHM systems have been challenging due to the lack of commercially available sensing units capable of sampling methods (e.g., random) consistent with the compressed sensing framework, often moving evaluation of CS techniques to simulation and post-processing. The research presented here describes implementation of a CS sampling scheme to the Narada wireless sensing node and the energy efficiencies observed in the deployed sensors. Of interest in this study is the compressibility of acceleration response signals collected from a multi-girder steel-concrete composite bridge. The study shows the benefit of CS in reducing data requirements while ensuring data analysis on compressed data remain accurate.

LAnd surface remote sensing Products VAlidation System (LAPVAS) and its preliminary application

NASA Astrophysics Data System (ADS)

Lin, Xingwen; Wen, Jianguang; Tang, Yong; Ma, Mingguo; Dou, Baocheng; Wu, Xiaodan; Meng, Lumin

2014-11-01

The long term record of remote sensing product shows the land surface parameters with spatial and temporal change to support regional and global scientific research widely. Remote sensing product with different sensors and different algorithms is necessary to be validated to ensure the high quality remote sensing product. Investigation about the remote sensing product validation shows that it is a complex processing both the quality of in-situ data requirement and method of precision assessment. A comprehensive validation should be needed with long time series and multiple land surface types. So a system named as land surface remote sensing product is designed in this paper to assess the uncertainty information of the remote sensing products based on a amount of in situ data and the validation techniques. The designed validation system platform consists of three parts: Validation databases Precision analysis subsystem, Inter-external interface of system. These three parts are built by some essential service modules, such as Data-Read service modules, Data-Insert service modules, Data-Associated service modules, Precision-Analysis service modules, Scale-Change service modules and so on. To run the validation system platform, users could order these service modules and choreograph them by the user interactive and then compete the validation tasks of remote sensing products (such as LAI ,ALBEDO ,VI etc.) . Taking SOA-based architecture as the framework of this system. The benefit of this architecture is the good service modules which could be independent of any development environment by standards such as the Web-Service Description Language(WSDL). The standard language: C++ and java will used as the primary programming language to create service modules. One of the key land surface parameter, albedo, is selected as an example of the system application. It is illustrated that the LAPVAS has a good performance to implement the land surface remote sensing product validation.

An Architecture for Automated Fire Detection Early Warning System Based on Geoprocessing Service Composition

NASA Astrophysics Data System (ADS)

Samadzadegan, F.; Saber, M.; Zahmatkesh, H.; Joze Ghazi Khanlou, H.

2013-09-01

Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementations to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in a Service Oriented Architecture (SOA) platform to become a widely accepted solution for processing remotely sensed data on the web. This paper presents an architecture design based on OGC web services for automated workflow for acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks for an automated fire detection early warning system are represented using web-based processing of remote sensing imageries utilizing MODIS data. A composition of WPS processes is proposed as a WPS service to extract fire events from MODIS data. Subsequently, the paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. A GeoPortal client with open-source software was developed to manage data, metadata, processes, and authorities. Investigating feasibility and benefits of proposed framework shows that this framework can be used for wide area of geospatial applications specially disaster management and environmental monitoring.

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches.

PubMed

Rawassizadeh, Reza; Tomitsch, Martin; Nourizadeh, Manouchehr; Momeni, Elaheh; Peery, Aaron; Ulanova, Liudmila; Pazzani, Michael

2015-09-08

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches

PubMed Central

Rawassizadeh, Reza; Tomitsch, Martin; Nourizadeh, Manouchehr; Momeni, Elaheh; Peery, Aaron; Ulanova, Liudmila; Pazzani, Michael

2015-01-01

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras. PMID:26370997

Strategic Environmental Assessment Framework for Landscape-Based, Temporal Analysis of Wetland Change in Urban Environments.

PubMed

Sizo, Anton; Noble, Bram F; Bell, Scott

2016-03-01

This paper presents and demonstrates a spatial framework for the application of strategic environmental assessment (SEA) in the context of change analysis for urban wetland environments. The proposed framework is focused on two key stages of the SEA process: scoping and environmental baseline assessment. These stages are arguably the most information-intense phases of SEA and have a significant effect on the quality of the SEA results. The study aims to meet the needs for proactive frameworks to assess and protect wetland habitat and services more efficiently, toward the goal of advancing more intelligent urban planning and development design. The proposed framework, adopting geographic information system and remote sensing tools and applications, supports the temporal evaluation of wetland change and sustainability assessment based on landscape indicator analysis. The framework was applied to a rapidly developing urban environment in the City of Saskatoon, Saskatchewan, Canada, analyzing wetland change and land-use pressures from 1985 to 2011. The SEA spatial scale was rescaled from administrative urban planning units to an ecologically meaningful area. Landscape change assessed was based on a suite of indicators that were subsequently rolled up into a single, multi-dimensional, and easy to understand and communicate index to examine the implications of land-use change for wetland sustainability. The results show that despite the recent extremely wet period in the Canadian prairie region, land-use change contributed to increasing threats to wetland sustainability.

Strategic Environmental Assessment Framework for Landscape-Based, Temporal Analysis of Wetland Change in Urban Environments

NASA Astrophysics Data System (ADS)

Sizo, Anton; Noble, Bram F.; Bell, Scott

2016-03-01

This paper presents and demonstrates a spatial framework for the application of strategic environmental assessment (SEA) in the context of change analysis for urban wetland environments. The proposed framework is focused on two key stages of the SEA process: scoping and environmental baseline assessment. These stages are arguably the most information-intense phases of SEA and have a significant effect on the quality of the SEA results. The study aims to meet the needs for proactive frameworks to assess and protect wetland habitat and services more efficiently, toward the goal of advancing more intelligent urban planning and development design. The proposed framework, adopting geographic information system and remote sensing tools and applications, supports the temporal evaluation of wetland change and sustainability assessment based on landscape indicator analysis. The framework was applied to a rapidly developing urban environment in the City of Saskatoon, Saskatchewan, Canada, analyzing wetland change and land-use pressures from 1985 to 2011. The SEA spatial scale was rescaled from administrative urban planning units to an ecologically meaningful area. Landscape change assessed was based on a suite of indicators that were subsequently rolled up into a single, multi-dimensional, and easy to understand and communicate index to examine the implications of land-use change for wetland sustainability. The results show that despite the recent extremely wet period in the Canadian prairie region, land-use change contributed to increasing threats to wetland sustainability.

Model-based reasoning in the physics laboratory: Framework and initial results

NASA Astrophysics Data System (ADS)

Zwickl, Benjamin M.; Hu, Dehui; Finkelstein, Noah; Lewandowski, H. J.

2015-12-01

[This paper is part of the Focused Collection on Upper Division Physics Courses.] We review and extend existing frameworks on modeling to develop a new framework that describes model-based reasoning in introductory and upper-division physics laboratories. Constructing and using models are core scientific practices that have gained significant attention within K-12 and higher education. Although modeling is a broadly applicable process, within physics education, it has been preferentially applied to the iterative development of broadly applicable principles (e.g., Newton's laws of motion in introductory mechanics). A significant feature of the new framework is that measurement tools (in addition to the physical system being studied) are subjected to the process of modeling. Think-aloud interviews were used to refine the framework and demonstrate its utility by documenting examples of model-based reasoning in the laboratory. When applied to the think-aloud interviews, the framework captures and differentiates students' model-based reasoning and helps identify areas of future research. The interviews showed how students productively applied similar facets of modeling to the physical system and measurement tools: construction, prediction, interpretation of data, identification of model limitations, and revision. Finally, we document students' challenges in explicitly articulating assumptions when constructing models of experimental systems and further challenges in model construction due to students' insufficient prior conceptual understanding. A modeling perspective reframes many of the seemingly arbitrary technical details of measurement tools and apparatus as an opportunity for authentic and engaging scientific sense making.

Computer Vision Research and its Applications to Automated Cartography

DTIC Science & Technology

1985-09-01

D Scene Geometry Thomas M. Strat and Martin A. Fischler Appendix D A New Sense for Depth of Field Alex P. Pentland iv 9.* qb CONTENTS (cont’d...D modeling. A. Baseline Stereo System As a framework for integration and evaluation of our research in modeling * 3-D scene geometry , as well as a...B. New Methods for Stereo Compilation As we previously indicated, the conventional approach to recovering scene geometry from a stereo pair of

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

PubMed

Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

2017-12-01

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

Higher-Order Theory for Functionally Graded Materials

NASA Technical Reports Server (NTRS)

Aboudi, Jacob; Pindera, Marek-Jerzy; Arnold, Steven M.

1999-01-01

This paper presents the full generalization of the Cartesian coordinate-based higher-order theory for functionally graded materials developed by the authors during the past several years. This theory circumvents the problematic use of the standard micromechanical approach, based on the concept of a representative volume element, commonly employed in the analysis of functionally graded composites by explicitly coupling the local (microstructural) and global (macrostructural) responses. The theoretical framework is based on volumetric averaging of the various field quantities, together with imposition of boundary and interfacial conditions in an average sense between the subvolumes used to characterize the composite's functionally graded microstructure. The generalization outlined herein involves extension of the theoretical framework to enable the analysis of materials characterized by spatially variable microstructures in three directions. Specialization of the generalized theoretical framework to previously published versions of the higher-order theory for materials functionally graded in one and two directions is demonstrated. In the applications part of the paper we summarize the major findings obtained with the one-directional and two-directional versions of the higher-order theory. The results illustrate both the fundamental issues related to the influence of microstructure on microscopic and macroscopic quantities governing the response of composites and the technologically important applications. A major issue addressed herein is the applicability of the classical homogenization schemes in the analysis of functionally graded materials. The technologically important applications illustrate the utility of functionally graded microstructures in tailoring the response of structural components in a variety of applications involving uniform and gradient thermomechanical loading.

Study of Water Pollution Early Warning Framework Based on Internet of Things

NASA Astrophysics Data System (ADS)

Chengfang, H.; Xiao, X.; Dingtao, S.; Bo, C.; Xiongfei, W.

2016-06-01

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

Predicting fruit fly's sensing rate with insect flight simulations.

PubMed

Chang, Song; Wang, Z Jane

2014-08-05

Without sensory feedback, flies cannot fly. Exactly how various feedback controls work in insects is a complex puzzle to solve. What do insects measure to stabilize their flight? How often and how fast must insects adjust their wings to remain stable? To gain insights into algorithms used by insects to control their dynamic instability, we develop a simulation tool to study free flight. To stabilize flight, we construct a control algorithm that modulates wing motion based on discrete measurements of the body-pitch orientation. Our simulations give theoretical bounds on both the sensing rate and the delay time between sensing and actuation. Interpreting our findings together with experimental results on fruit flies' reaction time and sensory motor reflexes, we conjecture that fruit flies sense their kinematic states every wing beat to stabilize their flight. We further propose a candidate for such a control involving the fly's haltere and first basalar motor neuron. Although we focus on fruit flies as a case study, the framework for our simulation and discrete control algorithms is applicable to studies of both natural and man-made fliers.

A model framework for actuation and sensing of ionic polymer-metal composites: prospective on frequency and shear response through simulation tools

NASA Astrophysics Data System (ADS)

Stalbaum, Tyler; Shen, Qi; Kim, Kwang J.

2017-04-01

Ionic polymer-metal composite (IPMC) is a promising material for soft-robotic actuator and sensor applications. This material system offers large deformation response for low input voltage and has an aptitude for operation in hydrated environments. Researchers have been developing IPMC actuators and sensors for applications with examples of self-sensing actuators, artificial fish fins and biomimicry of other aquatic lifeforms, and in medical operations such as in guided catheter devices. IPMCs have been developed in a range of geometric configurations, with tube or cylindrical and flat-plate rectangular as the most common shapes. Several mathematical and physics-based models have been developed for describing the transduction effects of IPMCs. In this work, the underlying theories of electromechanical and mechanoelectrical transduction in IPMCs are discussed, and simulated results of frequency response and shear response are presented. A model backbone is utilized which is primarily based on ion-transport and charge dynamics within the polymer membrane. The electromechanical model, that is with an IPMC as an actuator, is caused when an electric field is applied across the membrane causing ionic migration and swelling in the polymer membrane, which is based on the Poisson-Nernst-Planck equations and solid mechanics models. The mechanoelectric model is similar in underlying physics; however, the primary mechanisms of transduction are of different significance, where anion concentrations are as important as cations. COMSOL Multiphysics is utilized for simulations. Example applications of the modeling framework are presented. The simulated results provide additional support for the underlying physics theories discussed.

X-Windows Socket Widget Class

NASA Technical Reports Server (NTRS)

Barry, Matthew R.

2006-01-01

The X-Windows Socket Widget Class ("Class" is used here in the object-oriented-programming sense of the word) was devised to simplify the task of implementing network connections for graphical-user-interface (GUI) computer programs. UNIX Transmission Control Protocol/Internet Protocol (TCP/IP) socket programming libraries require many method calls to configure, operate, and destroy sockets. Most X Windows GUI programs use widget sets or toolkits to facilitate management of complex objects. The widget standards facilitate construction of toolkits and application programs. The X-Windows Socket Widget Class encapsulates UNIX TCP/IP socket-management tasks within the framework of an X Windows widget. Using the widget framework, X Windows GUI programs can treat one or more network socket instances in the same manner as that of other graphical widgets, making it easier to program sockets. Wrapping ISP socket programming libraries inside a widget framework enables a programmer to treat a network interface as though it were a GUI.

A luminescent Cd(II)-based metal-organic framework for detection of Fe(III) ions in aqueous solution

NASA Astrophysics Data System (ADS)

Li, Fen-Fang; Zhu, Miao-Li; Lu, Li-Ping

2018-05-01

A novel Cd((II)-organic framework [Cd(Hcbic)]n (H3cbic = 1-(4-carboxybenz-yl)-1H-benzoim-idazole-5, 6-dicarboxylic acid) was assembled and characterized by X-ray single crystal analysis. The Cd-MOF features one-dimensional left and right-handed double helical chains with screw-pitch of about 4.727 Å and the 4-methyl benzoic acid groups of Hcbic2- ligands in MOF-1 play many ribbons distributing in the two sides of the 2D networks. It is found that MOF-1 shows high selectivity (KSV = 1.8 × 105 L / mol) for Fe3+ ions in water solution with luminescent quenching because of the existence of uncoordinated carboxyl groups within open frameworks, which indicates that MOF-1 is a simple and reliable detection sensing reagent for Fe3+ in practical applications.

Flexibility transition and guest-driven reconstruction in a ferroelastic metal-organic framework†Electronic supplementary information (ESI) available: Atomic coordinates and lattice parameter data. CCDC 1016797. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/c4ce01572jClick here for additional data file.

PubMed

Hunt, Sarah J; Cliffe, Matthew J; Hill, Joshua A; Cairns, Andrew B; Funnell, Nicholas P; Goodwin, Andrew L

2015-01-14

The metal-organic framework copper(i) tricyanomethanide, Cu(tcm), undergoes a ferroelastic transition on cooling below T f = 240 K. Thermal expansion measurements reveal an order-of-magnitude variation in framework flexibility across T f . The low-temperature phase α-Cu(tcm) exhibits colossal positive and negative thermal expansion that is the strongest ever reported for a framework material. On exposure to acetonitrile, Cu(tcm) undergoes a reconstructive solid-phase transition to acetonitrilocopper(i) tricyanomethanide. This transition can be reversed by heating under vacuum. Infrared spectroscopy measurements are sensitive to the phase change, suggesting that Cu(tcm) may find application in solid-phase acetonitrile sensing.

Satellite Remote Sensing of Harmful Algal Blooms (HABs) and a Potential Synthesized Framework

PubMed Central

Shen, Li; Xu, Huiping; Guo, Xulin

2012-01-01

Harmful algal blooms (HABs) are severe ecological disasters threatening aquatic systems throughout the World, which necessitate scientific efforts in detecting and monitoring them. Compared with traditional in situ point observations, satellite remote sensing is considered as a promising technique for studying HABs due to its advantages of large-scale, real-time, and long-term monitoring. The present review summarizes the suitability of current satellite data sources and different algorithms for detecting HABs. It also discusses the spatial scale issue of HABs. Based on the major problems identified from previous literature, including the unsystematic understanding of HABs, the insufficient incorporation of satellite remote sensing, and a lack of multiple oceanographic explanations of the mechanisms causing HABs, this review also attempts to provide a comprehensive understanding of the complicated mechanism of HABs impacted by multiple oceanographic factors. A potential synthesized framework can be established by combining multiple accessible satellite remote sensing approaches including visual interpretation, spectra analysis, parameters retrieval and spatial-temporal pattern analysis. This framework aims to lead to a systematic and comprehensive monitoring of HABs based on satellite remote sensing from multiple oceanographic perspectives. PMID:22969372

Applications of the SWOT Mission to Reservoirs in the Mekong River Basin

NASA Astrophysics Data System (ADS)

Bonnema, M.; Hossain, F.

2017-12-01

The forthcoming Surface Water and Ocean Topography (SWOT) mission has the potential to significantly improve our ability to observe artificial reservoirs globally from a remote sensing perspective. By providing simultaneous estimates of reservoir water surface extent and elevation with near global coverage, reservoir storage changes can be estimated. Knowing how reservoir storage changes over time is critical for understanding reservoir impacts on river systems. In data limited regions, remote sensing is often the only viable method of retrieving such information about reservoir operations. When SWOT launches in 2021, it will join an array of satellite sensors with long histories of reservoir observation and monitoring capabilities. There are many potential synergies in the complimentary use of future SWOT observations with observations from current satellite sensors. The work presented here explores the potential benefits of utilizing SWOT observations over 20 reservoirs in the Mekong River Basin. The SWOT hydrologic simulator, developed by NASA Jet Propulsion Laboratory, is used to generate realistic SWOT observations, which are then inserted into a previously established remote sensing modeling framework of the 20 Mekong Basin reservoirs. This framework currently combines data from Landsat missions, Jason radar altimeters, and the Shuttle Radar and Topography Mission (SRTM), to provide monthly estimates of reservoir storage change. The incorporation of SWOT derived reservoir surface area and elevation into the model is explored in an effort to improve both accuracy and temporal resolution of observed reservoir operations.

Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk.

PubMed

Ceccato, P; Connor, S J; Jeanne, I; Thomson, M C

2005-03-01

Despite over 30 years of scientific research, algorithm development and multitudes of publications relating Remote Sensing (RS) information with the spatial and temporal distribution of malaria, it is only in recent years that operational products have been adopted by malaria control decision-makers. The time is ripe for the wealth of research knowledge and products from developed countries be made available to the decision-makers in malarious regions of the globe where this information is urgently needed. This paper reviews the capability of RS to provide useful information for operational malaria early warning systems. It also reviews the requirements for monitoring the major components influencing emergence of malaria and provides examples of applications that have been made. Discussion of the issues that have impeded implementation on a global scale and how those barriers are disappearing with recent economic, technological and political developments are explored; and help pave the way for implementation of an integrated Malaria Early Warning System framework using RS technologies.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing.

PubMed

Xu, Jason; Minin, Vladimir N

2015-07-01

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes.

Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries using Structural Coupling and Compressive Sensing

PubMed Central

Xi, Yan; Zhao, Jun; Bennett, James R.; Stacy, Mitchel R.; Sinusas, Albert J.; Wang, Ge

2016-01-01

Objective A unified reconstruction framework is presented for simultaneous CT-MRI reconstruction. Significance Combined CT-MRI imaging has the potential for improved results in existing preclinical and clinical applications, as well as opening novel research directions for future applications. Methods In an ideal CT-MRI scanner, CT and MRI acquisitions would occur simultaneously, and hence would be inherently registered in space and time. Alternatively, separately acquired CT and MRI scans can be fused to simulate an instantaneous acquisition. In this study, structural coupling and compressive sensing techniques are combined to unify CT and MRI reconstructions. A bidirectional image estimation method was proposed to connect images from different modalities. Hence, CT and MRI data serve as prior knowledge to each other for better CT and MRI image reconstruction than what could be achieved with separate reconstruction. Results Our integrated reconstruction methodology is demonstrated with numerical phantom and real-dataset based experiments, and has yielded promising results. PMID:26672028

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

PubMed Central

Xu, Jason; Minin, Vladimir N.

2016-01-01

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes. PMID:26949377

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls

PubMed Central

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-01-01

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense. PMID:26193275

Ultrasonic Device Would Open Pipe Bombs

NASA Technical Reports Server (NTRS)

El-Raheb, Michael S.; Adams, Marc A.; Zwissler, James G.

1991-01-01

Piezoelectric ultrasonic transducer, energized by frequency generator and power supply, vibrates shell of pipe bomb while hardly disturbing explosive inner material. Frequency-control circuitry senses resonance in shell and holds generator at that frequency to induce fatigue cracking in threads of end cap. In addition to disarming bombs, ultrasonically induced fatigue may have other applications. In manufacturing, replaces some machining and cutting operations. In repair of equipment, cleanly and quickly disassembles corroded parts. In demolition of buildings used to dismember steel framework safely and controllably.

Analysis of Drought in North Darfur Region of Sudan: Application of the DPSIR Framework on Long Term Data

NASA Astrophysics Data System (ADS)

Mohmmed, Alnail; Zhange, Ke; Makomere, Reuben; Twecan, Dalson; Mohamme, Mustafa

2017-04-01

Darfur region in western Sudan is located in one of the world's most inhospitable environments, adjacent to the Sahara desert, conflicts and drought have severely degraded this fragile area, devastating the environment, livestock and people. Northern Darfur is bedeviled with frequent drought due to insufficient water resources, high summer temperatures, and poor precipitation. Monitoring drought and providing timely seasonal predictions is important for integrated drought risk reduction in the region. This paper evaluates drought conditions in North Darfur by applying meteorological, remote sensing and crop production data, as well as the Driving force-Pressure-State-Impacts-Response (DPSIR) assessment framework. Interviews, group discussions and participant observations were conducted in order to understand the DPSIR framework indicators. The relationship between the Reconnaissance Drought Index (RDI), Vegetation Condition Index (VCI) and Soil Moisture Content Index (SMCI) were evaluated utilizing data from all five North Darfur counties during 10 growing seasons (2004-2013). Our results showed a strong correlation between RDI, VCI, and SMAI. Also, a significant agreement was noticed between Yield Anomaly Index (YAI) and Rainfall Anomaly Index (RAI). Generally, a high correlation coefficient was obtained between the meteorology drought index and remote sensing indices, which demonstrates the effectiveness of the above indices for evaluating agricultural drought in the sub-Saharan area. Keywords: Drought; Vegetation Condition Index; Reconnaissance Drought Index; Soil Moisture Content Index; North Darfur.

Sense of place among New England organic farmers and commercial fishermen: How social context shapes identity and environmentally responsible behavior

NASA Astrophysics Data System (ADS)

Mueller, Anneliese Marie

Given the prominence of sense of place in new environmental education curricula, this study aims to strengthen the conceptual and empirical foundations of sense of place, and to determine how sense of place may be linked to environmentally responsible behavior. For this study, five commercial fishermen and five organic farmers from the New England Seacoast region participated in a series of in-depth phenomenological interviews and observations. The data was systematically coded in order to allow themes and categories to emerge. The results indicate that aspects of the existing conceptual framework of sense of place, such as place attachment, ecological knowledge, and public involvement, do in fact describe the relationship between people and place. However, the results also indicate that two conceptual elements---attention to social context and awareness of moral theory---are missing from the current conceptual framework in EE theory. These results suggest that the current framework should be expanded to emphasize the role of human and non-human communities: the development of a sense of place and the learning of environmentally responsible behavior must be situated within a social context. This study lends support to the view that for sense of place to move people to ethical action, it is crucial for them to recognize, and to participate in, a community of support and care.

Sustainable Water Management & Satellite Remote Sensing

EPA Science Inventory

Eutrophication assessment frameworks such as the Australian National Water Quality Management Strategy, Oslo Paris (OSPAR) Commission Common Procedure, Water Framework Directive (WFD) of the European Union, Marine Strategy Framework Directive (MSFD) from the European Commission, ...

Electrogenerated Chemiluminescence Behavior of Au nanoparticles-hybridized Pb (II) metal-organic framework and its application in selective sensing hexavalent chromium.

PubMed

Ma, Hongmin; Li, Xiaojian; Yan, Tao; Li, Yan; Liu, Haiyang; Zhang, Yong; Wu, Dan; Du, Bin; Wei, Qin

2016-02-23

In this work, a novel electrochemiluminescence (ECL) sensor based on Au nanoparticles-hybridized Pb (II)-β-cyclodextrin (Pb-β-CD) metal-organic framework for detecting hexavalent chromium (Cr(VI)) was developed. Pb-β-CD shows excellent ECL behavior and unexpected reducing ability towards Au ions. Au nanoparticles could massively form on the surface of Pb-β-CD (Au@Pb-β-CD) without use of any additional reducing agent. In the presence of coreactant K2S2O8, the ECL emission of Pb-β-CD was enhanced by the formation of Au nanoparticles. Cr(VI) can collisionally quench the ECL behavior of Au@Pb-β-CD/S2O8(2-) system and the detection mechanism was investigated. This ECL sensor is found to have a linear response in the range of 0.01-100 μM and a low detection limit of 3.43 nM (S/N = 3) under the optimal conditions. These results suggest that metal-organic framework Au@Pb-β-CD has great potential in extending the application in the ECL field as an efficient luminophore.

Thermodynamic framework to assess low abundance DNA mutation detection by hybridization.

PubMed

Willems, Hanny; Jacobs, An; Hadiwikarta, Wahyu Wijaya; Venken, Tom; Valkenborg, Dirk; Van Roy, Nadine; Vandesompele, Jo; Hooyberghs, Jef

2017-01-01

The knowledge of genomic DNA variations in patient samples has a high and increasing value for human diagnostics in its broadest sense. Although many methods and sensors to detect or quantify these variations are available or under development, the number of underlying physico-chemical detection principles is limited. One of these principles is the hybridization of sample target DNA versus nucleic acid probes. We introduce a novel thermodynamics approach and develop a framework to exploit the specific detection capabilities of nucleic acid hybridization, using generic principles applicable to any platform. As a case study, we detect point mutations in the KRAS oncogene on a microarray platform. For the given platform and hybridization conditions, we demonstrate the multiplex detection capability of hybridization and assess the detection limit using thermodynamic considerations; DNA containing point mutations in a background of wild type sequences can be identified down to at least 1% relative concentration. In order to show the clinical relevance, the detection capabilities are confirmed on challenging formalin-fixed paraffin-embedded clinical tumor samples. This enzyme-free detection framework contains the accuracy and efficiency to screen for hundreds of mutations in a single run with many potential applications in molecular diagnostics and the field of personalised medicine.

Planning perception and action for cognitive mobile manipulators

NASA Astrophysics Data System (ADS)

Gaschler, Andre; Nogina, Svetlana; Petrick, Ronald P. A.; Knoll, Alois

2013-12-01

We present a general approach to perception and manipulation planning for cognitive mobile manipulators. Rather than hard-coding single purpose robot applications, a robot should be able to reason about its basic skills in order to solve complex problems autonomously. Humans intuitively solve tasks in real-world scenarios by breaking down abstract problems into smaller sub-tasks and use heuristics based on their previous experience. We apply a similar idea for planning perception and manipulation to cognitive mobile robots. Our approach is based on contingent planning and run-time sensing, integrated in our knowledge of volumes" planning framework, called KVP. Using the general-purpose PKS planner, we model information-gathering actions at plan time that have multiple possible outcomes at run time. As a result, perception and sensing arise as necessary preconditions for manipulation, rather than being hard-coded as tasks themselves. We demonstrate the e ectiveness of our approach on two scenarios covering visual and force sensing on a real mobile manipulator.

Synthesis of Co3O4/TiO2 composite by pyrolyzing ZIF-67 for detection of xylene

NASA Astrophysics Data System (ADS)

Bai, Shouli; Tian, Ke; Tian, Ye; Guo, Jun; Feng, Yongjun; Luo, Ruixian; Li, Dianqing; Chen, Aifan; Liu, Chung Chiun

2018-03-01

Co3O4/TiO2 composites with p-n heterojunction have been successfully prepared by pyrolyzing sacrificial template of Ti ion loaded Co-based Zeolitic imidazolate framework (ZIF-67). The structure and morphology of composite have been characterized by means of the analysis of XRD, FESEM, HRTEM and XPS spectra. The composite with a Co/Ti molar ratio of 4:1 exhibits the maximum sensing response of 6.17-50 ppm xylene, which is 5 times higher than pristine Co3O4. Moreover, Co3O4/TiO2 composite also shows good selectivity, long-term stability and rapid response and recovery. Such excellent sensing performances are attributed to material porous structure, high specific surface and the formation of abundant p-n heterojunction that permits the gas adsorption, diffusion and surface reaction and then improve the gas sensing performance. This work develops a promising synthesized approach of metal oxide composites for broader MOFs application in gas sensor field.

Optical touch sensing: practical bounds for design and performance

NASA Astrophysics Data System (ADS)

Bläßle, Alexander; Janbek, Bebart; Liu, Lifeng; Nakamura, Kanna; Nolan, Kimberly; Paraschiv, Victor

2013-02-01

Touch sensitive screens are used in many applications ranging in size from smartphones and tablets to display walls and collaborative surfaces. In this study, we consider optical touch sensing, a technology best suited for large-scale touch surfaces. Optical touch sensing utilizes cameras and light sources placed along the edge of the display. Within this framework, we first find a sufficient number of cameras necessary for identifying a convex polygon touching the screen, using a continuous light source on the boundary of a circular domain. We then find the number of cameras necessary to distinguish between two circular objects in a circular or rectangular domain. Finally, we use Matlab to simulate the polygonal mesh formed from distributing cameras and light sources on a circular domain. Using this, we compute the number of polygons in the mesh and the maximum polygon area to give us information about the accuracy of the configuration. We close with summary and conclusions, and pointers to possible future research directions.

Development of a software framework for data assimilation and its applications for streamflow forecasting in Japan

NASA Astrophysics Data System (ADS)

Noh, S. J.; Tachikawa, Y.; Shiiba, M.; Yorozu, K.; Kim, S.

2012-04-01

Data assimilation methods have received increased attention to accomplish uncertainty assessment and enhancement of forecasting capability in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modeling software are based on a deterministic approach. In this study, we developed a hydrological modeling framework for sequential data assimilation, so called MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modeling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. Sequential data assimilation based on the particle filters is available for any hydrologic models based on MPI-OHyMoS considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for short-term streamflow forecasting of several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and remotely-sensed rainfall data such as X-band or C-band radar is estimated and mitigated in the sequential data assimilation.

Accumulating pyramid spatial-spectral collaborative coding divergence for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Sun, Hao; Zou, Huanxin; Zhou, Shilin

2016-03-01

Detection of anomalous targets of various sizes in hyperspectral data has received a lot of attention in reconnaissance and surveillance applications. Many anomaly detectors have been proposed in literature. However, current methods are susceptible to anomalies in the processing window range and often make critical assumptions about the distribution of the background data. Motivated by the fact that anomaly pixels are often distinctive from their local background, in this letter, we proposed a novel hyperspectral anomaly detection framework for real-time remote sensing applications. The proposed framework consists of four major components, sparse feature learning, pyramid grid window selection, joint spatial-spectral collaborative coding and multi-level divergence fusion. It exploits the collaborative representation difference in the feature space to locate potential anomalies and is totally unsupervised without any prior assumptions. Experimental results on airborne recorded hyperspectral data demonstrate that the proposed methods adaptive to anomalies in a large range of sizes and is well suited for parallel processing.

Simulation Framework to Estimate the Performance of CO2 and O2 Sensing from Space and Airborne Platforms for the ASCENDS Mission Requirements Analysis

NASA Technical Reports Server (NTRS)

Plitau, Denis; Prasad, Narasimha S.

2012-01-01

The Active Sensing of CO2 Emissions over Nights Days and Seasons (ASCENDS) mission recommended by the NRC Decadal Survey has a desired accuracy of 0.3% in carbon dioxide mixing ratio (XCO2) retrievals requiring careful selection and optimization of the instrument parameters. NASA Langley Research Center (LaRC) is investigating 1.57 micron carbon dioxide as well as the 1.26-1.27 micron oxygen bands for our proposed ASCENDS mission requirements investigation. Simulation studies are underway for these bands to select optimum instrument parameters. The simulations are based on a multi-wavelength lidar modeling framework being developed at NASA LaRC to predict the performance of CO2 and O2 sensing from space and airborne platforms. The modeling framework consists of a lidar simulation module and a line-by-line calculation component with interchangeable lineshape routines to test the performance of alternative lineshape models in the simulations. As an option the line-by-line radiative transfer model (LBLRTM) program may also be used for line-by-line calculations. The modeling framework is being used to perform error analysis, establish optimum measurement wavelengths as well as to identify the best lineshape models to be used in CO2 and O2 retrievals. Several additional programs for HITRAN database management and related simulations are planned to be included in the framework. The description of the modeling framework with selected results of the simulation studies for CO2 and O2 sensing is presented in this paper.

Multispectral image enhancement processing for microsat-borne imager

NASA Astrophysics Data System (ADS)

Sun, Jianying; Tan, Zheng; Lv, Qunbo; Pei, Linlin

2017-10-01

With the rapid development of remote sensing imaging technology, the micro satellite, one kind of tiny spacecraft, appears during the past few years. A good many studies contribute to dwarfing satellites for imaging purpose. Generally speaking, micro satellites weigh less than 100 kilograms, even less than 50 kilograms, which are slightly larger or smaller than the common miniature refrigerators. However, the optical system design is hard to be perfect due to the satellite room and weight limitation. In most cases, the unprocessed data captured by the imager on the microsatellite cannot meet the application need. Spatial resolution is the key problem. As for remote sensing applications, the higher spatial resolution of images we gain, the wider fields we can apply them. Consequently, how to utilize super resolution (SR) and image fusion to enhance the quality of imagery deserves studying. Our team, the Key Laboratory of Computational Optical Imaging Technology, Academy Opto-Electronics, is devoted to designing high-performance microsat-borne imagers and high-efficiency image processing algorithms. This paper addresses a multispectral image enhancement framework for space-borne imagery, jointing the pan-sharpening and super resolution techniques to deal with the spatial resolution shortcoming of microsatellites. We test the remote sensing images acquired by CX6-02 satellite and give the SR performance. The experiments illustrate the proposed approach provides high-quality images.

Translational Imaging Spectroscopy for Proximal Sensing

PubMed Central

Rogass, Christian; Koerting, Friederike M.; Mielke, Christian; Brell, Maximilian; Boesche, Nina K.; Bade, Maria; Hohmann, Christian

2017-01-01

Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a precise pre-processing of hyperspectral data cubes to retrieve at-surface reflectance in order to conduct spectral feature-based comparison of unknown sample spectra to known library spectra. A new pre-processing chain called GeoMAP-Trans for at-surface reflectance retrieval is proposed here as an analogue to other algorithms published by the team of authors. It consists of a radiometric, a geometric and a spectral module. Each module consists of several processing steps that are described in detail. The processing chain was adapted to the broadly used HySPEX VNIR/SWIR imaging spectrometer system and tested using geological mineral samples. The performance was subjectively and objectively evaluated using standard artificial image quality metrics and comparative measurements of mineral and Lambertian diffuser standards with standard field and laboratory spectrometers. The proposed algorithm provides highly qualitative results, offers broad applicability through its generic design and might be the first one of its kind to be published. A high radiometric accuracy is achieved by the incorporation of the Reduction of Miscalibration Effects (ROME) framework. The geometric accuracy is higher than 1 μpixel. The critical spectral accuracy was relatively estimated by comparing spectra of standard field spectrometers to those from HySPEX for a Lambertian diffuser. The achieved spectral accuracy is better than 0.02% for the full spectrum and better than 98% for the absorption features. It was empirically shown that point and imaging spectrometers provide different results for non-Lambertian samples due to their different sensing principles, adjacency scattering impacts on the signal and anisotropic surface reflection properties. PMID:28800111

Compressed Sensing for Chemistry

NASA Astrophysics Data System (ADS)

Sanders, Jacob Nathan

Many chemical applications, from spectroscopy to quantum chemistry, involve measuring or computing a large amount of data, and then compressing this data to retain the most chemically-relevant information. In contrast, compressed sensing is an emergent technique that makes it possible to measure or compute an amount of data that is roughly proportional to its information content. In particular, compressed sensing enables the recovery of a sparse quantity of information from significantly undersampled data by solving an ℓ 1-optimization problem. This thesis represents the application of compressed sensing to problems in chemistry. The first half of this thesis is about spectroscopy. Compressed sensing is used to accelerate the computation of vibrational and electronic spectra from real-time time-dependent density functional theory simulations. Using compressed sensing as a drop-in replacement for the discrete Fourier transform, well-resolved frequency spectra are obtained at one-fifth the typical simulation time and computational cost. The technique is generalized to multiple dimensions and applied to two-dimensional absorption spectroscopy using experimental data collected on atomic rubidium vapor. Finally, a related technique known as super-resolution is applied to open quantum systems to obtain realistic models of a protein environment, in the form of atomistic spectral densities, at lower computational cost. The second half of this thesis deals with matrices in quantum chemistry. It presents a new use of compressed sensing for more efficient matrix recovery whenever the calculation of individual matrix elements is the computational bottleneck. The technique is applied to the computation of the second-derivative Hessian matrices in electronic structure calculations to obtain the vibrational modes and frequencies of molecules. When applied to anthracene, this technique results in a threefold speed-up, with greater speed-ups possible for larger molecules. The implementation of the method in the Q-Chem commercial software package is described. Moreover, the method provides a general framework for bootstrapping cheap low-accuracy calculations in order to reduce the required number of expensive high-accuracy calculations.

Investigation of the applicability of using the triple redundant hydrogen sensor for methane sensing

NASA Technical Reports Server (NTRS)

Lantz, J. B.; Wynveen, R. A.

1983-01-01

Application specifications for the methane sensor were assembled and design guidelines, development goals and evaluation criteria were formulated. This was done to provide a framework to evaluate sensor performance and any design adjustments to the preprototype sensor that could be required to provide methane sensitivity. Good response to hydrogen was experimentally established for four hydrogen sensor elements to be later evaluated for methane response. Prior results were assembled and analyzed for other prototype hydrogen sensor performance parameters to form a comparison base. The four sensor elements previously shown to have good hydrogen response were experimentally evaluated for methane response in 2.5% methane-in-air. No response was obtained for any of the elements, despite the high methane concentration used (50% of the Lower Flammability Limit). It was concluded that the preprototype sensing elements were insensitive to methane and were hydrogen specific. Alternative sensor operating conditions and hardware design changes were considered to provide methane sensitivity to the preprototype sensor, including a variety of different methane sensing techniques. Minor changes to the existing sensor elements, sensor geometry and operating conditions will not make the preprototype hydrogen sensor respond to methane. New sensor elements that will provide methane and hydrogen sensitivity require replacement of the existing thermistor type elements. Some hydrogen sensing characteristics of the modified sensor will be compromised (larger in situ calibration gas volume and H2 nonspecificity). The preprototype hydrogen sensor should be retained for hydrogen monitoring and a separate methane sensor should be developed.

Hyperspectral imaging utility for transportation systems

NASA Astrophysics Data System (ADS)

Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver

2015-03-01

The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.

Distributed MRI reconstruction using Gadgetron-based cloud computing.

PubMed

Xue, Hui; Inati, Souheil; Sørensen, Thomas Sangild; Kellman, Peter; Hansen, Michael S

2015-03-01

To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency. The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications). The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min. A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency. © 2014 Wiley Periodicals, Inc.

Spatiotemporal Local-Remote Senor Fusion (ST-LRSF) for Cooperative Vehicle Positioning.

PubMed

Jeong, Han-You; Nguyen, Hoa-Hung; Bhawiyuga, Adhitya

2018-04-04

Vehicle positioning plays an important role in the design of protocols, algorithms, and applications in the intelligent transport systems. In this paper, we present a new framework of spatiotemporal local-remote sensor fusion (ST-LRSF) that cooperatively improves the accuracy of absolute vehicle positioning based on two state estimates of a vehicle in the vicinity: a local sensing estimate, measured by the on-board exteroceptive sensors, and a remote sensing estimate, received from neighbor vehicles via vehicle-to-everything communications. Given both estimates of vehicle state, the ST-LRSF scheme identifies the set of vehicles in the vicinity, determines the reference vehicle state, proposes a spatiotemporal dissimilarity metric between two reference vehicle states, and presents a greedy algorithm to compute a minimal weighted matching (MWM) between them. Given the outcome of MWM, the theoretical position uncertainty of the proposed refinement algorithm is proven to be inversely proportional to the square root of matching size. To further reduce the positioning uncertainty, we also develop an extended Kalman filter model with the refined position of ST-LRSF as one of the measurement inputs. The numerical results demonstrate that the proposed ST-LRSF framework can achieve high positioning accuracy for many different scenarios of cooperative vehicle positioning.

Spatial conservation planning framework for assessing conservation opportunities in the Atlantic Forest of Brazil

PubMed Central

Giorgi, Ana Paula; Rovzar, Corey; Davis, Kelsey S.; Fuller, Trevon; Buermann, Wolfgang; Saatchi, Sassan; Smith, Thomas B.; Silveira, Luis Fabio; Gillespie, Thomas W.

2017-01-01

Historic rates of habitat change and growing exploitation of natural resources threaten avian biodiversity in the Brazilian Atlantic Forest, a global biodiversity hotspot. We implemented a twostage framework for conservation planning in the Atlantic Forest. First, we used ecological niche modeling to predict the distributions of 23 endemic bird species using 19 climatic metrics and 12 spectral and radar remote sensing metrics. Second, we utilized the principle of complementarity to prioritize new sites to augment the Atlantic Forest's existing reserves. The best predictors of bird distributions were precipitation metrics (the seasonality of rainfall) and radar remote sensing metrics (QSCAT). The existing protected areas do not include 10% of the habitat of each of the 23 endemic species. We propose a more economical set of protected areas by reducing the extent to which new sites duplicate the biodiversity content of existing protected areas. There is a high concordance between the proposed conservation areas that we designed using computerized algorithms and Important Bird Areas prioritized by BirdLife International. Insofar as deforestation in the Atlantic Forest is similar to land conversion in other biodiversity hotspots, our methodology is applicable to conservation efforts elsewhere in the world. PMID:28210009

In Situ Electrochemical Sensing and Real-Time Monitoring Live Cells Based on Freestanding Nanohybrid Paper Electrode Assembled from 3D Functionalized Graphene Framework.

PubMed

Zhang, Yan; Xiao, Jian; Lv, Qiying; Wang, Lu; Dong, Xulin; Asif, Muhammad; Ren, Jinghua; He, Wenshan; Sun, Yimin; Xiao, Fei; Wang, Shuai

2017-11-08

In this work, we develop a new type of freestanding nanohybrid paper electrode assembled from 3D ionic liquid (IL) functionalized graphene framework (GF) decorated by gold nanoflowers (AuNFs), and explore its practical application in in situ electrochemical sensing of live breast cell samples by real-time tracking biomarker H 2 O 2 released from cells. The AuNFs modified IL functionalized GF (AuNFs/IL-GF) was synthesized via a facile and efficient dopamine-assisted one-pot self-assembly strategy. The as-obtained nanohybrid assembly exhibits a typical 3D hierarchical porous structure, where the highly active electrocatalyst AuNFs are well dispersed on IL-GF scaffold. And the graft of hydrophilic IL molecules (i.e., 1-butyl-3-methylimidazolium tetrafluoroborate, BMIMBF 4 ) on graphene nanosheets not only avoids their agglomeration and disorder stacking during the self-assembly but also endows the integrated IL-GF monolithic material with unique hydrophilic properties, which enables it to be readily dispersed in aqueous solution and processed into freestanding paperlike material. Because of the unique structural properties and the combinational advantages of different components in the AuNFs/IL-GF composite, the resultant nanohybrid paper electrode exhibits good nonenzymatic electrochemical sensing performance toward H 2 O 2 . When used in real-time tracking H 2 O 2 secreted from different breast cells attached to the paper electrode without or with radiotherapy treatment, the proposed electrochemical sensor based on freestanding AuNFs/IL-GF paper electrode can distinguish the normal breast cell HBL-100 from the cancer breast cells MDA-MB-231 and MCF-7 cells, and assess the radiotherapy effects to different breast cancer cells, which opens a new horizon in real-time monitoring cancer cells by electrochemical sensing platform.

Building Capacity for Earth Observations in Support of the United Nations Sustainable Development Goals

NASA Astrophysics Data System (ADS)

Blevins, B.; Prados, A. I.; Hook, E.

2017-12-01

The Group on Earth Observations (GEO) looks to build a future where the international community uses Earth observations to make better, informed decisions. This includes application in international agreements such as the UN Sustainable Development Goals (SDGs), the Sendai Framework for Disaster Risk Reduction, and the Convention on Biological Diversity. To do this, decision makers first need to build the necessary skills. NASA's Applied Remote Sensing Training program (ARSET) seeks to build capacity through remote sensing training. In-person and online trainings raise awareness, enable data access, and demonstrate applications of Earth observations. Starting in 2017, ARSET began offering training focused on applying Earth data to the UN SDGs. These trainings offer insight into applications of satellite data in support of implementing, monitoring, and evaluating the SDGs. This presentation will provide an overview of the use of NASA satellite data to track progress towards increased food security, disaster risk reduction, and conservation of natural resources for societal benefit. It will also include a discussion on capacity building best practices and lessons learned for using Earth observations to meet SDG targets, based on feedback from engaging over 800 participants from 89 nations and 580 organizations in ARSET SDG trainings.

Demonstrating the Value of Near Real-time Satellite-based Earth Observations in a Research and Education Framework

NASA Astrophysics Data System (ADS)

Chiu, L.; Hao, X.; Kinter, J. L.; Stearn, G.; Aliani, M.

2017-12-01

The launch of GOES-16 series provides an opportunity to advance near real-time applications in natural hazard detection, monitoring and warning. This study demonstrates the capability and values of receiving real-time satellite-based Earth observations over a fast terrestrial networks and processing high-resolution remote sensing data in a university environment. The demonstration system includes 4 components: 1) Near real-time data receiving and processing; 2) data analysis and visualization; 3) event detection and monitoring; and 4) information dissemination. Various tools are developed and integrated to receive and process GRB data in near real-time, produce images and value-added data products, and detect and monitor extreme weather events such as hurricane, fire, flooding, fog, lightning, etc. A web-based application system is developed to disseminate near-real satellite images and data products. The images are generated with GIS-compatible format (GeoTIFF) to enable convenient use and integration in various GIS platforms. This study enhances the capacities for undergraduate and graduate education in Earth system and climate sciences, and related applications to understand the basic principles and technology in real-time applications with remote sensing measurements. It also provides an integrated platform for near real-time monitoring of extreme weather events, which are helpful for various user communities.

Framework Stability of Nanocrystalline NaY in Aqueous Solution at Varying pH

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

Petushkov, Anton; Freeman, Jasmine; Larsen, Sarah C.

Nanocrystalline zeolites (with crystal sizes of less than 50 nm) are versatile, porous nanomaterials with potential applications in a broad range of areas including bifunctional catalysis, drug delivery, environmental protection, and sensing, to name a few. The characterization of the properties of nanocrystalline zeolites on a fundamental level is critical to the realization of these innovative applications. Nanocrystalline zeolites have unique surface chemistry that is distinct from conventional microcrystalline zeolite materials and that will result in novel applications. In the proposed work, magnetic resonance techniques (solid state nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR)) will be used tomore » elucidate the structure and reactivity of nanocrystalline zeolites and to motivate bifunctional applications. Density functional theory (DFT) calculations will enhance data interpretation through chemical shift, quadrupole coupling constant, g-value and hyperfine calculations.« less

A Grassroots Remote Sensing Toolkit Using Live Coding, Smartphones, Kites and Lightweight Drones

PubMed Central

Anderson, K.; Griffiths, D.; DeBell, L.; Hancock, S.; Duffy, J. P.; Shutler, J. D.; Reinhardt, W. J.; Griffiths, A.

2016-01-01

This manuscript describes the development of an android-based smartphone application for capturing aerial photographs and spatial metadata automatically, for use in grassroots mapping applications. The aim of the project was to exploit the plethora of on-board sensors within modern smartphones (accelerometer, GPS, compass, camera) to generate ready-to-use spatial data from lightweight aerial platforms such as drones or kites. A visual coding ‘scheme blocks’ framework was used to build the application (‘app’), so that users could customise their own data capture tools in the field. The paper reports on the coding framework, then shows the results of test flights from kites and lightweight drones and finally shows how open-source geospatial toolkits were used to generate geographical information system (GIS)-ready GeoTIFF images from the metadata stored by the app. Two Android smartphones were used in testing–a high specification OnePlus One handset and a lower cost Acer Liquid Z3 handset, to test the operational limits of the app on phones with different sensor sets. We demonstrate that best results were obtained when the phone was attached to a stable single line kite or to a gliding drone. Results show that engine or motor vibrations from powered aircraft required dampening to ensure capture of high quality images. We demonstrate how the products generated from the open-source processing workflow are easily used in GIS. The app can be downloaded freely from the Google store by searching for ‘UAV toolkit’ (UAV toolkit 2016), and used wherever an Android smartphone and aerial platform are available to deliver rapid spatial data (e.g. in supporting decision-making in humanitarian disaster-relief zones, in teaching or for grassroots remote sensing and democratic mapping). PMID:27144310

A Grassroots Remote Sensing Toolkit Using Live Coding, Smartphones, Kites and Lightweight Drones.

PubMed

Anderson, K; Griffiths, D; DeBell, L; Hancock, S; Duffy, J P; Shutler, J D; Reinhardt, W J; Griffiths, A

2016-01-01

This manuscript describes the development of an android-based smartphone application for capturing aerial photographs and spatial metadata automatically, for use in grassroots mapping applications. The aim of the project was to exploit the plethora of on-board sensors within modern smartphones (accelerometer, GPS, compass, camera) to generate ready-to-use spatial data from lightweight aerial platforms such as drones or kites. A visual coding 'scheme blocks' framework was used to build the application ('app'), so that users could customise their own data capture tools in the field. The paper reports on the coding framework, then shows the results of test flights from kites and lightweight drones and finally shows how open-source geospatial toolkits were used to generate geographical information system (GIS)-ready GeoTIFF images from the metadata stored by the app. Two Android smartphones were used in testing-a high specification OnePlus One handset and a lower cost Acer Liquid Z3 handset, to test the operational limits of the app on phones with different sensor sets. We demonstrate that best results were obtained when the phone was attached to a stable single line kite or to a gliding drone. Results show that engine or motor vibrations from powered aircraft required dampening to ensure capture of high quality images. We demonstrate how the products generated from the open-source processing workflow are easily used in GIS. The app can be downloaded freely from the Google store by searching for 'UAV toolkit' (UAV toolkit 2016), and used wherever an Android smartphone and aerial platform are available to deliver rapid spatial data (e.g. in supporting decision-making in humanitarian disaster-relief zones, in teaching or for grassroots remote sensing and democratic mapping).

Zn/Cd/Cu- frameworks constructed by 3,3‧-diphenyldicarboxylate and 1,4-bis(1,2,4-triazol-1-yl)butane: Syntheses, structure, luminescence and luminescence sensing for metal ion in aqueous medium

NASA Astrophysics Data System (ADS)

Zhang, Mei-Na; Fan, Ting-Ting; Wang, Qiu-Shuang; Han, Hong-Liang; Li, Xia

2018-02-01

Three metal-organic frameworks (MOFs), [M(dpdc)(btb)0.5]n (M = Zn 1, Cd 2; dpdc = 3,3‧-diphenyldicarboxylate and btb = 1,4-bis(1,2,4-triazol-1-yl)butane) and [Cu3(dpdc)3(btb)2]n (3) were prepared and structurally determined. 1 is a 2D structure with the topology of {33·47·54·6}, while 2 possesses a 3D framework with the {312·429·514} topology. Complex 3 displays a 3D framework with the topology of {315.435.55}2{36.48.512.6.7}. 1-2 exhibit intense blue luminescence and high stability in water, which make them highly promising candidates as sensors using in aqueous medium. Complex 1 is a potential bi-functional chemosensor for Fe3+ and Al3+ ions while 2 displays a selective sensing ability to Fe3+ ion. Quenching mechanism of Fe3+ on the luminescence of 1-2 is attributed to the charge transfer process LMCT. 1 and 2 have same compositions but have different structures, thermally stabilities and different luminescence sensing functions. The relationship between MOF structures and luminescence sensing toward metal ions are further discussed.

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies.

PubMed

Lyseen, A K; Nøhr, C; Sørensen, E M; Gudes, O; Geraghty, E M; Shaw, N T; Bivona-Tellez, C

2014-08-15

The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health.

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies

PubMed Central

Nøhr, C.; Sørensen, E. M.; Gudes, O.; Geraghty, E. M.; Shaw, N. T.; Bivona-Tellez, C.

2014-01-01

Summary Objectives The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. Method The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. Results A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Conclusion Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health. PMID:25123730

Non-extensitivity vs. informative moments for financial models —A unifying framework and empirical results

NASA Astrophysics Data System (ADS)

Herrmann, K.

2009-11-01

Information-theoretic approaches still play a minor role in financial market analysis. Nonetheless, there have been two very similar approaches evolving during the last years, one in the so-called econophysics and the other in econometrics. Both generalize the notion of GARCH processes in an information-theoretic sense and are able to capture kurtosis better than traditional models. In this article we present both approaches in a more general framework. The latter allows the derivation of a wide range of new models. We choose a third model using an entropy measure suggested by Kapur. In an application to financial market data, we find that all considered models - with similar flexibility in terms of skewness and kurtosis - lead to very similar results.

Encapsulation of Hemin in Metal-Organic Frameworks for Catalyzing the Chemiluminescence Reaction of the H2O2-Luminol System and Detecting Glucose in the Neutral Condition.

PubMed

Luo, Fenqiang; Lin, Yaolin; Zheng, Liyan; Lin, Xiaomei; Chi, Yuwu

2015-06-03

Novel metal-organic frameworks (MOFs) based solid catalysts have been synthesized by encapsulating Hemin into the HKUST-1 MOF materials. These have been first applied in the chemiluminescence field with outstanding performance. The functionalized MOFs not only maintain an excellent catalytic activity inheriting from Hemin but also can be cyclically utilized as solid mimic peroxidases in the neutral condition. The synthesized Hemin@HKUST-1 composites have been used to develop practical sensors for H2O2 and glucose with wide response ranges and low detection limits. It was envisioned that catalyst-functionalized MOFs for chemiluminescence sensing would have promising applications in green, selective, and sensitive detection of target analytes in the future.

Reputation and Reward: Two Sides of the Same Bitcoin

PubMed Central

Delgado-Segura, Sergi; Tanas, Cristian; Herrera-Joancomartí, Jordi

2016-01-01

In Mobile Crowd Sensing (MCS), the power of the crowd, jointly with the sensing capabilities of the smartphones they wear, provides a new paradigm for data sensing. Scenarios involving user behavior or those that rely on user mobility are examples where standard sensor networks may not be suitable, and MCS provides an interesting solution. However, including human participation in sensing tasks presents numerous and unique research challenges. In this paper, we analyze three of the most important: user participation, data sensing quality and user anonymity. We tackle the three as a whole, since all of them are strongly correlated. As a result, we present PaySense, a general framework that incentivizes user participation and provides a mechanism to validate the quality of collected data based on the users’ reputation. All such features are performed in a privacy-preserving way by using the Bitcoin cryptocurrency. Rather than a theoretical one, our framework has been implemented, and it is ready to be deployed and complement any existing MCS system. PMID:27240373

Reputation and Reward: Two Sides of the Same Bitcoin.

PubMed

Delgado-Segura, Sergi; Tanas, Cristian; Herrera-Joancomartí, Jordi

2016-05-27

In Mobile Crowd Sensing (MCS), the power of the crowd, jointly with the sensing capabilities of the smartphones they wear, provides a new paradigm for data sensing. Scenarios involving user behavior or those that rely on user mobility are examples where standard sensor networks may not be suitable, and MCS provides an interesting solution. However, including human participation in sensing tasks presents numerous and unique research challenges. In this paper, we analyze three of the most important: user participation, data sensing quality and user anonymity. We tackle the three as a whole, since all of them are strongly correlated. As a result, we present PaySense, a general framework that incentivizes user participation and provides a mechanism to validate the quality of collected data based on the users' reputation. All such features are performed in a privacy-preserving way by using the Bitcoin cryptocurrency. Rather than a theoretical one, our framework has been implemented, and it is ready to be deployed and complement any existing MCS system.

Modern and prospective technologies for weather modification activities: Developing a framework for integrating autonomous unmanned aircraft systems

NASA Astrophysics Data System (ADS)

DeFelice, T. P.; Axisa, Duncan

2017-09-01

This paper builds upon the processes and framework already established for identifying, integrating and testing an unmanned aircraft system (UAS) with sensing technology for use in rainfall enhancement cloud seeding programs to carry out operational activities or to monitor and evaluate seeding operations. We describe the development and assessment methodologies of an autonomous and adaptive UAS platform that utilizes in-situ real time data to sense, target and implement seeding. The development of a UAS platform that utilizes remote and in-situ real-time data to sense, target and implement seeding deployed with a companion UAS ensures optimal, safe, secure, cost-effective seeding operations, and the dataset to quantify the results of seeding. It also sets the path for an innovative, paradigm shifting approach for enhancing precipitation independent of seeding mode. UAS technology is improving and their application in weather modification must be explored to lay the foundation for future implementation. The broader significance lies in evolving improved technology and automating cloud seeding operations that lowers the cloud seeding operational footprint and optimizes their effectiveness and efficiency, while providing the temporal and spatial sensitivities to overcome the predictability or sparseness of environmental parameters needed to identify conditions suitable for seeding, and how such might be implemented. The dataset from the featured approach will contain data from concurrent Eulerian and Lagrangian perspectives over sub-cloud scales that will facilitate the development of cloud seeding decision support tools.

Plasticity - Theory and finite element applications.

NASA Technical Reports Server (NTRS)

Armen, H., Jr.; Levine, H. S.

1972-01-01

A unified presentation is given of the development and distinctions associated with various incremental solution procedures used to solve the equations governing the nonlinear behavior of structures, and this is discussed within the framework of the finite-element method. Although the primary emphasis here is on material nonlinearities, consideration is also given to geometric nonlinearities acting separately or in combination with nonlinear material behavior. The methods discussed here are applicable to a broad spectrum of structures, ranging from simple beams to general three-dimensional bodies. The finite-element analysis methods for material nonlinearity are general in the sense that any of the available plasticity theories can be incorporated to treat strain hardening or ideally plastic behavior.

Observability-Based Guidance and Sensor Placement

NASA Astrophysics Data System (ADS)

Hinson, Brian T.

Control system performance is highly dependent on the quality of sensor information available. In a growing number of applications, however, the control task must be accomplished with limited sensing capabilities. This thesis addresses these types of problems from a control-theoretic point-of-view, leveraging system nonlinearities to improve sensing performance. Using measures of observability as an information quality metric, guidance trajectories and sensor distributions are designed to improve the quality of sensor information. An observability-based sensor placement algorithm is developed to compute optimal sensor configurations for a general nonlinear system. The algorithm utilizes a simulation of the nonlinear system as the source of input data, and convex optimization provides a scalable solution method. The sensor placement algorithm is applied to a study of gyroscopic sensing in insect wings. The sensor placement algorithm reveals information-rich areas on flexible insect wings, and a comparison to biological data suggests that insect wings are capable of acting as gyroscopic sensors. An observability-based guidance framework is developed for robotic navigation with limited inertial sensing. Guidance trajectories and algorithms are developed for range-only and bearing-only navigation that improve navigation accuracy. Simulations and experiments with an underwater vehicle demonstrate that the observability measure allows tuning of the navigation uncertainty.

A satellite-driven, client-server hydro-economic model prototype for agricultural water management

NASA Astrophysics Data System (ADS)

Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

2017-04-01

Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because this product is only available for the conterminous United States, the framework is currently only applicable in this region. To obtain information on crop phenology, productivity and transpiration at adequate spatial and temporal frequencies we blend high spatial resolution Landsat information with high temporal fidelity MODIS imagery. The result is a 30 m, 8-day fused dataset of crop greenness that is subsequently transformed into productivity and transpiration by adapting existing forest productivity and transpiration algorithms for agricultural applications. To ensure all involved agencies work with identical information and that end-users are sheltered from the computational burden of storing and processing remote sensing data, this modeling framework is integrated in a client-server architecture based on the Hydra platform (www.hydraplatform.org). Assimilation and processing of resource-intensive remote sensing information, as well as hydrologic and other ancillary data, occur on the server side. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. This architecture ensures that all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Advanced Image Processing for NASA Applications

NASA Technical Reports Server (NTRS)

LeMoign, Jacqueline

2007-01-01

The future of space exploration will involve cooperating fleets of spacecraft or sensor webs geared towards coordinated and optimal observation of Earth Science phenomena. The main advantage of such systems is to utilize multiple viewing angles as well as multiple spatial and spectral resolutions of sensors carried on multiple spacecraft but acting collaboratively as a single system. Within this framework, our research focuses on all areas related to sensing in collaborative environments, which means systems utilizing intracommunicating spatially distributed sensor pods or crafts being deployed to monitor or explore different environments. This talk will describe the general concept of sensing in collaborative environments, will give a brief overview of several technologies developed at NASA Goddard Space Flight Center in this area, and then will concentrate on specific image processing research related to that domain, specifically image registration and image fusion.

LEARNING SEMANTICS-ENHANCED LANGUAGE MODELS APPLIED TO UNSUEPRVISED WSD

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

VERSPOOR, KARIN; LIN, SHOU-DE

An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learnedmore » without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.« less

A Metal-Polydopamine Framework (MPDA) as an Effective Fluorescent Quencher for Highly Sensitive Detection of Hg (II) And Ag (I) ions Through Exonuclease III Activity.

PubMed

Ravikumar, Ayyanu; Panneerselvam, Perumal; Morad, Norhashimah

2018-05-24

In this paper, we propose a metal-polydopamine framework (MPDA) with specific molecular probe which appears to be the most promising approach to a strong fluorescence quencher. The MPDA framework quenching ability towards various organic fluorophore such as aminoethylcomarin acetate (AMCA), 6-carboxyfluorescein (FAM), carboxyteramethylrhodamine (TAMRA) and Cy5 are used to establish a fluorescent biosensor that can selectively recognize Hg2+ and Ag+ ion. The fluorescent quenching efficiency was sufficient to achieve more than 96%. The MPDA framework also exhibits different affinities with ssDNA and dsDNA. In addition, the FAM labelled ssDNA was adsorbed onto MPDA framework, based on their interaction with the complex formed between MPDA frameworks/ssDNA taken as a sensing platform. By taking advantage of this sensor highly sensitive and selective determination of Hg2+and Ag+ ions is achieved through Exonuclease III signal amplification activity. The detection limits of Hg2+and Ag+ achieved to be 1.2 pM and 34 pM respectively, were compared to co-existing metal ions and GO based sensors. Furthermore, the potential applications of this study establish the highly sensitive fluorescence detection targets in environmental and biological fields.

The Effect of EFL Teachers' Attitude toward English Language and English Language Proficiency on Their Sense of Efficacy

ERIC Educational Resources Information Center

Sabokrouh, Farzaneh

2014-01-01

Researchers in education have documented that teachers' sense of efficacy has strong impacts on various aspects of teaching and learning. Yet, in the field of TESOL, inquiry into teachers' sense of efficacy is extremely scarce. The present study, by adopting the notion of teachers' sense of efficacy as the theoretical framework, has explored…

Flexible Framework for Capacitive Sensing

NASA Technical Reports Server (NTRS)

Woodard, Stanley E. (Inventor); Taylor, Bryant D. (Inventor)

2006-01-01

A flexible framework supports electrically-conductive elements in a capacitive sensing arrangement. Identical frames are arranged end-to-end with adjacent frames being capable of rotational movement there between. Each frame has first and second passages extending therethrough and parallel to one another. Each of the first and second passages is adapted to receive an electrically-conductive element therethrough. Each frame further has a hollowed-out portion for the passage of a fluent material therethrough. The hollowed-out portion is sized and shaped to provide for capacitive sensing along a defined region between the electrically-conductive element in the first passage and the electrically-conductive element in the second passage.

Data Integration Framework Data Management Plan Remote Sensing Dataset

DTIC Science & Technology

2016-07-01

performed by the Coastal Observations and Analysis Branch (CEERD-HFA) of the Flood and Storm Protection Division (CEERD-HF), U.S. Army Engineer Research... Protection Division, Coastal Observations and Analysis Branch CESAM U.S. Army Corps of Engineers, Mobile District CESAM-OP-J U.S. Army Corps of Engineers...ER D C/ CH L SR -1 6- 2 Coastal Ocean Data Systems Program Data Integration Framework Data Management Plan Remote Sensing Dataset Co

U.S. Geological Survey Groundwater Modeling Software: Making Sense of a Complex Natural Resource

USGS Publications Warehouse

Provost, Alden M.; Reilly, Thomas E.; Harbaugh, Arlen W.; Pollock, David W.

2009-01-01

Computer models of groundwater systems simulate the flow of groundwater, including water levels, and the transport of chemical constituents and thermal energy. Groundwater models afford hydrologists a framework on which to organize their knowledge and understanding of groundwater systems, and they provide insights water-resources managers need to plan effectively for future water demands. Building on decades of experience, the U.S. Geological Survey (USGS) continues to lead in the development and application of computer software that allows groundwater models to address scientific and management questions of increasing complexity.

The knowledge-learning-instruction framework: bridging the science-practice chasm to enhance robust student learning.

PubMed

Koedinger, Kenneth R; Corbett, Albert T; Perfetti, Charles

2012-07-01

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. Copyright © 2012 Cognitive Science Society, Inc.

pH-Stable Eu- and Tb-organic-frameworks mediated by an ionic liquid for the aqueous-phase detection of 2,4,6-trinitrophenol (TNP).

PubMed

Qin, Jian-Hua; Wang, Hua-Rui; Han, Min-Le; Chang, Xin-Hong; Ma, Lu-Fang

2017-11-14

Two pH-stable luminescent metal-organic frameworks (LMOFs), {[Ln 2 (L) 2 (OH)(HCOO)]·[H 2 O]} n (Ln = Eu 1, Tb 2), based on a new π-conjugated organic building block involving both carboxylate and terpyridine groups were rationally synthesized under a combination of hydro/solvothermal and ionothermal conditions (H 2 L = 4'-(4-(3,5-dicarboxylphenoxy)phenyl)-4,2':6',4''-terpyridine). 1 and 2 are isostructural and feature noninterpenetrated open 3D condensed frameworks constructed by rod-shaped lanthanide-carboxylate building units. Their excellent water-stability and pH-stability allow them to be used in aquatic systems. 1 and 2 both exhibit selective and sensitive aqueous phase detection of the well-known nitroaromatic explosive environmental pollutant 2,4,6-trinitrophenol (TNP), which is highly desirable for practical applications. The presence of a free pyridine group on the LMOF particle surface was strategically utilized for the purpose of exclusive TNP-sensing.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments.

PubMed

Mora, Higinio; Gil, David; Terol, Rafael Muñoz; Azorín, Jorge; Szymanski, Julian

2017-10-10

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other 'things' ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers' heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

PubMed Central

Szymanski, Julian

2017-01-01

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries. PMID:28994743

A flexible metal–organic framework: Guest molecules controlled dynamic gas adsorption

DOE PAGES

Mahurin, Shannon Mark; Li, Man -Rong; Wang, Hailong; ...

2015-04-13

A flexible metal–organic framework (MOF) of [Zn 3(btca) 2(OH) 2]·(guest) n (H 2btca = 1,2,3-benzotriazole-5-carboxylic acid) that exhibits guest molecule-controlled dynamic gas adsorption is reported in which carbon dioxide molecules rather than N 2, He, and Ar induce a structural transition with a corresponding appearance of additional steps in the isotherms. Physical insights into the dynamic adsorption behaviors of flexible compound 1 were detected by gas adsorption at different temperatures and different pressures and confirmed by Fourier transform infrared spectroscopy and molecular simulations. Interestingly, by taking advantage of the flexible nature inherent to the framework, this MOF material enables highlymore » selective adsorption of CO 2/N 2, CO 2/Ar, and CO 2/He of 36.3, 32.6, and 35.9, respectively, at 298 K. Furthermore, this class of flexible MOFs has potential applications for controlled release, molecular sensing, noble gas separation, smart membranes, and nanotechnological devices.« less

Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing

PubMed Central

Bai, Ruiliang; Benjamini, Dan; Cheng, Jian; Basser, Peter J.

2016-01-01

Previously, we showed that compressive or compressed sensing (CS) can be used to reduce significantly the data required to obtain 2D-NMR relaxation and diffusion spectra when they are sparse or well localized. In some cases, an order of magnitude fewer uniformly sampled data were required to reconstruct 2D-MR spectra of comparable quality. Nonetheless, this acceleration may still not be sufficient to make 2D-MR spectroscopy practicable for many important applications, such as studying time-varying exchange processes in swelling gels or drying paints, in living tissue in response to various biological or biochemical challenges, and particularly for in vivo MRI applications. A recently introduced framework, marginal distributions constrained optimization (MADCO), tremendously accelerates such 2D acquisitions by using a priori obtained 1D marginal distribution as powerful constraints when 2D spectra are reconstructed. Here we exploit one important intrinsic property of the 2D-MR relaxation exchange spectra: the fact that the 1D marginal distributions of each 2D-MR relaxation exchange spectrum in both dimensions are equal and can be rapidly estimated from a single Carr–Purcell–Meiboom–Gill (CPMG) or inversion recovery prepared CPMG measurement. We extend the MADCO framework by further proposing to use the 1D marginal distributions to inform the subsequent 2D data-sampling scheme, concentrating measurements where spectral peaks are present and reducing them where they are not. In this way we achieve compression or acceleration that is an order of magnitude greater than that in our previous CS method while providing data in reconstructed 2D-MR spectral maps of comparable quality, demonstrated using several simulated and real 2D T2 – T2 experimental data. This method, which can be called “informed compressed sensing,” is extendable to other 2D- and even ND-MR exchange spectroscopy. PMID:27782473

Robust Methods for Sensing and Reconstructing Sparse Signals

ERIC Educational Resources Information Center

Carrillo, Rafael E.

2012-01-01

Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…

A land data assimilation system for sub-Saharan Africa food and water security applications

PubMed Central

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa. PMID:28195575

A land data assimilation system for sub-Saharan Africa food and water security applications

USGS Publications Warehouse

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications.

PubMed

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D; Verdin, James P

2017-02-14

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Data Descriptor: A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications

NASA Technical Reports Server (NTRS)

McNally, Amy; Arsenault, Krist; Kumar, Sujay; Shukla, Shraddhanand; Peter, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWSNETs operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Making an Informed Decision on Freshwater Management by Integrating Remote Sensing Data with Traditional Data

NASA Technical Reports Server (NTRS)

Hyon, Jason J.

2012-01-01

The US National Research Council (NRC) recommended that: "The U.S. government, working in concert with the private sector, academe, the public, and its international partners, should renew its investment in Earth-observing systems and restore its leadership in Earth science and applications." in response to the NASA Earth Science Division's request to prioritize research areas, observations, and notional missions to make those objectives. In this presentation, we will discuss our approach to connect remote sensing science to decision support applications by establishing a framework to integrate direct measurements, earth system models, inventories, and other information to accurately estimate fresh water resources in global, regional, and local scales. We will discuss our demonstration projects and lessons learned from the experience. Deploying a monitoring system that offers sustained, accurate, transparent and relevant information represents a challenge and opportunity to a broad community spanning earth science, water resource accounting and public policy. An introduction to some of the scientific and technical infrastructure issues associated with monitoring systems is offered here to encourage future treatment of these topics by other contributors as a concluding remark.

A land data assimilation system for sub-Saharan Africa food and water security applications

NASA Astrophysics Data System (ADS)

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-02-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Application of the GEM Inventory Data Capture Tools for Dynamic Vulnerability Assessment and Recovery Modelling

NASA Astrophysics Data System (ADS)

Verrucci, Enrica; Bevington, John; Vicini, Alessandro

2014-05-01

A set of open-source tools to create building exposure datasets for seismic risk assessment was developed from 2010-13 by the Inventory Data Capture Tools (IDCT) Risk Global Component of the Global Earthquake Model (GEM). The tools were designed to integrate data derived from remotely-sensed imagery, statistically-sampled in-situ field data of buildings to generate per-building and regional exposure data. A number of software tools were created to aid the development of these data, including mobile data capture tools for in-field structural assessment, and the Spatial Inventory Data Developer (SIDD) for creating "mapping schemes" - statistically-inferred distributions of building stock applied to areas of homogeneous urban land use. These tools were made publically available in January 2014. Exemplar implementations in Europe and Central Asia during the IDCT project highlighted several potential application areas beyond the original scope of the project. These are investigated here. We describe and demonstrate how the GEM-IDCT suite can be used extensively within the framework proposed by the EC-FP7 project SENSUM (Framework to integrate Space-based and in-situ sENSing for dynamic vUlnerability and recovery Monitoring). Specifically, applications in the areas of 1) dynamic vulnerability assessment (pre-event), and 2) recovery monitoring and evaluation (post-event) are discussed. Strategies for using the IDC Tools for these purposes are discussed. The results demonstrate the benefits of using advanced technology tools for data capture, especially in a systematic fashion using the taxonomic standards set by GEM. Originally designed for seismic risk assessment, it is clear the IDCT tools have relevance for multi-hazard risk assessment. When combined with a suitable sampling framework and applied to multi-temporal recovery monitoring, data generated from the tools can reveal spatio-temporal patterns in the quality of recovery activities and resilience trends can be inferred. Lastly, this work draws attention to the use of the IDCT suite as an education resource for inspiring and training new students and engineers in the field of disaster risk reduction.

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications.

PubMed

Le, Duc V; Nguyen, Thuong; Scholten, Hans; Havinga, Paul J M

2017-11-29

Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring.

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications

PubMed Central

Scholten, Hans; Havinga, Paul J. M.

2017-01-01

Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring. PMID:29186037

Lanthanide-Functionalized Metal-Organic Framework Hybrid Systems To Create Multiple Luminescent Centers for Chemical Sensing.

PubMed

Yan, Bing

2017-11-21

Metal-organic frameworks (MOFs) possess an important advantage over other candidate classes for chemosensory materials because of their exceptional structural tunability and properties. Luminescent sensing using MOFs is a simple, intuitive, and convenient method to recognize species, but the method has limitations, such as insufficient chemical selectivity and signal loss. MOFs contain versatile building blocks (linkers or ligands) with special chemical reactivity, and postsynthetic modification (PSM) provides an opportunity to exploit and expand their unique properties. The linkers in most MOFs contain aromatic subunits that can readily display luminescence after ultraviolet or visible (typically blue) excitation, and this is the main luminescent nature of most MOFs. The introduction of photoactive lanthanide ions (Ln 3+ ) into the MOF hosts may produce new luminescent signals at different positions from that of the MOF linker, but this depends on the intramolecular energy transfer (antenna effect) from the MOF (linkers) to the Ln 3+ ions. Controlling the Ln 3+ content in MOF hybrids may create multiple luminescent centers. The nature of the unique luminescent centers may cause different responses to sensing species (i.e., ratiometric sensing), which may provide a new opportunity for luminescence research with applications to chemical sensing. In this Account, recent research progress on using lanthanide-functionalized MOF hybrid materials to create multiple luminescent centers for chemical sensing is described. Here we propose a general strategy to functionalize MOF hosts with lanthanide ions, compounds, or other luminescent species (organic dyes or carbon dots) and to assemble types of photofunctional hybrid systems based on lanthanide-functionalized MOFs. Five main methods were used to functionalize the MOFs and assemble the hybrid materials: in situ composition, ionic doping, ionic exchange, covalent PSM, and coordinated PSM. Through the lanthanide functionalization, multiple (double or triple) luminescent centers were created with different luminescent bands in the visible region. Because of the different luminescent natures of the lanthanide ions, MOF linkers, and other species (organic dyes or carbon dots), they display different responses to sensing species. Currently, using these strategies, we have utilized a dual-response luminescent probe to realize chemical sensing of different types of cations (Fe 3+ /Fe 2+ , Hg 2+ , and Cd 2+ ), anions (Cr 2 O 7 2- /CrO 4 - and CO 3 2- ), molecules (volatile organic compounds and O 2 ), special air pollutants (formaldehyde), and biomarkers of food spoilage as well as pH and temperature. Additionally, we have achieved triple-luminescence-response sensing of ions (Ag + , Hg 2+ , and S 2- ) in complicated aqueous environments, which was developed using a logic operation.

Chemically Active, Porous 3D-Printed Thermoplastic Composites.

PubMed

Evans, Kent A; Kennedy, Zachary C; Arey, Bruce W; Christ, Josef F; Schaef, Herbert T; Nune, Satish K; Erikson, Rebecca L

2018-05-02

Metal-organic frameworks (MOFs) exhibit exceptional properties and are widely investigated because of their structural and functional versatility relevant to catalysis, separations, and sensing applications. However, their commercial or large-scale application is often limited by their powder forms which make integration into devices challenging. Here, we report the production of MOF-thermoplastic polymer composites in well-defined and customizable forms and with complex internal structural features accessed via a standard three-dimensional (3D) printer. MOFs (zeolitic imidazolate framework; ZIF-8) were incorporated homogeneously into both poly(lactic acid) (PLA) and thermoplastic polyurethane (TPU) matrices at high loadings (up to 50% by mass), extruded into filaments, and utilized for on-demand access to 3D structures by fused deposition modeling. Printed, rigid PLA/MOF composites display a large surface area (SA avg = 531 m 2 g -1 ) and hierarchical pore features, whereas flexible TPU/MOF composites achieve a high surface area (SA avg = 706 m 2 g -1 ) by employing a simple method developed to expose obstructed micropores postprinting. Critically, embedded particles in the plastic matrices retain their ability to participate in chemical interactions characteristic of the parent framework. The fabrication strategies were extended to other MOFs and illustrate the potential of 3D printing to create unique porous and high surface area chemically active structures.

Thermodynamic framework to assess low abundance DNA mutation detection by hybridization

PubMed Central

Willems, Hanny; Jacobs, An; Hadiwikarta, Wahyu Wijaya; Venken, Tom; Valkenborg, Dirk; Van Roy, Nadine; Vandesompele, Jo; Hooyberghs, Jef

2017-01-01

The knowledge of genomic DNA variations in patient samples has a high and increasing value for human diagnostics in its broadest sense. Although many methods and sensors to detect or quantify these variations are available or under development, the number of underlying physico-chemical detection principles is limited. One of these principles is the hybridization of sample target DNA versus nucleic acid probes. We introduce a novel thermodynamics approach and develop a framework to exploit the specific detection capabilities of nucleic acid hybridization, using generic principles applicable to any platform. As a case study, we detect point mutations in the KRAS oncogene on a microarray platform. For the given platform and hybridization conditions, we demonstrate the multiplex detection capability of hybridization and assess the detection limit using thermodynamic considerations; DNA containing point mutations in a background of wild type sequences can be identified down to at least 1% relative concentration. In order to show the clinical relevance, the detection capabilities are confirmed on challenging formalin-fixed paraffin-embedded clinical tumor samples. This enzyme-free detection framework contains the accuracy and efficiency to screen for hundreds of mutations in a single run with many potential applications in molecular diagnostics and the field of personalised medicine. PMID:28542229

The fusion of large scale classified side-scan sonar image mosaics.

PubMed

Reed, Scott; Tena, Ruiz Ioseba; Capus, Chris; Petillot, Yvan

2006-07-01

This paper presents a unified framework for the creation of classified maps of the seafloor from sonar imagery. Significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed. The techniques described are directly applicable to a range of remote sensing problems. Recent advances in side-scan data correction are incorporated to compensate for the sonar beam pattern and motion of the acquisition platform. The corrected images are segmented using pixel-based textural features and standard classifiers. In parallel, the navigation of the sonar device is processed using Kalman filtering techniques. A simultaneous localization and mapping framework is adopted to improve the navigation accuracy and produce georeferenced mosaics of the segmented side-scan data. These are fused within a Markovian framework and two fusion models are presented. The first uses a voting scheme regularized by an isotropic Markov random field and is applicable when the reliability of each information source is unknown. The Markov model is also used to inpaint regions where no final classification decision can be reached using pixel level fusion. The second model formally introduces the reliability of each information source into a probabilistic model. Evaluation of the two models using both synthetic images and real data from a large scale survey shows significant quantitative and qualitative improvement using the fusion approach.

Dynamic social community detection and its applications.

PubMed

Nguyen, Nam P; Dinh, Thang N; Shen, Yilin; Thai, My T

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

Dynamic Social Community Detection and Its Applications

PubMed Central

Nguyen, Nam P.; Dinh, Thang N.; Shen, Yilin; Thai, My T.

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods. PMID:24722164

Remote sensing of multimodal transportation systems : research brief.

DOT National Transportation Integrated Search

2016-09-01

Remote Sensing of Multimodal Transportation Systems : Rapid condition monitoring and performance evaluations of the vast and vulnerable transportation infrastructure has been elusive. The framework and models developed in this research will enable th...

Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Dou, Hao; Sun, Xiao; Li, Bin; Deng, Qianqian; Yang, Xubo; Liu, Di; Tian, Jinwen

2018-03-01

Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.

Remote sensing image denoising application by generalized morphological component analysis

NASA Astrophysics Data System (ADS)

Yu, Chong; Chen, Xiong

2014-12-01

In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.

Error Analysis and Selection of Optimal Excitation Parameters for the Sensing of CO2 and O2 from Space for ASCENDS Applications

NASA Technical Reports Server (NTRS)

Pliutau, Denis; Prasad, Narasimha S.

2012-01-01

Simulation studies to optimize sensing of CO2 and O2 from space are described. Uncertainties in line-by-line calculations unaccounted for in previous studies identified. Multivariate methods are employed for measurement wavelengths selection. The Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) recommended by NRC Decadal Survey has a stringent accuracy requirements of 0.5% or better in XCO2 retrievals. NASA LaRC and its partners are investigating the use of the 1.57 m band of CO2 and the 1.26-1.27 m band of oxygen for XCO2 measurements. As part of these efforts, we are carrying out simulation studies using a lidar modeling framework being developed at NASA LaRC to predict the performance of our proposed ASCENDS mission implementation [1]. Our study is aimed at predicting the sources and magnitudes of errors anticipated in XCO2 retrievals for further error minimization through the selection of optimum excitation parameters and development of better retrieval methods.

The Hico Image Processing System: A Web-Accessible Hyperspectral Remote Sensing Toolbox

NASA Astrophysics Data System (ADS)

Harris, A. T., III; Goodman, J.; Justice, B.

2014-12-01

As the quantity of Earth-observation data increases, the use-case for hosting analytical tools in geospatial data centers becomes increasingly attractive. To address this need, HySpeed Computing and Exelis VIS have developed the HICO Image Processing System, a prototype cloud computing system that provides online, on-demand, scalable remote sensing image processing capabilities. The system provides a mechanism for delivering sophisticated image processing analytics and data visualization tools into the hands of a global user community, who will only need a browser and internet connection to perform analysis. Functionality of the HICO Image Processing System is demonstrated using imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), an imaging spectrometer located on the International Space Station (ISS) that is optimized for acquisition of aquatic targets. Example applications include a collection of coastal remote sensing algorithms that are directed at deriving critical information on water and habitat characteristics of our vulnerable coastal environment. The project leverages the ENVI Services Engine as the framework for all image processing tasks, and can readily accommodate the rapid integration of new algorithms, datasets and processing tools.

Applying the Common Sense Model to Understand Representations of Arsenic Contaminated Well Water

PubMed Central

Severtson, Dolores J.; Baumann, Linda C.; Brown, Roger L.

2015-01-01

Theory-based research is needed to understand how people respond to environmental health risk information. The common sense model of self-regulation and the mental models approach propose that information shapes individual’s personal understandings that influence their decisions and actions. We compare these frameworks and explain how the common sense model (CSM) was applied to describe and measure mental representations of arsenic contaminated well water. Educational information, key informant interviews, and environmental risk literature were used to develop survey items to measure dimensions of cognitive representations (identity, cause, timeline, consequences, control) and emotional representations. Surveys mailed to 1067 private well users with moderate and elevated arsenic levels yielded an 84% response rate (n=897). Exploratory and confirmatory factor analyses of data from the elevated arsenic group identified a factor structure that retained the CSM representational structure and was consistent across moderate and elevated arsenic groups. The CSM has utility for describing and measuring representations of environmental health risks thus supporting its application to environmental health risk communication research. PMID:18726811

Data Quality in Remote Sensing

NASA Astrophysics Data System (ADS)

Batini, C.; Blaschke, T.; Lang, S.; Albrecht, F.; Abdulmutalib, H. M.; Barsi, Á.; Szabó, G.; Kugler, Zs.

2017-09-01

The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb "DQ" identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.

Recent Developments in 2D Nanomaterials for Chemiresistive-Type Gas Sensors

NASA Astrophysics Data System (ADS)

Choi, Seon-Jin; Kim, Il-Doo

2018-03-01

Two-dimensional (2D) nanostructures are gaining tremendous interests due to the fascinating physical, chemical, electrical, and optical properties. Recent advances in 2D nanomaterials synthesis have contributed to optimization of various parameters such as physical dimension and chemical structure for specific applications. In particular, development of high performance gas sensors is gaining vast importance for real-time and on-site environmental monitoring by detection of hazardous chemical species. In this review, we comprehensively report recent achievements of 2D nanostructured materials for chemiresistive-type gas sensors. Firstly, the basic sensing mechanism is described based on charge transfer behavior between gas species and 2D nanomaterials. Secondly, diverse synthesis strategies and characteristic gas sensing properties of 2D nanostructures such as graphene, metal oxides, transition metal dichalcogenides (TMDs), metal organic frameworks (MOFs), phosphorus, and MXenes are presented. In addition, recent trends in synthesis of 2D heterostructures by integrating two different types of 2D nanomaterials and their gas sensing properties are discussed. Finally, this review provides perspectives and future research directions for gas sensor technology using various 2D nanomaterials.

Evolutions Of Diff-Tomo For Sensing Subcanopy Deformations And Height-Varying Temporal Coherence

NASA Astrophysics Data System (ADS)

Lombardini, Fabrizio; Cai, Francesco

2012-01-01

Interest is continuing to grow in advanced interferometric SAR methods for sensing complex scenarios with multiple (layover or volumetric) scatterers mapped in the SAR cell. Multibaseline SAR tomographic (3D) elevation beam forming is a promising technique in this field. Recently, the Tomo concept has been integrated with the differential interferometry concept, producing the advanced “differential tomography” (Diff-Tomo, “4D”) processing mode which furnishes “space-time” signatures of multiple scatterer dynamics in the SAR cell. Advances in the application of this new framework are investigated for complex volume scattering scenarios including temporal signal variations, both from scatterer temporal decorrelation and deformation motions. In particular, new results are reported concerning the potentials of Diff-Tomo for the analysis of forest scenarios, based on the original concept of the space-time signatures of temporal decorrelation. E-SAR P-band data results are expanded of tomography robust to temporal decorrelation, and first trials are reported of separation of different temporal decorrelation mechanisms of canopy and ground, and of sensing possible sub-canopy subsidences.

The future of satellite remote sensing: A worldwide assessment and prediction

NASA Technical Reports Server (NTRS)

Spann, G. W.

1984-01-01

A frame-work in which to assess and predict the future prospects for satellite remote sensing markets is provided. The scope of the analysis is the satellite-related market for data, equipment, and services. It encompasses both domestic and international markets and contains an examination of the various market characteristics by market segment (e.g., Federal Government, State and Local Governments, Academic Organizations, Industrial Companies, and Individuals) and primary applications areas (e.g., Geology, Forestry, Land Resource Management, Agriculture and Cartography). The forecasts are derived from an analysis of both U.S. and foreign market data. The evolution and current status of U.S. and Foreign markets to arrive at market growth rates is evaluated. Circumstances and events which are likely to affect the future market development are examined. A market growth scenario is presented that is consistent with past data sales trends and takes into account the dynamic nature of the future satellite remote sensing market. Several areas of current and future business opportunities available in this market are discussed. Specific worldwide forecasts are presented in three market sectors for the period 1980 to 1990.

Recent Developments in 2D Nanomaterials for Chemiresistive-Type Gas Sensors

NASA Astrophysics Data System (ADS)

Choi, Seon-Jin; Kim, Il-Doo

2018-05-01

Two-dimensional (2D) nanostructures are gaining tremendous interests due to the fascinating physical, chemical, electrical, and optical properties. Recent advances in 2D nanomaterials synthesis have contributed to optimization of various parameters such as physical dimension and chemical structure for specific applications. In particular, development of high performance gas sensors is gaining vast importance for real-time and on-site environmental monitoring by detection of hazardous chemical species. In this review, we comprehensively report recent achievements of 2D nanostructured materials for chemiresistive-type gas sensors. Firstly, the basic sensing mechanism is described based on charge transfer behavior between gas species and 2D nanomaterials. Secondly, diverse synthesis strategies and characteristic gas sensing properties of 2D nanostructures such as graphene, metal oxides, transition metal dichalcogenides (TMDs), metal organic frameworks (MOFs), phosphorus, and MXenes are presented. In addition, recent trends in synthesis of 2D heterostructures by integrating two different types of 2D nanomaterials and their gas sensing properties are discussed. Finally, this review provides perspectives and future research directions for gas sensor technology using various 2D nanomaterials.

The Relationships between the Sense of Coherence, Demographic Characteristics, and Career Thought Processes among College Students with Disabilities

ERIC Educational Resources Information Center

Seo, Wonsun

2010-01-01

This study examined the relationship between sense of coherence, demographic characteristics, and career thought processes among college students with disabilities based on Antonovsky's conceptual framework of sense of coherence. Participants were college students with disabilities collected through the Resource Center for Persons with…

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

PubMed

Weiss, Christian; Zoubir, Abdelhak M

2017-05-01

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

PubMed

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

The research of differential reference electrode arrayed flexible IGZO glucose biosensor based on microfluidic framework

NASA Astrophysics Data System (ADS)

Chen, Jian-Syun; Chou, Jung-Chuan; Liao, Yi-Hung; Chen, Ruei-Ting; Huang, Min-Siang; Wu, Tong-Yu

2017-03-01

This study used a fast, simple, and low-cost method to fabricate arrayed flexible glucose biosensor, and the glucose biosensor was integrated with microfluidic framework for investigating sensing characteristics of glucose biosensor at the dynamic conditions. The indium gallium zinc oxide (IGZO) was adopted as sensing membrane and it was deposited on aluminum electrodes / polyethylene terephthalate (PET) substrate by the radio frequency sputtering system. Then, we utilized screen-printed technology to accomplish miniaturization of glucose biosensor. Finally, the glucose sensing membrane was composed of glucose oxidase (GOx) and nafion, which was dropped on IGZO sensing membrane to complete glucose biosensor. According to the experimental results, we found that optimal sensing characteristics of arrayed flexible IGZO glucose biosensor at the dynamic conditions were better than at the static conditions. The optimal average sensitivity and linearity of the arrayed flexible IGZO glucose biosensor were 7.255 mV/mM and 0.994 at 20 µL/min flow rate, respectively.

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

2011-01-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

System approach to distributed sensor management

NASA Astrophysics Data System (ADS)

Mayott, Gregory; Miller, Gordon; Harrell, John; Hepp, Jared; Self, Mid

2010-04-01

Since 2003, the US Army's RDECOM CERDEC Night Vision Electronic Sensor Directorate (NVESD) has been developing a distributed Sensor Management System (SMS) that utilizes a framework which demonstrates application layer, net-centric sensor management. The core principles of the design support distributed and dynamic discovery of sensing devices and processes through a multi-layered implementation. This results in a sensor management layer that acts as a System with defined interfaces for which the characteristics, parameters, and behaviors can be described. Within the framework, the definition of a protocol is required to establish the rules for how distributed sensors should operate. The protocol defines the behaviors, capabilities, and message structures needed to operate within the functional design boundaries. The protocol definition addresses the requirements for a device (sensors or processes) to dynamically join or leave a sensor network, dynamically describe device control and data capabilities, and allow dynamic addressing of publish and subscribe functionality. The message structure is a multi-tiered definition that identifies standard, extended, and payload representations that are specifically designed to accommodate the need for standard representations of common functions, while supporting the need for feature-based functions that are typically vendor specific. The dynamic qualities of the protocol enable a User GUI application the flexibility of mapping widget-level controls to each device based on reported capabilities in real-time. The SMS approach is designed to accommodate scalability and flexibility within a defined architecture. The distributed sensor management framework and its application to a tactical sensor network will be described in this paper.

Design of the smart scenic spot service platform

NASA Astrophysics Data System (ADS)

Yin, Min; Wang, Shi-tai

2015-12-01

With the deepening of the smart city construction, the model "smart+" is rapidly developing. Guilin, the international tourism metropolis fast constructing need smart tourism technology support. This paper studied the smart scenic spot service object and its requirements. And then constructed the smart service platform of the scenic spot application of 3S technology (Geographic Information System (GIS), Remote Sensing (RS) and Global Navigation Satellite System (GNSS)) and the Internet of things, cloud computing. Based on Guilin Seven-star Park scenic area as an object, this paper designed the Seven-star smart scenic spot service platform framework. The application of this platform will improve the tourists' visiting experience, make the tourism management more scientifically and standardly, increase tourism enterprises operating earnings.

SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System.

PubMed

Lin, Feng; Zhuang, Yan; Song, Chen; Wang, Aosen; Li, Yiran; Gu, Changzhan; Li, Changzhi; Xu, Wenyao

2017-02-01

Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive to use. To overcome these obstacles, a noncontact and cost-effective sleep monitoring system, named SleepSense, is proposed for continuous recognition of the sleep status, including on-bed movement, bed exit, and breathing section. SleepSense consists of three parts: a Doppler radar-based sensor, a robust automated radar demodulation module, and a sleep status recognition framework. Herein, several time-domain and frequency-domain features are extracted for the sleep recognition framework. A prototype of SleepSense is presented and evaluated using two sets of experiments. In the short-term controlled experiment, the SleepSense achieves an overall 95.1% accuracy rate in identifying various sleep status. In the 75-minute sleep study, SleepSense demonstrates wide usability in real life. The error rate for breathing rate extraction in this study is only 6.65%. These experimental results indicate that SleepSense is an effective and promising solution for in-home sleep monitoring.

Start making sense: Art informing health psychology

PubMed Central

Hughes, Brian M; Murray, Michael; Smyth, Joshua M

2018-01-01

Growing evidence suggests that the arts may be useful in health care and in the training of health care professionals. Four art genres – novels, films, paintings and music – are examined for their potential contribution to enhancing patient health and/or making better health care providers. Based on a narrative literature review, we examine the effects of passive (e.g. reading, watching, viewing and listening) and active (e.g. writing, producing, painting and performing) exposure to the four art genres, by both patients and health care providers. Overall, an emerging body of empirical evidence indicates positive effects on psychological and physiological outcome measures in patients and some benefits to medical training. Expressive writing/emotional disclosure, psychoneuroimmunology, Theory of Mind and the Common Sense Model of Self-Regulation are considered as possible theoretical frameworks to help incorporate art genres as sources of inspiration for the further development of health psychology research and clinical applications. PMID:29552350

Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder

NASA Astrophysics Data System (ADS)

August, Isaac; Oiknine, Yaniv; Abuleil, Marwan; Abdulhalim, Ibrahim; Stern, Adrian

2016-03-01

Spectroscopic imaging has been proved to be an effective tool for many applications in a variety of fields, such as biology, medicine, agriculture, remote sensing and industrial process inspection. However, due to the demand for high spectral and spatial resolution it became extremely challenging to design and implement such systems in a miniaturized and cost effective manner. Using a Compressive Sensing (CS) setup based on a single variable Liquid Crystal (LC) retarder and a sensor array, we present an innovative Miniature Ultra-Spectral Imaging (MUSI) system. The LC retarder acts as a compact wide band spectral modulator. Within the framework of CS, a sequence of spectrally modulated images is used to recover ultra-spectral image cubes. Using the presented compressive MUSI system, we demonstrate the reconstruction of gigapixel spatio-spectral image cubes from spectral scanning shots numbering an order of magnitude less than would be required using conventional systems.

Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder.

PubMed

August, Isaac; Oiknine, Yaniv; AbuLeil, Marwan; Abdulhalim, Ibrahim; Stern, Adrian

2016-03-23

Spectroscopic imaging has been proved to be an effective tool for many applications in a variety of fields, such as biology, medicine, agriculture, remote sensing and industrial process inspection. However, due to the demand for high spectral and spatial resolution it became extremely challenging to design and implement such systems in a miniaturized and cost effective manner. Using a Compressive Sensing (CS) setup based on a single variable Liquid Crystal (LC) retarder and a sensor array, we present an innovative Miniature Ultra-Spectral Imaging (MUSI) system. The LC retarder acts as a compact wide band spectral modulator. Within the framework of CS, a sequence of spectrally modulated images is used to recover ultra-spectral image cubes. Using the presented compressive MUSI system, we demonstrate the reconstruction of gigapixel spatio-spectral image cubes from spectral scanning shots numbering an order of magnitude less than would be required using conventional systems.

Ship detection in optical remote sensing images based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

Time-reversal MUSIC imaging of extended targets.

PubMed

Marengo, Edwin A; Gruber, Fred K; Simonetti, Francesco

2007-08-01

This paper develops, within a general framework that is applicable to rather arbitrary electromagnetic and acoustic remote sensing systems, a theory of time-reversal "MUltiple Signal Classification" (MUSIC)-based imaging of extended (nonpoint-like) scatterers (targets). The general analysis applies to arbitrary remote sensing geometry and sheds light onto how the singular system of the scattering matrix relates to the geometrical and propagation characteristics of the entire transmitter-target-receiver system and how to use this effect for imaging. All the developments are derived within exact scattering theory which includes multiple scattering effects. The derived time-reversal MUSIC methods include both interior sampling, as well as exterior sampling (or enclosure) approaches. For presentation simplicity, particular attention is given to the time-harmonic case where the informational wave modes employed for target interrogation are purely spatial, but the corresponding generalization to broadband fields is also given. This paper includes computer simulations illustrating the derived theory and algorithms.

Water security for productive economies: Applying an assessment framework in southern Africa

NASA Astrophysics Data System (ADS)

Holmatov, Bunyod; Lautze, Jonathan; Manthrithilake, Herath; Makin, Ian

2017-08-01

Achieving water security has emerged as a major objective in Africa, yet an analytical or diagnostic framework for assessing water security in African countries is not known to exist. This paper applies one key dimension of the 2016 Asian Development Bank's (ADB) Asian Water Development Outlook (AWDO) to assess levels of water security for productive economies in countries of the Southern African Development Community (SADC). Economic aspects of water security cover four areas: economic activities in the broad sense, agriculture, electricity, and industry. Water security in each area is measured through application of a set of indicators; results of indicator application are then aggregated to determine economic water security at a country-level. Results show that economic water security in SADC is greatest in the Seychelles and South Africa, and lowest in Madagascar and Malawi. Opportunities for strengthening economic water security in the majority of SADC countries exist through improving agricultural water productivity, strengthening resilience, and expanding sustainable electricity generation. More profoundly, this paper suggests that there is clear potential and utility in applying approaches used elsewhere to assess economic water security in southern Africa.

Metal-Organic Framework Thin Film Coated Optical Fiber Sensors: A Novel Waveguide-Based Chemical Sensing Platform.

PubMed

Kim, Ki-Joong; Lu, Ping; Culp, Jeffrey T; Ohodnicki, Paul R

2018-02-23

Integration of optical fiber with sensitive thin films offers great potential for the realization of novel chemical sensing platforms. In this study, we present a simple design strategy and high performance of nanoporous metal-organic framework (MOF) based optical gas sensors, which enables detection of a wide range of concentrations of small molecules based upon extremely small differences in refractive indices as a function of analyte adsorption within the MOF framework. Thin and compact MOF films can be uniformly formed and tightly bound on the surface of etched optical fiber through a simple solution method which is critical for manufacturability of MOF-based sensor devices. The resulting sensors show high sensitivity/selectivity to CO 2 gas relative to other small gases (H 2 , N 2 , O 2 , and CO) with rapid (

Spatiotemporal Local-Remote Senor Fusion (ST-LRSF) for Cooperative Vehicle Positioning

PubMed Central

Bhawiyuga, Adhitya

2018-01-01

Vehicle positioning plays an important role in the design of protocols, algorithms, and applications in the intelligent transport systems. In this paper, we present a new framework of spatiotemporal local-remote sensor fusion (ST-LRSF) that cooperatively improves the accuracy of absolute vehicle positioning based on two state estimates of a vehicle in the vicinity: a local sensing estimate, measured by the on-board exteroceptive sensors, and a remote sensing estimate, received from neighbor vehicles via vehicle-to-everything communications. Given both estimates of vehicle state, the ST-LRSF scheme identifies the set of vehicles in the vicinity, determines the reference vehicle state, proposes a spatiotemporal dissimilarity metric between two reference vehicle states, and presents a greedy algorithm to compute a minimal weighted matching (MWM) between them. Given the outcome of MWM, the theoretical position uncertainty of the proposed refinement algorithm is proven to be inversely proportional to the square root of matching size. To further reduce the positioning uncertainty, we also develop an extended Kalman filter model with the refined position of ST-LRSF as one of the measurement inputs. The numerical results demonstrate that the proposed ST-LRSF framework can achieve high positioning accuracy for many different scenarios of cooperative vehicle positioning. PMID:29617341

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

NASA Astrophysics Data System (ADS)

Ruiz-Pérez, Guiomar; Koch, Julian; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

2017-12-01

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment - the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

Dynamic experiment design regularization approach to adaptive imaging with array radar/SAR sensor systems.

PubMed

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

2011-01-01

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the "model-free" variational analysis (VA)-based image enhancement approach and the "model-based" descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.

Thermal Remote Sensing: A Powerful Tool in the Characterization of Landscapes on a Functional Basis

NASA Technical Reports Server (NTRS)

Jeffrey, Luvall C.; Kay, James; Fraser, Roydon

1999-01-01

Thermal remote sensing instruments can function as environmental measuring tools, with capabilities leading toward new directions in functional landscape ecology. Theoretical deduction and phenomenological observation leads us to believe that the second law of thermodynamics requires that all dynamically systems develop in a manner which dissipates gradients as rapidly as possible within the constraints of the system at hand. The ramification of this requirement is that dynamical systems will evolve dissipative structures which grow and complexify over time. This perspective has allowed us to develop a framework for discussing ecosystem development and integrity. In the context of this framework we have developed measures of development and integrity for ecosystems. One set of these measures is based on destruction of the exergy content of incoming solar energy. More developed ecosystems will be more effective at dissipating the solar gradient (destroying its exergy content). This can be measured by the effective surface temperature of the ecosystem on a landscape scale. These surface temperatures are measured using airborne thermal scanners such as the Thermal Infrared Multispectral Scanner (TIMS) and the Airborne Thermal/Visible Land Application Sensor(ATLAS) sensors. An analysis of agriculture and forest ecosystems will be used to illustrate the concept of ecological thermodynamics and the development of ecosystems.

Ultra-sensitive Trace-Water Optical Sensor with In situ- synthesized Metal-Organic Framework in Glass Paper.

PubMed

Ohira, Shin-Ichi; Nakamura, Nao; Endo, Masaaki; Miki, Yusuke; Hirose, Yasuo; Toda, Kei

2018-01-01

Monitoring of trace water in industrial gases is strongly recommended because contaminants cause serious problems during use, especially in the semiconductor industry. An ultra-sensitive trace-water sensor was developed with an in situ-synthesized metal-organic framework as the sensing material. The sample gas is passed through the sensing membrane and efficiently and rapidly collected by the sensing material in the newly designed gas collection/detection cell. The sensing membrane, glass paper impregnated with copper 1,3,5-benzenetricarboxylate (Cu-BTC), is also newly developed. The amount and density of the sensing material in the sensing membrane must be well balanced to achieve rapid and sensitive responses. In the present study, Cu-BTC was synthesized in situ in glass paper. The developed system gave high sensing performances with a limit of detection (signal/noise ratio = 3) of 9 parts per billion by volume (ppbv) H 2 O and a 90% response time of 86 s for 200 ppbv H 2 O. The reproducibility of the responses within and between lots had relative standard deviations for 500 ppbv H 2 O of 0.8% (n = 10) and 1.5% (n = 3), respectively. The long-term (2 weeks) stability was 7.3% for 400 ppbv H 2 O and one-year continuous monitoring test showed the sensitivity change of <∼3% before and after the study. Furthermore, the system response was in good agreement with the response achieved in cavity ring-down spectroscopy. These performances are sufficient for monitoring trace water in industrial gases. The integrated system with light and gas transparent structure for gas collection/absorbance detection can also be used for other target gases, using specific metal-organic frameworks.

High efficient optical remote sensing images acquisition for nano-satellite-framework

NASA Astrophysics Data System (ADS)

Li, Feng; Xin, Lei; Liu, Yang; Fu, Jie; Liu, Yuhong; Guo, Yi

2017-09-01

It is more difficult and challenging to implement Nano-satellite (NanoSat) based optical Earth observation missions than conventional satellites because of the limitation of volume, weight and power consumption. In general, an image compression unit is a necessary onboard module to save data transmission bandwidth and disk space. The image compression unit can get rid of redundant information of those captured images. In this paper, a new image acquisition framework is proposed for NanoSat based optical Earth observation applications. The entire process of image acquisition and compression unit can be integrated in the photo detector array chip, that is, the output data of the chip is already compressed. That is to say, extra image compression unit is no longer needed; therefore, the power, volume, and weight of the common onboard image compression units consumed can be largely saved. The advantages of the proposed framework are: the image acquisition and image compression are combined into a single step; it can be easily built in CMOS architecture; quick view can be provided without reconstruction in the framework; Given a certain compression ratio, the reconstructed image quality is much better than those CS based methods. The framework holds promise to be widely used in the future.

Senses and Your 8- to 12-Month-Old

MedlinePlus

... FrameworkServlet.doGet(FrameworkServlet.java:549) at javax.servlet.http.HttpServlet.service(HttpServlet.java:617) at javax.servlet.http.HttpServlet.service(HttpServlet.java:717) at org.apache. ...

A systematic framework for Monte Carlo simulation of remote sensing errors map in carbon assessments

Treesearch

S. Healey; P. Patterson; S. Urbanski

2014-01-01

Remotely sensed observations can provide unique perspective on how management and natural disturbance affect carbon stocks in forests. However, integration of these observations into formal decision support will rely upon improved uncertainty accounting. Monte Carlo (MC) simulations offer a practical, empirical method of accounting for potential remote sensing errors...

An Exploratory Study of a Story Problem Assessment: Understanding Children's Number Sense

ERIC Educational Resources Information Center

Shumway, Jessica F.; Westenskow, Arla; Moyer-Packenham, Patricia S.

2016-01-01

The purpose of this study was to identify and describe students' use of number sense as they solved story problem tasks. Three 8- and 9-year-old students participated in clinical interviews. Through a process of holistic and qualitative coding, researchers used the number sense view as a theoretical framework for exploring how students' number…

Neither property right nor heroic gift, neither sacrifice nor aporia: the benefit of the theoretical lens of sharing in donation ethics.

PubMed

Zeiler, Kristin

2014-05-01

Two ethical frameworks have dominated the discussion of organ donation for long: that of property rights and that of gift-giving. However, recent years have seen a drastic rise in the number of philosophical analyses of the meaning of giving and generosity, which has been mirrored in ethical debates on organ donation and in critical sociological, anthropological and ethnological work on the gift metaphor in this context. In order to capture the flourishing of this field, this article distinguishes between four frameworks for thinking about bodily exchanges in medicine: those of property rights, heroic gift-giving, sacrifice, and gift-giving as aporia. These frameworks represent four different ways of making sense of donation of organs as well as tissue, gametes and blood, draw on different conceptions of the relations between the self and the other, and bring out different ethical issues as core ones. The article presents these frameworks, argues that all of them run into difficulties when trying to make sense of reciprocity and relational interdependence in donation, and shows how the three gift-giving frameworks (of heroism, sacrifice and aporia) hang together in a critical discussion about what is at stake in organ donation. It also presents and argues in favour of an alternative intercorporeal framework of giving-through-sharing that more thoroughly explicates the gift metaphor in the context of donation, and offers tools for making sense of relational dimensions of live and post mortem donations.

Hydrogen sensing properties of nanocomposite graphene oxide/Co-based metal organic frameworks (Co-MOFs@GO)

NASA Astrophysics Data System (ADS)

Fardindoost, Somayeh; Hatamie, Shadie; Iraji Zad, Azam; Razi Astaraei, Fatemeh

2018-01-01

This paper reports on hydrogen sensing based graphene oxide hybrid with Co-based metal organic frameworks (Co-MOFs@GO) prepared by the hydrothermal process. The texture and morphology of the hybrid were characterized by powder x-ray diffraction, scanning electron microscopy and Brunauer-Emmett-Teller analysis. Porous flower like structures assembled from Co-MOFs and GO flakes with sufficient specific surface area are obtained, which are ideal for gas molecules diffusion and interactions. Sensing performance of Co-MOFs@GO were tested and also improved by sputtering platinum (Pt) as a catalyst. The Pt-sputtered Co-MOFs@GO show outstanding hydrogen resistive-sensing with response and recovery times below 12 s at 15 °C. Also, they show stable, repeatable and selective responses to the target gas which make it suitable for the development of a high performance hydrogen sensor.

Practical use of a framework for network science experimentation

NASA Astrophysics Data System (ADS)

Toth, Andrew; Bergamaschi, Flavio

2014-06-01

In 2006, the US Army Research Laboratory (ARL) and the UK Ministry of Defence (MoD) established a collaborative research alliance with academia and industry, called the International Technology Alliance (ITA)1 In Network and Information Sciences, to address fundamental issues concerning Network and Information Sciences that will enhance decision making for coalition operations and enable rapid, secure formation of ad hoc teams in coalition environments and enhance US and UK capabilities to conduct coalition warfare. Research conducted under the ITA was extended through collaboration between ARL and IBM UK to characterize and dene a software stack and tooling that has become the reference framework for network science experimentation in support for validation of theoretical research. This paper discusses the composition of the reference framework for experimentation resulting from the ARL/IBM UK collaboration and its use, by the Network Science Collaborative Technology Alliance (NS CTA)2 , in a recent network science experiment conducted at ARL. It also discusses how the experiment was modeled using the reference framework, the integration of two new components, the Apollo Fact-Finder3 tool and the Medusa Crowd Sensing4 application, the limitations identified and how they shall be addressed in future work.

Guest-induced emergent properties in Metal–Organic Frameworks

DOE PAGES

Allendorf, Mark D.; Foster, Michael E.; Léonard, François; ...

2015-03-19

Metal–Organic frameworks (MOFs) are crystalline nanoporous materials comprised of organic electron donors linked to metal ions by strong coordination bonds. Applications such as gas storage and separations are currently receiving considerable attention, but if the unique properties of MOFs could be extended to electronics, magnetics, and photonics, the impact on material science would greatly increase. Recently, we obtained “emergent properties,” such as electronic conductivity and energy transfer, by infiltrating MOF pores with “guest” molecules that interact with the framework electronic structure. In this Perspective, we define a path to emergent properties based on the Guest@MOF concept, using zinc-carboxylate and copper-paddlewheelmore » MOFs for illustration. Energy transfer and light harvesting are discussed for zinc carboxylate frameworks infiltrated with triplet-scavenging organometallic compounds and thiophene- and fullerene-infiltrated MOF-177. In addition, we discuss the mechanism of charge transport in TCNQ-infiltrated HKUST-1, the first MOF with electrical conductivity approaching conducting organic polymers. Lastly, these examples show that guest molecules in MOF pores should be considered not merely as impurities or analytes to be sensed but also as an important aspect of rational design.« less

MOF-templated synthesis of porous Co(3)O(4) concave nanocubes with high specific surface area and their gas sensing properties.

PubMed

Lü, Yinyun; Zhan, Wenwen; He, Yue; Wang, Yiting; Kong, Xiangjian; Kuang, Qin; Xie, Zhaoxiong; Zheng, Lansun

2014-03-26

Porous metal oxides nanomaterials with controlled morphology have received great attention because of their promising applications in catalysis, energy storage and conversion, gas sensing, etc. In this paper, porous Co3O4 concave nanocubes with extremely high specific surface area (120.9 m(2)·g(-1)) were synthesized simply by calcining Co-based metal-organic framework (Co-MOF, ZIF-67) templates at the optimized temperature (300 °C), and the formation mechanism of such highly porous structures as well as the influence of the calcination temperature are well explained by taking into account thermal behavior and intrinsic structural features of the Co-MOF precursors. The gas-sensing properties of the as-synthesized porous Co3O4 concave nanocubes were systematically tested towards volatile organic compounds including ethanol, acetone, toluene, and benzene. Experimental results reveal that the porous Co3O4 concave nanocubes present the highest sensitivity to ethanol with fast response/recovery time (< 10 s) and a low detection limit (at least 10 ppm). Such outstanding gas sensing performance of the porous Co3O4 concave nanocubes benefits from their high porosity, large specific surface area, and remarkable capabilities of surface-adsorbed oxygen.

An efficient and sensitive fluorescent pH sensor based on amino functional metal-organic frameworks in aqueous environment.

PubMed

Xu, Xiao-Yu; Yan, Bing

2016-04-28

A pH sensor is fabricated via a reaction between an Al(III) salt and 2-aminoterephthalic acid in DMF which leads to a MOF (Al-MIL-101-NH2) with free amino groups. The Al-MIL-101-NH2 samples show good luminescence and an intact structure in aqueous solutions with pH ranging from 4.0 to 7.7. Given its exceptional stability and pH-dependent fluorescence intensity, Al-MIL-101-NH2 has been applied to fluorescent pH sensing. Significantly, in the whole experimental pH range (4.0-7.7), the fluorescence intensity almost increases with increasing pH (R(2) = 0.99688) which can be rationalized using a linear equation: I = 2.33 pH + 26.04. In addition, error analysis and cycling experiments have demonstrated the accuracy and utilizability of the sensor. In practical applications (PBS and lake water), Al-MIL-101-NH2 also manifests its analytical efficiency in pH sensing. And the samples can be easily isolated from an aqueous solution by incorporating Fe3O4 nanoparticles. Moreover, the possible sensing mechanism based on amino protonation is discussed in detail. This work is on of the few cases for integrated pH sensing systems in aqueous solution based on luminescent MOFs.

Shape and Stress Sensing of Multilayered Composite and Sandwich Structures Using an Inverse Finite Element Method

NASA Technical Reports Server (NTRS)

Cerracchio, Priscilla; Gherlone, Marco; Di Sciuva, Marco; Tessler, Alexander

2013-01-01

The marked increase in the use of composite and sandwich material systems in aerospace, civil, and marine structures leads to the need for integrated Structural Health Management systems. A key capability to enable such systems is the real-time reconstruction of structural deformations, stresses, and failure criteria that are inferred from in-situ, discrete-location strain measurements. This technology is commonly referred to as shape- and stress-sensing. Presented herein is a computationally efficient shape- and stress-sensing methodology that is ideally suited for applications to laminated composite and sandwich structures. The new approach employs the inverse Finite Element Method (iFEM) as a general framework and the Refined Zigzag Theory (RZT) as the underlying plate theory. A three-node inverse plate finite element is formulated. The element formulation enables robust and efficient modeling of plate structures instrumented with strain sensors that have arbitrary positions. The methodology leads to a set of linear algebraic equations that are solved efficiently for the unknown nodal displacements. These displacements are then used at the finite element level to compute full-field strains, stresses, and failure criteria that are in turn used to assess structural integrity. Numerical results for multilayered, highly heterogeneous laminates demonstrate the unique capability of this new formulation for shape- and stress-sensing.

Ambiguity of Quality in Remote Sensing Data

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Leptoukh, Greg

2010-01-01

This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

A New Framework for Quantifying Lidar Uncertainty

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

Newman, Jennifer, F.; Clifton, Andrew; Bonin, Timothy A.

2017-03-24

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classificationmore » of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.« less

A Multifunctional Tb-MOF for Highly Discriminative Sensing of Eu3+ /Dy3+ and as a Catalyst Support of Ag Nanoparticles.

PubMed

Xu, Guo-Wang; Wu, Ya-Pan; Dong, Wen-Wen; Zhao, Jun; Wu, Xue-Qian; Li, Dong-Sheng; Zhang, Qichun

2017-06-01

Exploring novel multifunctional rare earth materials is very important because these materials have fundamental interests, such as new structural facts and connecting modes, as well as potential technological applications, including optics, magnetic properties, sorption, and catalytic behaviors. Especially, employing these nanomaterials for sensing or catalytic reactions is still very challenging. Herein, a new superstable, anionic terbium-metal-organic-framework, [H 2 N(CH 3 ) 2 ][Tb(cppa) 2 (H 2 O) 2 ], (China Three Gorges University (CTGU-1), H 2 cppa = 5-(4-carboxyphenyl)picolinic acid), is successfully prepared, which can be used as a turn-on, highly-sensitive fluorescent sensor to detect Eu 3+ and Dy 3+ , with a detection limitation of 5 × 10 -8 and 1 × 10 -4 m in dimethylformamide, respectively. This result represents the first example of lanthanide-metal-organic-frameworks (Ln-MOF) that can be employed as a discriminative fluorescent probe to recognize Eu 3+ and Dy 3+ . In addition, through ion exchanging at room temperature, Ag(I) can be readily reduced in situ and embedded in the anionic framework, which leads to the formation of nanometal-particle@Ln-MOF composite with uniform size and distribution. The as-prepared Ag@CTGU-1 shows remarkable catalytic performance to reduce 4-nitrophenol, with a reduction rate constant κ as large as 2.57 × 10 -2 s -1 ; almost the highest value among all reported noble-metal-nanoparticle@MOF composites. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Device-Independent Tests of Entropy

NASA Astrophysics Data System (ADS)

Chaves, Rafael; Brask, Jonatan Bohr; Brunner, Nicolas

2015-09-01

We show that the entropy of a message can be tested in a device-independent way. Specifically, we consider a prepare-and-measure scenario with classical or quantum communication, and develop two different methods for placing lower bounds on the communication entropy, given observable data. The first method is based on the framework of causal inference networks. The second technique, based on convex optimization, shows that quantum communication provides an advantage over classical communication, in the sense of requiring a lower entropy to reproduce given data. These ideas may serve as a basis for novel applications in device-independent quantum information processing.

Tetratopic phenyl compounds, related metal-organic framework materials and post-assembly elaboration

DOEpatents

Farha, Omar K.; Hupp, Joseph T.

2012-09-11

Disclosed are tetratopic carboxylic acid phenyl for use in metal-organic framework compounds. These compounds are useful in catalysis, gas storage, sensing, biological imaging, drug delivery and gas adsorption separation.

Tetratopic phenyl compounds, related metal-organic framework materials and post-assembly elaboration

DOEpatents

Farha, Omar K; Hupp, Joseph T

2013-06-25

Disclosed are tetratopic carboxylic acid phenyl for use in metal-organic framework compounds. These compounds are useful in catalysis, gas storage, sensing, biological imaging, drug delivery and gas adsorption separation.

Using Remotely Sensed Data for Climate Change Mitigation and Adaptation: A Collaborative Effort Between the Climate Change Adaptation Science Investigators Workgroup (CASI), NASA Johnson Space Center, and Jacobs Technology

NASA Technical Reports Server (NTRS)

Jagge, Amy

2016-01-01

With ever changing landscapes and environmental conditions due to human induced climate change, adaptability is imperative for the long-term success of facilities and Federal agency missions. To mitigate the effects of climate change, indicators such as above-ground biomass change must be identified to establish a comprehensive monitoring effort. Researching the varying effects of climate change on ecosystems can provide a scientific framework that will help produce informative, strategic and tactical policies for environmental adaptation. As a proactive approach to climate change mitigation, NASA tasked the Climate Change Adaptation Science Investigators Workgroup (CASI) to provide climate change expertise and data to Center facility managers and planners in order to ensure sustainability based on predictive models and current research. Generation of historical datasets that will be used in an agency-wide effort to establish strategies for climate change mitigation and adaptation at NASA facilities is part of the CASI strategy. Using time series of historical remotely sensed data is well-established means of measuring change over time. CASI investigators have acquired multispectral and hyperspectral optical and LiDAR remotely sensed datasets from NASA Earth Observation Satellites (including the International Space Station), airborne sensors, and astronaut photography using hand held digital cameras to create a historical dataset for the Johnson Space Center, as well as the Houston and Galveston area. The raster imagery within each dataset has been georectified, and the multispectral and hyperspectral imagery has been atmospherically corrected. Using ArcGIS for Server, the CASI-Regional Remote Sensing data has been published as an image service, and can be visualized through a basic web mapping application. Future work will include a customized web mapping application created using a JavaScript Application Programming Interface (API), and inclusion of the CASI data for the NASA Johnson Space Center into a NASA-Wide GIS Institutional Portal.

Framework GRASP: routine library for optimize processing of aerosol remote sensing observation

NASA Astrophysics Data System (ADS)

Fuertes, David; Torres, Benjamin; Dubovik, Oleg; Litvinov, Pavel; Lapyonok, Tatyana; Ducos, Fabrice; Aspetsberger, Michael; Federspiel, Christian

The present the development of a Framework for the Generalized Retrieval of Aerosol and Surface Properties (GRASP) developed by Dubovik et al., (2011). The framework is a source code project that attempts to strengthen the value of the GRASP inversion algorithm by transforming it into a library that will be used later for a group of customized application modules. The functions of the independent modules include the managing of the configuration of the code execution, as well as preparation of the input and output. The framework provides a number of advantages in utilization of the code. First, it implements loading data to the core of the scientific code directly from memory without passing through intermediary files on disk. Second, the framework allows consecutive use of the inversion code without the re-initiation of the core routine when new input is received. These features are essential for optimizing performance of the data production in processing of large observation sets, such as satellite images by the GRASP. Furthermore, the framework is a very convenient tool for further development, because this open-source platform is easily extended for implementing new features. For example, it could accommodate loading of raw data directly onto the inversion code from a specific instrument not included in default settings of the software. Finally, it will be demonstrated that from the user point of view, the framework provides a flexible, powerful and informative configuration system.

Rotaxane and catenane host structures for sensing charged guest species.

PubMed

Langton, Matthew J; Beer, Paul D

2014-07-15

CONSPECTUS: The promise of mechanically interlocked architectures, such as rotaxanes and catenanes, as prototypical molecular switches and shuttles for nanotechnological applications, has stimulated an ever increasing interest in their synthesis and function. The elaborate host cavities of interlocked structures, however, can also offer a novel approach toward molecular recognition: this Account describes the use of rotaxane and catenane host systems for binding charged guest species, and for providing sensing capability through an integrated optical or electrochemical reporter group. Particular attention is drawn to the exploitation of the unusual dynamic properties of interlocked molecules, such as guest-induced shuttling or conformational switching, as a sophisticated means of achieving a selective and functional sensor response. We initially survey interlocked host systems capable of sensing cationic guests, before focusing on our accomplishments in synthesizing rotaxanes and catenanes designed for the more challenging task of selective anion sensing. In our group, we have developed the use of discrete anionic templation to prepare mechanically interlocked structures for anion recognition applications. Removal of the anion template reveals an interlocked host system, possessing a unique three-dimensional geometrically restrained binding cavity formed between the interlocked components, which exhibits impressive selectivity toward complementary anionic guest species. By incorporating reporter groups within such systems, we have developed both electrochemical and optical anion sensors which can achieve highly selective sensing of anionic guests. Transition metals, lanthanides, and organic fluorophores integrated within the mechanically bonded structural framework of the receptor are perturbed by the binding of the guest, with a concomitant change in the emission profile. We have also exploited the unique dynamics of interlocked hosts by demonstrating that an anion-induced conformational change can be used as a means of signal transduction. Electrochemical sensing has been realized by integration of the redox-active ferrocene functionality within a range of rotaxane and catenanes; binding of an anion perturbs the metallocene, leading to a cathodic shift in the ferrocene/ferrocenium redox couple. In order to obtain practical sensors for target charged guest species, confinement of receptors at a surface is necessary in order to develop robust, reuseable devices. Surface confinement also offers advantages over solution based receptors, including amplification of signal, enhanced guest binding thermodynamics and the negation of solubility problems. We have fabricated anion-templated rotaxanes and catenanes on gold electrode surfaces and demonstrated that the resulting mechanically bonded self-assembled monolayers are electrochemically responsive to the binding of anions, a crucial first step toward the advancement of sophisticated, highly selective, anion sensory devices. Rotaxane and catenane host molecules may be engineered to offer a superior level of molecular recognition, and the incorporation of optical or electrochemical reporter groups within these interlocked frameworks can allow for guest sensing. Advances in synthetic templation strategies has facilitated the synthesis of interlocked architectures and widened their interest as prototype molecular machines. However, their unique host-guest properties are only now beginning to be exploited as a sophisticated approach to chemical sensing. The development of functional host-guest sensory systems such as these is of great interest to the interdisciplinary field of supramolecular chemistry.

JPRS Report, Science & Technology, China, Remote Sensing Systems, Applications.

DTIC Science & Technology

1991-01-17

Partial Contents: Short Introduction to Nation’s Remote Sensing Units, Domestic Airborne Remote - Sensing System, Applications in Monitoring Natural...Disasters, Applications of Imagery From Experimental Satellites Launched in 1985, 1986, Current Status, Future Prospects for Domestic Remote - Sensing -Satellite...Ground Station, and Radar Remote - Sensing Technology Used to Monitor Yellow River Delta,

Metal-Organic Frameworks-Derived Hierarchical Co3O4 Structures as Efficient Sensing Materials for Acetone Detection.

PubMed

Zhang, Rui; Zhou, Tingting; Wang, Lili; Zhang, Tong

2018-03-21

Highly sensitive and stable gas sensors have attracted much attention because they are the key to innovations in the fields of environment, health, energy savings and security, etc. Sensing materials, which influence the practical sensing performance, are the crucial parts for gas sensors. Metal-organic frameworks (MOFs) are considered as alluring sensing materials for gas sensors because of the possession of high specific surface area, unique morphology, abundant metal sites, and functional linkers. Herein, four kinds of porous hierarchical Co 3 O 4 structures have been selectively controlled by optimizing the thermal decomposition (temperature, rate, and atmosphere) using ZIF-67 as precursor that was obtained from coprecipitation method with the co-assistance of cobalt salt and 2-methylimidazole in the solution of methanol. These hierarchical Co 3 O 4 structures, with controllable cross-linked channels, meso-/micropores, and adjustable surface area, are efficient catalytic materials for gas sensing. Benefits from structural advantages, core-shell, and porous core-shell Co 3 O 4 exhibit enhanced sensing performance compared to those of porous popcorn and nanoparticle Co 3 O 4 to acetone gas. These novel MOF-templated Co 3 O 4 hierarchical structures are so fantastic that they can be expected to be efficient sensing materials for development of low-temperature operating gas sensors.

Centimetre-scale micropore alignment in oriented polycrystalline metal-organic framework films via heteroepitaxial growth.

PubMed

Falcaro, Paolo; Okada, Kenji; Hara, Takaaki; Ikigaki, Ken; Tokudome, Yasuaki; Thornton, Aaron W; Hill, Anita J; Williams, Timothy; Doonan, Christian; Takahashi, Masahide

2017-03-01

The fabrication of oriented, crystalline films of metal-organic frameworks (MOFs) is a critical step toward their application to advanced technologies such as optics, microelectronics, microfluidics and sensing. However, the direct synthesis of MOF films with controlled crystalline orientation remains a significant challenge. Here we report a one-step approach, carried out under mild conditions, that exploits heteroepitaxial growth for the rapid fabrication of oriented polycrystalline MOF films on the centimetre scale. Our methodology employs crystalline copper hydroxide as a substrate and yields MOF films with oriented pore channels on scales that primarily depend on the dimensions of the substrate. To demonstrate that an anisotropic crystalline morphology can translate to a functional property, we assembled a centimetre-scale MOF film in the presence of a dye and showed that the optical response could be switched 'ON' or 'OFF' by simply rotating the film.

Emerging Multifunctional Metal-Organic Framework Materials.

PubMed

Li, Bin; Wen, Hui-Min; Cui, Yuanjing; Zhou, Wei; Qian, Guodong; Chen, Banglin

2016-10-01

Metal-organic frameworks (MOFs), also known as coordination polymers, represent an interesting type of solid crystalline materials that can be straightforwardly self-assembled through the coordination of metal ions/clusters with organic linkers. Owing to the modular nature and mild conditions of MOF synthesis, the porosities of MOF materials can be systematically tuned by judicious selection of molecular building blocks, and a variety of functional sites/groups can be introduced into metal ions/clusters, organic linkers, or pore spaces through pre-designing or post-synthetic approaches. These unique advantages enable MOFs to be used as a highly versatile and tunable platform for exploring multifunctional MOF materials. Here, the bright potential of MOF materials as emerging multifunctional materials is highlighted in some of the most important applications for gas storage and separation, optical, electric and magnetic materials, chemical sensing, catalysis, and biomedicine. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Highly sensitive detection of dipicolinic acid with a water-dispersible terbium-metal organic framework.

PubMed

Bhardwaj, Neha; Bhardwaj, Sanjeev; Mehta, Jyotsana; Kim, Ki-Hyun; Deep, Akash

2016-12-15

The sensitive detection of dipicolinic acid (DPA) is strongly associated with the sensing of bacterial organisms in food and many types of environmental samples. To date, the demand for a sensitive detection method for bacterial toxicity has increased remarkably. Herein, we investigated the DPA detection potential of a water-dispersible terbium-metal organic framework (Tb-MOF) based on the fluorescence quenching mechanism. The Tb-MOF showed a highly sensitive ability to detect DPA at a limit of detection of 0.04nM (linear range of detection: 1nM to 5µM) and also offered enhanced selectivity from other commonly associated organic molecules. The present study provides a basis for the application of Tb-MOF for direct, convenient, highly sensitive, and specific detection of DPA in the actual samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Covalent Organic Framework Functionalized with 8-Hydroxyquinoline as a Dual-Mode Fluorescent and Colorimetric pH Sensor.

PubMed

Chen, Long; He, Linwei; Ma, Fuyin; Liu, Wei; Wang, Yaxing; Silver, Mark A; Chen, Lanhua; Zhu, Lin; Gui, Daxiang; Diwu, Juan; Chai, Zhifang; Wang, Shuao

2018-05-09

Real-time and accurate detection of pH in aqueous solution is of great significance in chemical, environmental, and engineering-related fields. We report here the use of 8-hydroxyquinoline-functionalized covalent organic framework (COF-HQ) for dual-mode pH sensing. In the fluorescent mode, the emission intensity of COF-HQ weakened as the pH decreased, and also displayed a good linear relationship against pH in the range from 1 to 5. In addition, COF-HQ showed discernible color changes from yellow to black as the acidity increased and can be therefore used as a colorimetric pH sensor. All these changes are reversible and COF-HQ can be recycled for multiple detection runs owing to its high hydrolytical stability. It can be further assembled into a mixed matrix membrane for practical applications.

Social Change: A Framework for Inclusive Leadership Development in Nursing Education.

PubMed

Read, Catherine Y; Pino Betancourt, Debra M; Morrison, Chenille

2016-03-01

The social change model (SCM) promotes equity, social justice, self-knowledge, service, and collaboration. It is a relevant framework for extracurricular leadership development programs that target students who may not self-identify as leaders. Application of the SCM in a leadership development program for prelicensure nursing students from underresourced or underrepresented backgrounds is described. Students' opinions about leadership for social change were explored through a focus group and a pilot test of an instrument designed to assess the values of the SCM. Students lack the experience required to feel comfortable with change, but they come into nursing with a sense of commitment that can be nurtured toward leadership for social change and health equity through best practices derived from the SCM. These include sociocultural conversations, mentoring relationships, community service, and membership in off-campus organizations. Nurse educators can cultivate inclusive leadership for social change using the SCM as a guide. Copyright 2016, SLACK Incorporated.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

PubMed

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

PubMed Central

Sotiropoulos, Konstantinos

2018-01-01

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. PMID:29662043

AWIPS II+: An Open-Source SOA Solution Enabling Environmental Remote Sensing Integration, Analysis, and Decision Support

NASA Astrophysics Data System (ADS)

Ardanuy, P. E.; Hood, C. A.; Moran, S. G.; Ritchie, A. A.; Tarro, A. M.; Nappi, A. J.

2008-12-01

Our shared future demands a renewed focus on sound environment stewardship-on the GEOSS socioeconomic imperatives, as well as the interdisciplinary relationships interconnecting our environment, climate, ecosystems, energy, carbon, water-and national security. Data volumes are now measured in the many petabytes. An increasingly urgent and accelerated tempo of changing requirements and responsive solutions demands data exploitation, and transparent, seamless, effortless, bidirectional, and interdisciplinary interoperability across models and observations. There is today a robust working paradigm established with the Advanced Weather Interactive Processing System (AWIPS)-NOAA/NWS's information integration and fusion capability. This process model extends vertically, and seamlessly, from environmental sensing through the direct delivery of societal benefit. NWS, via AWIPS, is the primary source of weather forecast and warning information in the nation. AWIPS is the tested and proven "the nerve center of operations" at all 122 NWS Weather Forecast Offices and 13 River Forecast Centers. Raytheon, in partnership with NOAA, has now evolved AWIPS into an open-source 2nd generation capability to satisfy climate, ecosystems, weather, and water mission goals. Just as AWIPS II supports NOAA decision- making, it is at the same time a platform funded by Raytheon IRAD and Government investment that can be cost-effectively leveraged across all of the GEOSS and IEOS societal benefit areas. The core principles in the AWIPS II evolution to a service-oriented architecture (SOA) were to minimize coupling, increase cohesion, minimize size of code base, maximize simplicity, and incorporate a pull-style data flow. We focused on "ilities" to drive the new AWIPS architecture-our shared architecture framework vision included six elements: - Create a new, low-cost framework for hosting a full range of environmental services, including thick-client visualization via virtual Earth's and GIS - Scale down framework to a small laptop and through workstations to clusters of enterprise servers without software change - "Plug-n-play"- plug-ins can be hot deployable, or system cycled to pick up new plug-ins - Base the framework on highly reusable design patterns that maximize reuse and have datatype independence and fast adaptability - Open Source leveraged to maximize reuse - "Gaming-style" interaction with the data This talk addresses the challenges that we meet to realize benefits in applications that couple environmental data from many disparate remote sensing and ancillary sources and disciplines. By leveraging the existing AWIPS II weather, water, ecosystems, and climate functionality and these six elements, along with well- thought-out displays with the end user's specific needs in mind, we demonstrate an easily adapted, extremely powerful, open-source remote sensing software tool that will help non-geospatial-experts make better use of these remote sensing resources to enhance environmental mapping and analysis and help guide environmental decision making at the national, regional, local and citizen levels.

Waggle: A Framework for Intelligent Attentive Sensing and Actuation

NASA Astrophysics Data System (ADS)

Sankaran, R.; Jacob, R. L.; Beckman, P. H.; Catlett, C. E.; Keahey, K.

2014-12-01

Advances in sensor-driven computation and computationally steered sensing will greatly enable future research in fields including environmental and atmospheric sciences. We will present "Waggle," an open-source hardware and software infrastructure developed with two goals: (1) reducing the separation and latency between sensing and computing and (2) improving the reliability and longevity of sensing-actuation platforms in challenging and costly deployments. Inspired by "deep-space probe" systems, the Waggle platform design includes features that can support longitudinal studies, deployments with varying communication links, and remote management capabilities. Waggle lowers the barrier for scientists to incorporate real-time data from their sensors into their computations and to manipulate the sensors or provide feedback through actuators. A standardized software and hardware design allows quick addition of new sensors/actuators and associated software in the nodes and enables them to be coupled with computational codes both insitu and on external compute infrastructure. The Waggle framework currently drives the deployment of two observational systems - a portable and self-sufficient weather platform for study of small-scale effects in Chicago's urban core and an open-ended distributed instrument in Chicago that aims to support several research pursuits across a broad range of disciplines including urban planning, microbiology and computer science. Built around open-source software, hardware, and Linux OS, the Waggle system comprises two components - the Waggle field-node and Waggle cloud-computing infrastructure. Waggle field-node affords a modular, scalable, fault-tolerant, secure, and extensible platform for hosting sensors and actuators in the field. It supports insitu computation and data storage, and integration with cloud-computing infrastructure. The Waggle cloud infrastructure is designed with the goal of scaling to several hundreds of thousands of Waggle nodes. It supports aggregating data from sensors hosted by the nodes, staging computation, relaying feedback to the nodes and serving data to end-users. We will discuss the Waggle design principles and their applicability to various observational research pursuits, and demonstrate its capabilities.

Homochiral metal-organic frameworks and their application in chromatography enantioseparations.

PubMed

Peluso, Paola; Mamane, Victor; Cossu, Sergio

2014-10-10

The last frontier in the chiral stationary phases (CSPs) field for chromatography enantioseparations is represented by homochiral metal-organic frameworks (MOFs), a class of organic-inorganic hybrid materials built from metal-connecting nodes and organic-bridging ligands. The modular nature of these materials allows to design focused structures by combining properly metal, organic ligands and rigid polytopic spacers. Intriguingly, chiral ligands introduce molecular chirality in the MOF-network as well as homochirality in the secondary structure of materials (such as homohelicity) producing homochiral nets in a manner mimicking biopolymers (proteins, polysaccharides) which are characterized by a definite helical sense associated with the chirality of their building blocks (amino acids or sugars). Nowadays, robust and flexible materials characterized by high porosity and surface area became available by using preparative procedures typical of the so-called reticular synthesis. This review focuses on recent developments in the synthesis and applications of homochiral MOFs as supports for chromatography enantioseparations. Indeed, despite this field is in its infancy, interesting results have been produced and a critical overview of the 12 reported applications for gas chromatography (GC) and high-performance liquid chromatography (HPLC) can orient the reader approaching the field. Mechanistic aspects are shortly discussed and a view regarding future trends in this field is provided. Copyright © 2014 Elsevier B.V. All rights reserved.

A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

PubMed

Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

2014-03-01

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

Energy-efficient sensing in wireless sensor networks using compressed sensing.

PubMed

Razzaque, Mohammad Abdur; Dobson, Simon

2014-02-12

Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

X-Windows Information Sharing Protocol Widget Class

NASA Technical Reports Server (NTRS)

Barry, Matthew R.

2006-01-01

The X-Windows Information Sharing Protocol (ISP) Widget Class ("Class") is used here in the object-oriented-programming sense of the word) was devised to simplify the task of implementing ISP graphical-user-interface (GUI) computer programs. ISP programming tasks require many method calls to identify, query, and interpret the connections and messages exchanged between a client and an ISP server. Most X-Windows GUI programs use widget sets or toolkits to facilitate management of complex objects. The widget standards facilitate construction of toolkits and application programs. The X-Windows Information Sharing Protocol (ISP) Widget Class encapsulates the client side of the ISP programming libraries within the framework of an X-Windows widget. Using the widget framework, X-Windows GUI programs can interact with ISP services in an abstract way and in the same manner as that of other graphical widgets, making it easier to write ISP GUI client programs. Wrapping ISP client services inside a widget framework enables a programmer to treat an ISP server interface as though it were a GUI. Moreover, an alternate subclass could implement another communication protocol in the same sort of widget.

GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments.

PubMed

Monroy, Javier; Hernandez-Bennets, Victor; Fan, Han; Lilienthal, Achim; Gonzalez-Jimenez, Javier

2017-06-23

This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment.

GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments

PubMed Central

Hernandez-Bennetts, Victor; Fan, Han; Lilienthal, Achim; Gonzalez-Jimenez, Javier

2017-01-01

This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment. PMID:28644375

Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications

PubMed Central

Mathur, Rohit; Xing, Jia; Gilliam, Robert; Sarwar, Golam; Hogrefe, Christian; Pleim, Jonathan; Pouliot, George; Roselle, Shawn; Spero, Tanya L.; Wong, David C.; Young, Jeffrey

2018-01-01

The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modelled processes were examined and enhanced to suitably represent the extended space and time scales for such applications. Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting (WRF) model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. The expansion of CMAQ to simulate the hemispheric scales provides a framework to examine interactions between atmospheric processes occurring at various spatial and temporal scales with physical, chemical, and dynamical consistency. PMID:29681922

Fortified Anonymous Communication Protocol for Location Privacy in WSN: A Modular Approach

PubMed Central

Abuzneid, Abdel-Shakour; Sobh, Tarek; Faezipour, Miad; Mahmood, Ausif; James, John

2015-01-01

Wireless sensor network (WSN) consists of many hosts called sensors. These sensors can sense a phenomenon (motion, temperature, humidity, average, max, min, etc.) and represent what they sense in a form of data. There are many applications for WSNs including object tracking and monitoring where in most of the cases these objects need protection. In these applications, data privacy itself might not be as important as the privacy of source location. In addition to the source location privacy, sink location privacy should also be provided. Providing an efficient end-to-end privacy solution would be a challenging task to achieve due to the open nature of the WSN. The key schemes needed for end-to-end location privacy are anonymity, observability, capture likelihood, and safety period. We extend this work to allow for countermeasures against multi-local and global adversaries. We present a network model protected against a sophisticated threat model: passive /active and local/multi-local/global attacks. This work provides a solution for end-to-end anonymity and location privacy as well. We will introduce a framework called fortified anonymous communication (FAC) protocol for WSN. PMID:25763649

Pan Sharpening Quality Investigation of Turkish In-Operation Remote Sensing Satellites: Applications with Rasat and GÖKTÜRK-2 Images

NASA Astrophysics Data System (ADS)

Ozendi, Mustafa; Topan, Hüseyin; Cam, Ali; Bayık, Çağlar

2016-10-01

Recently two optical remote sensing satellites, RASAT and GÖKTÜRK-2, launched successfully by the Republic of Turkey. RASAT has 7.5 m panchromatic, and 15 m visible bands whereas GÖKTÜRK-2 has 2.5 m panchromatic and 5 m VNIR (Visible and Near Infrared) bands. These bands with various resolutions can be fused by pan-sharpening methods which is an important application area of optical remote sensing imagery. So that, the high geometric resolution of panchromatic band and the high spectral resolution of VNIR bands can be merged. In the literature there are many pan-sharpening methods. However, there is not a standard framework for quality investigation of pan-sharpened imagery. The aim of this study is to investigate pan-sharpening performance of RASAT and GÖKTÜRK-2 images. For this purpose, pan-sharpened images are generated using most popular pan-sharpening methods IHS, Brovey and PCA at first. This procedure is followed by quantitative evaluation of pan-sharpened images using Correlation Coefficient (CC), Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE), Spectral Angle Mapper (SAM) and Erreur Relative Globale Adimensionnelle de Synthése (ERGAS) metrics. For generation of pan-sharpened images and computation of metrics SharpQ tool is used which is developed with MATLAB computing language. According to metrics, PCA derived pan-sharpened image is the most similar one to multispectral image for RASAT, and Brovey derived pan-sharpened image is the most similar one to multispectral image for GÖKTÜRK-2. Finally, pan-sharpened images are evaluated qualitatively in terms of object availability and completeness for various land covers (such as urban, forest and flat areas) by a group of operators who are experienced in remote sensing imagery.

Photoluminescent Metal–Organic Frameworks for Gas Sensing

PubMed Central

Lin, Rui‐Biao; Liu, Si‐Yang; Ye, Jia‐Wen; Li, Xu‐Yu

2016-01-01

Luminescence of porous coordination polymers (PCPs) or metal–organic frameworks (MOFs) is sensitive to the type and concentration of chemical species in the surrounding environment, because these materials combine the advantages of the highly regular porous structures and various luminescence mechanisms, as well as diversified host‐guest interactions. In the past few years, luminescent MOFs have attracted more and more attention for chemical sensing of gas‐phase analytes, including common gases and vapors of solids/liquids. While liquid‐phase and gas‐phase luminescence sensing by MOFs share similar mechanisms such as host‐guest electron and/or energy transfer, exiplex formation, and guest‐perturbing of excited‐state energy level and radiation pathways, via various types of host‐guest interactions, gas‐phase sensing has its unique advantages and challenges, such as easy utilization of encapsulated guest luminophores and difficulty for accurate measurement of the intensity change. This review summarizes recent progresses by using luminescent MOFs as reusable sensing materials for detection of gases and vapors of solids/liquids especially for O2, highlighting various strategies for improving the sensitivity, selectivity, stability, and accuracy, reducing the materials cost, and developing related devices. PMID:27818903

Metal–Organic Framework Thin Film Coated Optical Fiber Sensors: A Novel Waveguide-Based Chemical Sensing Platform

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

Kim, Ki-Joong; Lu, Ping; Culp, Jeffrey T.

Integration of optical fiber with sensitive thin films offers great potential for the realization of novel chemical sensing platforms. In this study, we present a simple design strategy and high performance of nanoporous metal–organic framework (MOF) based optical gas sensors, which enables detection of a wide range of concentrations of small molecules based upon extremely small differences in refractive indices as a function of analyte adsorption within the MOF framework. Thin and compact MOF films can be uniformly formed and tightly bound on the surface of etched optical fiber through a simple solution method which is critical for manufacturability ofmore » MOF-based sensor devices. The resulting sensors show high sensitivity/selectivity to CO 2 gas relative to other small gases (H 2, N 2, O 2, and CO) with rapid (< tens of seconds) response time and excellent reversibility, which can be well correlated to the physisorption of gases into a nanoporous MOF. We propose a refractive index based sensing mechanism for the MOF-integrated optical fiber platform which results in an amplification of inherent optical absorption present within the MOF-based sensing layer with increasing values of effective refractive index associated with adsorption of gases.« less

Metal–Organic Framework Thin Film Coated Optical Fiber Sensors: A Novel Waveguide-Based Chemical Sensing Platform

DOE PAGES

Kim, Ki-Joong; Lu, Ping; Culp, Jeffrey T.; ...

2018-01-18

Integration of optical fiber with sensitive thin films offers great potential for the realization of novel chemical sensing platforms. In this study, we present a simple design strategy and high performance of nanoporous metal–organic framework (MOF) based optical gas sensors, which enables detection of a wide range of concentrations of small molecules based upon extremely small differences in refractive indices as a function of analyte adsorption within the MOF framework. Thin and compact MOF films can be uniformly formed and tightly bound on the surface of etched optical fiber through a simple solution method which is critical for manufacturability ofmore » MOF-based sensor devices. The resulting sensors show high sensitivity/selectivity to CO 2 gas relative to other small gases (H 2, N 2, O 2, and CO) with rapid (< tens of seconds) response time and excellent reversibility, which can be well correlated to the physisorption of gases into a nanoporous MOF. We propose a refractive index based sensing mechanism for the MOF-integrated optical fiber platform which results in an amplification of inherent optical absorption present within the MOF-based sensing layer with increasing values of effective refractive index associated with adsorption of gases.« less

Forensic pedology, forensic geology, forensic geoscience, geoforensics and soil forensics.

PubMed

Ruffell, Alastair

2010-10-10

We now have a confusing set of five commonly used terms for the application of Earth evidence in forensic science. This confusion is resulting in Earth scientists who use these methods mentioning different terms, sometimes for the same type of study. Likewise, forensic scientists, police/law enforcement officers and those employed by courts of law are becoming confused as to what each term means. A nomenclatural framework (based on the first use of each term) is proposed to encourage consistency in the use of terminology. Generally, the number of Earth science applications has grown through time, from soil and sediment analysis to remote sensing and GIS. The issue of where forensic biology and microbiology sits with these uses of Earth evidence is considered. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

Role of satellite remote sensing in the geographic information economics in France

NASA Astrophysics Data System (ADS)

Denégre, Jean

In national and international economics, geographic information plays a role which is generally acknowledged to be important but which is however, difficult to assess quantitatively, its applications being rather miscellaneous and indirect. Computer graphics and telecommunications increae that importance still more and justify many investments and research into new cartographic forms. As part of its responsibility for participating in the promotion of those developments, by taking into account needs expressed by public or private users, the National Council for Geographic Information (C.N.I.G.) has undertaken a general evaluation of the economic and social utility of geographic information in France. The study involves an estimation of the cost of production and research activities, which are probably about 0.1% of the Cross National Product—similar to many other countries. It also devised a method of estimating "cost/advantage" ratios applicable to these "intangible" benefits. Within that framework, remote sensing emphasizes particular aspects related both to the increase of economic performances in cartographic production and to the advent of new products and new ways of utilization. A review of some significant sectors shows effective earnings of about 10-20%, or even 50% or 100% of the costs, and these are doubtless much greater for the efficacy in the exploitation of products. Finally, many applications, entirely new result from extensions in various fields which would have been impossible without remote sensing: here the "cost advantage" ratio cannot even be compared with previous processes. Studies were undertaken in parallel for defining different types of products derived from satellite imagery, as well as those domains where development effort is required in order to make new advances.

Plasmonic nanopatch array with integrated metal–organic framework for enhanced infrared absorption gas sensing

DOE PAGES

Chong, Xinyuan; Kim, Ki-joong; Zhang, Yujing; ...

2017-06-06

In this letter, we present a nanophotonic device consisting of plasmonic nanopatch array (NPA) with integrated metal–organic framework (MOF) for enhanced infrared absorption gas sensing. By designing a gold NPA on a sapphire substrate, we are able to achieve enhanced optical field that spatially overlaps with the MOF layer, which can adsorb carbon dioxide (CO 2) with high capacity. Additionally, experimental results show that this hybrid plasmonic–MOF device can effectively increase the infrared absorption path of on-chip gas sensors by more than 1100-fold. Lastly, the demonstration of infrared absorption spectroscopy of CO 2 using the hybrid plasmonic–MOF device proves amore » promising strategy for future on-chip gas sensing with ultra-compact size.« less

Plasmonic nanopatch array with integrated metal–organic framework for enhanced infrared absorption gas sensing

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

Chong, Xinyuan; Kim, Ki-joong; Zhang, Yujing

In this letter, we present a nanophotonic device consisting of plasmonic nanopatch array (NPA) with integrated metal–organic framework (MOF) for enhanced infrared absorption gas sensing. By designing a gold NPA on a sapphire substrate, we are able to achieve enhanced optical field that spatially overlaps with the MOF layer, which can adsorb carbon dioxide (CO 2) with high capacity. Additionally, experimental results show that this hybrid plasmonic–MOF device can effectively increase the infrared absorption path of on-chip gas sensors by more than 1100-fold. Lastly, the demonstration of infrared absorption spectroscopy of CO 2 using the hybrid plasmonic–MOF device proves amore » promising strategy for future on-chip gas sensing with ultra-compact size.« less

Using the Five Senses of Success framework to understand the experiences of midwifery students enroled in an undergraduate degree program.

PubMed

Sidebotham, M; Fenwick, J; Carter, A; Gamble, J

2015-01-01

developing a student's sense of capability, purpose, resourcefulness, identity and connectedness (five-senses of success) are key factors that may be important in predicting student satisfaction and progression within their university program. the study aimed to examine the expectations and experiences of second and third year midwifery students enroled in a Bachelor of Midwifery program and identify barriers and enablers to success. a descriptive exploratory qualitative design was used. Fifty-six students enroled in either year 2 or 3 of the Bachelor of Midwifery program in SE Queensland participated in an anonymous survey using open-ended questions. In addition, 16 students participated in two year-level focus groups. Template analysis, using the Five Senses Framework, was used to analyse the data set. early exposure to 'hands on' clinical midwifery practice as well as continuity of care experiences provided students with an opportunity to link theory to practice and increased their perception of capability as they transitioned through the program. Students' sense of identity, purpose, resourcefulness, and capability was strongly influenced by the programs embedded meta-values, including a 'woman centred' approach. In addition, a student's ability to form strong positive relationships with women, peers, lecturers and supportive clinicians was central to developing connections and ultimately a sense of success. A sense of connection not only fostered an ongoing belief that challenges could be overcome but that students themselves could initiate or influence change. the five senses framework provided a useful lens through which to analyse the student experience. Key factors to student satisfaction and retention within a Bachelor of Midwifery program include: a clearly articulated midwifery philosophy, strategies to promote student connectedness including the use of social media, and further development of clinicians' skills in preceptorship, clinical teaching and facilitation. Program delivery methods and student support systems should be designed to enable maximum flexibility to promote capability and resourcefulness and embed sense of purpose and identity early in the program. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Interactive Web-Based Analysis Framework for Remote Sensing Cloud Computing

NASA Astrophysics Data System (ADS)

Wang, X. Z.; Zhang, H. M.; Zhao, J. H.; Lin, Q. H.; Zhou, Y. C.; Li, J. H.

2015-07-01

Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.

Remote Sensing and the Kyoto Protocol: A Workshop Summary

NASA Technical Reports Server (NTRS)

Rosenqvist, Ake; Imhoff, Marc; Milne, Anthony; Dobson, Craig

2000-01-01

The Kyoto Protocol to the United Nations Framework Convention on Climate Change contains quantified, legally binding commitments to limit or reduce greenhouse gas emissions to 1990 levels and allows carbon emissions to be balanced by carbon sinks represented by vegetation. The issue of using vegetation cover as an emission offset raises a debate about the adequacy of current remote sensing systems and data archives to both assess carbon stocks/sinks at 1990 levels, and monitor the current and future global status of those stocks. These concerns and the potential ratification of the Protocol among participating countries is stimulating policy debates and underscoring a need for the exchange of information between the international legal community and the remote sensing community. On October 20-22 1999, two working groups of the International Society for Photogrammetry and Remote Sensing (ISPRS) joined with the University of Michigan (Michigan, USA) to convene discussions on how remote sensing technology could contribute to the information requirements raised by implementation of, and compliance with, the Kyoto Protocol. The meeting originated as a joint effort between the Global Monitoring Working Group and the Radar Applications Working Group in Commission VII of the ISPRS, co-sponsored by the University of Michigan. Tile meeting was attended by representatives from national government agencies and international organizations and academic institutions. Some of the key themes addressed were: (1) legal aspects of transnational remote sensing in the context of the Kyoto Protocol; (2) a review of the current and future and remote sensing technologies that could be applied to the Kyoto Protocol; (3) identification of areas where additional research is needed in order to advance and align remote sensing technology with the requirements and expectations of the Protocol; and 94) the bureaucratic and research management approaches needed to align the remote sensing community with both the science and policy communities.

Luminescent microporous metal–organic framework with functional Lewis basic sites on the pore surface: Quantifiable evaluation of luminescent sensing mechanisms towards Fe{sup 3+}

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

Jin, Jun-Cheng; Technology Promotion Center of Nano Composite Material of Biomimetic Sensor and Detecting Technology, Preparation and Application, Anhui Provincial Laboratory West Anhui University, Anhui 237012; Guo, Rui-Li

2016-11-15

A systematic study has been conducted on a novel luminescent metal-organic framework, ([Zn(bpyp)(L-OH)]·DMF·2H{sub 2}O){sub n} (1), to explore its sensing mechanisms to Fe{sup 3+}. Structure analyses show that compound 1 exist pyridine N atoms and -OH groups on the pore surface for specific sensing of metal ions via Lewis acid-base interactions. On this consideration, the quenching mechanisms are studied and the processes are controlled by multiple mechanisms in which dynamic and static mechanisms are calculated, achieving the quantification evaluation of the quenching process. This work not only achieves the quantitative evaluation of the luminescence quenching but also provides certain insightsmore » into the quenching process, and the possible mechanisms explored in this work may inspire future research and design of target luminescent metal-organic frameworks (LMOFs) with specific functions. - Graphical abstract: A systematic study has been conducted on a novel luminescent metal-organic framework to explore its sensing mechanisms to Fe{sup 3+}. The quenching mechanisms are studied and the processes are controlled by multiple mechanisms in which dynamic and static mechanisms are calculated, achieving the quantification evaluation of the quenching process. - Highlights: • A novel porous luminescent MOF containing uncoordinated groups in interlayer channels was successfully synthesized. • The compound 1 can exhibit significant luminescent sensitivity to Fe{sup 3+}, which make its good candidate as luminescent sensor. • The corresponding dynamic and static quenching constants are calculated, achieving the quantification evaluation of the quenching process.« less

Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

2017-12-01

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

Monitoring of changes in areas of conflicts: the example of Darfur

NASA Astrophysics Data System (ADS)

Thunig, H.; Michel, U.

2012-10-01

Rapid change detection is used in cases of natural hazards and disasters. This analysis leads to rapid information on areas of damage. In certain cases the lack of information after catastrophe events is obstructing supporting measures within disaster management. Earthquakes, tsunamis, civil war, volcanic eruption, droughts and floods have much in common: people are directly affected, landscapes and buildings are destroyed. In every case geospatial data is necessary to gain knowledge as basement for decision support. Where to go first? Which infrastructure is usable? How much area is affected? These are essential question which need to be answered before appropriate, eligible help can be established. This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of lack of remote sensing knowledge. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

Fusion and quality analysis for remote sensing images using contourlet transform

NASA Astrophysics Data System (ADS)

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

2013-05-01

Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.

Photofunctional hybrids of lanthanide functionalized bio-MOF-1 for fluorescence tuning and sensing.

PubMed

Shen, Xiang; Yan, Bing

2015-08-01

A series of luminescent Ln(3+)@bio-MOF-1 (Ln=Eu, Tb, bio-MOF-1=Zn8(ad)4(BPDC)6O⋅2Me2NH2 (ad=adeninate, BPDC=biphenyldicarboxylate)) are synthesized via postsynthetic cation exchange by encapsulating lanthanide ions into an anionic metal-organic framework (MOF), and their photophysical properties are studied. After loading 2-thenoyltrifluroacetone (TTA) as sensitized ligand by a gas diffusion ("ship-in-bottle") method, it is found that the luminescent intensity of Eu(3+) is enhanced. Especially, when loading two different lanthanide cations into bio-MOF-1, the luminescent color can be tuned to close white (light pink) light output. Additionally, bio-MOF-1 and Eu(3+)@bio-MOF-1 are selected as representative samples for sensing metal ions. When bio-MOF-1 is immersed in the aqueous solutions of different metal ions, it shows highly sensitive sensing for Fe(3+) as well as Eu(3+)@bio-MOF-1 immersed in the DMF solutions of different metal ion. The results are benefit for the further application of functionalized bio-MOFs in practical fields. Copyright © 2015 Elsevier Inc. All rights reserved.

Metal organic frameworks enhanced graphene oxide electrode for humidity sensor

NASA Astrophysics Data System (ADS)

Zhang, Wen; Meng, Siyu; Wang, Hui; He, Yongning

2018-03-01

Copper benzene-1,3,5-tricarboxylate (Cu-BTC), a typical metal organic framework, is deposited on the graphene oxide (GO) film to prepare a resistance humidity sensor (Cu- BTC/GO) for improving humidity sensing. The characteristics of Cu-BTC, GO and Cu- BTC/GO were measured by scanning electron microscopy (SEM), X-ray diffraction (XRD), nitrogen isotherm adsorption and electrochemical impedance spectroscopy (EIS). The humidity sensing properties of the Cu-BTC/GO were investigated in detail. The obtained Cu-BTC/GO demonstrates good sensitivity and repeatability over 11%-85% relative humidity (RH) measurements. The Cu-BTC/GO coated device shows high normalized response (S) value (6200%), which is much higher than that of pure GO coated device. Sensing mechanism of Cu- BTC/GO is discussed based on different RH and the results indicate that moderate amounts of Cu-BTC deposition can enhance sensing abilities of GO. High specific surface area and interfacial conductivity are crucial factors to fabricate humidity sensors with high performance.

Sensing and capture of toxic and hazardous gases and vapors by metal–organic frameworks

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

Wang, Hao; Lustig, William P.; Li, Jing

This review summaries recent progress in the luminescent detection and adsorptive removal of harmful gases and vapors by metal–organic frameworks, as well as the principles and strategies guiding the design of these materials.

Sensing and capture of toxic and hazardous gases and vapors by metal–organic frameworks

DOE PAGES

Wang, Hao; Lustig, William P.; Li, Jing

2018-01-01

This review summaries recent progress in the luminescent detection and adsorptive removal of harmful gases and vapors by metal–organic frameworks, as well as the principles and strategies guiding the design of these materials.

A Review of Mechanoluminescence in Inorganic Solids: Compounds, Mechanisms, Models and Applications

PubMed Central

2018-01-01

Mechanoluminescence (ML) is the non-thermal emission of light as a response to mechanical stimuli on a solid material. While this phenomenon has been observed for a long time when breaking certain materials, it is now being extensively explored, especially since the discovery of non-destructive ML upon elastic deformation. A great number of materials have already been identified as mechanoluminescent, but novel ones with colour tunability and improved sensitivity are still urgently needed. The physical origin of the phenomenon, which mainly involves the release of trapped carriers at defects with the help of stress, still remains unclear. This in turn hinders a deeper research, either theoretically or application oriented. In this review paper, we have tabulated the known ML compounds according to their structure prototypes based on the connectivity of anion polyhedra, highlighting structural features, such as framework distortion, layered structure, elastic anisotropy and microstructures, which are very relevant to the ML process. We then review the various proposed mechanisms and corresponding mathematical models. We comment on their contribution to a clearer understanding of the ML phenomenon and on the derived guidelines for improving properties of ML phosphors. Proven and potential applications of ML in various fields, such as stress field sensing, light sources, and sensing electric (magnetic) fields, are summarized. Finally, we point out the challenges and future directions in this active and emerging field of luminescence research. PMID:29570650

Building Extraction from Remote Sensing Data Using Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Bittner, K.; Cui, S.; Reinartz, P.

2017-05-01

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems

PubMed Central

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

2011-01-01

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations. PMID:22163859

Graph-cut based discrete-valued image reconstruction.

PubMed

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

Coarse-to-fine wavelet-based airport detection

NASA Astrophysics Data System (ADS)

Li, Cheng; Wang, Shuigen; Pang, Zhaofeng; Zhao, Baojun

2015-10-01

Airport detection on optical remote sensing images has attracted great interest in the applications of military optics scout and traffic control. However, most of the popular techniques for airport detection from optical remote sensing images have three weaknesses: 1) Due to the characteristics of optical images, the detection results are often affected by imaging conditions, like weather situation and imaging distortion; and 2) optical images contain comprehensive information of targets, so that it is difficult for extracting robust features (e.g., intensity and textural information) to represent airport area; 3) the high resolution results in large data volume, which makes real-time processing limited. Most of the previous works mainly focus on solving one of those problems, and thus, the previous methods cannot achieve the balance of performance and complexity. In this paper, we propose a novel coarse-to-fine airport detection framework to solve aforementioned three issues using wavelet coefficients. The framework includes two stages: 1) an efficient wavelet-based feature extraction is adopted for multi-scale textural feature representation, and support vector machine(SVM) is exploited for classifying and coarsely deciding airport candidate region; and then 2) refined line segment detection is used to obtain runway and landing field of airport. Finally, airport recognition is achieved by applying the fine runway positioning to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy and processing efficiency.

The impact of non-motor manifestations of Parkinson's disease on partners: understanding and application of chronic sorrow theory.

PubMed

Mercer, Christine J

2015-09-01

Parkinson's disease (PD) can cause many emotions, including grief and a sense of isolation for both the person with PD (referred to as Parkinsonian) and their partner. Such ongoing grief and emotional turmoil can be termed chronic sorrow. The aim of this research is to present accounts of partners' perspectives, analysed in the context of chronic sorrow theory, to offer health professionals an insight into the impact of non-motor PD symptoms on partners. A group of partners of Parkinsonians provided the data through individual stories. These stories were subjected to thematic analysis, using a seven-step process leading to the establishment of themes. Caregiver burden and chronic sorrow is not related to providing physical care, but the emotional care of attempting to minimise the effect of PD, coping with disturbance to sleep, and helping the Parkinsonian to maintain as much independence as possible. Contributors to this article found chronic sorrow theory provided a framework for understanding their emotions. Sharing their experiences with others provided an opportunity to be heard, and enabled them to make sense of individual situations. Chronic sorrow theory provides a useful framework for both partners of Parkinsonians in understanding their emotional responses, and for health professionals in considering the challenges partners face in coping with living with a person with PD.

Applications for fiber optic sensing in the upstream oil and gas industry

NASA Astrophysics Data System (ADS)

Baldwin, Chris S.

2015-05-01

Fiber optic sensing has been used in an increasing number of applications in the upstream oil and gas industry over the past 20 years. In some cases, fiber optic sensing is providing measurements where traditional measurement technologies could not. This paper will provide a general overview of these applications and describe how the use of fiber optic sensing is enabling these applications. Technologies such as Bragg gratings, distributed temperature and acoustic sensing, interferometric sensing, and Brillouin scattering will be discussed. Applications for optic sensing include a range of possibilities from a single pressure measurement point in the wellbore to multizone pressure and flow monitoring. Some applications make use of fully distributed measurements including thermal profiling of the well. Outside of the wellbore, fiber optic sensors are used in applications for flowline and pipeline monitoring and for riser integrity monitoring. Applications to be described in this paper include in-flow profiling, well integrity, production monitoring, and steam chamber growth. These applications will cover well types such as injectors, producers, hydraulic fracturing, and thermal recovery. Many of these applications use the measurements provided by fiber optic sensing to improve enhanced oil recovery operations. The growing use of fiber optic sensors is providing improved measurement capabilities leading to the generation of actionable data for enhanced production optimization. This not only increases the recovered amount of production fluids but can also enhance wellbore integrity and safety.

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Evaluating ESA CCI soil moisture in East Africa.

PubMed

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P

2016-06-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Multiaxis sensing using metal organic frameworks

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

Talin, Albert Alec; Allendorf, Mark D.; Leonard, Francois

2017-01-17

A sensor device including a sensor substrate; and a thin film comprising a porous metal organic framework (MOF) on the substrate that presents more than one transduction mechanism when exposed to an analyte. A method including exposing a porous metal organic framework (MOF) on a substrate to an analyte; and identifying more than one transduction mechanism in response to the exposure to the analyte.

Assembly of ZIF-67 Metal-Organic Framework over Tin Oxide Nanoparticles for Synergistic Chemiresistive CO2 Gas Sensing.

PubMed

DMello, Marilyn Esclance; Sundaram, Nalini G; Kalidindi, Suresh Babu

2018-05-03

Metal-organic frameworks (MOFs) are widely known for their record storage capacities of small gas molecules (H 2 , CO 2 , and CH 4 ). Assembly of such porous materials onto well-known chemiresistive gas sensing elements such as SnO 2 could be an attractive prospect to achieve novel sensing properties as this affects the surface chemistry of SnO 2 . Cobalt-imidazole based ZIF-67 MOF was grown onto preformed SnO 2 nanoparticles to realize core-shell like architecture and explored for greenhouse gas CO 2 sensing. CO 2 sensing over SnO 2 is a challenge because its interaction with SnO 2 surface is minimal. The ZIF-67 coating over SnO 2 improved the response of SnO 2 up to 12-fold (for 50 % CO 2 ). The SnO 2 @ZIF-67 also showed a response of 16.5±2.1 % for 5000 ppm CO 2 (threshold limit value (TLV)) at 205 °C, one of the best values reported for a SnO 2 -based sensor. The observed novel CO 2 sensing characteristics are assigned to electronic structure changes at the interface of ZIF-67 and SnO 2 . © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Chemically Active, Porous 3D-Printed Thermoplastic Composites

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

Evans, Kent A.; Kennedy, Zachary C.; Arey, Bruce W.

Metal-organic frameworks (MOFs) exhibit exceptional properties and are widely investigated because of their structural and functional versatility relevant to catalysis, separations, and sensing applications. However, their commercial or large-scale application is often limited by their powder forms. To address this, we report the production of MOF-thermoplastic polymer composites accessed via a standard 3D printer. MOFs (Zeolitic imidazolate framework; ZIF-8) were successfully incorporated homogeneously into both poly(lactic acid) (PLA) and thermoplastic polyurethane (TPU) matrices, extruded into filaments, and utilized for on-demand access to 3D structures by fused-deposition modeling. Printed rigid PLA-MOF composites displayed good structural integrity, high surface area ((SA)avg =more » 531 m2 g-1) and hierarchical pore features. Flexible TPU-MOF composites (SAavg = 706 m2 g-1) were achieved by employing a sacrificial fluoropolymer readily removed post-printing. Critically, embedded particles in the plastic matrices retain their ability to participate in chemical interactions characteristic of the parent MOF. The fabrication strategies can be extended to other MOFs and illustrate the potential of 3D printing to create unique porous and high surface area chemically-active structures.« less

Macroscopically Oriented Porous Materials with Periodic Ordered Structures: From Zeolites and Metal-Organic Frameworks to Liquid-Crystal-Templated Mesoporous Materials.

PubMed

Cho, Joonil; Ishida, Yasuhiro

2017-07-01

Porous materials with molecular-sized periodic structures, as exemplified by zeolites, metal-organic frameworks, or mesoporous silica, have attracted increasing attention due to their range of applications in storage, sensing, separation, and transformation of small molecules. Although the components of such porous materials have a tendency to pack in unidirectionally oriented periodic structures, such ideal types of packing cannot continue indefinitely, generally ceasing when they reach a micrometer scale. Consequently, most porous materials are composed of multiple randomly oriented domains, and overall behave as isotropic materials from a macroscopic viewpoint. However, if their channels could be unidirectionally oriented over a macroscopic scale, the resultant porous materials might serve as powerful tools for manipulating molecules. Guest molecules captured in macroscopically oriented channels would have their positions and directions well-defined, so that molecular events in the channels would proceed in a highly controlled manner. To realize such an ideal situation, numerous efforts have been made to develop various porous materials with macroscopically oriented channels. An overview of recent studies on the synthesis, properties, and applications of macroscopically oriented porous materials is presented. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Luminescent Porous Polymers Based on Aggregation-Induced Mechanism: Design, Synthesis and Functions.

PubMed

Dalapati, Sasanka; Gu, Cheng; Jiang, Donglin

2016-12-01

Enormous research efforts are focusing on the design and synthesis of advanced luminescent systems, owing to their diverse capability in scientific studies and technological developments. In particular, fluorescence systems based on aggregation-induced emission (AIE) have emerged to show great potential for sensing, bio-imaging, and optoelectronic applications. Among them, integrating AIE mechanisms to design porous polymers is unique because it enables the combination of porosity and luminescence activity in one molecular skeleton for functional design. In recent years rapid progress in exploring AIE-based porous polymers has developed a new class of luminescent materials that exhibit broad structural diversity, outstanding properties and functions and promising applications. By classifying the structural nature of the skeleton, herein the design principle, synthetic development and structural features of different porous luminescent materials are elucidated, including crystalline covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and amorphous porous organic polymers (POPs). The functional exploration of these luminescent porous polymers are highlighted by emphasizing electronic interplay within the confined nanospace, fundamental issues to be addressed are disclosed, and future directions from chemistry, physics and materials science perspectives are proposed. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Five Senses of Christmas Chemistry

ERIC Educational Resources Information Center

Jackson, Derek A.; Dicks, Andrew P.

2012-01-01

This article describes the organic chemistry of five compounds that are directly associated with the Christmas season. These substances and related materials are presented within the framework of the five senses: silver fulminate (sound), alpha-pinene (sight), sodium acetate (touch), tryptophan (taste), and gingerol (smell). Connections with the…

Bridging Human Reliability Analysis and Psychology, Part 2: A Cognitive Framework to Support HRA

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

April M. Whaley; Stacey M. L. Hendrickson; Ronald L. Boring

This is the second of two papers that discuss the literature review conducted as part of the U.S. Nuclear Regulatory Commission (NRC) effort to develop a hybrid human reliability analysis (HRA) method in response to Staff Requirements Memorandum (SRM) SRM-M061020. This review was conducted with the goal of strengthening the technical basis within psychology, cognitive science and human factors for the hybrid HRA method being proposed. An overview of the literature review approach and high-level structure is provided in the first paper, whereas this paper presents the results of the review. The psychological literature review encompassed research spanning the entiretymore » of human cognition and performance, and consequently produced an extensive list of psychological processes, mechanisms, and factors that contribute to human performance. To make sense of this large amount of information, the results of the literature review were organized into a cognitive framework that identifies causes of failure of macrocognition in humans, and connects those proximate causes to psychological mechanisms and performance influencing factors (PIFs) that can lead to the failure. This cognitive framework can serve as a tool to inform HRA. Beyond this, however, the cognitive framework has the potential to also support addressing human performance issues identified in Human Factors applications.« less

Curvelet-based compressive sensing for InSAR raw data

NASA Astrophysics Data System (ADS)

Costa, Marcello G.; da Silva Pinho, Marcelo; Fernandes, David

2015-10-01

The aim of this work is to evaluate the compression performance of SAR raw data for interferometry applications collected by airborne from BRADAR (Brazilian SAR System operating in X and P bands) using the new approach based on compressive sensing (CS) to achieve an effective recovery with a good phase preserving. For this framework is desirable a real-time capability, where the collected data can be compressed to reduce onboard storage and bandwidth required for transmission. In the CS theory, a sparse unknown signals can be recovered from a small number of random or pseudo-random measurements by sparsity-promoting nonlinear recovery algorithms. Therefore, the original signal can be significantly reduced. To achieve the sparse representation of SAR signal, was done a curvelet transform. The curvelets constitute a directional frame, which allows an optimal sparse representation of objects with discontinuities along smooth curves as observed in raw data and provides an advanced denoising optimization. For the tests were made available a scene of 8192 x 2048 samples in range and azimuth in X-band with 2 m of resolution. The sparse representation was compressed using low dimension measurements matrices in each curvelet subband. Thus, an iterative CS reconstruction method based on IST (iterative soft/shrinkage threshold) was adjusted to recover the curvelets coefficients and then the original signal. To evaluate the compression performance were computed the compression ratio (CR), signal to noise ratio (SNR), and because the interferometry applications require more reconstruction accuracy the phase parameters like the standard deviation of the phase (PSD) and the mean phase error (MPE) were also computed. Moreover, in the image domain, a single-look complex image was generated to evaluate the compression effects. All results were computed in terms of sparsity analysis to provides an efficient compression and quality recovering appropriated for inSAR applications, therefore, providing a feasibility for compressive sensing application.

Luminescent Lanthanide MOFs: A Unique Platform for Chemical Sensing

PubMed Central

Zhao, Shu-Na; Wang, Guangbo

2018-01-01

In recent years, lanthanide metal–organic frameworks (LnMOFs) have developed to be an interesting subclass of MOFs. The combination of the characteristic luminescent properties of Ln ions with the intriguing topological structures of MOFs opens up promising possibilities for the design of LnMOF-based chemical sensors. In this review, we present the most recent developments of LnMOFs as chemical sensors by briefly introducing the general luminescence features of LnMOFs, followed by a comprehensive investigation of the applications of LnMOF sensors for cations, anions, small molecules, nitroaromatic explosives, gases, vapors, pH, and temperature, as well as biomolecules. PMID:29642458

Ratiometric near infrared luminescent thermometer based on lanthanide metal-organic frameworks

NASA Astrophysics Data System (ADS)

Yue, Dan; Zhang, Jun; Zhao, Dian; Lian, Xiusheng; Cui, Yuanjing; Yang, Yu; Qian, Guodong

2016-09-01

A near infrared luminescent MOFs thermometer (Nd0.676Yb0.324BTC) was prepared via a simple solvothermal method using Ln3+ (Ln=Nd, Yb) ions and 1, 3, 5-benznenetricarboxylic acid (H3BTC), and characterized by PXRD, TGA, ICP, and photoluminescence (PL) spectrum. These results indicate that the Nd0.676Yb0.324BTC displays high relative sensitivity and excellent repeatability in the physiological temperature range (288-323 K), and the maximum relative sensitivity is determined to be 1.187% K-1 at 323 K. These NIR luminescent MOFs may have potential applications in physiological temperature sensing.

Development of data processing interpretation and analysis system for the remote sensing of trace atmospheric gas species

NASA Technical Reports Server (NTRS)

Casas, J. C.; Koziana, J. V.; Saylor, M. S.; Kindle, E. C.

1982-01-01

Problems associated with the development of the measurement of air pollution from satellites (MAPS) experiment program are addressed. The primary thrust of this research was the utilization of the MAPS experiment data in three application areas: low altitude aircraft flights (one to six km); mid altitude aircraft flights (eight to 12 km); and orbiting space platforms. Extensive research work in four major areas of data management was the framework for implementation of the MAPS experiment technique. These areas are: (1) data acquisition; (2) data processing, analysis and interpretation algorithms; (3) data display techniques; and (4) information production.

A multifunctional chemical sensor based on a three-dimensional lanthanide metal-organic framework

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

Du, Pei-Yao; Liao, Sheng-Yun; Gu, Wen, E-mail: guwen68@nankai.edu.cn

2016-12-15

A 3D lanthanide MOF with formula [Sm{sub 2}(abtc){sub 1.5}(H{sub 2}O){sub 3}(DMA)]·H{sub 2}O·DMA (1) has been successfully synthesized via solvothermal method. Luminescence studies reveal that 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. In addition, 1 displays a turn-on luminescence sensing with respect to ethanol among different alcohol molecules, which suggests that 1 is also a promising luminescent probe for high selective sensing of ethanol. - Highlights: • A three-dimensional lanthanide metal-organic framework has been synthesized. • Complex 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. • Complex 1 displays amore » turn-on luminescence sensing with respect to ethanol among different alcohol molecules.« less

Study on Building Extraction from High-Resolution Images Using Mbi

NASA Astrophysics Data System (ADS)

Ding, Z.; Wang, X. Q.; Li, Y. L.; Zhang, S. S.

2018-04-01

Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.

Flood risks in urbanized areas - multi-sensoral approaches using remotely sensed data for risk assessment

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Wurm, M.; Netzband, M.; Zwenzner, H.; Roth, A.; Rahman, A.; Dech, S.

2011-02-01

Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.

Compressed digital holography: from micro towards macro

NASA Astrophysics Data System (ADS)

Schretter, Colas; Bettens, Stijn; Blinder, David; Pesquet-Popescu, Béatrice; Cagnazzo, Marco; Dufaux, Frédéric; Schelkens, Peter

2016-09-01

signal processing methods from software-driven computer engineering and applied mathematics. The compressed sensing theory in particular established a practical framework for reconstructing the scene content using few linear combinations of complex measurements and a sparse prior for regularizing the solution. Compressed sensing found direct applications in digital holography for microscopy. Indeed, the wave propagation phenomenon in free space mixes in a natural way the spatial distribution of point sources from the 3-dimensional scene. As the 3-dimensional scene is mapped to a 2-dimensional hologram, the hologram samples form a compressed representation of the scene as well. This overview paper discusses contributions in the field of compressed digital holography at the micro scale. Then, an outreach on future extensions towards the real-size macro scale is discussed. Thanks to advances in sensor technologies, increasing computing power and the recent improvements in sparse digital signal processing, holographic modalities are on the verge of practical high-quality visualization at a macroscopic scale where much higher resolution holograms must be acquired and processed on the computer.

Civil Engineering Applications of Ground Penetrating Radar Recent Advances @ the ELEDIA Research Center

NASA Astrophysics Data System (ADS)

Salucci, Marco; Tenuti, Lorenza; Nardin, Cristina; Oliveri, Giacomo; Viani, Federico; Rocca, Paolo; Massa, Andrea

2014-05-01

The application of non-destructive testing and evaluation (NDT/NDE) methodologies in civil engineering has raised a growing interest during the last years because of its potential impact in several different scenarios. As a consequence, Ground Penetrating Radar (GPR) technologies have been widely adopted as an instrument for the inspection of the structural stability of buildings and for the detection of cracks and voids. In this framework, the development and validation of GPR algorithms and methodologies represents one of the most active research areas within the ELEDIA Research Center of the University of Trento. More in detail, great efforts have been devoted towards the development of inversion techniques based on the integration of deterministic and stochastic search algorithms with multi-focusing strategies. These approaches proved to be effective in mitigating the effects of both nonlinearity and ill-posedness of microwave imaging problems, which represent the well-known issues arising in GPR inverse scattering formulations. More in detail, a regularized multi-resolution approach based on the Inexact Newton Method (INM) has been recently applied to subsurface prospecting, showing a remarkable advantage over a single-resolution implementation [1]. Moreover, the use of multi-frequency or frequency-hopping strategies to exploit the information coming from GPR data collected in time domain and transformed into its frequency components has been proposed as well. In this framework, the effectiveness of the multi-resolution multi-frequency techniques has been proven on synthetic data generated with numerical models such as GprMax [2]. The application of inversion algorithms based on Bayesian Compressive Sampling (BCS) [3][4] to GPR is currently under investigation, as well, in order to exploit their capability to provide satisfactory reconstructions in presence of single and multiple sparse scatterers [3][4]. Furthermore, multi-scaling approaches exploiting level-set-based optimization have been developed for the qualitative reconstruction of multiple and disconnected homogeneous scatterers [5]. Finally, the real-time detection and classification of subsurface scatterers has been investigated by means of learning-by-examples (LBE) techniques, such as Support Vector Machines (SVM) [6]. Acknowledgment - This work was partially supported by COST Action TU1208 'Civil Engineering Applications of Ground Penetrating Radar' References [1] M. Salucci, D. Sartori, N. Anselmi, A. Randazzo, G. Oliveri, and A. Massa, 'Imaging Buried Objects within the Second-Order Born Approximation through a Multiresolution Regularized Inexact-Newton Method', 2013 International Symposium on Electromagnetic Theory (EMTS), (Hiroshima, Japan), May 20-24 2013 (invited). [2] A. Giannopoulos, 'Modelling ground penetrating radar by GprMax', Construct. Build. Mater., vol. 19, no. 10, pp.755 -762 2005 [3] L. Poli, G. Oliveri, P. Rocca, and A. Massa, "Bayesian compressive sensing approaches for the reconstruction of two-dimensional sparse scatterers under TE illumination," IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 5, pp. 2920-2936, May. 2013. [4] L. Poli, G. Oliveri, and A. Massa, "Imaging sparse metallic cylinders through a Local Shape Function Bayesian Compressive Sensing approach," Journal of Optical Society of America A, vol. 30, no. 6, pp. 1261-1272, 2013. [5] M. Benedetti, D. Lesselier, M. Lambert, and A. Massa, "Multiple shapes reconstruction by means of multi-region level sets," IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 5, pp. 2330-2342, May 2010. [6] L. Lizzi, F. Viani, P. Rocca, G. Oliveri, M. Benedetti and A. Massa, "Three-dimensional real-time localization of subsurface objects - From theory to experimental validation," 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. II-121-II-124, 12-17 July 2009.

Health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.

DOT National Transportation Integrated Search

2017-05-31

The overarching goal of this project was to integrate data from commercial remote sensing and spatial information (CRS&SI) technologies to create a novel data-driven decision making framework that empowers the railroad industry to monitor, assess, an...

Getting the science right for the right reasons: the environmental sensing revolution that just happened.

NASA Astrophysics Data System (ADS)

Selker, J. S.

2014-12-01

Noting that cool phone in your pocket, and your car have more sensors and wireless capabilities than your new Campbell weather station, does it ever feel like there is a mismatch between the world of science and that of consumer products? How can we understand our place in the "sensing ecosystem," and sort between the transformative opportunities of sensing technology and technological land mines that will expend your budget and be unreliable? Here I review the impact of three technological frameworks on biogeochemical observation: distributed fiber optic sensing; low-power radio and GSM communication; and 3-D printing. From the fiber optic sensing applications in air, soil, rivers, oceans and wells, we see that this truly does qualify as a revolutionary observational platform. Specifically, it densely spans the critical 0.1 m to 10,000 m spatial scales and 1 to 1,000,000 s temporal scales, providing opportunity to address long-standing fundamental open questions. This is placed in contrast to the unfulfilled promises touted by the self-organizing mesh network radio technology. We argue that this outcome reflects a lack of candor of technology insiders in the selling of this technology with respect to the potential given the 1/r^3 energy of radio communication combined with the challenges of environmental settings for wave propagation (e.g., intense rain, snow laden branches, and long periods of low solar radiation). This is contrasted with the excellent outcomes of GSM-based monitoring approaches that leveraged the massive infrastructure of cellular telephones. Finally, I will venture to explain why open-source 3-D printing technology will provide the next transformative opportunity for Biogeosicences by re-inventing point-sensing instrumentation.

Field Data Collection: an Essential Element in Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Pettinger, L. R.

1971-01-01

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Intelligent Sensors: Strategies for an Integrated Systems Approach

NASA Technical Reports Server (NTRS)

Chitikeshi, Sanjeevi; Mahajan, Ajay; Bandhil, Pavan; Utterbach, Lucas; Figueroa, Fernando

2005-01-01

This paper proposes the development of intelligent sensors as an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Intelligent Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS).

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

NASA Technical Reports Server (NTRS)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

The liminal self in people with multiple sclerosis: an interpretative phenomenological exploration of being diagnosed.

PubMed

Strickland, Karen; Worth, Allison; Kennedy, Catriona

2017-06-01

To explore the lived experience of the meaning of being diagnosed with multiple sclerosis on the individual's sense of self. The time leading up to and immediately following the diagnosis of multiple sclerosis has been identified as a time period shrouded by uncertainty and one where individuals have a heightened desire to seek accurate information and support. The diagnosis brings changes to the way one views the self which has consequences for biographical construction. A hermeneutic phenomenological study. In-depth qualitative interviews were conducted with 10 people recently diagnosed with multiple sclerosis. The data were analysed using interpretative phenomenological analysis. This study presents the three master themes: the 'road to diagnosis', 'the liminal self' and 'learning to live with multiple sclerosis'. The diagnosis of multiple sclerosis may be conceptualised as a 'threshold moment' where the individual's sense of self is disrupted from the former taken-for-granted way of being and propose a framework which articulates the transition. The findings highlight the need for healthcare professionals to develop interventions to better support people affected by a new diagnosis of multiple sclerosis. The conceptual framework which has been developed from the data and presented in this study provides a new way of understanding the impact of the diagnosis on the individual's sense of self when affected by a new diagnosis of multiple sclerosis. This framework can guide healthcare professionals in the provision of supportive care around the time of diagnosis. The findings provide practitioners with a new way of understanding the impact of the diagnosis on the individual's sense of self and a framework which can guide them in the provision of supportive care around the time of diagnosis. © 2016 John Wiley & Sons Ltd.

Capacity-Delay Trade-Off in Collaborative Hybrid Ad-Hoc Networks with Coverage Sensing.

PubMed

Chen, Lingyu; Luo, Wenbin; Liu, Chen; Hong, Xuemin; Shi, Jianghong

2017-01-26

The integration of ad hoc device-to-device (D2D) communications and open-access small cells can result in a networking paradigm called hybrid the ad hoc network, which is particularly promising in delivering delay-tolerant data. The capacity-delay performance of hybrid ad hoc networks has been studied extensively under a popular framework called scaling law analysis. These studies, however, do not take into account aspects of interference accumulation and queueing delay and, therefore, may lead to over-optimistic results. Moreover, focusing on the average measures, existing works fail to give finer-grained insights into the distribution of delays. This paper proposes an alternative analytical framework based on queueing theoretic models and physical interference models. We apply this framework to study the capacity-delay performance of a collaborative cellular D2D network with coverage sensing and two-hop relay. The new framework allows us to fully characterize the delay distribution in the transform domain and pinpoint the impacts of coverage sensing, user and base station densities, transmit power, user mobility and packet size on the capacity-delay trade-off. We show that under the condition of queueing equilibrium, the maximum throughput capacity per device saturates to an upper bound of 0.7239 λ b / λ u bits/s/Hz, where λ b and λ u are the densities of base stations and mobile users, respectively.

Capacity-Delay Trade-Off in Collaborative Hybrid Ad-Hoc Networks with Coverage Sensing

PubMed Central

Chen, Lingyu; Luo, Wenbin; Liu, Chen; Hong, Xuemin; Shi, Jianghong

2017-01-01

The integration of ad hoc device-to-device (D2D) communications and open-access small cells can result in a networking paradigm called hybrid the ad hoc network, which is particularly promising in delivering delay-tolerant data. The capacity-delay performance of hybrid ad hoc networks has been studied extensively under a popular framework called scaling law analysis. These studies, however, do not take into account aspects of interference accumulation and queueing delay and, therefore, may lead to over-optimistic results. Moreover, focusing on the average measures, existing works fail to give finer-grained insights into the distribution of delays. This paper proposes an alternative analytical framework based on queueing theoretic models and physical interference models. We apply this framework to study the capacity-delay performance of a collaborative cellular D2D network with coverage sensing and two-hop relay. The new framework allows us to fully characterize the delay distribution in the transform domain and pinpoint the impacts of coverage sensing, user and base station densities, transmit power, user mobility and packet size on the capacity-delay trade-off. We show that under the condition of queueing equilibrium, the maximum throughput capacity per device saturates to an upper bound of 0.7239 λb/λu bits/s/Hz, where λb and λu are the densities of base stations and mobile users, respectively. PMID:28134769

The Priority Heuristic: Making Choices without Trade-Offs

ERIC Educational Resources Information Center

Brandstatter, Eduard; Gigerenzer, Gerd; Hertwig, Ralph

2006-01-01

Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, the authors generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic…

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Inorganic Nanoparticles/Metal Organic Framework Hybrid Membrane Reactors for Efficient Photocatalytic Conversion of CO2.

PubMed

Maina, James W; Schütz, Jürg A; Grundy, Luke; Des Ligneris, Elise; Yi, Zhifeng; Kong, Lingxue; Pozo-Gonzalo, Cristina; Ionescu, Mihail; Dumée, Ludovic F

2017-10-11

Photocatalytic conversion of carbon dioxide (CO 2 ) to useful products has potential to address the adverse environmental impact of global warming. However, most photocatalysts used to date exhibit limited catalytic performance, due to poor CO 2 adsorption capacity, inability to efficiently generate photoexcited electrons, and/or poor transfer of the photogenerated electrons to CO 2 molecules adsorbed on the catalyst surface. The integration of inorganic semiconductor nanoparticles across metal organic framework (MOF) materials has potential to yield new hybrid materials, combining the high CO 2 adsorption capacity of MOF and the ability of the semiconductor nanoparticles to generate photoexcited electrons. Herein, controlled encapsulation of TiO 2 and Cu-TiO 2 nanoparticles within zeolitic imidazolate framework (ZIF-8) membranes was successfully accomplished, using rapid thermal deposition (RTD), and their photocatalytic efficiency toward CO 2 conversion was investigated under UV irradiation. Methanol and carbon monoxide (CO) were found to be the only products of the CO 2 reduction, with yields strongly dependent upon the content and composition of the dopant semiconductor particles. CuTiO 2 nanoparticle doped membranes exhibited the best photocatalytic performance, with 7 μg of the semiconductor nanoparticle enhancing CO yield of the pristine ZIF-8 membrane by 233%, and methanol yield by 70%. This work opens new routes for the fabrication of hybrid membranes containing inorganic nanoparticles and MOFs, with potential application not only in catalysis but also in electrochemical, separation, and sensing applications.

Prognostics and Health Management of Wind Turbines: Current Status and Future Opportunities

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

Sheng, Shuangwen

Prognostics and health management is not a new concept. It has been used in relatively mature industries, such as aviation and electronics, to help improve operation and maintenance (O&M) practices. In the wind industry, prognostics and health management is relatively new. The level for both wind industry applications and research and development (R&D) has increased in recent years because of its potential for reducing O&M cost of wind power, especially for turbines installed offshore. The majority of wind industry application efforts has been focused on diagnosis based on various sensing and feature extraction techniques. For R&D, activities are being conductedmore » in almost all areas of a typical prognostics and health management framework (i.e., sensing, data collection, feature extraction, diagnosis, prognosis, and maintenance scheduling). This presentation provides an overview of the current status of wind turbine prognostics and health management that focuses on drivetrain condition monitoring through vibration, oil debris, and oil condition analysis techniques. It also discusses turbine component health diagnosis through data mining and modeling based on supervisory control and data acquisition system data. Finally, it provides a brief survey of R&D activities for wind turbine prognostics and health management, along with future opportunities.« less

Logic-centered architecture for ubiquitous health monitoring.

PubMed

Lewandowski, Jacek; Arochena, Hisbel E; Naguib, Raouf N G; Chao, Kuo-Ming; Garcia-Perez, Alexeis

2014-09-01

One of the key points to maintain and boost research and development in the area of smart wearable systems (SWS) is the development of integrated architectures for intelligent services, as well as wearable systems and devices for health and wellness management. This paper presents such a generic architecture for multiparametric, intelligent and ubiquitous wireless sensing platforms. It is a transparent, smartphone-based sensing framework with customizable wireless interfaces and plug'n'play capability to easily interconnect third party sensor devices. It caters to wireless body, personal, and near-me area networks. A pivotal part of the platform is the integrated inference engine/runtime environment that allows the mobile device to serve as a user-adaptable personal health assistant. The novelty of this system lays in a rapid visual development and remote deployment model. The complementary visual Inference Engine Editor that comes with the package enables artificial intelligence specialists, alongside with medical experts, to build data processing models by assembling different components and instantly deploying them (remotely) on patient mobile devices. In this paper, the new logic-centered software architecture for ubiquitous health monitoring applications is described, followed by a discussion as to how it helps to shift focus from software and hardware development, to medical and health process-centered design of new SWS applications.

Near Real-Time Monitoring of Global Evapotranspiration and its Application to Water Resource Management

NASA Astrophysics Data System (ADS)

Halverson, G. H.; Fisher, J.; Jewell, L. A.; Moore, G.; Verma, M.; McDonald, T.; Kim, S.; Muniz, A.

2016-12-01

Water scarcity and its impact on agriculture is a pressing world concern. At the heart of this crisis is the balance of water exchange between the land and the atmosphere. The ability to monitor evapotranspiration provides a solution by enabling sustainable irrigation practices. The Priestley-Taylor Jet Propulsion Laboratory model of evapotranspiration has been implemented to meet this need as a daily MODIS product with 1 to 5 km resolution. An automated data pipeline for this model implementation provides daily data with global coverage and near real-time latency using the Geospatial Data Abstraction Library. An interactive map providing on-demand statistical analysis enables water resource managers to monitor rates of water loss. To demonstrate the application of remotely-sensed evapotranspiration to water resource management, a partnership has been arranged with the New Mexico Office of the State Engineer (NMOSE). The online water research management tool was developed to meet the specifications of NMOSE using the Leaflet, GeoServer, and Django frameworks. NMOSE will utilize this tool to monitor drought and fire risk and manage irrigation. Through this test-case, it is hoped that real-time, user-friendly remote sensing tools will be adopted globally to make resource management decisions informed by the NASA Earth Observation System.

Compressed sensing with cyclic-S Hadamard matrix for terahertz imaging applications

NASA Astrophysics Data System (ADS)

Ermeydan, Esra Şengün; ćankaya, Ilyas

2018-01-01

Compressed Sensing (CS) with Cyclic-S Hadamard matrix is proposed for single pixel imaging applications in this study. In single pixel imaging scheme, N = r . c samples should be taken for r×c pixel image where . denotes multiplication. CS is a popular technique claiming that the sparse signals can be reconstructed with samples under Nyquist rate. Therefore to solve the slow data acquisition problem in Terahertz (THz) single pixel imaging, CS is a good candidate. However, changing mask for each measurement is a challenging problem since there is no commercial Spatial Light Modulators (SLM) for THz band yet, therefore circular masks are suggested so that for each measurement one or two column shifting will be enough to change the mask. The CS masks are designed using cyclic-S matrices based on Hadamard transform for 9 × 7 and 15 × 17 pixel images within the framework of this study. The %50 compressed images are reconstructed using total variation based TVAL3 algorithm. Matlab simulations demonstrates that cyclic-S matrices can be used for single pixel imaging based on CS. The circular masks have the advantage to reduce the mechanical SLMs to a single sliding strip, whereas the CS helps to reduce acquisition time and energy since it allows to reconstruct the image from fewer samples.

Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications

PubMed Central

Moccia, Antonio

2014-01-01

Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection. PMID:25105154

An Uncertainty Quantification Framework for Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Hobbs, J.

2017-12-01

Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.

A luminescent ytterbium(III)-organic framework for highly selective sensing of 2,4,6-trinitrophenol

NASA Astrophysics Data System (ADS)

Xin, Xuelian; Zhang, Minghui; Ji, Shijie; Dong, Hanxiao; Zhang, Liangliang

2018-06-01

An ytterbium(III)-organic framework, [Yb4(abtc)3(HCOO) (H2O)]·(C2H8N) (H2O) (UPC-22, H4abtc = 3,3‧,5,5‧-azobenzene-tetracarboxylic acid) was synthesized under solvothermal conditions and characterized. UPC-22 exhibited strong H4abtc-based luminescence and can be used for sensing nitroaromatic compounds (NACs) in an ethanol suspension with outstanding selectivity and sensitivity. The most striking property of UPC-22 is its ability to selectively detect 2,4,6-trinitrophenol (TNP), thereby rendering it a promising TNP-selective luminescence probe.

Metal-organic framework thin films on a surface of optical fibre long period grating for chemical sensing

NASA Astrophysics Data System (ADS)

Hromadka, J.; Tokay, B.; James, S.; Korposh, S.

2017-04-01

An optical fibre long period grating (LPG) modified with a thin film of HKUST-1, a material from metal organic framework (MOF) family, was employed for the detection of carbon dioxide. The sensing mechanism is based on the measurement of the change of the refractive index (RI) of the coating that is induced by the penetration of CO2 molecules into the HKUST-1 pores. The responses of the resonance bands in the transmission spectrum of an LPG modified with 40 layers of HKUST-1 upon exposure to carbon dioxide in mixture with nitrogen were investigated.

Common and Innovative Visuals: A sparsity modeling framework for video.

PubMed

Abdolhosseini Moghadam, Abdolreza; Kumar, Mrityunjay; Radha, Hayder

2014-05-02

Efficient video representation models are critical for many video analysis and processing tasks. In this paper, we present a framework based on the concept of finding the sparsest solution to model video frames. To model the spatio-temporal information, frames from one scene are decomposed into two components: (i) a common frame, which describes the visual information common to all the frames in the scene/segment, and (ii) a set of innovative frames, which depicts the dynamic behaviour of the scene. The proposed approach exploits and builds on recent results in the field of compressed sensing to jointly estimate the common frame and the innovative frames for each video segment. We refer to the proposed modeling framework by CIV (Common and Innovative Visuals). We show how the proposed model can be utilized to find scene change boundaries and extend CIV to videos from multiple scenes. Furthermore, the proposed model is robust to noise and can be used for various video processing applications without relying on motion estimation and detection or image segmentation. Results for object tracking, video editing (object removal, inpainting) and scene change detection are presented to demonstrate the efficiency and the performance of the proposed model.

Framework of sensor-based monitoring for pervasive patient care.

PubMed

Triantafyllidis, Andreas K; Koutkias, Vassilis G; Chouvarda, Ioanna; Adami, Ilia; Kouroubali, Angelina; Maglaveras, Nicos

2016-09-01

Sensor-based health systems can often become difficult to use, extend and sustain. The authors propose a framework for designing sensor-based health monitoring systems aiming to provide extensible and usable monitoring services in the scope of pervasive patient care. The authors' approach relies on a distributed system for monitoring the patient health status anytime-anywhere and detecting potential health complications, for which healthcare professionals and patients are notified accordingly. Portable or wearable sensing devices measure the patient's physiological parameters, a smart mobile device collects and analyses the sensor data, a Medical Center system receives notifications on the detected health condition, and a Health Professional Platform is used by formal caregivers in order to review the patient condition and configure monitoring schemas. A Service-oriented architecture is utilised to provide extensible functional components and interoperable interactions among the diversified system components. The framework was applied within the REMOTE ambient-assisted living project in which a prototype system was developed, utilising Bluetooth to communicate with the sensors and Web services for data exchange. A scenario of using the REMOTE system and preliminary usability results show the applicability, usefulness and virtue of our approach.

Framework of sensor-based monitoring for pervasive patient care

PubMed Central

Koutkias, Vassilis G.; Chouvarda, Ioanna; Adami, Ilia; Kouroubali, Angelina; Maglaveras, Nicos

2016-01-01

Sensor-based health systems can often become difficult to use, extend and sustain. The authors propose a framework for designing sensor-based health monitoring systems aiming to provide extensible and usable monitoring services in the scope of pervasive patient care. The authors’ approach relies on a distributed system for monitoring the patient health status anytime-anywhere and detecting potential health complications, for which healthcare professionals and patients are notified accordingly. Portable or wearable sensing devices measure the patient's physiological parameters, a smart mobile device collects and analyses the sensor data, a Medical Center system receives notifications on the detected health condition, and a Health Professional Platform is used by formal caregivers in order to review the patient condition and configure monitoring schemas. A Service-oriented architecture is utilised to provide extensible functional components and interoperable interactions among the diversified system components. The framework was applied within the REMOTE ambient-assisted living project in which a prototype system was developed, utilising Bluetooth to communicate with the sensors and Web services for data exchange. A scenario of using the REMOTE system and preliminary usability results show the applicability, usefulness and virtue of our approach. PMID:27733920

Strategic Sense: The Key to Reflective Leadership in School Principals.

ERIC Educational Resources Information Center

Hall, Gene E.

The use of reflective leadership among school principals is examined in this paper to develop a framework for the process of reflective decision making, with a focus on administrators'"strategic sense." Field notes and interviews with principals and their teachers were used to identify leadership roles and principals' perceptions of those roles.…

Teachers' Sense-Making about Comprehensive School Reform: The Influence of Emotions

ERIC Educational Resources Information Center

Schmidt, Michele; Datnow, Amanda

2005-01-01

The paper examines teachers' emotions in the process of making sense of educational reforms. We draw upon concepts from sociological theory and education to inform our framework for understanding how emotions, as a social construct, directly and indirectly, influence teachers' understandings. Using qualitative data gathered in a study of…

Improving Online Social Presence through Asynchronous Video

ERIC Educational Resources Information Center

Borup, Jered; West, Richard E.; Graham, Charles R.

2012-01-01

Online learning has become a reality for many students in higher education. Unfortunately, something that has also become a reality is a sense of isolation in online courses, and Moore (1980) has warned that students' sense of distance can threaten their ability to learn. The community of inquiry framework (Garrison, Anderson, & Archer, 2000) has…

The layered sensing operations center: a modeling and simulation approach to developing complex ISR networks

NASA Astrophysics Data System (ADS)

Curtis, Christopher; Lenzo, Matthew; McClure, Matthew; Preiss, Bruce

2010-04-01

In order to anticipate the constantly changing landscape of global warfare, the United States Air Force must acquire new capabilities in the field of Intelligence, Surveillance, and Reconnaissance (ISR). To meet this challenge, the Air Force Research Laboratory (AFRL) is developing a unifying construct of "Layered Sensing" which will provide military decision-makers at all levels with the timely, actionable, and trusted information necessary for complete battlespace awareness. Layered Sensing is characterized by the appropriate combination of sensors and platforms (including those for persistent sensing), infrastructure, and exploitation capabilities to enable this synergistic awareness. To achieve the Layered Sensing vision, AFRL is pursuing a Modeling & Simulation (M&S) strategy through the Layered Sensing Operations Center (LSOC). An experimental ISR system-of-systems test-bed, the LSOC integrates DoD standard simulation tools with commercial, off-the-shelf video game technology for rapid scenario development and visualization. These tools will help facilitate sensor management performance characterization, system development, and operator behavioral analysis. Flexible and cost-effective, the LSOC will implement a non-proprietary, open-architecture framework with well-defined interfaces. This framework will incentivize the transition of current ISR performance models to service-oriented software design for maximum re-use and consistency. This paper will present the LSOC's development and implementation thus far as well as a summary of lessons learned and future plans for the LSOC.

Amino-Functionalized Luminescent Metal-Organic Framework Test Paper for Rapid and Selective Sensing of SO2 Gas and Its Derivatives by Luminescence Turn-On Effect.

PubMed

Wang, Meng; Guo, Lin; Cao, Dapeng

2018-03-06

Rapid and selective sensing of sulfur dioxide (SO 2 ) gas has attracted more and more attention because SO 2 not only causes environmental pollution but also severely affects the health of human beings. Here we report an amino-functionalized luminescent metal-organic framework (MOF) material (i.e., MOF-5-NH 2 ) and further investigate its sensing property for SO 2 gas and its derivatives as a luminescent probe. The results indicate that the MOF-5-NH 2 probe can selectively and sensitively sense SO 2 derivatives (i.e., SO 3 2- ) in real time by a luminescence turn-on effect with a lower detection limit of 0.168 ppm and a response time of less than 15 s. Importantly, the luminescence turn-on phenomenon can be observed by the naked eye. We also assembled MOF-5-NH 2 into a test paper to achieve the aim of portable detection, and the lower-limit concentration of the test paper for sensing SO 2 in real time was found to be about 0.05 ppm. Moreover, MOF-5-NH 2 also shows good anti-interference ability, strong luminescence stability, and reusability, which means that this material is an excellent sensing candidate. The amino functionalization may also provide a modification strategy to design luminescent sensors for other atmospheric pollutants.

Ethnography of Communication in Praxis in the Literature Classroom

ERIC Educational Resources Information Center

Hepburn, Carol

2016-01-01

In this article, I suggest that an applied communication approach using Dell Hymes' framework of "ethnography of communication" could serve as an intervention strategy in order to promote a greater sense of shared community within the college literature classroom. I explore this framework with consideration on how this communication…

A near infrared luminescent metal-organic framework for temperature sensing in the physiological range.

PubMed

Lian, Xiusheng; Zhao, Dian; Cui, Yuanjing; Yang, Yu; Qian, Guodong

2015-12-28

A near infrared pumped luminescent metal-organic framework thermometer Nd(0.577)Yb(0.423)BDC-F4, with near infrared fluorescence and excellent sensitivity in the physiological temperature range (293-313 K), has been first realized, and might be potentially applied for biomedical systems.

Remote sensing applications for transportation and traffic engineering studies: A review of the literature

NASA Technical Reports Server (NTRS)

Epps, J. W.

1973-01-01

Current references were surveyed for the application of remote sensing to traffic and transportation studies. The major problems are presented that concern traffic engineers and transportation managers, and the literature references that discuss remote sensing applications are summarized.

EGIS - An Environmental GIS Developed for NASA Field Center Applications

NASA Technical Reports Server (NTRS)

Smoot, James; Cohan, Tyrus; O'Connor, Christina; Johnson, Gary; Carr, Hugh

2001-01-01

As the principal center for Environmental Geographic Information Systems (EGIS), the John C. Stennis Space Center (SSC), located in Hancock County, Mississippi, has been assigned technical support requirements to design and to implement a basic EGIS data base for all NASA Field Centers. The intent of this Phase I effort is to produce a baseline EGIS data base incorporating newly available remotely sensed data as well as existing environmental data. A example application of the use of the data base at Stennis Space Center will be to illustrate baseline environmental conditions for consideration with proposed propulsion test stand development and operation. To effectively answer questions related to environmental issues at each center, organization of the data layers and sources will include the following categories: Cadastral/Geodetic; Geopolitical; Hydrography; Infrastructure; Physical Geography; Socioeconomic; Remote Sensing Imagery; Associated Metadata. As part of a Phase II effort, site-specific data and applications will be implemented and added to the data base at each Field Center. This poster illustrates the framework of the design and implementation of a basic EGIS data base. Shown are example data sources, hardware and software, and data base delivery and installation. The poster also depicts future recommendations for a centrally located server to house each of the NASA Field Center data bases. The server will allow real-time data base updates with additional layers and models for each center. Expansion of the EGIS data base will continue to grow as site-specific applications are developed addressing the ongoing evolution of environmental concerns at all NASA Field Centers.

Recent Trends in Monitoring of European Water Framework Directive Priority Substances Using Micro-Sensors: A 2007–2009 Review

PubMed Central

Namour, Philippe; Lepot, Mathieu; Jaffrezic-Renault, Nicole

2010-01-01

This review discusses from a critical perspective the development of new sensors for the measurement of priority pollutants targeted in the E.U. Water Framework Directive. Significant advances are reported in the paper and their advantages and limitations are also discussed. Future perspectives in this area are also pointed out in the conclusions. This review covers publications appeared since December 2006 (the publication date of the Swift report). Among priority substances, sensors for monitoring the four WFD metals represent 81% of published papers. None of analyzed publications present a micro-sensor totally validated in laboratory, ready for tests under real conditions in the field. The researches are mainly focused on the sensing part of the micro-sensors. Nevertheless, the main factor limiting micro-sensor applications in the environment is the ruggedness of the receptor towards environmental conditions. This point constitutes the first technological obstacle to be overcome for any long-term field tests. PMID:22163635

Multichannel blind iterative image restoration.

PubMed

Sroubek, Filip; Flusser, Jan

2003-01-01

Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately in a single-channel framework, serious conceptual and numerical problems are often encountered. Very recently, an eigenvector-based method (EVAM) was proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied. We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.

Towards a theoretical clarification of biomimetics using conceptual tools from engineering design.

PubMed

Drack, M; Limpinsel, M; de Bruyn, G; Nebelsick, J H; Betz, O

2017-12-13

Many successful examples of biomimetic products are available, and most research efforts in this emerging field are directed towards the development of specific applications. The theoretical and conceptual underpinnings of the knowledge transfer between biologists, engineers and architects are, however, poorly investigated. The present article addresses this gap. We use a 'technomorphic' approach, i.e. the application of conceptual tools derived from engineering design, to better understand the processes operating during a typical biomimetic research project. This helps to elucidate the formal connections between functions, working principles and constructions (in a broad sense)-because the 'form-function-relationship' is a recurring issue in biology and engineering. The presented schema also serves as a conceptual framework that can be implemented for future biomimetic projects. The concepts of 'function' and 'working principle' are identified as the core elements in the biomimetic knowledge transfer towards applications. This schema not only facilitates the development of a common language in the emerging science of biomimetics, but also promotes the interdisciplinary dialogue among its subdisciplines.

U.S. Geological Survey shrub/grass products provide new approach to shrubland monitoring

USGS Publications Warehouse

Young, Steven M.

2017-12-11

In the Western United States, shrubland ecosystems provide vital ecological, hydrological, biological, agricultural, and recreational services. However, disturbances such as livestock grazing, exotic species invasion, conversion to agriculture, climate change, urban expansion, and energy development are altering these ecosystems.Improving our understanding of how shrublands are distributed, where they are changing, the extent of the historical change, and likely future change directions is critical for successful management of these ecosystems. Remote-sensing technologies provide the most likely data source for large-area monitoring of ecosystem disturbance—both near-real time and historically. A monitoring framework supported by remote-sensing data can offer efficient and accurate analysis of change across a range of spatial and temporal scales.The U.S. Geological Survey has been working to develop new remote-sensing data, tools, and products to characterize and monitor these changing shrubland landscapes. Nine individual map products (components) have been developed that quantify the percent of shrub, sagebrush, big sagebrush, herbaceous, annual herbaceous, litter, bare ground, shrub height, and sagebrush height at 1-percent intervals in each 30-meter grid cell. These component products are designed to be combined and customized to widely support different applications in rangeland monitoring, analysis of wildlife habitat, resource inventory, adaptive management, and environmental review.

A closed-loop neurobotic system for fine touch sensing

NASA Astrophysics Data System (ADS)

Bologna, L. L.; Pinoteau, J.; Passot, J.-B.; Garrido, J. A.; Vogel, J.; Ros Vidal, E.; Arleo, A.

2013-08-01

Objective. Fine touch sensing relies on peripheral-to-central neurotransmission of somesthetic percepts, as well as on active motion policies shaping tactile exploration. This paper presents a novel neuroengineering framework for robotic applications based on the multistage processing of fine tactile information in the closed action-perception loop. Approach. The integrated system modules focus on (i) neural coding principles of spatiotemporal spiking patterns at the periphery of the somatosensory pathway, (ii) probabilistic decoding mechanisms mediating cortical-like tactile recognition and (iii) decision-making and low-level motor adaptation underlying active touch sensing. We probed the resulting neural architecture through a Braille reading task. Main results. Our results on the peripheral encoding of primary contact features are consistent with experimental data on human slow-adapting type I mechanoreceptors. They also suggest second-order processing by cuneate neurons may resolve perceptual ambiguities, contributing to a fast and highly performing online discrimination of Braille inputs by a downstream probabilistic decoder. The implemented multilevel adaptive control provides robustness to motion inaccuracy, while making the number of finger accelerations covariate with Braille character complexity. The resulting modulation of fingertip kinematics is coherent with that observed in human Braille readers. Significance. This work provides a basis for the design and implementation of modular neuromimetic systems for fine touch discrimination in robotics.

High Resolution Sensing and Control of Urban Water Networks

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Wong, B. P.; Kerkez, B.

2016-12-01

We present a framework to enable high-resolution sensing, modeling, and control of urban watersheds using (i) a distributed sensor network based on low-cost cellular-enabled motes, (ii) hydraulic models powered by a cloud computing infrastructure, and (iii) automated actuation valves that allow infrastructure to be controlled in real time. This platform initiates two major advances. First, we achieve a high density of measurements in urban environments, with an anticipated 40+ sensors over each urban area of interest. In addition to new measurements, we also illustrate the design and evaluation of a "smart" control system for real-world hydraulic networks. This control system improves water quality and mitigates flooding by using real-time hydraulic models to adaptively control releases from retention basins. We evaluate the potential of this platform through two ongoing deployments: (i) a flood monitoring network in the Dallas-Fort Worth metropolitan area that detects and anticipates floods at the level of individual roadways, and (ii) a real-time hydraulic control system in the city of Ann Arbor, MI—soon to be one of the most densely instrumented urban watersheds in the United States. Through these applications, we demonstrate that distributed sensing and control of water infrastructure can improve flash flood predictions, emergency response, and stormwater contaminant mitigation.

A new sensor system for accurate and precise determination of sediment dynamics and position.

NASA Astrophysics Data System (ADS)

Maniatis, Georgios; Hoey, Trevor; Sventek, Joseph; Hodge, Rebecca

2014-05-01

Sediment transport processes control many significant geomorphological changes. Consequently, sediment transport dynamics are studied across a wide range of scales leading to application of a variety of conceptually different mathematical descriptions (models) and data acquisition techniques (sensing). For river sediment transport processes both Eulerian and Lagrangian formulations are used. Data are gathered using a very wide range of sensing techniques that are not always compatible with the conceptual formulation applied. We are concerned with small to medium sediment grain-scale motion in gravel-bed rivers, and other coarse-grained environments, and: a) are developing a customised environmental sensor capable of providing coherent data that reliably record the motion; and, b) provide a mathematical framework in which these data can be analysed and interpreted, this being compatible with current stochastic approaches to sediment transport theory. Here we present results from three different aspects of the above developmental process. Firstly, we present a requirement analysis for the sensor based on the state of the art of the existing technologies. We focus on the factors that enhance data coherence and representativeness, extending the common practice for optimization which is based exclusively on electronics/computing related criteria. This analysis leads to formalization of a method that permits accurate control on the physical properties of the sensor using contemporary rapid prototyping techniques [Maniatis et al. 2013]. Secondly the first results are presented from a series of entrainment experiments in a 5 x 0.8 m flume in which a prototype sensor was deployed to monitor entrainment dynamics under increasing flow conditions (0.037 m3.s-1). The sensor was enclosed in an idealized spherical case (111 mm diameter) and placed on a constructed bed of hemispheres of the same diameter. We measured 3-axial inertial acceleration (as a measure of flow stress), with sampling frequency 4 to 10Hz, for two different initial positions over a range of slopes (from 0.026 to 0.57). The results reveal forces during the pre-entrainment phase and show the effect of slope on the temporal characteristics of the process. Finally we present results from the simulations using a mathematical framework developed to integrate the inertial-dynamics data (corresponding to the above experimental procedure and sensing conceptualization) [Abeywardana et al. 2012] with the mathematical techniques used in contemporary localization applications [Zanella et al. 2012]. We specifically assess different signal filtering techniques in terms of: a) how informative they are regarding the complexity of sediment movement; and, b) how possible it is to reduce rapidly accumulating errors that occur during sensing and increase positional accuracy. References Maniatis, G.; Hoey, T.; Sventek, J. Sensor Enclosures: Example Application and Implications for Data Coherence. J. Sens. Actuator Netw. 2013, 2, 761-779. Abeywardana, D. K., A. P. Hu, and N. Kularatna. "IPT charged wireless sensor module for river sedimentation detection." Sensors Applications Symposium (SAS), 2012 IEEE. IEEE, 2012. Zannella, Fillipo, and Angelo Cenedese. "Multi-agent tracking in wireless sensor networks: implementation." WSEAS Int. Conf. on Information Technology and Computer Networks (ITCN). 2012.

Intelligent infrastructure for sustainable potable water: a roundtable for emerging transnational research and technology development needs.

PubMed

Adriaens, Peter; Goovaerts, Pierre; Skerlos, Steven; Edwards, Elizabeth; Egli, Thomas

2003-12-01

Recent commercial and residential development have substantially impacted the fluxes and quality of water that recharge the aquifers and discharges to streams, lakes and wetlands and, ultimately, is recycled for potable use. Whereas the contaminant sources may be varied in scope and composition, these issues of urban water sustainability are of public health concern at all levels of economic development worldwide, and require cheap and innovative environmental sensing capabilities and interactive monitoring networks, as well as tailored distributed water treatment technologies. To address this need, a roundtable was organized to explore the potential role of advances in biotechnology and bioengineering to aid in developing causative relationships between spatial and temporal changes in urbanization patterns and groundwater and surface water quality parameters, and to address aspects of socioeconomic constraints in implementing sustainable exploitation of water resources. An interactive framework for quantitative analysis of the coupling between human and natural systems requires integrating information derived from online and offline point measurements with Geographic Information Systems (GIS)-based remote sensing imagery analysis, groundwater-surface water hydrologic fluxes and water quality data to assess the vulnerability of potable water supplies. Spatially referenced data to inform uncertainty-based dynamic models can be used to rank watershed-specific stressors and receptors to guide researchers and policymakers in the development of targeted sensing and monitoring technologies, as well as tailored control measures for risk mitigation of potable water from microbial and chemical environmental contamination. The enabling technologies encompass: (i) distributed sensing approaches for microbial and chemical contamination (e.g. pathogens, endocrine disruptors); (ii) distributed application-specific, and infrastructure-adaptive water treatment systems; (iii) geostatistical integration of monitoring data and GIS layers; and (iv) systems analysis of microbial and chemical proliferation in distribution systems. This operational framework is aimed at technology implementation while maximizing economic and public health benefits. The outcomes of the roundtable will further research agendas in information technology-based monitoring infrastructure development, integration of processes and spatial analysis, as well as in new educational and training platforms for students, practitioners and regulators. The potential for technology diffusion to emerging economies with limited financial resources is substantial.

Toward a theoretical framework for trustworthy cyber sensing

NASA Astrophysics Data System (ADS)

Xu, Shouhuai

2010-04-01

Cyberspace is an indispensable part of the economy and society, but has been "polluted" with many compromised computers that can be abused to launch further attacks against the others. Since it is likely that there always are compromised computers, it is important to be aware of the (dynamic) cyber security-related situation, which is however challenging because cyberspace is an extremely large-scale complex system. Our project aims to investigate a theoretical framework for trustworthy cyber sensing. With the perspective of treating cyberspace as a large-scale complex system, the core question we aim to address is: What would be a competent theoretical (mathematical and algorithmic) framework for designing, analyzing, deploying, managing, and adapting cyber sensor systems so as to provide trustworthy information or input to the higher layer of cyber situation-awareness management, even in the presence of sophisticated malicious attacks against the cyber sensor systems?

Defining the Application Readiness of Products when Developing Earth Observing Remote Sensing Data Products

NASA Astrophysics Data System (ADS)

Escobar, V. M.

2017-12-01

Satellite remote sensing technology has contributed to the transformation of multiple earth science domains, putting space observations at the forefront of innovation in Earth Science. With new satellite missions being launched every year, new types of Earth Science data are being incorporated into science models and decision-making systems in a broad array of organizations. These applications help hazard mitigation and decision-making in government, private, and civic institutions working to reduce its impact on human wellbeing. Policy guidance and knowledge of product maturity can influence mission design as well as development of product applications in user organizations. Ensuring that satellite missions serve both the scientific and user communities without becoming unfocused and overly expensive is a critical outcome from engagement of user communities. Tracking the applications and product maturity help improve the use of data. NASA's Applications Readiness Levels reduce cost and increase the confidence in applications. ARLs help identify areas where NASA products are most useful while allowing the user to leverage products in early development as well as those ready for operational uses. By considering the needs of the user community early on in the mission-design process, agencies can use ARLs to ensure that satellites meet the needs of multiple constituencies and the development of products are integrated into user organizations organically. ARLs and user integration provide a perspective on the maturity and readiness of a products ability to influence policy and decision-making. This paper describes the mission application development process at NASA and within the Earth Science Directorate. We present the successes and challenges faced by NASA data users and explain how ARLs helps link NASA science to the appropriate policies and decision frameworks. The methods presented here can be adapted to other programs and institutions seeking to rapidly move scientific research to applications that have societal impact.

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

PubMed Central

Silva, Rogers F.; Plis, Sergey M.; Sui, Jing; Pattichis, Marios S.; Adalı, Tülay; Calhoun, Vince D.

2016-01-01

In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting “networks” represented as the underlying latent sources. While the broad success in learning latent representations from multiple datasets has promoted the wide presence of BSS in modern neuroscience, it also introduced a wide variety of objective functions, underlying graphical structures, and parameter constraints for each method. Such diversity, combined with a host of datatype-specific know-how, can cause a sense of disorder and confusion, hampering a practitioner’s judgment and impeding further development. We organize the diverse landscape of BSS models by exposing its key features and combining them to establish a novel unifying view of the area. In the process, we unveil important connections among models according to their properties and subspace structures. Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model for their applications. Equipped with that knowledge, we review the current state of BSS applications to neuroimaging. The gained insight into model connections elicits a broader sense of generalization, highlighting several directions for model development. In light of that, we discuss emerging multi-dataset multidimensional (MDM) models and summarize their benefits for the study of the healthy brain and disease-related changes. PMID:28461840

Polarimetric Interferometry - Remote Sensing Applications

DTIC Science & Technology

2007-02-01

This lecture is mainly based on the work of S.R. Cloude and presents examples for remote sensing applications Polarimetric SAR Interferometry...PolInSAR). PolInSAR has its origins in remote sensing and was first developed for applications in 1997 using SIRC L-Band data [1,2]. In its original form it

Application of remote sensing to solution of ecological problems

NASA Technical Reports Server (NTRS)

Adelman, A.

1972-01-01

The application of remote sensing techniques to solving ecological problems is discussed. The three phases of environmental ecological management are examined. The differences between discovery and exploitation of natural resources and their ecological management are described. The specific application of remote sensing to water management is developed.

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Evaluating ESA CCI Soil Moisture in East Africa

NASA Technical Reports Server (NTRS)

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.

2016-01-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

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

NASA Astrophysics Data System (ADS)

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.

Computational Design for Multifunctional Microstructural Composites

NASA Astrophysics Data System (ADS)

Chen, Yuhang; Zhou, Shiwei; Li, Qing

As an important class of natural and engineered materials, periodic microstructural composites have drawn substantial attention from the material research community for their excellent flexibility in tailoring various desirable physical behaviors. To develop periodic cellular composites for multifunctional applications, this paper presents a unified design framework for combining stiffness and a range of physical properties governed by quasi-harmonic partial differential equations. A multiphase microstructural configuration is sought within a periodic base-cell design domain using topology optimization. To deal with conflicting properties, e.g. conductivity/permeability versus bulk modulus, the optimum is sought in a Pareto sense. Illustrative examples demonstrate the capability of the presented procedure for the design of multiphysical composites and tissue scaffolds.

Bridging the Dialectic: Diversity, Psychological Sense of Community, and Inclusion.

PubMed

Brodsky, Anne E

2017-06-01

Although, there are many times when P/SOC and diversity appear in opposition, I argue that this conflict is not inherent to the concepts or their joint value, but to social contexts in which they are enacted in real life. The primary values of community psychology-building and supporting positive communities, social change, and social justice within a framework that recognizes the centrality of diversity, culture, inclusion, power, and privilege-actually bind diversity and community together. Thus, we can bridge this seeming dialectic through deeper reflection about the real and intended meaning, operationalization, and application of these two terms, and a reliance on the central values of our field. © Society for Community Research and Action 2017.

Making Sense in the Edge of Chaos: A Framework for Effective Initial Response Efforts to Large-Scale Incidents

DTIC Science & Technology

2010-09-01

working with equally experienced partners who can, cumulatively, help each other make sense of chaotic situations. “Human brains collect, organize...but a process reinforced by years of Fire Department training. No matter what we do, even an optimally functioning human brain will prepare for...trick or reorganize the brain of those who will be first responding incident commanders to an edge-of-chaos event into creatively making sense of

Algorithms in the historical emergence of word senses.

PubMed

Ramiro, Christian; Srinivasan, Mahesh; Malt, Barbara C; Xu, Yang

2018-03-06

Human language relies on a finite lexicon to express a potentially infinite set of ideas. A key result of this tension is that words acquire novel senses over time. However, the cognitive processes that underlie the historical emergence of new word senses are poorly understood. Here, we present a computational framework that formalizes competing views of how new senses of a word might emerge by attaching to existing senses of the word. We test the ability of the models to predict the temporal order in which the senses of individual words have emerged, using an historical lexicon of English spanning the past millennium. Our findings suggest that word senses emerge in predictable ways, following an historical path that reflects cognitive efficiency, predominantly through a process of nearest-neighbor chaining. Our work contributes a formal account of the generative processes that underlie lexical evolution.

Classification and data acquisition with incomplete data

NASA Astrophysics Data System (ADS)

Williams, David P.

In remote-sensing applications, incomplete data can result when only a subset of sensors (e.g., radar, infrared, acoustic) are deployed at certain regions. The limitations of single sensor systems have spurred interest in employing multiple sensor modalities simultaneously. For example, in land mine detection tasks, different sensor modalities are better-suited to capture different aspects of the underlying physics of the mines. Synthetic aperture radar sensors may be better at detecting surface mines, while infrared sensors may be better at detecting buried mines. By employing multiple sensor modalities to address the detection task, the strengths of the disparate sensors can be exploited in a synergistic manner to improve performance beyond that which would be achievable with either single sensor alone. When multi-sensor approaches are employed, however, incomplete data can be manifested. If each sensor is located on a separate platform ( e.g., aircraft), each sensor may interrogate---and hence collect data over---only partially overlapping areas of land. As a result, some data points may be characterized by data (i.e., features) from only a subset of the possible sensors employed in the task. Equivalently, this scenario implies that some data points will be missing features. Increasing focus in the future on using---and fusing data from---multiple sensors will make such incomplete-data problems commonplace. In many applications involving incomplete data, it is possible to acquire the missing data at a cost. In multi-sensor remote-sensing applications, data is acquired by deploying sensors to data points. Acquiring data is usually an expensive, time-consuming task, a fact that necessitates an intelligent data acquisition process. Incomplete data is not limited to remote-sensing applications, but rather, can arise in virtually any data set. In this dissertation, we address the general problem of classification when faced with incomplete data. We also address the closely related problem of active data acquisition, which develops a strategy to acquire missing features and labels that will most benefit the classification task. We first address the general problem of classification with incomplete data, maintaining the view that all data (i.e., information) is valuable. We employ a logistic regression framework within which we formulate a supervised classification algorithm for incomplete data. This principled, yet flexible, framework permits several interesting extensions that allow all available data to be utilized. One extension incorporates labeling error, which permits the usage of potentially imperfectly labeled data in learning a classifier. A second major extension converts the proposed algorithm to a semi-supervised approach by utilizing unlabeled data via graph-based regularization. Finally, the classification algorithm is extended to the case in which (image) data---from which features are extracted---are available from multiple resolutions. Taken together, this family of incomplete-data classification algorithms exploits all available data in a principled manner by avoiding explicit imputation. Instead, missing data is integrated out analytically with the aid of an estimated conditional density function (conditioned on the observed features). This feat is accomplished by invoking only mild assumptions. We also address the problem of active data acquisition by determining which missing data should be acquired to most improve performance. Specifically, we examine this data acquisition task when the data to be acquired can be either labels or features. The proposed approach is based on a criterion that accounts for the expected benefit of the acquisition. This approach, which is applicable for any general missing data problem, exploits the incomplete-data classification framework introduced in the first part of this dissertation. This data acquisition approach allows for the acquisition of both labels and features. Moreover, several types of feature acquisition are permitted, including the acquisition of individual or multiple features for individual or multiple data points, which may be either labeled or unlabeled. Furthermore, if different types of data acquisition are feasible for a given application, the algorithm will automatically determine the most beneficial type of data to acquire. Experimental results on both benchmark machine learning data sets and real (i.e., measured) remote-sensing data demonstrate the advantages of the proposed incomplete-data classification and active data acquisition algorithms.

Research and development of web oriented remote sensing image publication system based on Servlet technique

NASA Astrophysics Data System (ADS)

Juanle, Wang; Shuang, Li; Yunqiang, Zhu

2005-10-01

According to the requirements of China National Scientific Data Sharing Program (NSDSP), the research and development of web oriented RS Image Publication System (RSIPS) is based on Java Servlet technique. The designing of RSIPS framework is composed of 3 tiers, which is Presentation Tier, Application Service Tier and Data Resource Tier. Presentation Tier provides user interface for data query, review and download. For the convenience of users, visual spatial query interface is included. Served as a middle tier, Application Service Tier controls all actions between users and databases. Data Resources Tier stores RS images in file and relationship databases. RSIPS is developed with cross platform programming based on Java Servlet tools, which is one of advanced techniques in J2EE architecture. RSIPS's prototype has been developed and applied in the geosciences clearinghouse practice which is among the experiment units of NSDSP in China.

A remote instruction system empowered by tightly shared haptic sensation

NASA Astrophysics Data System (ADS)

Nishino, Hiroaki; Yamaguchi, Akira; Kagawa, Tsuneo; Utsumiya, Kouichi

2007-09-01

We present a system to realize an on-line instruction environment among physically separated participants based on a multi-modal communication strategy. In addition to visual and acoustic information, commonly used communication modalities in network environments, our system provides a haptic channel to intuitively conveying partners' sense of touch. The human touch sensation, however, is very sensitive for delays and jitters in the networked virtual reality (NVR) systems. Therefore, a method to compensate for such negative factors needs to be provided. We show an NVR architecture to implement a basic framework that can be shared by various applications and effectively deals with the problems. We take a hybrid approach to implement both data consistency by client-server and scalability by peer-to-peer models. As an application system built on the proposed architecture, a remote instruction system targeted at teaching handwritten characters and line patterns on a Korea-Japan high-speed research network also is mentioned.

Sense of Community as Construct and Theory: Authors' Response to McMillan

ERIC Educational Resources Information Center

Nowell, Branda; Boyd, Neil

2011-01-01

In this article, we respond to criticisms posed by McMillan (2011) of our recent paper, "Viewing Community as Responsibility as well as a Resource: Deconstructing the Theoretical Roots of Psychological Sense of Community." We clarify that the focus of our article was to explore the macro theoretical frameworks and second-order assumptions that…

The Effect Different Synchronous Computer Mediums Have on Distance Education Graduate Students' Sense of Community and Feelings of Loneliness

ERIC Educational Resources Information Center

Heuvelman-Hutchinson, Lorene R.

2012-01-01

Because distance education is such a rapidly developing educational venue, knowing what factors impact success must be known. Loneliness and sense of connectedness, or community, are issues facing graduate distance education students. These issues may influence retention. The theoretical framework of a Community of Practice assisted in…

A Write to Know: Using Autoethnographic Writing to Explore Marginalization and Sense of Belonging in Community College Students

ERIC Educational Resources Information Center

Bracamontes, Brent Ignacio

2017-01-01

This dissertation used qualitative, interpretive methods to explore African American and Latino/a community college students' use of autoethnographic writing to express experiences of marginalization and sense of belonging on their college campus. Using postmodernism and critical race theory as theoretical frameworks, I investigated how students…

In the Sandbox: Individuals and Collectives in Organizational Learning as Sense-Making through Play

ERIC Educational Resources Information Center

Popova-Nowak, Irina V.

2014-01-01

This study was conducted to develop a grounded theory of connections between individual and collective (group and organizational) levels of analysis through the examination of play and sense-making as integral parts of organizational learning (OL) by relying on the meta-paradigm theoretical framework. The study employed grounded theory as its…

Community Organizations and Sense of Community: Further Development in Theory and Measurement

ERIC Educational Resources Information Center

Peterson, N. Andrew; Speer, Paul W.; Hughey, Joseph; Armstead, Theresa L.; Schneider, John E.; Sheffer, Megan A.

2008-01-01

The Community Organization Sense of Community Scale (COSOC) is a frequently used or cited measure of the construct in community psychology and other disciplines, despite a lack of confirmation of its underlying 4-factor framework. Two studies were conducted to test the hypothesized structure of the COSOC, the potential effects of method bias on…

Making sense of 'place': Reflections on pluralism and positionality in place research

Treesearch

Daniel R. Williams

2014-01-01

Drawing on critical pluralism and positionality, this essay offers a four-part framework for making sense of the manifold ways place has been studied and applied to landscape planning and management. The first element highlights how diverse intellectual origins behind place research have inhibited a transdisciplinary understanding of place as an object of study in...

Sense of place: Mount Desert Island residents and Acadia National Park

Treesearch

Nicole L. Ballinger; Robert E. Manning

1998-01-01

The framework of sense of place, developed by humanistic geographers, has been employed by researchers in their efforts to explain the range of attachments, values, and meanings assigned to natural areas. This study used an exploratory approach to address the range of values and meanings assigned by local residents to places in Acadia National Park. Qualitative...

Exploring Sense of Community and Persistence in the Community College

ERIC Educational Resources Information Center

Bengfort, Randall R.

2012-01-01

As concern grows about the level of college completion in the U.S., higher education leaders are seeking ways to help more students attain their educational objectives. This study sought to aid that effort by determining if a theoretical framework of sense of community developed by McMillan and Chavis (1986) influences students' decisions to…

Summaries of the thematic conferences on remote sensing for exploration geology

NASA Technical Reports Server (NTRS)

1989-01-01

The Thematic Conference series was initiated to address the need for concentrated discussion of particular remote sensing applications. The program is primarily concerned with the application of remote sensing to mineral and hydrocarbon exploration, with special emphasis on data integration, methodologies, and practical solutions for geologists. Some fifty invited papers are scheduled for eleven plenary sessions, formulated to address such important topics as basement tectonics and their surface expressions, spectral geology, applications for hydrocarbon exploration, and radar applications and future systems. Other invited presentations will discuss geobotanical remote sensing, mineral exploration, engineering and environmental applications, advanced image processing, and integration and mapping.

Literature relevant to remote sensing of water quality

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Marcell, R. F.

1983-01-01

References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

XAL Application Framework and Bricks GUI Builder

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

Pelaia II, Tom

2007-01-01

The XAL [1] Application Framework is a framework for rapidly developing document based Java applications with a common look and feel along with many built-in user interface behaviors. The Bricks GUI builder consists of a modern application and framework for rapidly building user interfaces in support of true Model-View-Controller (MVC) compliant Java applications. Bricks and the XAL Application Framework allow developers to rapidly create quality applications.

Supporting Multidisciplinary Networks through Relationality and a Critical Sense of Belonging: Three "Gardening Tools" and the "Relational Agency Framework"

ERIC Educational Resources Information Center

Duhn, Iris; Fleer, Marilyn; Harrison, Linda

2016-01-01

This article focuses on the "Relational Agency Framework" (RAF), an analytical tool developed for an Australian review and evaluation study of an early years' policy initiative. We explore Anne Edward's concepts of "relational expertise", "building common knowledge" and "relational agency" to explore how…

Making Sense and Facing Tensions: An Investigation of Core Practice Complexities

ERIC Educational Resources Information Center

Neel, Michael A.

2017-01-01

Recently, scholars have called for a practice-based framework for teacher education and some have argued more narrowly for a framework built around "core practices of teaching." These efforts, in part, are intended to make teacher education practice public and available for collective improvement. The purpose of this paper is to…

Installing the earth station of Ka-band satellite frequency in Malaysia: conceptual framework for site decision

NASA Astrophysics Data System (ADS)

Mahmud, M. R.; Reba, M. N. M.; Jaw, S. W.; Arsyad, A.; Ibrahim, M. A. M.

2017-05-01

This paper developed a conceptual framework in determining the suitable location in installing the earth station for Ka-band satellite communication in Malaysia. This current evolution of high throughput satellites experienced major challenge due to Malaysian climate. Because Ka-band frequency is highly attenuated by the rainfall; it is an enormous challenge to define the most appropriate site for the static communication. Site diversity, a measure to anticipate this conflict by choosing less attenuated region and geographically change the transmission strategy on season basis require accurate spatio-temporal information on the geographical, environmental and hydro-climatology at local scale. Prior to that request, this study developed a conceptual framework to cater the needs. By using the digital spatial data, acquired from site measurement and remote sensing, the proposed framework applied a multiple criteria analysis to perform the tasks of site selection. With the advancement of high resolution remotely sensed data, site determination can be conducted as in Malaysia; accommodating a new, fast, and effective satellite communication. The output of this study is one of the pioneer contributions to create a high tech-society.

Biosafety and Biosecurity: A Relative Risk-Based Framework for Safer, More Secure, and Sustainable Laboratory Capacity Building.

PubMed

Dickmann, Petra; Sheeley, Heather; Lightfoot, Nigel

2015-01-01

Laboratory capacity building is characterized by a paradox between endemicity and resources: countries with high endemicity of pathogenic agents often have low and intermittent resources (water, electricity) and capacities (laboratories, trained staff, adequate regulations). Meanwhile, countries with low endemicity of pathogenic agents often have high-containment facilities with costly infrastructure and maintenance governed by regulations. The common practice of exporting high biocontainment facilities and standards is not sustainable and concerns about biosafety and biosecurity require careful consideration. A group at Chatham House developed a draft conceptual framework for safer, more secure, and sustainable laboratory capacity building. The draft generic framework is guided by the phrase "LOCAL - PEOPLE - MAKE SENSE" that represents three major principles: capacity building according to local needs (local) with an emphasis on relationship and trust building (people) and continuous outcome and impact measurement (make sense). This draft generic framework can serve as a blueprint for international policy decision-making on improving biosafety and biosecurity in laboratory capacity building, but requires more testing and detailing development.

Exploiting passive polarimetric imagery for remote sensing applications

NASA Astrophysics Data System (ADS)

Vimal Thilak Krishna, Thilakam

Polarization is a property of light or electromagnetic radiation that conveys information about the orientation of the transverse electric and magnetic fields. The polarization of reflected light complements other electromagnetic radiation attributes such as intensity, frequency, or spectral characteristics. A passive polarization based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. The polarization due to surface reflections from such objects contains information about the targets that can be exploited in remote sensing applications such as target detection, target classification, object recognition and shape extraction/recognition. In recent years, there has been renewed interest in the use of passive polarization information in remote sensing applications. The goal of our research is to design image processing algorithms for remote sensing applications by utilizing physics-based models that describe the polarization imparted by optical scattering from an object. In this dissertation, we present a method to estimate the complex index of refraction and reflection angle from multiple polarization measurements. This method employs a polarimetric bidirectional reflectance distribution function (pBRDF) that accounts for polarization due to specular scattering. The parameters of interest are derived by utilizing a nonlinear least squares estimation algorithm, and computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Furthermore, laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle. We also study the use of extracted index of refraction as a feature vector in designing two important image processing applications, namely image segmentation and material classification so that the resulting systems are largely invariant to illumination source location. This is in contrast to most passive polarization-based image processing algorithms proposed in the literature that employ quantities such as Stokes vectors and the degree of polarization and which are not robust to changes in illumination conditions. The estimated index of refraction, on the other hand, is invariant to illumination conditions and hence can be used as an input to image processing algorithms. The proposed estimation framework also is extended to the case where the position of the observer (camera) moves between measurements while that of the source remains fixed. Finally, we explore briefly the topic of parameter estimation for a generalized model that accounts for both specular and volumetric scattering. A combination of simulation and experimental results are provided to evaluate the effectiveness of the above methods.

Crystal engineering, structure–function relationships, and the future of metal–organic frameworks

DOE PAGES

Allendorf, Mark D.; Stavila, Vitalie

2014-10-15

Metal-Organic Frameworks (MOFs) are a rapidly expanding class of hybrid organic-inorganic materials that can be rationally designed and assembled through crystal engineering. The explosion of interest in this subclass of coordination polymers results from their outstanding properties and myriad possible applications that include traditional uses of microporous materials, such as gas storage, separations, and catalysis, to new realms in biomedicine, electronic devices, and and information storage. The objective of this Highlight article is to provide the reader with a sense of where the field stands after roughly fifteen years of research. Remarkable progress has been made, but the barriers tomore » practical and commercial advances are also illuminated. We discuss the basic elements of MOF assembly and present a conceptual hierarchy of structural elements that assists in understanding how unique properties in these materials can be achieved. Structure-function relationships are then discussed; several are now well understood as a result of the focused efforts of many research groups over the past decade. Prospects for practical applications of MOFs in membranes, catalysis, biomedicine, and as active components in electronic and photonic devices are also discussed. Finally, we list key challenges that, in our view, must be addressed for these materials to realize their full potential in the marketplace.« less

Mercury⊕: An evidential reasoning image classifier

NASA Astrophysics Data System (ADS)

Peddle, Derek R.

1995-12-01

MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.

Bilinear Inverse Problems: Theory, Algorithms, and Applications

NASA Astrophysics Data System (ADS)

Ling, Shuyang

We will discuss how several important real-world signal processing problems, such as self-calibration and blind deconvolution, can be modeled as bilinear inverse problems and solved by convex and nonconvex optimization approaches. In Chapter 2, we bring together three seemingly unrelated concepts, self-calibration, compressive sensing and biconvex optimization. We show how several self-calibration problems can be treated efficiently within the framework of biconvex compressive sensing via a new method called SparseLift. More specifically, we consider a linear system of equations y = DAx, where the diagonal matrix D (which models the calibration error) is unknown and x is an unknown sparse signal. By "lifting" this biconvex inverse problem and exploiting sparsity in this model, we derive explicit theoretical guarantees under which both x and D can be recovered exactly, robustly, and numerically efficiently. In Chapter 3, we study the question of the joint blind deconvolution and blind demixing, i.e., extracting a sequence of functions [special characters omitted] from observing only the sum of their convolutions [special characters omitted]. In particular, for the special case s = 1, it becomes the well-known blind deconvolution problem. We present a non-convex algorithm which guarantees exact recovery under conditions that are competitive with convex optimization methods, with the additional advantage of being computationally much more efficient. We discuss several applications of the proposed framework in image processing and wireless communications in connection with the Internet-of-Things. In Chapter 4, we consider three different self-calibration models of practical relevance. We show how their corresponding bilinear inverse problems can be solved by both the simple linear least squares approach and the SVD-based approach. As a consequence, the proposed algorithms are numerically extremely efficient, thus allowing for real-time deployment. Explicit theoretical guarantees and stability theory are derived and the number of sampling complexity is nearly optimal (up to a poly-log factor). Applications in imaging sciences and signal processing are discussed and numerical simulations are presented to demonstrate the effectiveness and efficiency of our approach.

Supervised Semantic Classification for Nuclear Proliferation Monitoring

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

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

Selective and sensitive aqueous-phase detection of 2,4,6-trinitrophenol (TNP) by an amine-functionalized metal-organic framework.

PubMed

Joarder, Biplab; Desai, Aamod V; Samanta, Partha; Mukherjee, Soumya; Ghosh, Sujit K

2015-01-12

Highly selective and sensitive aqueous-phase detection of nitro explosive 2,4,6-trinitrophenol (TNP) by a hydrolytically stable 3D luminescent metal-organic framework is reported. The compound senses TNP exclusively even in the presence of other nitro-compounds, with an unprecedented sensitivity in the MOF regime by means of strategic deployment of its free amine groups. Such an accurate sensing of TNP, widely recognized as a harmful environmental contaminant in water media, establishes this new strategic approach as one of the frontiers to tackle present-day security and health concerns in a real-time scenario. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An expanded conceptual framework for solution-focused management of chemical pollution in European waters.

PubMed

Munthe, John; Brorström-Lundén, Eva; Rahmberg, Magnus; Posthuma, Leo; Altenburger, Rolf; Brack, Werner; Bunke, Dirk; Engelen, Guy; Gawlik, Bernd Manfred; van Gils, Jos; Herráez, David López; Rydberg, Tomas; Slobodnik, Jaroslav; van Wezel, Annemarie

2017-01-01

This paper describes a conceptual framework for solutions-focused management of chemical contaminants built on novel and systematic approaches for identifying, quantifying and reducing risks of these substances. The conceptual framework was developed in interaction with stakeholders representing relevant authorities and organisations responsible for managing environmental quality of water bodies. Stakeholder needs were compiled via a survey and dialogue. The content of the conceptual framework was thereafter developed with inputs from relevant scientific disciplines. The conceptual framework consists of four access points: Chemicals, Environment, Abatement and Society, representing different aspects and approaches to engaging in the issue of chemical contamination of surface waters. It widens the scope for assessment and management of chemicals in comparison to a traditional (mostly) perchemical risk assessment approaches by including abatement- and societal approaches as optional solutions. The solution-focused approach implies an identification of abatement- and policy options upfront in the risk assessment process. The conceptual framework was designed for use in current and future chemical pollution assessments for the aquatic environment, including the specific challenges encountered in prioritising individual chemicals and mixtures, and is applicable for the development of approaches for safe chemical management in a broader sense. The four access points of the conceptual framework are interlinked by four key topics representing the main scientific challenges that need to be addressed, i.e.: identifying and prioritising hazardous chemicals at different scales; selecting relevant and efficient abatement options; providing regulatory support for chemicals management; predicting and prioritising future chemical risks. The conceptual framework aligns current challenges in the safe production and use of chemicals. The current state of knowledge and implementation of these challenges is described. The use of the conceptual framework, and addressing the challenges, is intended to support: (1) forwarding sustainable use of chemicals, (2) identification of pollutants of priority concern for cost-effective management, (3) the selection of optimal abatement options and (4) the development and use of optimised legal and policy instruments.

Remote sensing for restoration ecology: Application for restoring degraded, damaged, transformed, or destroyed ecosystems.

PubMed

Reif, Molly K; Theel, Heather J

2017-07-01

Restoration monitoring is generally perceived as costly and time consuming, given the assumptions of successfully restoring ecological functions and services of a particular ecosystem or habitat. Opportunities exist for remote sensing to bolster the restoration science associated with a wide variety of injured resources, including resources affected by fire, hydropower operations, chemical releases, and oil spills, among others. In the last decade, the role of remote sensing to support restoration monitoring has increased, in part due to the advent of high-resolution satellite sensors as well as other sensor technology, such as lidar. Restoration practitioners in federal agencies require monitoring standards to assess restoration performance of injured resources. This review attempts to address a technical need and provides an introductory overview of spatial data and restoration metric considerations, as well as an in-depth review of optical (e.g., spaceborne, airborne, unmanned aerial vehicles) and active (e.g., radar, lidar) sensors and examples of restoration metrics that can be measured with remotely sensed data (e.g., land cover, species or habitat type, change detection, quality, degradation, diversity, and pressures or threats). To that end, the present article helps restoration practitioners assemble information not only about essential restoration metrics but also about the evolving technological approaches that can be used to best assess them. Given the need for monitoring standards to assess restoration success of injured resources, a universal monitoring framework should include a range of remote sensing options with which to measure common restoration metrics. Integr Environ Assess Manag 2017;13:614-630. Published 2016. This article is a US Government work and is in the public domain in the USA. Published 2016. This article is a US Government work and is in the public domain in the USA.

Structural health management of aerospace hotspots under fatigue loading

NASA Astrophysics Data System (ADS)

Soni, Sunilkumar

Sustainability and life-cycle assessments of aerospace systems, such as aircraft structures and propulsion systems, represent growing challenges in engineering. Hence, there has been an increasing demand in using structural health monitoring (SHM) techniques for continuous monitoring of these systems in an effort to improve safety and reduce maintenance costs. The current research is part of an ongoing multidisciplinary effort to develop a robust SHM framework resulting in improved models for damage-state awareness and life prediction, and enhancing capability of future aircraft systems. Lug joints, a typical structural hotspot, were chosen as the test article for the current study. The thesis focuses on integrated SHM techniques for damage detection and characterization in lug joints. Piezoelectric wafer sensors (PZTs) are used to generate guided Lamb waves as they can be easily used for onboard applications. Sensor placement in certain regions of a structural component is not feasible due to the inaccessibility of the area to be monitored. Therefore, a virtual sensing concept is introduced to acquire sensor data from finite element (FE) models. A full three dimensional FE analysis of lug joints with piezoelectric transducers, accounting for piezoelectrical-mechanical coupling, was performed in Abaqus and the sensor signals were simulated. These modeled sensors are called virtual sensors. A combination of real data from PZTs and virtual sensing data from FE analysis is used to monitor and detect fatigue damage in aluminum lug joints. Experiments were conducted on lug joints under fatigue loads and sensor signals collected were used to validate the simulated sensor response. An optimal sensor placement methodology for lug joints is developed based on a detection theory framework to maximize the detection rate and minimize the false alarm rate. The placement technique is such that the sensor features can be directly correlated to damage. The technique accounts for a number of factors, such as actuation frequency and strength, minimum damage size, damage detection scheme, material damping, signal to noise ratio and sensing radius. Advanced information processing methodologies are discussed for damage diagnosis. A new, instantaneous approach for damage detection, localization and quantification is proposed for applications to practical problems associated with changes in reference states under different environmental and operational conditions. Such an approach improves feature extraction for state awareness, resulting in robust life prediction capabilities.

Radar Remote Sensing of Waves and Currents in the Nearshore Zone

DTIC Science & Technology

2006-01-01

and application of novel microwave, acoustic, and optical remote sensing techniques. The objectives of this effort are to determine the extent to which...Doppler radar techniques are useful for nearshore remote sensing applications. Of particular interest are estimates of surf zone location and extent...surface currents, waves, and bathymetry. To date, optical (video) techniques have been the primary remote sensing technology used for these applications. A key advantage of the radar is its all weather day-night operability.

Graphene Hybrid Materials in Gas Sensing Applications †

PubMed Central

Latif, Usman; Dickert, Franz L.

2015-01-01

Graphene, a two dimensional structure of carbon atoms, has been widely used as a material for gas sensing applications because of its large surface area, excellent conductivity, and ease of functionalization. This article reviews the most recent advances in graphene hybrid materials developed for gas sensing applications. In this review, synthetic approaches to fabricate graphene sensors, the nano structures of hybrid materials, and their sensing mechanism are presented. Future perspectives of this rapidly growing field are also discussed. PMID:26690156

An Integrated Patient Information and In-Home Health Monitoring System Using Smartphones and Web Services.

PubMed

Sorwar, Golam; Ali, Mortuza; Islam, Md Kamrul; Miah, Mohammad Selim

2016-01-01

Modern healthcare systems are undergoing a paradigm shift from in-hospital care to in-home monitoring, leveraging the emerging technologies in the area of bio-sensing, wireless communication, mobile computing, and artificial intelligence. In-home monitoring promises to significantly reduce healthcare spending by preventing unnecessary hospital admissions and visits to healthcare professionals. Most of the in-home monitoring systems, proposed in the literature, focus on monitoring a set of specific vital signs. However, from the perspective of caregivers it is infeasible to maintain a collection of specialized monitoring systems. In this paper, we view the problem of in-home monitoring from the perspective of caregivers and present a framework that supports various monitoring capabilities while making the complexity transparent to the end users. The essential idea of the framework is to define a 'general purpose architecture' where the system specifies a particular protocol for communication and makes it public. Then any bio-sensing system can communicate with the system as long as it conforms to the protocol. We then argue that as the system grows in terms of number of patients and bio-sensing systems, artificial intelligence technologies need to be employed for patients' risk assessment, prioritization, and recommendation. Finally, we present an initial prototype of the system designed according to the proposed framework.

An examination of applications of remote sensing data to Metropolitan Washington Council of Governments' planning requirements

NASA Technical Reports Server (NTRS)

Mallon, H. J.; Howard, J. Y.; Karch, K. M.

1971-01-01

A comprehensive inventory of a series of remote sensing applications for a variety of regional planning programs in metropolitan Washington was undertaken. Examples of application, methods for data utilization, and corresponding photographic illustrations are provided illustrating how remote sensing would prove particularly useful as a unique and/or supplemental data source.

Identifying Opportunities for Grade One Children to Acquire Foundational Number Sense: Developing a Framework for Cross Cultural Classroom Analyses

ERIC Educational Resources Information Center

Andrews, Paul; Sayers, Judy

2015-01-01

It is known that an appropriately developed foundational number sense (FONS), or the ability to operate flexibly with number and quantity, is a powerful predictor of young children's later mathematical achievement. However, until now not only has FONS been definitionally elusive but instruments for identifying opportunities for children to acquire…

Exploring Latent Class Based on Growth Rates in Number Sense Ability

ERIC Educational Resources Information Center

Kim, Dongil; Shin, Jaehyun; Lee, Kijyung

2013-01-01

The purpose of this study was to explore latent class based on growth rates in number sense ability by using latent growth class modeling (LGCM). LGCM is one of the noteworthy methods for identifying growth patterns of the progress monitoring within the response to intervention framework in that it enables us to analyze latent sub-groups based not…

Critical Care and Problematizing Sense of School Belonging as a Response to Inequality for Immigrants and Children of Immigrants

ERIC Educational Resources Information Center

DeNicolo, Christina Passos; Yu, Min; Crowley, Christopher B.; Gabel, Susan L.

2017-01-01

This chapter examines the factors that contribute to a sense of school belonging for immigrant and immigrant-origin youth. Through a review of the education research on critical care, the authors propose a framework informed by "cariño conscientizado"--critically conscious and authentic care--as central to reconceptualizing notions of…

Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions

USDA-ARS?s Scientific Manuscript database

The ability of remote sensing-based surface energy balance (SEB) models to track water stress in rain-fed switchgrass has not been explored yet. In this paper, the theoretical framework of crop water stress index (CWSI) was utilized to estimate CWSI in rain-fed switchgrass (Panicum virgatum L.) usin...

Towards a Balanced Literacy Instruction: Understanding Reading Skills within a Whole Language Paradigm.

ERIC Educational Resources Information Center

Lavadenz, Magaly

The goals outlined in the California Language Arts Framework (1987) include a call for Language Arts instruction that promotes a love of reading through a sense of personal fulfillment, a sense of effectiveness through which students acquire a range of lifelong learning strategies that foster full participation in the world of work and the access…

A Framework for Mathematics Graphical Tasks: The Influence of the Graphic Element on Student Sense Making

ERIC Educational Resources Information Center

Lowrie, Tom; Diezmann, Carmel M.; Logan, Tracy

2012-01-01

Graphical tasks have become a prominent aspect of mathematics assessment. From a conceptual stance, the purpose of this study was to better understand the composition of graphical tasks commonly used to assess students' mathematics understandings. Through an iterative design, the investigation described the sense making of 11-12-year-olds as they…

Learning to Teach English Language Learners: A Study of Elementary School Teachers' Sense-Making in an ELL Endorsement Program

ERIC Educational Resources Information Center

Daniel, Shannon M.; Pray, Lisa

2017-01-01

Using Jarvis's (2009) framework of adult learning, this study examines how in-service elementary school teachers make sense of instruction that is responsive to multilingual learners. Case studies of two teachers reveal their nuanced attempts to improve practice during a 1-year, graduate-level, add-on certification program for teaching English…

Principal Sensemaking of Inclusion: A Multi-Case Study of Five Urban School Principals

ERIC Educational Resources Information Center

DeMatthews, David Edward

2012-01-01

This study examined how five principals working in one urban school district made sense of inclusion. I employed a multi-case study guided by the theoretical framework of sensemaking. Weick's sensemaking theory was useful in examining the way principals made sense of inclusion. Each of the seven characteristics of Weick's sensemaking…

CoP Sensing Framework on Web-Based Environment

NASA Astrophysics Data System (ADS)

Mustapha, S. M. F. D. Syed

The Web technologies and Web applications have shown similar high growth rate in terms of daily usages and user acceptance. The Web applications have not only penetrated in the traditional domains such as education and business but have also encroached into areas such as politics, social, lifestyle, and culture. The emergence of Web technologies has enabled Web access even to the person on the move through PDAs or mobile phones that are connected using Wi-Fi, HSDPA, or other communication protocols. These two phenomena are the inducement factors toward the need of building Web-based systems as the supporting tools in fulfilling many mundane activities. In doing this, one of the many focuses in research has been to look at the implementation challenges in building Web-based support systems in different types of environment. This chapter describes the implementation issues in building the community learning framework that can be supported on the Web-based platform. The Community of Practice (CoP) has been chosen as the community learning theory to be the case study and analysis as it challenges the creativity of the architectural design of the Web system in order to capture the presence of learning activities. The details of this chapter describe the characteristics of the CoP to understand the inherent intricacies in modeling in the Web-based environment, the evidences of CoP that need to be traced automatically in a slick manner such that the evidence-capturing process is unobtrusive, and the technologies needed to embrace a full adoption of Web-based support system for the community learning framework.

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation

PubMed Central

Shen, Liang; Huang, Xiaotao; Fan, Chongyi

2018-01-01

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm. PMID:29724013

Distributed Event-Based Set-Membership Filtering for a Class of Nonlinear Systems With Sensor Saturations Over Sensor Networks.

PubMed

Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos

2017-11-01

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation.

PubMed

Shen, Liang; Huang, Xiaotao; Fan, Chongyi

2018-05-01

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm.

Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

PubMed Central

Chen, Yen-Lin; Liang, Wen-Yew; Chiang, Chuan-Yen; Hsieh, Tung-Ju; Lee, Da-Cheng; Yuan, Shyan-Ming; Chang, Yang-Lang

2011-01-01

This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions. PMID:22163990

Application of remote sensing to water resources problems

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1972-01-01

The following conclusions were reached concerning the applications of remote sensing to water resources problems: (1) Remote sensing methods provide the most practical method of obtaining data for many water resources problems; (2) the multi-disciplinary approach is essential to the effective application of remote sensing to water resource problems; (3) there is a correlation between the amount of suspended solids in an effluent discharged into a water body and reflected energy; (4) remote sensing provides for more effective and accurate monitoring, discovery and characterization of the mixing zone of effluent discharged into a receiving water body; and (5) it is possible to differentiate between blue and blue-green algae.

A Conceptual Framework for Evaluating Attrition in Online Courses

ERIC Educational Resources Information Center

Laing, C. Linda; Laing, Gregory K.

2015-01-01

The purpose of this paper is to develop a conceptual framework that considers the role that the sense of isolation and alienation play in contributing to attrition in online courses in the higher education sector. The approach adopted in this paper is a theoretical study aimed at synthesizing existing theories. The ultimate contribution of this…

A State Cyber Hub Operations Framework

DTIC Science & Technology

2016-06-01

to communicate and sense or interact with their internal states or the external environment. Machine Learning: A type of artificial intelligence that... artificial intelligence , and computational linguistics concerned with the interactions between computers and human (natural) languages. Patching: A piece...formalizing a proof of concept for cyber initiatives and developed frameworks for operationalizing the data and intelligence produced across state

Using Campinha-Bacote's Framework to Examine Cultural Competence from an Interdisciplinary International Service Learning Program

ERIC Educational Resources Information Center

Wall-Bassett, Elizabeth DeVane; Hegde, Archana Vasudeva; Craft, Katelyn; Oberlin, Amber Louise

2018-01-01

The purpose of this study was to investigate an interdisciplinary international service learning program and its impact on student sense of cultural awareness and competence using the Campinha-Bacote's (2002) framework of cultural competency model. Seven undergraduate and one graduate student from Human Development and Nutrition Science…

Using a Framework for Three Levels of Sense Making in a Mathematics Classroom

ERIC Educational Resources Information Center

Moss, Diana L.; Lamberg, Teruni

2016-01-01

This discussion-based lesson is designed to support Year 6 students in their initial understanding of using letters to represent numbers, expressions, and equations in algebra. The three level framework is designed for: (1) making thinking explicit, (2) exploring each other's solutions, and (3) developing new mathematical insights. In each level…

Seeping Deficit Thinking Assumptions Maintain the Neoliberal Education Agenda: Exploring Three Conceptual Frameworks of Deficit Thinking in Inner-City Schools

ERIC Educational Resources Information Center

Sharma, Manu

2018-01-01

This article draws awareness to the subtle and seeping "common sense" mentality of neoliberalism and deficit thinking assumptions about racially marginalized students in inner-city schools. From a literature review conducted on deficit thinking and deficit practices in schools, I developed three different frameworks for understanding the…

Satellite imaging coral reef resilience at regional scale. A case-study from Saudi Arabia.

PubMed

Rowlands, Gwilym; Purkis, Sam; Riegl, Bernhard; Metsamaa, Liisa; Bruckner, Andrew; Renaud, Philip

2012-06-01

We propose a framework for spatially estimating a proxy for coral reef resilience using remote sensing. Data spanning large areas of coral reef habitat were obtained using the commercial QuickBird satellite, and freely available imagery (NASA, Google Earth). Principles of coral reef ecology, field observation, and remote observations, were combined to devise mapped indices. These capture important and accessible components of coral reef resilience. Indices are divided between factors known to stress corals, and factors incorporating properties of the reef landscape that resist stress or promote coral growth. The first-basis for a remote sensed resilience index (RSRI), an estimate of expected reef resilience, is proposed. Developed for the Red Sea, the framework of our analysis is flexible and with minimal adaptation, could be extended to other reef regions. We aim to stimulate discussion as to use of remote sensing to do more than simply deliver habitat maps of coral reefs. Copyright © 2012 Elsevier Ltd. All rights reserved.

Advances on Aryldiazonium Salt Chemistry Based Interfacial Fabrication for Sensing Applications.

PubMed

Cao, Chaomin; Zhang, Yin; Jiang, Cheng; Qi, Meng; Liu, Guozhen

2017-02-15

Aryldiazonium salts as coupling agents for surface chemistry have evidenced their wide applications for the development of sensors. Combined with advances in nanomaterials, current trends in sensor science and a variety of particular advantages of aryldiazonium salt chemistry in sensing have driven the aryldiazonium salt-based sensing strategies to grow at an astonishing pace. This review focuses on the advances in the use of aryldiazonium salts for modifying interfaces in sensors and biosensors during the past decade. It will first summarize the current methods for modification of interfaces with aryldiazonium salts, and then discuss the sensing applications of aryldiazonium salts modified on different transducers (bulky solid electrodes, nanomaterials modified bulky solid electrodes, and nanoparticles). Finally, the challenges and perspectives that aryldiazonium salt chemistry is facing in sensing applications are critically discussed.

Active and Passive 3D Vector Radiative Transfer with Preferentially-Aligned Ice Particles

NASA Technical Reports Server (NTRS)

Adams, Ian S.; Munchak, Stephen J.; Pelissier, Craig S.; Kuo, Kwo-Sen; Heymsfield, Gerald M.

2017-01-01

For the purposes of interpreting active (radar) and passive (radiometer) microwave and millimeter wave remote sensing data, we have constructed a consistent radiative transfer modeling framework to simulate the responses for arbitrary sensors with differing sensing geometries and hardware configurations. As part of this work, we have implemented a recent method for calculating the electromagnetic properties of individual ice crystals and snow flakes. These calculations will allow us to exploit polarized remote sensing observations to discriminate different particles types and elucidate dynamics of cloud and precipitating systems.

Applications of remote sensing in resource management in Nebraska

NASA Technical Reports Server (NTRS)

Drew, J. V.

1974-01-01

The project is reported for studying the application of remote sensing in land use classification and delineation of major tectonic lineaments in Nebraska. Other research reported include the use of aircraft and ERTS-1 satellite imagery in detecting and estimating the acreage of irrigated land, and the application of remote sensing in estimating evapotranspiration in the Platte River Basin.

Remote sensing sensors and applications in environmental resources mapping and modeling

USGS Publications Warehouse

Melesse, Assefa M.; Weng, Qihao; Thenkabail, Prasad S.; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

The role of advanced sensing in smart cities.

PubMed

Hancke, Gerhard P; Silva, Bruno de Carvalho E; Hancke, Gerhard P

2012-12-27

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

The Role of Advanced Sensing in Smart Cities

PubMed Central

Hancke, Gerhard P.; de Carvalho e Silva, Bruno; Hancke, Gerhard P.

2013-01-01

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities. PMID:23271603

Hybrid graphene/geopolymeric cement as a superionic conductor for structural health monitoring applications

NASA Astrophysics Data System (ADS)

Saafi, M.; Piukovics, G.; Ye, J.

2016-10-01

In this paper, we demonstrate for the first time a novel hybrid superionic long gauge sensor for structural health monitoring applications. The sensor consists of two graphene electrodes and a superionic conductor film made entirely of fly ash geopolymeric material. The sensor employs ion hopping as a conduction mechanism for high precision temperature and tensile strain sensing in structures. The design, fabrication and characterization of the sensor are presented. The temperature and strain sensing mechanisms of the sensor are also discussed. The experimental results revealed that the crystal structure of the superionic film is a 3D sodium-poly(sialate-siloxo) framework, with a room temperature ionic conductivity between 1.54 × 10-2 and 1.72 × 10-2 S m-1 and, activation energy of 0.156 eV, which supports the notion that ion hopping is the main conduction mechanism for the sensor. The sensor showed high sensitivity to both temperature and tensile strain. The sensor exhibited temperature sensitivity as high as 21.5 kΩ °C-1 and tensile strain sensitivity (i.e., gauge factor) as high as 358. The proposed sensor is relatively inexpensive and can easily be manufactured with long gauges to measure temperature and bulk strains in structures. With further development and characterization, the sensor can be retrofitted onto existing structures such as bridges, buildings, pipelines and wind turbines to monitor their structural integrity.

Modeling asset price processes based on mean-field framework

NASA Astrophysics Data System (ADS)

Ieda, Masashi; Shiino, Masatoshi

2011-12-01

We propose a model of the dynamics of financial assets based on the mean-field framework. This framework allows us to construct a model which includes the interaction among the financial assets reflecting the market structure. Our study is on the cutting edge in the sense of a microscopic approach to modeling the financial market. To demonstrate the effectiveness of our model concretely, we provide a case study, which is the pricing problem of the European call option with short-time memory noise.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

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

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Space Gator: a giant leap for fiber optic sensing

NASA Astrophysics Data System (ADS)

Evenblij, R. S.; Leijtens, J. A. P.

2017-11-01

Fibre Optic Sensing is a rapidly growing application field for Photonics Integrated Circuits (PIC) technology. PIC technology is regarded enabling for required performances and miniaturization of next generation fibre optic sensing instrumentation. So far a number of Application Specific Photonics Integrated Circuits (ASPIC) based interrogator systems have been realized as operational system-on-chip devices. These circuits have shown that all basic building blocks are working and complete interrogator on chip solutions can be produced. Within the Saristu (FP7) project several high reliability solutions for fibre optic sensing in Aeronautics are being developed, combining the specifically required performance aspects for the different sensing applications: damage detection, impact detection, load monitoring and shape sensing (including redundancy aspects and time division features). Further developments based on devices and taking into account specific space requirements (like radiation aspects) will lead to the Space Gator, which is a radiation tolerant highly integrated Fibre Bragg Grating (FBG) interrogator on chip. Once developed and qualified the Space Gator will be a giant leap for fibre optic sensing in future space applications.

An Update of NASA Public Health Applications Projects using Remote Sensing Data

NASA Technical Reports Server (NTRS)

Estes, Sue M.; Haynes, J. A.

2009-01-01

Satellite earth observations present a unique vantage point of the earth s environment from space which offers a wealth of health applications for the imaginative investigator. The session will present research results of the remote sensing environmental observations of earth and health applications. This session will an overview of many of the NASA public health applications using Remote Sensing Data and will also discuss opportunities to become a research collaborator with NASA.

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

Research investigations in and demonstrations of remote sensing applications to urban environmental problems

NASA Technical Reports Server (NTRS)

Hidalgo, J. U.

1975-01-01

The applicability of remote sensing to transportation and traffic analysis, urban quality, and land use problems is discussed. Other topics discussed include preliminary user analysis, potential uses, traffic study by remote sensing, and urban condition analysis using ERTS.

VTT's Fabry-Perot interferometer technologies for hyperspectral imaging and mobile sensing applications

NASA Astrophysics Data System (ADS)

Rissanen, Anna; Guo, Bin; Saari, Heikki; Näsilä, Antti; Mannila, Rami; Akujärvi, Altti; Ojanen, Harri

2017-02-01

VTT's Fabry-Perot interferometers (FPI) technology enables creation of small and cost-efficient microspectrometers and hyperspectral imagers - these robust and light-weight sensors are currently finding their way into a variety of novel applications, including emerging medical products, automotive sensors, space instruments and mobile sensing devices. This presentation gives an overview of our core FPI technologies with current advances in generation of novel sensing applications including recent mobile technology demonstrators of a hyperspectral iPhone and a mobile phone CO2 sensor, which aim to advance mobile spectroscopic sensing.

Present and future development of remote sensing in China

NASA Astrophysics Data System (ADS)

Pan, H. R.; Jiang, J. S.; Hu, D. Y.; Wang, C. Y.

This paper summarizes the program that has been established during the past decade and the present situation in remote sensing techniques and applications in China. Special attention is given to the recent results that have been achieved in remote sensing applications, such as the successful applications of aerial photography and satellite images to a wide range of grassland surveys in Xinjians province, and to real time flood monitoring in the Tons-Tins Lake drainage basin in 1985, etc. The paper also touches upon the future trends for developing remote sensing in China.

Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

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

Forrest, Stephanie; He, Wenbo; Groat, Michael

2013-01-01

Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

Data governance in predictive toxicology: A review.

PubMed

Fu, Xin; Wojak, Anna; Neagu, Daniel; Ridley, Mick; Travis, Kim

2011-07-13

Due to recent advances in data storage and sharing for further data processing in predictive toxicology, there is an increasing need for flexible data representations, secure and consistent data curation and automated data quality checking. Toxicity prediction involves multidisciplinary data. There are hundreds of collections of chemical, biological and toxicological data that are widely dispersed, mostly in the open literature, professional research bodies and commercial companies. In order to better manage and make full use of such large amount of toxicity data, there is a trend to develop functionalities aiming towards data governance in predictive toxicology to formalise a set of processes to guarantee high data quality and better data management. In this paper, data quality mainly refers in a data storage sense (e.g. accuracy, completeness and integrity) and not in a toxicological sense (e.g. the quality of experimental results). This paper reviews seven widely used predictive toxicology data sources and applications, with a particular focus on their data governance aspects, including: data accuracy, data completeness, data integrity, metadata and its management, data availability and data authorisation. This review reveals the current problems (e.g. lack of systematic and standard measures of data quality) and desirable needs (e.g. better management and further use of captured metadata and the development of flexible multi-level user access authorisation schemas) of predictive toxicology data sources development. The analytical results will help to address a significant gap in toxicology data quality assessment and lead to the development of novel frameworks for predictive toxicology data and model governance. While the discussed public data sources are well developed, there nevertheless remain some gaps in the development of a data governance framework to support predictive toxicology. In this paper, data governance is identified as the new challenge in predictive toxicology, and a good use of it may provide a promising framework for developing high quality and easy accessible toxicity data repositories. This paper also identifies important research directions that require further investigation in this area.

Data governance in predictive toxicology: A review

PubMed Central

2011-01-01

Background Due to recent advances in data storage and sharing for further data processing in predictive toxicology, there is an increasing need for flexible data representations, secure and consistent data curation and automated data quality checking. Toxicity prediction involves multidisciplinary data. There are hundreds of collections of chemical, biological and toxicological data that are widely dispersed, mostly in the open literature, professional research bodies and commercial companies. In order to better manage and make full use of such large amount of toxicity data, there is a trend to develop functionalities aiming towards data governance in predictive toxicology to formalise a set of processes to guarantee high data quality and better data management. In this paper, data quality mainly refers in a data storage sense (e.g. accuracy, completeness and integrity) and not in a toxicological sense (e.g. the quality of experimental results). Results This paper reviews seven widely used predictive toxicology data sources and applications, with a particular focus on their data governance aspects, including: data accuracy, data completeness, data integrity, metadata and its management, data availability and data authorisation. This review reveals the current problems (e.g. lack of systematic and standard measures of data quality) and desirable needs (e.g. better management and further use of captured metadata and the development of flexible multi-level user access authorisation schemas) of predictive toxicology data sources development. The analytical results will help to address a significant gap in toxicology data quality assessment and lead to the development of novel frameworks for predictive toxicology data and model governance. Conclusions While the discussed public data sources are well developed, there nevertheless remain some gaps in the development of a data governance framework to support predictive toxicology. In this paper, data governance is identified as the new challenge in predictive toxicology, and a good use of it may provide a promising framework for developing high quality and easy accessible toxicity data repositories. This paper also identifies important research directions that require further investigation in this area. PMID:21752279

A Rabi, an Imam, and a Priest Walk into a Bar ... Or, What Can Music Education Philosophy Learn from Comparative Cosmopolitanism?

ERIC Educational Resources Information Center

Schmidt, Patrick

2013-01-01

In the face of globalization, speed communication, and the mashing up of once clearly drawn borders, it seems both pertinent and constructive that music education philosophers make use of comparative frameworks to make sense--and make new sense--of educational and musical events, products, and interactions. However, comparison that is merely…

A Rabi, an Imam, and a Priest Walk into a Bar . . . or , What Can Music Education Philosophy Learn from Comparative Cosmopolitanism?

ERIC Educational Resources Information Center

Schmidt, Patrick

2013-01-01

In the face of globalization, speed communication, and the mashing up of once clearly drawn borders, it seems both pertinent and constructive that music education philosophers make use of comparative frameworks to make sense--and make new sense--of educational and musical events, products, and interactions. However, comparison that is merely…

Supplemental Analysis on Compressed Sensing Based Interior Tomography

PubMed Central

Yu, Hengyong; Yang, Jiansheng; Jiang, Ming; Wang, Ge

2010-01-01

Recently, in the compressed sensing framework we proved that an interior ROI can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant. In the proofs, we implicitly utilized the property that if an artifact image assumes a constant value within the ROI then this constant must be zero. Here we prove this property in the space of square integrable functions. PMID:19717891

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

NASA Technical Reports Server (NTRS)

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.

Remote sensing applications in water resources - An opportunity for research in developing countries

NASA Technical Reports Server (NTRS)

Menenti, M.

1992-01-01

A review is presented of first-hand experience with remote sensing research in developing countries to illustrate the inherent semiempirical basis of remote sensing applications. This task is accomplished by means of examples drawn from actual research work. Results of case studies in different farming systems and countries are summarized to exemplify the relative, application-dependent, weight of satellite versus ground information.

MapSentinel: Can the Knowledge of Space Use Improve Indoor Tracking Further?

PubMed Central

Jia, Ruoxi; Jin, Ming; Zou, Han; Yesilata, Yigitcan; Xie, Lihua; Spanos, Costas

2016-01-01

Estimating an occupant’s location is arguably the most fundamental sensing task in smart buildings. The applications for fine-grained, responsive building operations require the location sensing systems to provide location estimates in real time, also known as indoor tracking. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphone to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel combines the noisy sensor readings with the floormap information to estimate locations. One key observation supporting our work is that occupants exhibit distinctive motion characteristics at different locations on the floormap, e.g., constrained motion along the corridor or in the cubicle zones, and free movement in the open space. While extensive research has been performed on using a floormap as a tool to obtain correct walking trajectories without wall-crossings, there have been few attempts to incorporate the knowledge of space use available from the floormap into the location estimation. This paper argues that the knowledge of space use as an additional information source presents new opportunities for indoor tracking. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework, and the Context-Augmented Particle Filtering algorithm is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve accuracy improvement of 31.3% compared with the purely WiFi-based tracking system. PMID:27049387

MapSentinel: Can the Knowledge of Space Use Improve Indoor Tracking Further?

PubMed

Jia, Ruoxi; Jin, Ming; Zou, Han; Yesilata, Yigitcan; Xie, Lihua; Spanos, Costas

2016-04-02

Estimating an occupant's location is arguably the most fundamental sensing task in smart buildings. The applications for fine-grained, responsive building operations require the location sensing systems to provide location estimates in real time, also known as indoor tracking. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphone to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel combines the noisy sensor readings with the floormap information to estimate locations. One key observation supporting our work is that occupants exhibit distinctive motion characteristics at different locations on the floormap, e.g., constrained motion along the corridor or in the cubicle zones, and free movement in the open space. While extensive research has been performed on using a floormap as a tool to obtain correct walking trajectories without wall-crossings, there have been few attempts to incorporate the knowledge of space use available from the floormap into the location estimation. This paper argues that the knowledge of space use as an additional information source presents new opportunities for indoor tracking. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework, and the Context-Augmented Particle Filtering algorithm is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve accuracy improvement of 31.3% compared with the purely WiFi-based tracking system.

Postsynthetic Tuning of Metal-Organic Frameworks for Targeted Applications.

PubMed

Islamoglu, Timur; Goswami, Subhadip; Li, Zhanyong; Howarth, Ashlee J; Farha, Omar K; Hupp, Joseph T

2017-04-18

Metal-organic frameworks (MOFs) are periodic, hybrid, atomically well-defined porous materials that typically form by self-assembly and consist of inorganic nodes (metal ions or clusters) and multitopic organic linkers. MOFs as a whole offer many intriguing properties, including ultrahigh porosity, tunable chemical functionality, and low density. These properties point to numerous potential applications, including gas storage, chemical separations, catalysis, light harvesting, and chemical sensing, to name a few. Reticular chemistry, or the linking of molecular building blocks into predetermined network structures, has been employed to synthesize thousands of MOFs. Given the vast library of candidate nodes and linkers, the number of potentially synthetically accessible MOFs is enormous. Nevertheless, a powerful complementary approach to obtain specific structures with desired chemical functionality is to modify known MOFs after synthesis. This approach is particularly useful when incorporation of particular chemical functionalities via direct synthesis is challenging or impossible. The challenges may stem from limited stability or solubility of precursors, unwanted secondary reactivity of precursors, or incompatibility of functional groups with the conditions needed for direct synthesis. MOFs can be postsynthetically modified by replacing the metal nodes and/or organic linkers or via functionalization of the metal nodes and/or organic linkers. Here we describe some of our efforts toward the development and application of postsynthetic strategies for imparting desired chemical functionalities in MOFs of known topology. The techniques include methods for functionalizing MOF nodes, i.e., solvent-assisted ligand incorporation (SALI) and atomic layer deposition in MOFs (AIM) as well as a method to replace structural linkers, termed solvent-assisted linker exchange (SALE), also known as postsynthethic exchange (PSE). For each functionalization strategy, we first describe its chemical basis along with the requirements for its successful implementation. We then present a small number of examples, with an emphasis on those that (a) convey the underlying concepts and/or (b) lead to functional structures (e.g., catalysts) that would be difficult or impossible to access via direct routes. The examples, however, are only illustrative, and a significant body of work exists from both our lab and others, especially for the SALE/PSE strategy. We refer readers to the papers cited and to the references therein. More exciting, in our view, will be new examples and new applications of the functionalization strategies-especially applications made possible by creatively combining the strategies. Underexplored (again, in our view) are implementations that impart electrical conductivity, enable increasingly selective chemical sensing, or facilitate cascade catalysis. It will be interesting to see where these strategies and others take this compelling field over the next few years.

Remote sensing with unmanned aircraft systems for precision agriculture applications

USDA-ARS?s Scientific Manuscript database

The Federal Aviation Administration is revising regulations for using unmanned aircraft systems (UAS) in the national airspace. An important potential application of UAS may be as a remote-sensing platform for precision agriculture, but simply down-scaling remote sensing methodologies developed usi...

Remote sensing for mined area reclamation: Application inventory

NASA Technical Reports Server (NTRS)

1971-01-01

Applications of aerial remote sensing to coal mined area reclamation are documented, and information concerning available data banks for coal producing areas in the east and midwest is given. A summary of mined area information requirements to which remote sensing methods might contribute is included.

Optical sampling of the flux tower footprint

NASA Astrophysics Data System (ADS)

Gamon, J. A.

2015-03-01

The purpose of this review is to address the reasons and methods for conducting optical remote sensing within the flux tower footprint. Fundamental principles and conclusions gleaned from over two decades of proximal remote sensing at flux tower sites are reviewed. An organizing framework is the light-use efficiency (LUE) model, both because it is widely used, and because it provides a useful theoretical construct for integrating optical remote sensing with flux measurements. Multiple ways of driving this model, ranging from meteorological measurements to remote sensing, have emerged in recent years, making it a convenient conceptual framework for comparative experimental studies. New interpretations of established optical sampling methods, including the Photochemical Reflectance Index (PRI) and Solar-Induced Fluorescence (SIF), are discussed within the context of the LUE model. Multi-scale analysis across temporal and spatial axes is a central theme, because such scaling can provide links between ecophysiological mechanisms detectable at the level of individual organisms and broad patterns emerging at larger scales, enabling evaluation of emergent properties and extrapolation to the flux footprint and beyond. Proper analysis of sampling scale requires an awareness of sampling context that is often essential to the proper interpretation of optical signals. Additionally, the concept of optical types, vegetation exhibiting contrasting optical behavior in time and space, is explored as a way to frame our understanding of the controls on surface-atmosphere fluxes. Complementary NDVI and PRI patterns across ecosystems are offered as an example of this hypothesis, with the LUE model and light-response curve providing an integrating framework. We conclude that experimental approaches allowing systematic exploration of plant optical behavior in the context of the flux tower network provides a unique way to improve our understanding of environmental constraints and ecophysiological function. In addition to an enhanced mechanistic understanding of ecosystem processes, this integration of remote sensing with flux measurements offers many rich opportunities for upscaling, satellite validation, and informing practical management objectives ranging form assessing ecosystem health and productivity to quantifying biospheric carbon sequestration.

Real time network traffic monitoring for wireless local area networks based on compressed sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza

2017-05-01

A wireless local area network (WLAN) is an important type of wireless networks which connotes different wireless nodes in a local area network. WLANs suffer from important problems such as network load balancing, large amount of energy, and load of sampling. This paper presents a new networking traffic approach based on Compressed Sensing (CS) for improving the quality of WLANs. The proposed architecture allows reducing Data Delay Probability (DDP) to 15%, which is a good record for WLANs. The proposed architecture is increased Data Throughput (DT) to 22 % and Signal to Noise (S/N) ratio to 17 %, which provide a good background for establishing high qualified local area networks. This architecture enables continuous data acquisition and compression of WLAN's signals that are suitable for a variety of other wireless networking applications. At the transmitter side of each wireless node, an analog-CS framework is applied at the sensing step before analog to digital converter in order to generate the compressed version of the input signal. At the receiver side of wireless node, a reconstruction algorithm is applied in order to reconstruct the original signals from the compressed signals with high probability and enough accuracy. The proposed algorithm out-performs existing algorithms by achieving a good level of Quality of Service (QoS). This ability allows reducing 15 % of Bit Error Rate (BER) at each wireless node.

Assessing the Sense of Initiative and Entrepreneurship in Vocational Students Using the European Qualification Framework

ERIC Educational Resources Information Center

Morselli, Daniele; Ajello, Annamaria

2016-01-01

Purpose: The purpose of this paper is to find a framework for the assessment of the learning outcomes of entrepreneurship education as a cross-curricular subject. The problem is twofold: the first difficulty is the relationship to the general issues regarding competence and its assessment; the second difficulty is the assessment of competencies in…

The Human Nervous System: A Framework for Teaching and the Teaching Brain

ERIC Educational Resources Information Center

Rodriguez, Vanessa

2013-01-01

The teaching brain is a new concept that mirrors the complex, dynamic, and context-dependent nature of the learning brain. In this article, I use the structure of the human nervous system and its sensing, processing, and responding components as a framework for a re-conceptualized teaching system. This teaching system is capable of responses on an…

Fusion and Sense Making of Heterogeneous Sensor Network and Other Sources

DTIC Science & Technology

2017-03-16

multimodal fusion framework that uses both training data and web resources for scene classification, the experimental results on the benchmark datasets...show that the proposed text-aided scene classification framework could significantly improve classification performance. Experimental results also show...human whose adaptability is achieved by reliability- dependent weighting of different sensory modalities. Experimental results show that the proposed

Software Framework for Development of Web-GIS Systems for Analysis of Georeferenced Geophysical Data

NASA Astrophysics Data System (ADS)

Okladnikov, I.; Gordov, E. P.; Titov, A. G.

2011-12-01

Georeferenced datasets (meteorological databases, modeling and reanalysis results, remote sensing products, etc.) are currently actively used in numerous applications including modeling, interpretation and forecast of climatic and ecosystem changes for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their size which might constitute up to tens terabytes for a single dataset at present studies in the area of climate and environmental change require a special software support. A dedicated software framework for rapid development of providing such support information-computational systems based on Web-GIS technologies has been created. The software framework consists of 3 basic parts: computational kernel developed using ITTVIS Interactive Data Language (IDL), a set of PHP-controllers run within specialized web portal, and JavaScript class library for development of typical components of web mapping application graphical user interface (GUI) based on AJAX technology. Computational kernel comprise of number of modules for datasets access, mathematical and statistical data analysis and visualization of results. Specialized web-portal consists of web-server Apache, complying OGC standards Geoserver software which is used as a base for presenting cartographical information over the Web, and a set of PHP-controllers implementing web-mapping application logic and governing computational kernel. JavaScript library aiming at graphical user interface development is based on GeoExt library combining ExtJS Framework and OpenLayers software. Based on the software framework an information-computational system for complex analysis of large georeferenced data archives was developed. Structured environmental datasets available for processing now include two editions of NCEP/NCAR Reanalysis, JMA/CRIEPI JRA-25 Reanalysis, ECMWF ERA-40 Reanalysis, ECMWF ERA Interim Reanalysis, MRI/JMA APHRODITE's Water Resources Project Reanalysis, meteorological observational data for the territory of the former USSR for the 20th century, and others. Current version of the system is already involved into a scientific research process. Particularly, recently the system was successfully used for analysis of Siberia climate changes and its impact in the region. The software framework presented allows rapid development of Web-GIS systems for geophysical data analysis thus providing specialists involved into multidisciplinary research projects with reliable and practical instruments for complex analysis of climate and ecosystems changes on global and regional scales. This work is partially supported by RFBR grants #10-07-00547, #11-05-01190, and SB RAS projects 4.31.1.5, 4.31.2.7, 4, 8, 9, 50 and 66.

Multi-Sensor Based State Prediction for Personal Mobility Vehicles

PubMed Central

Gupta, Pankaj; Umata, Ichiro; Watanabe, Atsushi; Even, Jani; Suyama, Takayuki; Ishii, Shin

2016-01-01

This paper presents a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle) in an indoor labyrinth-like environment. The study reports findings on the habituation of human stress response during self-driving. In addition, the effects of “loss of controllability”, change in the role of the driver to a passenger, are investigated via an autonomous driving modality. The multi-modal emotional state detector sensing framework consists of four sensing devices: electroencephalograph (EEG), heart inter-beat interval (IBI), galvanic skin response (GSR) and stressor level lever (in the case of autonomous riding). Physiological emotional state measurement characteristics are organized by time-scale, in terms of capturing slower changes (long-term) and quicker changes from moment-to-moment. Experimental results with fifteen participants regarding subjective emotional state reports and commercial software measurements validated the proposed emotional state detector. Short-term GSR and heart signal characterizations captured moment-to-moment emotional state during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001). Short-term GSR and EEG characterizations reliably captured moment-to-moment emotional state during self-driving (Classification accuracy; 69.7). Finally, long-term GSR and heart characterizations were confirmed to reliably capture slow changes during autonomous riding and also of emotional state during participant resting state. The purpose of this study and the exploration of various algorithms and sensors in a structured framework is to provide a comprehensive background for multi-modal emotional state prediction experiments and/or applications. Additional discussion regarding the feasibility and utility of the possibilities of these concepts are given. PMID:27732589

Multi-Sensor Based State Prediction for Personal Mobility Vehicles.

PubMed

Abdur-Rahim, Jamilah; Morales, Yoichi; Gupta, Pankaj; Umata, Ichiro; Watanabe, Atsushi; Even, Jani; Suyama, Takayuki; Ishii, Shin

2016-01-01

This paper presents a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle) in an indoor labyrinth-like environment. The study reports findings on the habituation of human stress response during self-driving. In addition, the effects of "loss of controllability", change in the role of the driver to a passenger, are investigated via an autonomous driving modality. The multi-modal emotional state detector sensing framework consists of four sensing devices: electroencephalograph (EEG), heart inter-beat interval (IBI), galvanic skin response (GSR) and stressor level lever (in the case of autonomous riding). Physiological emotional state measurement characteristics are organized by time-scale, in terms of capturing slower changes (long-term) and quicker changes from moment-to-moment. Experimental results with fifteen participants regarding subjective emotional state reports and commercial software measurements validated the proposed emotional state detector. Short-term GSR and heart signal characterizations captured moment-to-moment emotional state during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001). Short-term GSR and EEG characterizations reliably captured moment-to-moment emotional state during self-driving (Classification accuracy; 69.7). Finally, long-term GSR and heart characterizations were confirmed to reliably capture slow changes during autonomous riding and also of emotional state during participant resting state. The purpose of this study and the exploration of various algorithms and sensors in a structured framework is to provide a comprehensive background for multi-modal emotional state prediction experiments and/or applications. Additional discussion regarding the feasibility and utility of the possibilities of these concepts are given.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

PubMed Central

Melesse, Assefa M.; Weng, Qihao; S.Thenkabail, Prasad; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling. PMID:28903290

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile; Bauer-Gottwein, Peter; Nielsen, Karina; Tøttrup, Christian

2017-04-01

Knowledge on hydrological regimes of river basins is crucial for water management. However, data requirements often limit the applicability of hydrological models in basins with scarce in-situ data. Remote sensing provides a unique possibility to acquire information on hydrological variables in these basins. This study explores how multi-mission remote sensing data can inform a hydrological model. The Ogooué basin in Gabon is used as study area. No previous modelling efforts have been conducted for the basin and only historical flow and precipitation observations are available. Publicly available remote sensing observations are used to parametrize, force, calibrate and validate a hydrological model of the Ogooué. The modelling framework used in the study, is a lumped conceptual rainfall-runoff model based on the Budyko framework coupled to a Muskingum routing scheme. Precipitation is a crucial driver of the land-surface water balance, therefore two satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) product 3B42 version 7 and Famine Early Warning System - Rainfall Estimate (FEWS-RFE), are compared. The comparison shows good seasonal and spatial agreement between the products; however, TRMM consistently predicts significantly more precipitation: 1726 mm on average per year against 1556 mm for FEWS-RFE. Best modeling results are obtained with the TRMM precipitation forcing. Model calibration combines historical in-situ flow observations and GRACE total water storage observations using the Jet Propulsion Laboratory (JPL) mascon solution in a multi-objective approach. The two models are calibrated using flow duration curves and climatology benchmarks to overcome the lack of simultaneity between simulated and observed discharge. The objectives are aggregated into a global objective function, and the models are calibrated using the Shuffled Complex Evolution Algorithm. Water height observations from drifting orbit altimetry missions are extracted along the river line, using a detailed water mask based on Sentinel-1 SAR imagery. 1399 single CryoSat-2 altimetry observations and 48 ICESat observations are acquired. Additionally, water heights have been measured by the repeat-orbit satellite missions Envisat and Jason-2 at 12 virtual stations along the river. The four missions show generally good agreement in terms of mean annual water height amplitudes. The altimetry observations are used to validate the hydrological model of the Ogooué River. By combining hydrological modelling and remote sensing, new information on an otherwise unstudied basin is obtained. The study shows the potential of using remote sensing observations to parameterize, force, calibrate and validate models of poorly gauged river basins. Specifically, the study shows how Sentinel-1 SAR imagery supports the extraction of satellite altimetry data over rivers. The model can be used to assess climate change scenarios, evaluate hydraulic infrastructure development projects and predict the impact of irrigation diversions.

Making sense in a complex landscape: how the Cynefin Framework from Complex Adaptive Systems Theory can inform health promotion practice.

PubMed

Van Beurden, Eric K; Kia, Annie M; Zask, Avigdor; Dietrich, Uta; Rose, Lauren

2013-03-01

Health promotion addresses issues from the simple (with well-known cause/effect links) to the highly complex (webs and loops of cause/effect with unpredictable, emergent properties). Yet there is no conceptual framework within its theory base to help identify approaches appropriate to the level of complexity. The default approach favours reductionism--the assumption that reducing a system to its parts will inform whole system behaviour. Such an approach can yield useful knowledge, yet is inadequate where issues have multiple interacting causes, such as social determinants of health. To address complex issues, there is a need for a conceptual framework that helps choose action that is appropriate to context. This paper presents the Cynefin Framework, informed by complexity science--the study of Complex Adaptive Systems (CAS). It introduces key CAS concepts and reviews the emergence and implications of 'complex' approaches within health promotion. It explains the framework and its use with examples from contemporary practice, and sets it within the context of related bodies of health promotion theory. The Cynefin Framework, especially when used as a sense-making tool, can help practitioners understand the complexity of issues, identify appropriate strategies and avoid the pitfalls of applying reductionist approaches to complex situations. The urgency to address critical issues such as climate change and the social determinants of health calls for us to engage with complexity science. The Cynefin Framework helps practitioners make the shift, and enables those already engaged in complex approaches to communicate the value and meaning of their work in a system that privileges reductionist approaches.

Some Defence Applications of Civilian Remote Sensing Satellite Images

DTIC Science & Technology

1993-11-01

This report is on a pilot study to demonstrate some of the capabilities of remote sensing in intelligence gathering. A wide variety of issues, both...colour images. The procedure will be presented in a companion report. Remote sensing , Satellite imagery, Image analysis, Military applications, Military intelligence.

Active microwave remote sensing of oceans, chapter 3

NASA Technical Reports Server (NTRS)

1975-01-01

A rationale is developed for the use of active microwave sensing in future aerospace applications programs for the remote sensing of the world's oceans, lakes, and polar regions. Summaries pertaining to applications, local phenomena, and large-scale phenomena are given along with a discussion of orbital errors.

An experimental study of graph connectivity for unsupervised word sense disambiguation.

PubMed

Navigli, Roberto; Lapata, Mirella

2010-04-01

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most "important" node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.

A new simple concept for ocean colour remote sensing using parallel polarisation radiance

PubMed Central

He, Xianqiang; Pan, Delu; Bai, Yan; Wang, Difeng; Hao, Zengzhou

2014-01-01

Ocean colour remote sensing has supported research on subjects ranging from marine ecosystems to climate change for almost 35 years. However, as the framework for ocean colour remote sensing is based on the radiation intensity at the top-of-atmosphere (TOA), the polarisation of the radiation, which contains additional information on atmospheric and water optical properties, has largely been neglected. In this study, we propose a new simple concept to ocean colour remote sensing that uses parallel polarisation radiance (PPR) instead of the traditional radiation intensity. We use vector radiative transfer simulation and polarimetric satellite sensing data to demonstrate that using PPR has two significant advantages in that it effectively diminishes the sun glint contamination and enhances the ocean colour signal at the TOA. This concept may open new doors for ocean colour remote sensing. We suggest that the next generation of ocean colour sensors should measure PPR to enhance observational capability. PMID:24434904

Automatic Training of Rat Cyborgs for Navigation.

PubMed

Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

2016-01-01

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs.

Automatic Training of Rat Cyborgs for Navigation

PubMed Central

Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

2016-01-01

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs. PMID:27436999

Polyacrylonitrile nanofibers with added zeolitic imidazolate frameworks (ZIF-7) to enhance mechanical and thermal stability

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

Lee, Min Wook; An, Seongpil; Song, Kyo Yong

2015-12-28

Zeolitic imidazolate framework 7/polyacrylonitrile (ZIF-7/PAN) nanofiber mat of high porosity and surface area can be used as a flexible fibrous filtration membrane that is subjected to various modes of mechanical loading resulting in stresses and strains. Therefore, the stress-strain relation of ZIF-7/PAN nanofiber mats in the elastic and plastic regimes of deformation is of significant importance for numerous practical applications, including hydrogen storage, carbon dioxide capture, and molecular sensing. Here, we demonstrated the fabrication of ZIF-7/PAN nanofiber mats via electrospinning and report their mechanical properties measured in tensile tests covering the elastic and plastic domains. The effect of the matmore » fabrication temperature on the mechanical properties is elucidated. We showed the superior mechanical strength and thermal stability of the compound ZIF-7/PAN nanofiber mats in comparison with that of pure PAN nanofiber mats. Material characterization including scanning electron microscope, energy-dispersive X-ray spectroscopy, tensile tests, differential scanning calorimetry, and Fourier transform infrared spectroscopy revealed the enhanced chemical bonds of the ZIF-7/PAN complex.« less

Supporting Dynamic Spectrum Access in Heterogeneous LTE+ Networks

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

Luiz A. DaSilva; Ryan E. Irwin; Mike Benonis

As early as 2014, mobile network operators’ spectral capac- ity is expected to be overwhelmed by the demand brought on by new devices and applications. With Long Term Evo- lution Advanced (LTE+) networks likely as the future one world 4G standard, network operators may need to deploy a Dynamic Spectrum Access (DSA) overlay in Heterogeneous Networks (HetNets) to extend coverage, increase spectrum efficiency, and increase the capacity of these networks. In this paper, we propose three new management frameworks for DSA in an LTE+ HetNet: Spectrum Accountability Client, Cell Spectrum Management, and Domain Spectrum Man- agement. For these spectrum managementmore » frameworks, we define protocol interfaces and operational signaling scenar- ios to support cooperative sensing, spectrum lease manage- ment, and alarm scenarios for rule adjustment. We also quan- tify, through integer programs, the benefits of using DSA in an LTE+ HetNet, that can opportunistically reuse vacant TV and GSM spectrum. Using integer programs, we consider a topology using Geographic Information System data from the Blacksburg, VA metro area to assess the realistic benefits of DSA in an LTE+ HetNet.« less

Mixed finite-element formulations in piezoelectricity and flexoelectricity.

PubMed

Mao, Sheng; Purohit, Prashant K; Aravas, Nikolaos

2016-06-01

Flexoelectricity, the linear coupling of strain gradient and electric polarization, is inherently a size-dependent phenomenon. The energy storage function for a flexoelectric material depends not only on polarization and strain, but also strain-gradient. Thus, conventional finite-element methods formulated solely on displacement are inadequate to treat flexoelectric solids since gradients raise the order of the governing differential equations. Here, we introduce a computational framework based on a mixed formulation developed previously by one of the present authors and a colleague. This formulation uses displacement and displacement-gradient as separate variables which are constrained in a 'weighted integral sense' to enforce their known relation. We derive a variational formulation for boundary-value problems for piezo- and/or flexoelectric solids. We validate this computational framework against available exact solutions. Our new computational method is applied to more complex problems, including a plate with an elliptical hole, stationary cracks, as well as tension and shear of solids with a repeating unit cell. Our results address several issues of theoretical interest, generate predictions of experimental merit and reveal interesting flexoelectric phenomena with potential for application.

Amphiphilic inclusion spaces for various guests and regulation of fluorescence intensity of 1,8-bis(4-aminophenyl)anthracene crystals.

PubMed

Sugino, Misa; Hatanaka, Keisuke; Araki, Yusuke; Hisaki, Ichiro; Miyata, Mikiji; Tohnai, Norimitsu

2014-03-10

A host framework for inclusion of various guest molecules was investigated by preparation of inclusion crystals of 1,8-bis(4-aminophenyl)anthracene (1,8-BAPA) with organic solvents. X-ray crystallographic analysis revealed construction of the same inclusion space incorporating 1,8-BAPA and eight guest molecules including both non-polar (benzene) and polar guests (N,N-dimethylformamide, DMF). Fluorescence efficiencies varied depending on guest molecule polarity; DMF inclusion crystals exhibited the highest fluorescence intensity (ΦF=0.40), four times as high as that of a benzene inclusion crystal (ΦF=0.10). According to systematic investigations of inclusion phenomena, strong host–guest interactions and filling of the inclusion space led to a high fluorescence intensity. Temperature-dependent fluorescence spectral measurements revealed these factors effectively immobilised the host framework. Although hydrogen bonding commonly decreases fluorescence intensity, the present study demonstrated that such strong interactions provide excellent conditions for fluorescence enhancement. Thus, this remarkable behaviour has potential application toward sensing of highly polar molecules, such as biogenic compounds.

Design and Validation of a 150 MHz HFFQCM Sensor for Bio-Sensing Applications

PubMed Central

Fernández, Román; García, Pablo; García, María; Jiménez, Yolanda; Arnau, Antonio

2017-01-01

Acoustic wave resonators have become suitable devices for a broad range of sensing applications due to their sensitivity, low cost, and integration capability, which are all factors that meet the requirements for the resonators to be used as sensing elements for portable point of care (PoC) platforms. In this work, the design, characterization, and validation of a 150 MHz high fundamental frequency quartz crystal microbalance (HFF-QCM) sensor for bio-sensing applications are introduced. Finite element method (FEM) simulations of the proposed design are in good agreement with the electrical characterization of the manufactured resonators. The sensor is also validated for bio-sensing applications. For this purpose, a specific sensor cell was designed and manufactured that addresses the critical requirements associated with this type of sensor and application. Due to the small sensing area and the sensor’s fragility, these requirements include a low-volume flow chamber in the nanoliter range, and a system approach that provides the appropriate pressure control for assuring liquid confinement while maintaining the integrity of the sensor with a good base line stability and easy sensor replacement. The sensor characteristics make it suitable for consideration as the elemental part of a sensor matrix in a multichannel platform for point of care applications. PMID:28885551

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

NASA Astrophysics Data System (ADS)

Carpentieri, Bruno; Pizzolante, Raffaele

2017-12-01

Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

Spatial aggregation query in dynamic geosensor networks

NASA Astrophysics Data System (ADS)

Yi, Baolin; Feng, Dayang; Xiao, Shisong; Zhao, Erdun

2007-11-01

Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. In many of these applications, the researches mainly aim at building sensor network based systems to leverage the sensed data to applications. However, the existing works seldom exploited spatial aggregation query considering the dynamic characteristics of sensor networks. In this paper, we investigate how to process spatial aggregation query over dynamic geosensor networks where both the sink node and sensor nodes are mobile and propose several novel improvements on enabling techniques. The mobility of sensors makes the existing routing protocol based on information of fixed framework or the neighborhood infeasible. We present an improved location-based stateless implicit geographic forwarding (IGF) protocol for routing a query toward the area specified by query window, a diameter-based window aggregation query (DWAQ) algorithm for query propagation and data aggregation in the query window, finally considering the location changing of the sink node, we present two schemes to forward the result to the sink node. Simulation results show that the proposed algorithms can improve query latency and query accuracy.