in-situ data fusion

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Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

Features in the environment, are the product of many interacting processes, including physical, chemical and biological. They are determined with exceedingly complex interactions. To overcome the difficulty of predicting this intricate distribution, it is required to treat the variation as if it is random (Matheron, 1963). The measurement points from the on-line soil sensor and yield sensor required a method of interpolation, to provide a continuous data set across the locations. Kriging was selected as a non-biased approach to predict the values between the sample points, where semi-variograms were first produced and then applied in Kriging interpolation calculations. The interpolated data were then converted into a common 5 m raster grid in ArcGIS (Esri, USA) in order to assist data fusion (Frogbrook and Oliver, 2007). The raster squares of the layers were converted into this common grid of points by extracting the value at the midpoint of each raster square. A smaller resolution has no practical implementation, due to the limitations of the size and response time of the precision farming equipment. The 5 m grid size provided a balance between adequately characterising the spatial variation and practical farm management. These steps ensured that all layers consisted of a common set of 5 m grid point-values, to allow the application of parametric modelling to be carried out. This method allowed data from a diverse range of soil and crop property surveys, measured at different resolutions, to be merged (Khosla R. et al., 2008). The different soil and crop layers of a 5 by 5 m grid were subjected to the VNRX-LN detailed in the following section.
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Assessing mechanical integrity of spinal fusion by in situ endochondral osteoinduction in the murine model

Assessing mechanical integrity of spinal fusion by in situ endochondral osteoinduction in the murine model

Following euthanasia, spines were harvested from the first lumbar to the first sacral vertebrae with all sur- rounding musculature and pelvis intact. The harvested spines were fixed and stored in formaldehyde until ready for testing. Of note, it is unclear what effect, if any, fixation has on the mechanical attributes of the tis- sue. For mechanical testing spines were first cast in the center of a 2 × 1 × 4 cm block of dental Alginate impression material (Henry Schein, INC., Melville, NY). Next, spines were imaged on high resolution Xray in flexion, neutral, and extension using the custom crafted flexion and extension cells described below. The images were then analyzed using computer-assisted methods on Quantitative Motion Analysis (Medical Metrics, Hous- ton, TX) that has been previously validated [29] and used to assess the mechanical integrity of spinal fusions in human patients. The computer-assisted analysis quantified the amount of intervertebral motion within ±0.1 that occurred in flexion and extension. Following the mechanical testing, the spine was imaged at 14 micron resolution using the micro-CT system. From the micro-CT data, three dimensional reconstructions of the vertebrae and any mineralized tissue were made (eXplore MicroView, v. 2.0, GE Healthcare, London, Ontario). A surgeon blindly reviewed the mouse spine CTs for fusions. Accuracy of spine fusion identification
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Original Article ROS1 gene rearrangement and clinicopathological characteristics in Chinese NSCLC patients

Original Article ROS1 gene rearrangement and clinicopathological characteristics in Chinese NSCLC patients

Abstract: Objective: Since Rikova et al. reported c-ros oncogene 1 (ROS1) rearrangements in non-small cell lung cancer (NSCLC) in 2007, data on the clinicopathological characteristics of ROS1-positive patients in China are scarce. We aim to examine the correlation between clinicopathological characteristics of NSCLC patients and the frequency of ROS1-rearrangements. Methods: The cancer tissues of 1720 patients with NSCLC were analyzed us- ing fluorescence in situ hybridization (FISH) assay to assess the presence of ROS1 gene fusions. Polymerase Chain Reaction (PCR) direct sequencing was performed to identify the fusion genes in positive tissues. Clinicopathological characteristics of the patients and the corresponding frequency of ROS1-rearrangement were analyzed. Results: Among the 1720 NSCLC patients, 31 (1.8%) were tested positive for ROS1-rearrangement. Compared to the ROS1- negative group, they were significantly younger and more likely to be never-smokers (each P<0.05). All of the ROS1- positive tumors were adenocarcinomas, and tend to be higher grade cancer (P<0.05), however there was no signifi- cant preference in gender (P>0.05). Four ROS1 fusions were observed in the samples, they were CD74-ROSl (n=9), SLC34A2-ROSl (n=7), SDC4-ROSl (n=8) and TPM3-ROSl (n=7). Conclusions: ROSl-rearrangements were recognized in 1.8% of the Chinese NSCLC patients studied, similar to the prevalence of 1-2% that had been reported. The clini- copathological characteristics of these patients were clearly associated with ROS1-rearrangements. Specifically, ROS1-rearrangements were significantly more prevalent in the younger and never-smoking lung adenocarcinoma patients.
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Original Article Surgical management of spondylolisthesis----intentional reduction or in situ fusion: a meta-analysis

Original Article Surgical management of spondylolisthesis----intentional reduction or in situ fusion: a meta-analysis

Abstract: Objective background context: To clarify the potential difference of surgical management with intentional reduction or in situ fusion for spondylolisthesis. Methods: A comprehensive search of the NGC, the Cochrane Li- brary, WOS, PubMed, Embase databases was conducted to identify eligible studies by the date of October 1, 2017. Three authors independently selected qualified studies, assessed methodological quality, and extracted the data. Results: 17 studies involved 992 patients were eligible for this meta-analysis (546 in reduction group and 446 in in situ fusion group). There were no significant differences in Visual Analog Scale (VAS), Japanese Orthopedic Associa- tion (JOA) scale, fusion rates and complication rates between two groups. In addition, regarding to operative time, our study indicated that in situ fusion group was associated with shorter operative time compared with reduction group. Reduction group was correlated with lower mean ODI, shorter length of stay (in low-grade), less slippage and blood loss (in high-grade) compared with in situ fusion group. Conclusion: Surgical interventions with intentional reduction did not significantly improve patient-reported outcomes, main clinical outcomes or reduce perioperative complications in low-grade spondylolisthesis. Therefore, intentional reduction may not be a requirement in the sur- gical management of spondylolisthesis. Randomized control studies with relatively large population and long-term follow up should be carried out to clarify this issue in the future.
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Vibrational characterization of in-situ stair performance using  energy-harvester data

Vibrational characterization of in-situ stair performance using energy-harvester data

Both energy harvesting and vibration mitigation are well- developed fields that are increasingly being leveraged to produce novel devices for energy solutions. Examples of commercial energy harvesting devices include, Pavegen™ and Perpetuum™ that use live load vibrations to generate power. Some of these devices provide additional data like footfall and traffic patterns, but the primary use is to power ancillary sensors or systems. Additionally, structural health monitoring, remote sensor networks and in-situ sensors are becoming ubiquitous for monitoring and retrofit solutions to vibration problems. The rapid growth in these two distinct fields has prompted the authors to explore the feasibility of using energy harvesting devices to simultaneously scavenge power and characterize in-situ structural response. While significant work has been completed on identification of occupant traffic through sensing of structural vibrations [1, 2], this study explores using energy harvester output (voltage) to characterize structural performance and occupant behavior. Accordingly, the performance of a prototype magneto- induction floor harvester is presented as well as the method for benchmarking to the dynamic response of in-situ stair vibration excitations. Note that this research focuses on the feasibility of the dual-use of a harvester to assess in-situ vibrations while providing usable power, it does not focus on analysis and post-
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Prognostic Features of BCR ABL Genetic Variations in Acute Lymphoblastic Leukemia

Prognostic Features of BCR ABL Genetic Variations in Acute Lymphoblastic Leukemia

BCR/ABL fusion gene analysis by ES-FISH may serve as a powerful prognostic marker in adulthood ALL. The age, TLC and t(9; 22) represent the significant standard prognostic factors in relation to patient’s outcome. Moreover, phili- delphia chromosome with additional chromosomal abnormalities and gene am- plification affecting BCR/ABL are efficiently detected by ES-FISH and show sig- nificant association with patient’s outcome that may be used as prognostic indi- cators for therapeutic response.

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Hierarchical data fusion for Smart Healthcare

Hierarchical data fusion for Smart Healthcare

The Internet of Things (IoT) facilitates creation of smart spaces by converting existing environments into sensor-rich data-centric cyber-physical systems with an increasing degree of automation, giving rise to Industry 4.0. When adopted in commercial/indus- trial contexts, this trend is revolutionising many aspects of our everyday life, including the way people access and receive healthcare services. As we move towards Health- care Industry 4.0, the underlying IoT systems of Smart Healthcare spaces are growing in size and complexity, making it important to ensure that extreme amounts of collected data are properly processed to provide valuable insights and decisions according to requirements in place. This paper focuses on the Smart Healthcare domain and addresses the issue of data fusion in the context of IoT networks, consisting of edge devices, network and communications units, and Cloud platforms. We propose a distributed hierarchical data fusion architecture, in which different data sources are combined at each level of the IoT taxonomy to produce timely and accurate results. This way, mission-critical decisions, as demonstrated by the presented Smart Health- care scenario, are taken with minimum time delay, as soon as necessary information is generated and collected. The proposed approach was implemented using the Com- plex Event Processing technology, which natively supports the hierarchical processing model and specifically focuses on handling streaming data ‘on the fly’—a key require- ment for storage-limited IoT devices and time-critical application domains. Initial experiments demonstrate that the proposed approach enables fine-grained decision taking at different data fusion levels and, as a result, improves the overall performance and reaction time of public healthcare services, thus promoting the adoption of the IoT technologies in Healthcare Industry 4.0.
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Common and distinct components in data fusion

Common and distinct components in data fusion

In many areas of science multiple sets of data are collected per- taining to the same system. Examples are food products which are characterized by different sets of variables, bio-processes which are on-line sampled with different instruments, or biological systems of which different genomics measurements are obtained. Data fusion is concerned with analyzing such sets of data simultaneously to arrive at a global view of the system under study. One of the upcoming areas of data fusion is exploring whether the data sets have some- thing in common or not. This gives insight into common and distinct variation in each data set, thereby facilitating understanding the rela- tionships between the data sets. Unfortunately, research on methods to distinguish common and distinct components is fragmented, both in terminology as well as in methods: there is no common ground which hampers comparing methods and understanding their relative merits. This paper provides a unifying framework for this subfield of data fusion by using rigorous arguments from linear algebra. The most frequently used methods for distinguishing common and distinct components are explained in this framework and some practical ex- amples are given of these methods in the areas of (medical) biology and food science.
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INFORMATION FUSION FOR MULTI-SENSORY DATA

INFORMATION FUSION FOR MULTI-SENSORY DATA

In present time sensory resources are used extensively to develop various autonomous applications. Multi sensory networks produce large amounts of data that needs to be processed, delivered, and assessed according to the application objectives. Some fundamental problems are how to collect the sensory data and generate the inference parameter to take intelligent decisions for autonomous systems. There are some performance parameters which ought to consider while developing such applications or system, for example, reliability, computational time, accuracy and so on. Information fusion technique compute information gathered by multiple, and eventually heterogeneous sensors to generate inference not obtainable with single sensor. This paper gives detail information about basic concepts of information fusion such as existing methodologies, Algorithms, architectures, models. In addition highlighted and described the methodology of proposed system with mathematical formation and analysis of unsupervised decision making is done by using probability and theory of computation concepts.
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Recurrent LRP1-SNRNP25 and KCNMB4-CCND3 fusion genes promote tumor cell motility in human osteosarcoma

Recurrent LRP1-SNRNP25 and KCNMB4-CCND3 fusion genes promote tumor cell motility in human osteosarcoma

Osteosarcoma is the most common histological form of primary bone sarcoma and predominantly inflicts chil- dren and young adults. While it is often characterized as a genomically unstable bone sarcoma, no recurrent gene translocation and fusion genes has ever been reported in human osteosarcoma, except for one report which re- vealed exon 6 of the cAMP-responsive element binding protein 3-like 1 gene (CREB3L1) fused in-frame to the EWSR1 exon 11 in a case of small cell osteosarcoma [23].The most important discovery of the present study is the detection of two recurrent fusion genes, LRP1- SNRNP25 and KCNMB4-CCND3, which are validated by RT-PCR, Sanger sequencing and FISH. Furthermore, preliminary functional studies show that expression of LRP1-SNRNP25 and KCNMB4-CCND3 fusion genes promotes SAOS-2 osteosarcoma cell migration, while LRP1-SNRNP25 expression also promotes invasion. Taken together, these data suggest that LRP1-SNRNP25 and KCNMB4-CCND3 fusions confer an oncogenic ef- fect in human osteosarcoma by enhancing cancer cell motility.
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Titanium Alloys Manufactured by In Situ Alloying During Laser Powder Bed Fusion

Titanium Alloys Manufactured by In Situ Alloying During Laser Powder Bed Fusion

gradually increased with the Cu contents. It was suggested that the intermetallic phase in Ti6Al4V- x%Cu alloys could result in higher values of hard- ness and UTS and, at the same time, some embrit- tlement of in situ alloyed samples. 22 The solid solution strengthening effect of Cu in titanium alloy and the refinement of martensitic lamella are the other possible factors influencing the enhancement observed in hardness and UTS in the Ti6Al4V- 1.38%Cu alloy under investigation. Nevertheless, the mechanisms behind the strengthening effect of Cu in in situ alloyed Ti6Al4V-xCu have to be investigated more thoroughly and be proven by experimental investigations and phase identifica- tions in sintered LPBF material.
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Analysis of in Situ Posterior Spinal Fusion with Pedicle Screws and Rods in Grade I and Grade II Isthmic and Degenerative Spondylolisthesis

Analysis of in Situ Posterior Spinal Fusion with Pedicle Screws and Rods in Grade I and Grade II Isthmic and Degenerative Spondylolisthesis

A variation of PLIF is unilateral PLIF or transforaminal lumbar interbody fusion. Originally described by Blume, unilateral PLIF produced successful results in 80% of patients treated for lumbar disc pathology. Unique to this procedure is the preservation of the ligamentum flavum by approaching the disc in the foraminal region after unilateral facetectomy. This approach theoretically avoids epidural scarring and excessive postoperative instability because the spinal canal is not opened, and the interspinous-supraspinous ligament complex, lamina and the contralateral facet are left intact. Harms et al., reported successful arthrodesis with TLIF fusion in 97% of patients. Radiographic analysis of isthmic spondylolisthesis treated with TLIF fusion showed restoration of disc space height and reduction of anterior listhesis. Improvement of sagittal alignment depends on anterior placement of the cage.
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Spatial data fusion with visual feedback

Spatial data fusion with visual feedback

This paper outlines our ongoing work towards developing a system for extracting patterns embedded in heterogeneous data streams that contain people’s recorded movements in both physical and virtual spaces. Examples of such spatial data sources are satellite-based sensors (GPS), ultrasound acoustic trackers, radio frequency (WLAN, Bluetooth, UWB) and infrared-based sensors. The core work on pattern extraction relies on the spatial data fusion component aiming to bring various data types to a common format. The additional benefit of this system will consist in the graphical interface that will enable interactive visualisation of the extracted patterns. The rationale of this work is outlined through the relevance of location aware system in the context of ubiquitous computing, which so far have received limited benefits from fields such as Human Computer Interaction (HCI) and interactive visualisation.
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Advances in the modelling of in-situ powder diffraction data

Advances in the modelling of in-situ powder diffraction data

For in-situ powder diffraction a series of measurements is performed in dependence on an external variable (temperature, pressure, time, …) 4 . In in-situ powder diffraction usually a huge number of datasets needs to be analysed. Conventionally each powder diffraction pattern is analysed by itself and for all further investigations (e.g. fitting with empirical or physically based functions), the values obtained in those single refinements are used. The approach of parametric Rietveld refinement (Stinton & Evans, 2007) allows refining a series of powder diffraction pattern simultaneously as in this case functional dependencies of parameters are refined instead of refining the single parameter values. This approach is advantageous as the correlation between parameters and the final standard uncertainty can be reduced and non-crystallographic parameters can be refined directly from the measured powder patterns (Stinton & Evans, 2007).
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Multi-satellite Monitoring SST Data Fusion based on the Adaptive Threshold Clustering Algorithm

Multi-satellite Monitoring SST Data Fusion based on the Adaptive Threshold Clustering Algorithm

However, the research results mentioned above are all used traditional rigid fusion method, which means the rigid processing method is used through the fusion process to filter out fuzzy and inconsistent information, and finally certain results are got, but it doesn’t have a precise description to the accuracy and reliability of the fusion point, and user can't learn a conflict among the data sources just from those products, they have no idea of which points are fused in certain circumstances, and which are in hesitation. This paper proposes a more advanced fusion method with the adaptive threshold clustering algorithm, and it’s divided into two steps, the pretreatment model and the fusion center model, each forms a relative independent model, and the two models have a progressive relationship. The former is used for consistency evaluation, data cleaning and eliminating invalid data, while the latter provides fusion results and variable precision fusion expression by the adaptive threshold clustering algorithm. The first part of this paper introduces the classic fixed threshold clustering algorithms; The second part proposes the adaptive threshold clustering algorithm to make up the insufficient of the fixed threshold clustering algorithm; The third part is the case analysis of the adaptive threshold clustering algorithm; The fourth part is fusion result comparison and analysis.
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Customized Scalable Tracing with in-situ Data Analysis.

Customized Scalable Tracing with in-situ Data Analysis.

One of the most frequently used techniques for root cause diagnosis is tracing, where specific events in the application are identified and traced as part of an execution of the application. Tracing differs from profiling in that it tries to preserve more data about the execution, includ- ing the chronology of events that took place, while profiling is inherently lossy and focuses on overall performance metrics related to specific event types. With trace-based tools, it becomes increasingly difficult to isolate problems since their causes are often subtle to identify and the instrumentation cost can be prohibitive. Tools generally try to instrument interfaces exhaus- tively because developers cannot predict the subset of interfaces that might be pertinent to an application’s problem. Using these tools might be prohibitive and expensive for two reasons. First, instrumenting more interfaces than necessary might introduce perturbations that can mask the problem at hand. Second, instrumenting more interfaces results in more data, thus impairing scalability.
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Remote Fingerprinting and Multisensor Data Fusion

Remote Fingerprinting and Multisensor Data Fusion

It is inferred from this that the bots in the Kelihos/Hlux botnet were all configured using the same configuration script and that all of the bots were running an Nginx webserver which could act as a reverse proxy. Using these characteristics a signature to identify hosts belonging to this specific botnet has been determined. While this may not be valid in the future, it will remain useful as long as the botnet is functioning in it’s present form. The bots have maintained the same HTTP responses for three weeks after initially gathering this data. Any phishing payloads present on these bots are only be accessible through a specific URL, as the bot acts as a reverse proxy,forwarding data from the “mothership”, thus HTTP GET requests to the root directory on the web server are likely to only return details regarding the initial configuration of the bot.
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CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measure- ments, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950.
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Implementation of Multisensor Data Fusion Algorithm

Implementation of Multisensor Data Fusion Algorithm

HUMANS accept input from five sense organs and senses: touch, smell, taste, sound, and sight in different physical formats (and even the sixth sense as mystics tell us) [1-3]. By some incredible process, not yet fully understood, humans transform input from these organs within the brain into the sensation of being in some “reality.” We need to feel or be assured that we are somewhere, in some coordinates, in some place, and at some time. Thus, we obtain a more complete picture of an observed scene than would have been possible otherwise (i. e., using only one sense organ or sensor). The human activities of planning, acting, investigating, market analysis, military intelligence, complex art work, complex dance sequences, creation of music, and journalism are good examples of activities that use advanced data fusion (DF) aspects and concepts that we do not yet fully understand. Perhaps, the human brain combines such data or information without using any automatic aids, because it has a powerful associative reasoning ability, evolved over thousands of years.
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Global Ranking via Data Fusion

Global Ranking via Data Fusion

Global ranking, a new information re- trieval (IR) technology, uses a ranking model for cases in which there exist re- lationships between the objects to be ranked. In the ranking task, the ranking model is defined as a function of the properties of the objects as well as the relations between the objects. Existing global ranking approaches address the problem by “learning to rank”. In this paper, we propose a global ranking framework that solves the problem via data fusion. The idea is to take each re- trieved document as a pseudo-IR sys- tem. Each document generates a pseu- do-ranked list by a global function. The data fusion algorithm is then adapted to generate the final ranked list. Taking a biomedical information extraction task, namely, interactor normalization task (INT), as an example, we explain how the problem can be formulated as a global ranking problem, and demon- strate how the proposed fusion-based framework outperforms baseline me- thods. By using the proposed frame- work, we improve the performance of the top 1 INT system by 3.2% using the official evaluation metric of the BioCreAtIvE challenge. In addition, by employing the standard ranking quality measure, NDCG, we demonstrate that
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