Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks.

PubMed

Deng, Zhi-An; Wang, Guofeng; Qin, Danyang; Na, Zhenyu; Cui, Yang; Chen, Juan

2016-09-05

To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning (PDR), and landmarks, we propose a novel fusion approach based on an extended Kalman filter (EKF). For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning.

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

PubMed

Jiang, Baichuan; Gao, Wenpeng; Kacher, Daniel; Nevo, Erez; Fetics, Barry; Lee, Thomas C; Jayender, Jagadeesan

2018-04-01

In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

PubMed

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

Decision-Level Fusion of Spatially Scattered Multi-Modal Data for Nondestructive Inspection of Surface Defects

PubMed Central

Heideklang, René; Shokouhi, Parisa

2016-01-01

This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique’s robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate. PMID:26784200

An efficient method for the fusion of light field refocused images

NASA Astrophysics Data System (ADS)

Wang, Yingqian; Yang, Jungang; Xiao, Chao; An, Wei

2018-04-01

Light field cameras have drawn much attention due to the advantage of post-capture adjustments such as refocusing after exposure. The depth of field in refocused images is always shallow because of the large equivalent aperture. As a result, a large number of multi-focus images are obtained and an all-in-focus image is demanded. Consider that most multi-focus image fusion algorithms do not particularly aim at large numbers of source images and traditional DWT-based fusion approach has serious problems in dealing with lots of multi-focus images, causing color distortion and ringing effect. To solve this problem, this paper proposes an efficient multi-focus image fusion method based on stationary wavelet transform (SWT), which can deal with a large quantity of multi-focus images with shallow depth of fields. We compare SWT-based approach with DWT-based approach on various occasions. And the results demonstrate that the proposed method performs much better both visually and quantitatively.

A Hybrid Brain-Computer Interface Based on the Fusion of P300 and SSVEP Scores.

PubMed

Yin, Erwei; Zeyl, Timothy; Saab, Rami; Chau, Tom; Hu, Dewen; Zhou, Zongtan

2015-07-01

The present study proposes a hybrid brain-computer interface (BCI) with 64 selectable items based on the fusion of P300 and steady-state visually evoked potential (SSVEP) brain signals. With this approach, row/column (RC) P300 and two-step SSVEP paradigms were integrated to create two hybrid paradigms, which we denote as the double RC (DRC) and 4-D spellers. In each hybrid paradigm, the target is simultaneously detected based on both P300 and SSVEP potentials as measured by the electroencephalogram. We further proposed a maximum-probability estimation (MPE) fusion approach to combine the P300 and SSVEP on a score level and compared this approach to other approaches based on linear discriminant analysis, a naïve Bayes classifier, and support vector machines. The experimental results obtained from thirteen participants indicated that the 4-D hybrid paradigm outperformed the DRC paradigm and that the MPE fusion achieved higher accuracy compared with the other approaches. Importantly, 12 of the 13 participants, using the 4-D paradigm achieved an accuracy of over 90% and the average accuracy was 95.18%. These promising results suggest that the proposed hybrid BCI system could be used in the design of a high-performance BCI-based keyboard.

Low-energy fusion dynamics of weakly bound nuclei: A time dependent perspective

NASA Astrophysics Data System (ADS)

Diaz-Torres, A.; Boselli, M.

2016-05-01

Recent dynamical fusion models for weakly bound nuclei at low incident energies, based on a time-dependent perspective, are briefly presented. The main features of both the PLATYPUS model and a new quantum approach are highlighted. In contrast to existing timedependent quantum models, the present quantum approach separates the complete and incomplete fusion from the total fusion. Calculations performed within a toy model for 6Li + 209Bi at near-barrier energies show that converged excitation functions for total, complete and incomplete fusion can be determined with the time-dependent wavepacket dynamics.

A practical approach for active camera coordination based on a fusion-driven multi-agent system

NASA Astrophysics Data System (ADS)

Bustamante, Alvaro Luis; Molina, José M.; Patricio, Miguel A.

2014-04-01

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.

Multi-focus image fusion based on window empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Zheng, Jiaoyue; Hu, Gang; Wang, Jiao

2017-09-01

In order to improve multi-focus image fusion quality, a novel fusion algorithm based on window empirical mode decomposition (WEMD) is proposed. This WEMD is an improved form of bidimensional empirical mode decomposition (BEMD), due to its decomposition process using the adding window principle, effectively resolving the signal concealment problem. We used WEMD for multi-focus image fusion, and formulated different fusion rules for bidimensional intrinsic mode function (BIMF) components and the residue component. For fusion of the BIMF components, the concept of the Sum-modified-Laplacian was used and a scheme based on the visual feature contrast adopted; when choosing the residue coefficients, a pixel value based on the local visibility was selected. We carried out four groups of multi-focus image fusion experiments and compared objective evaluation criteria with other three fusion methods. The experimental results show that the proposed fusion approach is effective and performs better at fusing multi-focus images than some traditional methods.

An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China

NASA Astrophysics Data System (ADS)

Huang, Zhijiong; Hu, Yongtao; Zheng, Junyu; Zhai, Xinxin; Huang, Ran

2018-05-01

Lateral boundary conditions (LBCs) are essential for chemical transport models to simulate regional transport; however they often contain large uncertainties. This study proposes an optimized data fusion approach to reduce the bias of LBCs by fusing gridded model outputs, from which the daughter domain's LBCs are derived, with ground-level measurements. The optimized data fusion approach follows the framework of a previous interpolation-based fusion method but improves it by using a bias kriging method to correct the spatial bias in gridded model outputs. Cross-validation shows that the optimized approach better estimates fused fields in areas with a large number of observations compared to the previous interpolation-based method. The optimized approach was applied to correct LBCs of PM2.5 concentrations for simulations in the Pearl River Delta (PRD) region as a case study. Evaluations show that the LBCs corrected by data fusion improve in-domain PM2.5 simulations in terms of the magnitude and temporal variance. Correlation increases by 0.13-0.18 and fractional bias (FB) decreases by approximately 3%-15%. This study demonstrates the feasibility of applying data fusion to improve regional air quality modeling.

Fluorescent Protein Approaches in Alpha Herpesvirus Research

PubMed Central

Hogue, Ian B.; Bosse, Jens B.; Engel, Esteban A.; Scherer, Julian; Hu, Jiun-Ruey; del Rio, Tony; Enquist, Lynn W.

2015-01-01

In the nearly two decades since the popularization of green fluorescent protein (GFP), fluorescent protein-based methodologies have revolutionized molecular and cell biology, allowing us to literally see biological processes as never before. Naturally, this revolution has extended to virology in general, and to the study of alpha herpesviruses in particular. In this review, we provide a compendium of reported fluorescent protein fusions to herpes simplex virus 1 (HSV-1) and pseudorabies virus (PRV) structural proteins, discuss the underappreciated challenges of fluorescent protein-based approaches in the context of a replicating virus, and describe general strategies and best practices for creating new fluorescent fusions. We compare fluorescent protein methods to alternative approaches, and review two instructive examples of the caveats associated with fluorescent protein fusions, including describing several improved fluorescent capsid fusions in PRV. Finally, we present our future perspectives on the types of powerful experiments these tools now offer. PMID:26610544

A Bio-Inspired Herbal Tea Flavour Assessment Technique

PubMed Central

Zakaria, Nur Zawatil Isqi; Masnan, Maz Jamilah; Zakaria, Ammar; Shakaff, Ali Yeon Md

2014-01-01

Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers' performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied. PMID:25010697

Base of the Measles Virus Fusion Trimer Head Receives the Signal That Triggers Membrane Fusion*

PubMed Central

Apte-Sengupta, Swapna; Negi, Surendra; Leonard, Vincent H. J.; Oezguen, Numan; Navaratnarajah, Chanakha K.; Braun, Werner; Cattaneo, Roberto

2012-01-01

The measles virus (MV) fusion (F) protein trimer executes membrane fusion after receiving a signal elicited by receptor binding to the hemagglutinin (H) tetramer. Where and how this signal is received is understood neither for MV nor for other paramyxoviruses. Because only the prefusion structure of the parainfluenza virus 5 (PIV5) F-trimer is available, to study signal receipt by the MV F-trimer, we generated and energy-refined a homology model. We used two approaches to predict surface residues of the model interacting with other proteins. Both approaches measured interface propensity values for patches of residues. The second approach identified, in addition, individual residues based on the conservation of physical chemical properties among F-proteins. Altogether, about 50 candidate interactive residues were identified. Through iterative cycles of mutagenesis and functional analysis, we characterized six residues that are required specifically for signal transmission; their mutation interferes with fusion, although still allowing efficient F-protein processing and cell surface transport. One residue is located adjacent to the fusion peptide, four line a cavity in the base of the F-trimer head, while the sixth residue is located near this cavity. Hydrophobic interactions in the cavity sustain the fusion process and contacts with H. The cavity is flanked by two different subunits of the F-trimer. Tetrameric H-stalks may be lodged in apposed cavities of two F-trimers. Because these insights are based on a PIV5 homology model, the signal receipt mechanism may be conserved among paramyxoviruses. PMID:22859308

Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

PubMed

V S, Unni; Mishra, Deepak; Subrahmanyam, G R K S

2016-12-01

The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang

2015-01-01

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule. PMID:26295399

Remote sensing fusion based on guided image filtering

NASA Astrophysics Data System (ADS)

Zhao, Wenfei; Dai, Qinling; Wang, Leiguang

2015-12-01

In this paper, we propose a novel remote sensing fusion approach based on guided image filtering. The fused images can well preserve the spectral features of the original multispectral (MS) images, meanwhile, enhance the spatial details information. Four quality assessment indexes are also introduced to evaluate the fusion effect when compared with other fusion methods. Experiments carried out on Gaofen-2, QuickBird, WorldView-2 and Landsat-8 images. And the results show an excellent performance of the proposed method.

Remotely controlled fusion of selected vesicles and living cells: a key issue review

NASA Astrophysics Data System (ADS)

Bahadori, Azra; Moreno-Pescador, Guillermo; Oddershede, Lene B.; Bendix, Poul M.

2018-03-01

Remote control over fusion of single cells and vesicles has a great potential in biological and chemical research allowing both transfer of genetic material between cells and transfer of molecular content between vesicles. Membrane fusion is a critical process in biology that facilitates molecular transport and mixing of cellular cytoplasms with potential formation of hybrid cells. Cells precisely regulate internal membrane fusions with the aid of specialized fusion complexes that physically provide the energy necessary for mediating fusion. Physical factors like membrane curvature, tension and temperature, affect biological membrane fusion by lowering the associated energy barrier. This has inspired the development of physical approaches to harness the fusion process at a single cell level by using remotely controlled electromagnetic fields to trigger membrane fusion. Here, we critically review various approaches, based on lasers or electric pulses, to control fusion between individual cells or between individual lipid vesicles and discuss their potential and limitations for present and future applications within biochemistry, biology and soft matter.

Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators.

PubMed

Restaino, Rocco; Vivone, Gemine; Dalla Mura, Mauro; Chanussot, Jocelyn

2016-04-20

Nonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor.

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

PubMed Central

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

2017-01-01

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

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

NASA Astrophysics Data System (ADS)

Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

2012-12-01

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

A fusion approach for coarse-to-fine target recognition

NASA Astrophysics Data System (ADS)

Folkesson, Martin; Grönwall, Christina; Jungert, Erland

2006-04-01

A fusion approach in a query based information system is presented. The system is designed for querying multimedia data bases, and here applied to target recognition using heterogeneous data sources. The recognition process is coarse-to-fine, with an initial attribute estimation step and a following matching step. Several sensor types and algorithms are involved in each of these two steps. An independence of the matching results, on the origin of the estimation results, is observed. It allows for distribution of data between algorithms in an intermediate fusion step, without risk of data incest. This increases the overall chance of recognising the target. An implementation of the system is described.

Establishment and characterization of an open mini-thoracotomy surgical approach to an ovine thoracic spine fusion model.

PubMed

Yong, Mostyn R N O; Saifzadeh, Siamak; Askin, Geoffrey N; Labrom, Robert D; Hutmacher, Dietmar W; Adam, Clayton J

2014-01-01

A large animal model is required for the assessment of minimally invasive, tissue-engineering-based approaches to thoracic spine fusion, with relevance to deformity correction surgery for human adolescent idiopathic scoliosis. Here, we develop a novel open mini-thoracotomy approach in an ovine model of thoracic interbody fusion that allows the assessment of various fusion constructs, with a focus on novel, tissue-engineering-based interventions. The open mini-thoracotomy surgical approach was developed through a series of mock surgeries, and then applied in a live sheep study. Customized scaffolds were manufactured to conform with intervertebral disc space clearances that were required of the study. Six male Merino sheep aged 4-6 years and weighing 35-45 kg underwent the procedure mentioned earlier and were alloted a survival timeline of 6 months. Each sheep underwent a three-level discectomy (T6/7, T8/9, and T10/11) with a randomly allocated implantation of a different graft substitute at each of the following three levels: (1) polycaprolactone (PCL)-based scaffold plus 0.54 μg recombinant human bone morphogenetic protein-2 (rhBMP-2); (2) PCL-based scaffold alone; or (3) autograft. The sheep were closely monitored postoperatively for signs of pain (i.e., gait abnormalities/teeth gnawing/social isolation). Fusion assessments were conducted postsacrifice using computed tomography and hard-tissue histology. All scientific work was undertaken in accordance with the study protocol that was approved by the Institute's committee on animal research. All six sheep were successfully operated on and reached the allotted survival timeline, thereby demonstrating the feasibility of the surgical procedure and postoperative care. There were no significant complications and during the postoperative period, the animals did not exhibit marked signs of distress according to the previously described assessment criteria. Computed tomographic scanning demonstrated higher fusion grades in the rhBMP-2 plus PCL-based scaffold group in comparison to either PCL-based scaffold alone or autograft. These results were supported by a histological evaluation of the respective groups. This novel open mini-thoracotomy surgical approach to the ovine thoracic spine represents a safe surgical method that can reproducibly form the platform for research into various spine-tissue-engineered constructs and their fusion-promoting properties.

Quality dependent fusion of intramodal and multimodal biometric experts

NASA Astrophysics Data System (ADS)

Kittler, J.; Poh, N.; Fatukasi, O.; Messer, K.; Kryszczuk, K.; Richiardi, J.; Drygajlo, A.

2007-04-01

We address the problem of score level fusion of intramodal and multimodal experts in the context of biometric identity verification. We investigate the merits of confidence based weighting of component experts. In contrast to the conventional approach where confidence values are derived from scores, we use instead raw measures of biometric data quality to control the influence of each expert on the final fused score. We show that quality based fusion gives better performance than quality free fusion. The use of quality weighted scores as features in the definition of the fusion functions leads to further improvements. We demonstrate that the achievable performance gain is also affected by the choice of fusion architecture. The evaluation of the proposed methodology involves 6 face and one speech verification experts. It is carried out on the XM2VTS data base.

Image Fusion of CT and MR with Sparse Representation in NSST Domain

PubMed Central

Qiu, Chenhui; Wang, Yuanyuan; Zhang, Huan

2017-01-01

Multimodal image fusion techniques can integrate the information from different medical images to get an informative image that is more suitable for joint diagnosis, preoperative planning, intraoperative guidance, and interventional treatment. Fusing images of CT and different MR modalities are studied in this paper. Firstly, the CT and MR images are both transformed to nonsubsampled shearlet transform (NSST) domain. So the low-frequency components and high-frequency components are obtained. Then the high-frequency components are merged using the absolute-maximum rule, while the low-frequency components are merged by a sparse representation- (SR-) based approach. And the dynamic group sparsity recovery (DGSR) algorithm is proposed to improve the performance of the SR-based approach. Finally, the fused image is obtained by performing the inverse NSST on the merged components. The proposed fusion method is tested on a number of clinical CT and MR images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation. PMID:29250134

Image Fusion of CT and MR with Sparse Representation in NSST Domain.

PubMed

Qiu, Chenhui; Wang, Yuanyuan; Zhang, Huan; Xia, Shunren

2017-01-01

Multimodal image fusion techniques can integrate the information from different medical images to get an informative image that is more suitable for joint diagnosis, preoperative planning, intraoperative guidance, and interventional treatment. Fusing images of CT and different MR modalities are studied in this paper. Firstly, the CT and MR images are both transformed to nonsubsampled shearlet transform (NSST) domain. So the low-frequency components and high-frequency components are obtained. Then the high-frequency components are merged using the absolute-maximum rule, while the low-frequency components are merged by a sparse representation- (SR-) based approach. And the dynamic group sparsity recovery (DGSR) algorithm is proposed to improve the performance of the SR-based approach. Finally, the fused image is obtained by performing the inverse NSST on the merged components. The proposed fusion method is tested on a number of clinical CT and MR images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation.

Spatial resolution enhancement of satellite image data using fusion approach

NASA Astrophysics Data System (ADS)

Lestiana, H.; Sukristiyanti

2018-02-01

Object identification using remote sensing data has a problem when the spatial resolution is not in accordance with the object. The fusion approach is one of methods to solve the problem, to improve the object recognition and to increase the objects information by combining data from multiple sensors. The application of fusion image can be used to estimate the environmental component that is needed to monitor in multiple views, such as evapotranspiration estimation, 3D ground-based characterisation, smart city application, urban environments, terrestrial mapping, and water vegetation. Based on fusion application method, the visible object in land area has been easily recognized using the method. The variety of object information in land area has increased the variation of environmental component estimation. The difficulties in recognizing the invisible object like Submarine Groundwater Discharge (SGD), especially in tropical area, might be decreased by the fusion method. The less variation of the object in the sea surface temperature is a challenge to be solved.

Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing.

PubMed

Riniker, Sereina; Fechner, Nikolas; Landrum, Gregory A

2013-11-25

The concept of data fusion - the combination of information from different sources describing the same object with the expectation to generate a more accurate representation - has found application in a very broad range of disciplines. In the context of ligand-based virtual screening (VS), data fusion has been applied to combine knowledge from either different active molecules or different fingerprints to improve similarity search performance. Machine-learning (ML) methods based on fusion of multiple homogeneous classifiers, in particular random forests, have also been widely applied in the ML literature. The heterogeneous version of classifier fusion - fusing the predictions from different model types - has been less explored. Here, we investigate heterogeneous classifier fusion for ligand-based VS using three different ML methods, RF, naïve Bayes (NB), and logistic regression (LR), with four 2D fingerprints, atom pairs, topological torsions, RDKit fingerprint, and circular fingerprint. The methods are compared using a previously developed benchmarking platform for 2D fingerprints which is extended to ML methods in this article. The original data sets are filtered for difficulty, and a new set of challenging data sets from ChEMBL is added. Data sets were also generated for a second use case: starting from a small set of related actives instead of diverse actives. The final fused model consistently outperforms the other approaches across the broad variety of targets studied, indicating that heterogeneous classifier fusion is a very promising approach for ligand-based VS. The new data sets together with the adapted source code for ML methods are provided in the Supporting Information .

Towards a Unified Approach to Information Integration - A review paper on data/information fusion

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

Whitney, Paul D.; Posse, Christian; Lei, Xingye C.

2005-10-14

Information or data fusion of data from different sources are ubiquitous in many applications, from epidemiology, medical, biological, political, and intelligence to military applications. Data fusion involves integration of spectral, imaging, text, and many other sensor data. For example, in epidemiology, information is often obtained based on many studies conducted by different researchers at different regions with different protocols. In the medical field, the diagnosis of a disease is often based on imaging (MRI, X-Ray, CT), clinical examination, and lab results. In the biological field, information is obtained based on studies conducted on many different species. In military field, informationmore » is obtained based on data from radar sensors, text messages, chemical biological sensor, acoustic sensor, optical warning and many other sources. Many methodologies are used in the data integration process, from classical, Bayesian, to evidence based expert systems. The implementation of the data integration ranges from pure software design to a mixture of software and hardware. In this review we summarize the methodologies and implementations of data fusion process, and illustrate in more detail the methodologies involved in three examples. We propose a unified multi-stage and multi-path mapping approach to the data fusion process, and point out future prospects and challenges.« less

SOLVING THE STAND-OFF PROBLEM FOR MAGNETIZED TARGET FUSION: PLASMA STREAMS AS DISPOSABLE ELECTRODES, PLUS A LOCAL SPHERICAL BLANKET

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

Ryutov, D D; Thio, Y F

In a fusion reactor based on the Magnetized Target Fusion approach, the permanent power supply has to deliver currents up to a few mega-amperes to the target dropped into the reaction chamber. All the structures situated around the target will be destroyed after every pulse and have to be replaced at a frequency of 1 to 10 Hz. In this paper, an approach based on the use of spherical blanket surrounding the target, and pulsed plasma electrodes connecting the target to the power supply, is discussed. A brief physic analysis of the processes associated with creation of plasma electrodes ismore » discussed.« less

Frequency domain surface EMG sensor fusion for estimating finger forces.

PubMed

Potluri, Chandrasekhar; Kumar, Parmod; Anugolu, Madhavi; Urfer, Alex; Chiu, Steve; Naidu, D; Schoen, Marco P

2010-01-01

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

An Indoor Positioning Method for Smartphones Using Landmarks and PDR.

PubMed

Wang, Xi; Jiang, Mingxing; Guo, Zhongwen; Hu, Naijun; Sun, Zhongwei; Liu, Jing

2016-12-15

Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.

An Indoor Positioning Method for Smartphones Using Landmarks and PDR †

PubMed Central

Wang, Xi; Jiang, Mingxing; Guo, Zhongwen; Hu, Naijun; Sun, Zhongwei; Liu, Jing

2016-01-01

Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m. PMID:27983670

Segmentation by fusion of histogram-based k-means clusters in different color spaces.

PubMed

Mignotte, Max

2008-05-01

This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an input image expressed in different color spaces. Our fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation), and has been successfully applied on the Berkeley image database. The experiments herein reported in this paper illustrate the potential of this approach compared to the state-of-the-art segmentation methods recently proposed in the literature.

Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS

NASA Astrophysics Data System (ADS)

Saadeddin, Kamal; Abdel-Hafez, Mamoun F.; Jaradat, Mohammad A.; Jarrah, Mohammad Amin

2013-12-01

In this paper, a low-cost navigation system that fuses the measurements of the inertial navigation system (INS) and the global positioning system (GPS) receiver is developed. First, the system's dynamics are obtained based on a vehicle's kinematic model. Second, the INS and GPS measurements are fused using an extended Kalman filter (EKF) approach. Subsequently, an artificial intelligence based approach for the fusion of INS/GPS measurements is developed based on an Input-Delayed Adaptive Neuro-Fuzzy Inference System (IDANFIS). Experimental tests are conducted to demonstrate the performance of the two sensor fusion approaches. It is found that the use of the proposed IDANFIS approach achieves a reduction in the integration development time and an improvement in the estimation accuracy of the vehicle's position and velocity compared to the EKF based approach.

Rational decision making in a wide scenario of different minimally invasive lumbar interbody fusion approaches and devices.

PubMed

Pimenta, Luiz; Tohmeh, Antoine; Jones, David; Amaral, Rodrigo; Marchi, Luis; Oliveira, Leonardo; Pittman, Bruce C; Bae, Hyun

2018-03-01

With the proliferation of a variety of modern MIS spine surgery procedures, it is mandatory that the surgeon dominate all aspects involved in surgical indication. The information related to the decision making in patient selection for specific procedures is mandatory for surgical success. The objective of this study is to present decision-making criteria in minimally invasive surgery (MIS) selection for a variety of patients and pathologies. In this article, practicing surgeons who specialize in various MIS approaches for spinal fusion were engaged to provide expert opinion and literature review on decision making criteria for several MIS procedures. Pros, cons, relative limitations, and case examples are provided for patient selection in treatment with MIS posterolateral fusion (MIS-PLF), mini anterior lumbar interbody fusion (mini-ALIF), lateral interbody fusion (LLIF), MIS posterior lumbar interbody fusion (MIS-PLIF) and MIS transforaminal lumbar interbody fusion (MIS-TLIF). There is a variety of aspects to consider when deciding which modern MIS surgical approach is most appropriate to use based on patient and pathologic characteristics. The surgeon must adapt them to the characteristic of each type of patients, helping them to choose the most effective and efficient therapeutic option for each case.

A hybrid sensing approach for pure and adulterated honey classification.

PubMed

Subari, Norazian; Mohamad Saleh, Junita; Md Shakaff, Ali Yeon; Zakaria, Ammar

2012-10-17

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

An Approach for Reducing the Error Rate in Automated Lung Segmentation

PubMed Central

Gill, Gurman; Beichel, Reinhard R.

2016-01-01

Robust lung segmentation is challenging, especially when tens of thousands of lung CT scans need to be processed, as required by large multi-center studies. The goal of this work was to develop and assess a method for the fusion of segmentation results from two different methods to generate lung segmentations that have a lower failure rate than individual input segmentations. As basis for the fusion approach, lung segmentations generated with a region growing and model-based approach were utilized. The fusion result was generated by comparing input segmentations and selectively combining them using a trained classification system. The method was evaluated on a diverse set of 204 CT scans of normal and diseased lungs. The fusion approach resulted in a Dice coefficient of 0.9855 ± 0.0106 and showed a statistically significant improvement compared to both input segmentation methods. In addition, the failure rate at different segmentation accuracy levels was assessed. For example, when requiring that lung segmentations must have a Dice coefficient of better than 0.97, the fusion approach had a failure rate of 6.13%. In contrast, the failure rate for region growing and model-based methods was 18.14% and 15.69%, respectively. Therefore, the proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis of lungs. Also, to enable a comparison with other methods, results on the LOLA11 challenge test set are reported. PMID:27447897

A data fusion framework for meta-evaluation of intelligent transportation system effectiveness

DOT National Transportation Integrated Search

This study presents a framework for the meta-evaluation of Intelligent Transportation System effectiveness. The framework is based on data fusion approaches that adjust for data biases and violations of other standard statistical assumptions. Operati...

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

PubMed

Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

2016-07-01

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

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

Bhateja, Vikrant, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja

Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Componentmore » Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).« less

Gradient-based multiresolution image fusion.

PubMed

Petrović, Valdimir S; Xydeas, Costas S

2004-02-01

A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.

Multisource image fusion method using support value transform.

PubMed

Zheng, Sheng; Shi, Wen-Zhong; Liu, Jian; Zhu, Guang-Xi; Tian, Jin-Wen

2007-07-01

With the development of numerous imaging sensors, many images can be simultaneously pictured by various sensors. However, there are many scenarios where no one sensor can give the complete picture. Image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors. In this paper, we proposed a new image fusion method using the support value transform, which uses the support value to represent the salient features of image. This is based on the fact that, in support vector machines (SVMs), the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. The mapped least squares SVM (mapped LS-SVM) is used to efficiently compute the support values of image. The support value analysis is developed by using a series of multiscale support value filters, which are obtained by filling zeros in the basic support value filter deduced from the mapped LS-SVM to match the resolution of the desired level. Compared with the widely used image fusion methods, such as the Laplacian pyramid, discrete wavelet transform methods, the proposed method is an undecimated transform-based approach. The fusion experiments are undertaken on multisource images. The results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (Q(AB/F)), the mutual information, etc.

Improvement of information fusion-based audio steganalysis

NASA Astrophysics Data System (ADS)

Kraetzer, Christian; Dittmann, Jana

2010-01-01

In the paper we extend an existing information fusion based audio steganalysis approach by three different kinds of evaluations: The first evaluation addresses the so far neglected evaluations on sensor level fusion. Our results show that this fusion removes content dependability while being capable of achieving similar classification rates (especially for the considered global features) if compared to single classifiers on the three exemplarily tested audio data hiding algorithms. The second evaluation enhances the observations on fusion from considering only segmental features to combinations of segmental and global features, with the result of a reduction of the required computational complexity for testing by about two magnitudes while maintaining the same degree of accuracy. The third evaluation tries to build a basis for estimating the plausibility of the introduced steganalysis approach by measuring the sensibility of the models used in supervised classification of steganographic material against typical signal modification operations like de-noising or 128kBit/s MP3 encoding. Our results show that for some of the tested classifiers the probability of false alarms rises dramatically after such modifications.

Overview of Heavy Ion Fusion Accelerator Research in the U. S.

NASA Astrophysics Data System (ADS)

Friedman, Alex

2002-12-01

This article provides an overview of current U.S. research on accelerators for Heavy Ion Fusion, that is, inertial fusion driven by intense beams of heavy ions with the goal of energy production. The concept, beam requirements, approach, and major issues are introduced. An overview of a number of new experiments is presented. These include: the High Current Experiment now underway at Lawrence Berkeley National Laboratory; studies of advanced injectors (and in particular an approach based on the merging of multiple beamlets), being investigated experimentally at Lawrence Livermore National Laboratory); the Neutralized (chamber) Transport Experiment being assembled at Lawrence Berkeley National Laboratory; and smaller experiments at the University of Maryland and at Princeton Plasma Physics Laboratory. The comprehensive program of beam simulations and theory is outlined. Finally, prospects and plans for further development of this promising approach to fusion energy are discussed.

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. PMID:23202033

Design and Evaluation of Fusion Approach for Combining Brain and Gaze Inputs for Target Selection

PubMed Central

Évain, Andéol; Argelaguet, Ferran; Casiez, Géry; Roussel, Nicolas; Lécuyer, Anatole

2016-01-01

Gaze-based interfaces and Brain-Computer Interfaces (BCIs) allow for hands-free human–computer interaction. In this paper, we investigate the combination of gaze and BCIs. We propose a novel selection technique for 2D target acquisition based on input fusion. This new approach combines the probabilistic models for each input, in order to better estimate the intent of the user. We evaluated its performance against the existing gaze and brain–computer interaction techniques. Twelve participants took part in our study, in which they had to search and select 2D targets with each of the evaluated techniques. Our fusion-based hybrid interaction technique was found to be more reliable than the previous gaze and BCI hybrid interaction techniques for 10 participants over 12, while being 29% faster on average. However, similarly to what has been observed in hybrid gaze-and-speech interaction, gaze-only interaction technique still provides the best performance. Our results should encourage the use of input fusion, as opposed to sequential interaction, in order to design better hybrid interfaces. PMID:27774048

A Multiplexed Amplicon Approach for Detecting Gene Fusions by Next-Generation Sequencing.

PubMed

Beadling, Carol; Wald, Abigail I; Warrick, Andrea; Neff, Tanaya L; Zhong, Shan; Nikiforov, Yuri E; Corless, Christopher L; Nikiforova, Marina N

2016-03-01

Chromosomal rearrangements that result in oncogenic gene fusions are clinically important drivers of many cancer types. Rapid and sensitive methods are therefore needed to detect a broad range of gene fusions in clinical specimens that are often of limited quantity and quality. We describe a next-generation sequencing approach that uses a multiplex PCR-based amplicon panel to interrogate fusion transcripts that involve 19 driver genes and 94 partners implicated in solid tumors. The panel also includes control assays that evaluate the 3'/5' expression ratios of 12 oncogenic kinases, which might be used to infer gene fusion events when the partner is unknown or not included on the panel. There was good concordance between the solid tumor fusion gene panel and other methods, including fluorescence in situ hybridization, real-time PCR, Sanger sequencing, and other next-generation sequencing panels, because 40 specimens known to harbor gene fusions were correctly identified. No specific fusion reads were observed in 59 fusion-negative specimens. The 3'/5' expression ratio was informative for fusions that involved ALK, RET, and NTRK1 but not for BRAF or ROS1 fusions. However, among 37 ALK or RET fusion-negative specimens, four exhibited elevated 3'/5' expression ratios, indicating that fusions predicted solely by 3'/5' read ratios require confirmatory testing. Copyright © 2016 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

NASA Astrophysics Data System (ADS)

Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

2011-02-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

A local approach for focussed Bayesian fusion

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael; Goussev, Igor; Beyerer, Jürgen

2009-04-01

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential.

Fusion and Gaussian mixture based classifiers for SONAR data

NASA Astrophysics Data System (ADS)

Kotari, Vikas; Chang, KC

2011-06-01

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

Hierarchical Multi-atlas Label Fusion with Multi-scale Feature Representation and Label-specific Patch Partition

PubMed Central

Wu, Guorong; Kim, Minjeong; Sanroma, Gerard; Wang, Qian; Munsell, Brent C.; Shen, Dinggang

2014-01-01

Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images, after registration a label is assigned to each target point in the target image by determining the similarity between the underlying target image patch (centered at the target point) and the aligned image patch in each atlas image. To achieve the highest level of accuracy during the label fusion process it’s critical the chosen patch similarity measurement accurately captures the tissue/shape appearance of the anatomical structure. One major limitation of existing state-of-the-art label fusion methods is that they often apply a fixed size image patch throughout the entire label fusion procedure. Doing so may severely affect the fidelity of the patch similarity measurement, which in turn may not adequately capture complex tissue appearance patterns expressed by the anatomical structure. To address this limitation, we advance state-of-the-art by adding three new label fusion contributions: First, each image patch now characterized by a multi-scale feature representation that encodes both local and semi-local image information. Doing so will increase the accuracy of the patch-based similarity measurement. Second, to limit the possibility of the patch-based similarity measurement being wrongly guided by the presence of multiple anatomical structures in the same image patch, each atlas image patch is further partitioned into a set of label-specific partial image patches according to the existing labels. Since image information has now been semantically divided into different patterns, these new label-specific atlas patches make the label fusion process more specific and flexible. Lastly, in order to correct target points that are mislabeled during label fusion, a hierarchically approach is used to improve the label fusion results. In particular, a coarse-to-fine iterative label fusion approach is used that gradually reduces the patch size. To evaluate the accuracy of our label fusion approach, the proposed method was used to segment the hippocampus in the ADNI dataset and 7.0 tesla MR images, sub-cortical regions in LONI LBPA40 dataset, mid-brain regions in SATA dataset from MICCAI 2013 segmentation challenge, and a set of key internal gray matter structures in IXI dataset. In all experiments, the segmentation results of the proposed hierarchical label fusion method with multi-scale feature representations and label-specific atlas patches are more accurate than several well-known state-of-the-art label fusion methods. PMID:25463474

Joint sparsity based heterogeneous data-level fusion for target detection and estimation

NASA Astrophysics Data System (ADS)

Niu, Ruixin; Zulch, Peter; Distasio, Marcello; Blasch, Erik; Shen, Dan; Chen, Genshe

2017-05-01

Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.

Single-Scale Fusion: An Effective Approach to Merging Images.

PubMed

Ancuti, Codruta O; Ancuti, Cosmin; De Vleeschouwer, Christophe; Bovik, Alan C

2017-01-01

Due to its robustness and effectiveness, multi-scale fusion (MSF) based on the Laplacian pyramid decomposition has emerged as a popular technique that has shown utility in many applications. Guided by several intuitive measures (weight maps) the MSF process is versatile and straightforward to be implemented. However, the number of pyramid levels increases with the image size, which implies sophisticated data management and memory accesses, as well as additional computations. Here, we introduce a simplified formulation that reduces MSF to only a single level process. Starting from the MSF decomposition, we explain both mathematically and intuitively (visually) a way to simplify the classical MSF approach with minimal loss of information. The resulting single-scale fusion (SSF) solution is a close approximation of the MSF process that eliminates important redundant computations. It also provides insights regarding why MSF is so effective. While our simplified expression is derived in the context of high dynamic range imaging, we show its generality on several well-known fusion-based applications, such as image compositing, extended depth of field, medical imaging, and blending thermal (infrared) images with visible light. Besides visual validation, quantitative evaluations demonstrate that our SSF strategy is able to yield results that are highly competitive with traditional MSF approaches.

Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

NASA Astrophysics Data System (ADS)

Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

2017-03-01

Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

A Novel Heat Shock Protein 70-based Vaccine Prepared from DC-Tumor Fusion Cells.

PubMed

Weng, Desheng; Calderwood, Stuart K; Gong, Jianlin

2018-01-01

We have developed an enhanced molecular chaperone-based vaccine through rapid isolation of Hsp70 peptide complexes after the fusion of tumor and dendritic cells (Hsp70.PC-F). In this approach, the tumor antigens are introduced into the antigen processing machinery of dendritic cells through the cell fusion process and thus we can obtain antigenic tumor peptides or their intermediates that have been processed by dendritic cells. Our results show that Hsp70.PC-F has increased immunogenicity compared to preparations from tumor cells alone and therefore constitutes an improved formulation of chaperone protein-based tumor vaccine.

Biometric identification based on feature fusion with PCA and SVM

NASA Astrophysics Data System (ADS)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina

2018-04-01

Biometric identification is gaining ground compared to traditional identification methods. Many biometric measurements may be used for secure human identification. The most reliable among them is the iris pattern because of its uniqueness, stability, unforgeability and inalterability over time. The approach presented in this paper is a fusion of different feature descriptor methods such as HOG, LIOP, LBP, used for extracting iris texture information. The classifiers obtained through the SVM and PCA methods demonstrate the effectiveness of our system applied to one and both irises. The performances measured are highly accurate and foreshadow a fusion system with a rate of identification approaching 100% on the UPOL database.

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.

Improved Accuracy Using Recursive Bayesian Estimation Based Language Model Fusion in ERP-Based BCI Typing Systems

PubMed Central

Orhan, U.; Erdogmus, D.; Roark, B.; Oken, B.; Purwar, S.; Hild, K. E.; Fowler, A.; Fried-Oken, M.

2013-01-01

RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive Bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve Bayesian fusion approach. The results indicate the superiority of the recursive Bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach. PMID:23366432

Three-dimensional fusion of spaceborne and ground radar reflectivity data using a neural network-based approach

NASA Astrophysics Data System (ADS)

Kou, Leilei; Wang, Zhuihui; Xu, Fen

2018-03-01

The spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission satellite (TRMM PR) can provide good measurement of the vertical structure of reflectivity, while ground radar (GR) has a relatively high horizontal resolution and greater sensitivity. Fusion of TRMM PR and GR reflectivity data may maximize the advantages from both instruments. In this paper, TRMM PR and GR reflectivity data are fused using a neural network (NN)-based approach. The main steps included are: quality control of TRMM PR and GR reflectivity data; spatiotemporal matchup; GR calibration bias correction; conversion of TRMM PR data from Ku to S band; fusion of TRMM PR and GR reflectivity data with an NN method; interpolation of reflectivity data that are below PR's sensitivity; blind areas compensation with a distance weighting-based merging approach; combination of three types of data: data with the NN method, data below PR's sensitivity and data within compensated blind areas. During the NN fusion step, the TRMM PR data are taken as targets of the training NNs, and gridded GR data after horizontal downsampling at different heights are used as the input. The trained NNs are then used to obtain 3D high-resolution reflectivity from the original GR gridded data. After 3D fusion of the TRMM PR and GR reflectivity data, a more complete and finer-scale 3D radar reflectivity dataset incorporating characteristics from both the TRMM PR and GR observations can be obtained. The fused reflectivity data are evaluated based on a convective precipitation event through comparison with the high resolution TRMM PR and GR data with an interpolation algorithm.

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

PubMed

Benkarim, Oualid M; Piella, Gemma; González Ballester, Miguel Angel; Sanroma, Gerard

2017-12-01

Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image. Accurately estimating the contribution of each atlas labelmap to the final segmentation is a critical step for the success of multi-atlas segmentation. Common approaches to label fusion either rely on local patch similarity, probabilistic statistical frameworks or a combination of both. In this work, we propose a probabilistic label fusion framework based on atlas label confidences computed at each voxel of the structure of interest. Maximum likelihood atlas confidences are estimated using a supervised approach, explicitly modeling the relationship between local image appearances and segmentation errors produced by each of the atlases. We evaluate different spatial pooling strategies for modeling local segmentation errors. We also present a novel type of label-dependent appearance features based on atlas labelmaps that are used during confidence estimation to increase the accuracy of our label fusion. Our approach is evaluated on the segmentation of seven subcortical brain structures from the MICCAI 2013 SATA Challenge dataset and the hippocampi from the ADNI dataset. Overall, our results indicate that the proposed label fusion framework achieves superior performance to state-of-the-art approaches in the majority of the evaluated brain structures and shows more robustness to registration errors. Copyright © 2017 Elsevier B.V. All rights reserved.

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

PubMed Central

Yang, Guocheng; Li, Meiling; Chen, Leiting; Yu, Jie

2015-01-01

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices. PMID:26557871

Fusion of approaches to the treatment of organ failure.

PubMed

Ogle, Brenda; Cascalho, Marilia; Platt, Jeffrey L

2004-01-01

Because organ transplantation is the preferred treatment for organ failure, the demand for human organs for transplantation is large and growing. From this demand, several fields based on new technologies for the replacement or repair of damaged tissues and organs have emerged. These fields include stem cell biology, cloning, tissue engineering and xenotransplantation. Here we evaluate the potential contribution of these to the devising of alternative approaches to organ replacement. We present our vision for the development of two structurally complex organs - the lung and the kidney - based on a 'fusion' of new and established technologies.

Multiclassifier information fusion methods for microarray pattern recognition

NASA Astrophysics Data System (ADS)

Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Herzig-Marx, Rachel

2004-04-01

This paper addresses automatic recognition of microarray patterns, a capability that could have a major significance for medical diagnostics, enabling development of diagnostic tools for automatic discrimination of specific diseases. The paper presents multiclassifier information fusion methods for microarray pattern recognition. The input space partitioning approach based on fitness measures that constitute an a-priori gauging of classification efficacy for each subspace is investigated. Methods for generation of fitness measures, generation of input subspaces and their use in the multiclassifier fusion architecture are presented. In particular, two-level quantification of fitness that accounts for the quality of each subspace as well as the quality of individual neighborhoods within the subspace is described. Individual-subspace classifiers are Support Vector Machine based. The decision fusion stage fuses the information from mulitple SVMs along with the multi-level fitness information. Final decision fusion stage techniques, including weighted fusion as well as Dempster-Shafer theory based fusion are investigated. It should be noted that while the above methods are discussed in the context of microarray pattern recognition, they are applicable to a broader range of discrimination problems, in particular to problems involving a large number of information sources irreducible to a low-dimensional feature space.

Case Study: Organotypic human in vitro models of embryonic ...

EPA Pesticide Factsheets

Morphogenetic fusion of tissues is a common event in embryonic development and disruption of fusion is associated with birth defects of the eye, heart, neural tube, phallus, palate, and other organ systems. Embryonic tissue fusion requires precise regulation of cell-cell and cell-matrix interactions that drive proliferation, differentiation, and morphogenesis. Chemical low-dose exposures can disrupt morphogenesis across space and time by interfering with key embryonic fusion events. The Morphogenetic Fusion Task uses computer and in vitro models to elucidate consequences of developmental exposures. The Morphogenetic Fusion Task integrates multiple approaches to model responses to chemicals that leaad to birth defects, including integrative mining on ToxCast DB, ToxRefDB, and chemical structures, advanced computer agent-based models, and human cell-based cultures that model disruption of cellular and molecular behaviors including mechanisms predicted from integrative data mining and agent-based models. The purpose of the poster is to indicate progress on the CSS 17.02 Virtual Tissue Models Morphogenesis Task 1 products for the Board of Scientific Counselors meeting on Nov 16-17.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection

PubMed Central

Wei, Pan; Anderson, Derek T.

2018-01-01

A significant challenge in object detection is accurate identification of an object’s position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications. PMID:29562609

Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.

PubMed

Zhang, Xinzheng; Rad, Ahmad B; Wong, Yiu-Kwong

2012-01-01

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

Layer-Based Approach for Image Pair Fusion.

PubMed

Son, Chang-Hwan; Zhang, Xiao-Ping

2016-04-20

Recently, image pairs, such as noisy and blurred images or infrared and noisy images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.

ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation

NASA Astrophysics Data System (ADS)

Pal, S. K.; Majumdar, T. J.; Bhattacharya, Amit K.

Fusion of optical and synthetic aperture radar data has been attempted in the present study for mapping of various lithologic units over a part of the Singhbhum Shear Zone (SSZ) and its surroundings. ERS-2 SAR data over the study area has been enhanced using Fast Fourier Transformation (FFT) based filtering approach, and also using Frost filtering technique. Both the enhanced SAR imagery have been then separately fused with histogram equalized IRS-1C LISS III image using Principal Component Analysis (PCA) technique. Later, Feature-oriented Principal Components Selection (FPCS) technique has been applied to generate False Color Composite (FCC) images, from which corresponding geological maps have been prepared. Finally, GIS techniques have been successfully used for change detection analysis in the lithological interpretation between the published geological map and the fusion based geological maps. In general, there is good agreement between these maps over a large portion of the study area. Based on the change detection studies, few areas could be identified which need attention for further detailed ground-based geological studies.

Mitigating Information Overload: The Impact of Context-Based Approach to the Design of Tools for Intelligence Analysts

DTIC Science & Technology

2008-03-01

amount of arriving data, extract actionable information, and integrate it with prior knowledge. Add to that the pressures of today’s fusion center...information, and integrate it with prior knowledge. Add to that the pressures of today’s fusion center climate and it becomes clear that analysts, police... fusion centers, including specifics about how these problems manifest at the Illinois State Police (ISP) Statewide Terrorism and Intelligence Center

Sensor fusion display evaluation using information integration models in enhanced/synthetic vision applications

NASA Technical Reports Server (NTRS)

Foyle, David C.

1993-01-01

Based on existing integration models in the psychological literature, an evaluation framework is developed to assess sensor fusion displays as might be implemented in an enhanced/synthetic vision system. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The pilot's performance with the sensor fusion image is compared to models' predictions based on the pilot's performance when viewing the original component sensor images prior to fusion. This allows for the determination as to when a sensor fusion system leads to: poorer performance than one of the original sensor displays, clearly an undesirable system in which the fused sensor system causes some distortion or interference; better performance than with either single sensor system alone, but at a sub-optimal level compared to model predictions; optimal performance compared to model predictions; or, super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays.

Infrared and visible image fusion with spectral graph wavelet transform.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Zong, Jing-guo

2015-09-01

Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.

Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion.

PubMed

Wang, Yang; Zhang, Wenjie; Wu, Lin; Lin, Xuemin; Zhao, Xiang

2017-01-01

Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature representations may combat this problem from different aspects; as visual data objects described by multiple features can be decomposed into multiple views, thus often provide complementary information. In this paper, we propose a cross-view fusion algorithm that leads to a similarity metric for multiview data by systematically fusing multiple similarity measures. Unlike existing paradigms, we focus on learning distance measure by exploiting a graph structure of data samples, where an input similarity matrix can be improved through a propagation of graph random walk. In particular, we construct multiple graphs with each one corresponding to an individual view, and a cross-view fusion approach based on graph random walk is presented to derive an optimal distance measure by fusing multiple metrics. Our method is scalable to a large amount of data by enforcing sparsity through an anchor graph representation. To adaptively control the effects of different views, we dynamically learn view-specific coefficients, which are leveraged into graph random walk to balance multiviews. However, such a strategy may lead to an over-smooth similarity metric where affinities between dissimilar samples may be enlarged by excessively conducting cross-view fusion. Thus, we figure out a heuristic approach to controlling the iteration number in the fusion process in order to avoid over smoothness. Extensive experiments conducted on real-world data sets validate the effectiveness and efficiency of our approach.

Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization.

PubMed

Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

2015-01-05

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

PubMed Central

Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

2015-01-01

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m. PMID:25569750

A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

NASA Astrophysics Data System (ADS)

JafarGandomi, Arash; Binley, Andrew

2013-09-01

We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is successful not only in enhancing the subsurface information but also as a survey design tool to identify the appropriate combination of the geophysical tools and show whether application of an individual method for further investigation of a specific site is beneficial.

Data Fusion Based on Optical Technology for Observation of Human Manipulation

NASA Astrophysics Data System (ADS)

Falco, Pietro; De Maria, Giuseppe; Natale, Ciro; Pirozzi, Salvatore

2012-01-01

The adoption of human observation is becoming more and more frequent within imitation learning and programming by demonstration approaches (PbD) to robot programming. For robotic systems equipped with anthropomorphic hands, the observation phase is very challenging and no ultimate solution exists. This work proposes a novel mechatronic approach to the observation of human hand motion during manipulation tasks. The strategy is based on the combined use of an optical motion capture system and a low-cost data glove equipped with novel joint angle sensors, based on optoelectronic technology. The combination of the two information sources is obtained through a sensor fusion algorithm based on the extended Kalman filter (EKF) suitably modified to tackle the problem of marker occlusions, typical of optical motion capture systems. This approach requires a kinematic model of the human hand. Another key contribution of this work is a new method to calibrate this model.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues

NASA Astrophysics Data System (ADS)

Adams, W. H.; Iyengar, Giridharan; Lin, Ching-Yung; Naphade, Milind Ramesh; Neti, Chalapathy; Nock, Harriet J.; Smith, John R.

2003-12-01

We present a learning-based approach to the semantic indexing of multimedia content using cues derived from audio, visual, and text features. We approach the problem by developing a set of statistical models for a predefined lexicon. Novel concepts are then mapped in terms of the concepts in the lexicon. To achieve robust detection of concepts, we exploit features from multiple modalities, namely, audio, video, and text. Concept representations are modeled using Gaussian mixture models (GMM), hidden Markov models (HMM), and support vector machines (SVM). Models such as Bayesian networks and SVMs are used in a late-fusion approach to model concepts that are not explicitly modeled in terms of features. Our experiments indicate promise in the proposed classification and fusion methodologies: our proposed fusion scheme achieves more than 10% relative improvement over the best unimodal concept detector.

A fusion algorithm for infrared and visible based on guided filtering and phase congruency in NSST domain

NASA Astrophysics Data System (ADS)

Liu, Zhanwen; Feng, Yan; Chen, Hang; Jiao, Licheng

2017-10-01

A novel and effective image fusion method is proposed for creating a highly informative and smooth surface of fused image through merging visible and infrared images. Firstly, a two-scale non-subsampled shearlet transform (NSST) is employed to decompose the visible and infrared images into detail layers and one base layer. Then, phase congruency is adopted to extract the saliency maps from the detail layers and a guided filtering is proposed to compute the filtering output of base layer and saliency maps. Next, a novel weighted average technique is used to make full use of scene consistency for fusion and obtaining coefficients map. Finally the fusion image was acquired by taking inverse NSST of the fused coefficients map. Experiments show that the proposed approach can achieve better performance than other methods in terms of subjective visual effect and objective assessment.

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE PAGES

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

2017-02-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

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

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Distributed service-based approach for sensor data fusion in IoT environments.

PubMed

Rodríguez-Valenzuela, Sandra; Holgado-Terriza, Juan A; Gutiérrez-Guerrero, José M; Muros-Cobos, Jesús L

2014-10-15

The Internet of Things (IoT) enables the communication among smart objects promoting the pervasive presence around us of a variety of things or objects that are able to interact and cooperate jointly to reach common goals. IoT objects can obtain data from their context, such as the home, office, industry or body. These data can be combined to obtain new and more complex information applying data fusion processes. However, to apply data fusion algorithms in IoT environments, the full system must deal with distributed nodes, decentralized communication and support scalability and nodes dynamicity, among others restrictions. In this paper, a novel method to manage data acquisition and fusion based on a distributed service composition model is presented, improving the data treatment in IoT pervasive environments.

Distributed Service-Based Approach for Sensor Data Fusion in IoT Environments

PubMed Central

Rodríguez-Valenzuela, Sandra; Holgado-Terriza, Juan A.; Gutiérrez-Guerrero, José M.; Muros-Cobos, Jesús L.

2014-01-01

The Internet of Things (IoT) enables the communication among smart objects promoting the pervasive presence around us of a variety of things or objects that are able to interact and cooperate jointly to reach common goals. IoT objects can obtain data from their context, such as the home, office, industry or body. These data can be combined to obtain new and more complex information applying data fusion processes. However, to apply data fusion algorithms in IoT environments, the full system must deal with distributed nodes, decentralized communication and support scalability and nodes dynamicity, among others restrictions. In this paper, a novel method to manage data acquisition and fusion based on a distributed service composition model is presented, improving the data treatment in IoT pervasive environments. PMID:25320907

HEDP and new directions for fusion energy

NASA Astrophysics Data System (ADS)

Kirkpatrick, Ronald C.

2010-06-01

Magnetic-confinement fusion energy and inertia-confinement fusion energy (IFE) represent two extreme approaches to the quest for the application of thermonuclear fusion to electrical energy generation. Blind pursuit of these extreme approaches has long delayed the achievement of their common goal. We point out the possibility of an intermediate approach that promises cheaper, and consequently more rapid development of fusion energy. For example, magneto-inertial fusion appears to be possible over a broad range of parameter space. It is further argued that imposition of artificial constraints impedes the discovery of physics solutions for the fusion energy problem.

A fusion-protein approach enabling mammalian cell production of tumor targeting protein domains for therapeutic development.

PubMed

Hu, Jia; Chen, Xiang; Zhang, Xuhua; Yuan, Xiaopeng; Yang, Mingjuan; Dai, Hui; Yang, Wei; Zhou, Qinghua; Wen, Weihong; Wang, Qirui; Qin, Weijun; Zhao, Aizhi

2018-05-01

A single chain Fv fragment (scFv) is a fusion of the variable regions of heavy (V H ) and light (V L ) chains of immunoglobulins. They are important elements of chimeric antigen receptors for cancer therapy. We sought to produce a panel of 16 extracellular protein domains of tumor markers for use in scFv yeast library screenings. A series of vectors comprising various combinations of expression elements was made, but expression was unpredictable and more than half of the protein domains could not be produced using any of the constructs. Here we describe a novel fusion expression system based on mouse TEM7 (tumor endothelial marker 7), which could facilitate protein expression. With this approach we could produce all but one of the tumor marker domains that could not otherwise be expressed. In addition, we demonstrated that the tumor associated antigen hFZD10 produced as a fusion protein with mTEM7 could be used to enrich scFv antibodies from a yeast display library. Collectively our study demonstrates the potential of specific fusion proteins based on mTEM7 in enabling mammalian cell production of tumor targeting protein domains for therapeutic development. © 2018 The Protein Society.

Case presentation and short perspective on management of foraminal/far lateral discs and stenosis.

PubMed

Epstein, Nancy E

2018-01-01

The management of lumbar foraminal/far lateral discs (FOR/FLD) with stenosis remains controversial. Operative choices should be based on each patient's preoperative dynamic X-ray findings, magnetic resonance (MR), and computed tomography (CT) studies. Here we reviewed several options for decompression alone vs. decompression with fusion. Safe excision of FOR/FLD with stenosis should begin at the level above the disc herniation, as identification of the superior, foraminally, and far laterally exiting nerve root is critical. Performing an undercutting laminectomy and utilizing an operating microscope usually preserves the facet joints, and in many cases, avoids the need for fusion. Other decompressive techniques include; the intertransverse (ITT), and Wiltse approaches. Fusions following complete unilateral full facetectomy may be; noninstrumented (e.g., older, osteoporotic patients) vs. instrumented (e.g., posterolateral fusion or occasionally transforaminal lumbar interbody fusion). Here we present a patient with L2-L5 stenosis, and a left L3-L4 FOR/FLD, and multiple synovial cysts who was successfully managed with an l2-L5 laminecotmy, left L34 FOR/FLD diksectomy without fusion. Postoperatively, the patient was neurologically intact, and stability was maintained. Adjunctive measures for FOR/FLD diksectomy should include; intraoperative monitoring, use of the operating microscope, and an intraoperative film with a radiopaque marker in the correct disc space to confirm the correct level of diskectomy. There are multiple approaches to the excision of FOR/FLD with stenosis. These include; decompression alone vs. decompression with non-instrumented vs. instrumented fusion. Surgical choices must be based on individual patient's X-ray, MR, and CT findings. The aim should be to maximize the safety of disc excision with decompression of stenosis, and to preserve stability, reducing the need for fusion, while minimizing morbidity.

Target Engageability Improvement Through Adaptive Data Fusion and Sensor Management: An Approach Based on the Fire Control Radar Search to Lock-On Time

DTIC Science & Technology

2008-05-01

ch based on the fire control radar search to l o ck - o n t i m e F. Rhéaume A. Benaskeur DRDC Valcartier Defence R& D Canada...recherche visant à développer et démontrer des concepts avancés de fusion de données adaptative et de gestion de res- sources. Les systèmes C2 navals...militaires sont en grande partie appuyés par des techno- logies de fusion de données et de gestion de ressources. Le C2 naval militaire doit

Multiscale Medical Image Fusion in Wavelet Domain

PubMed Central

Khare, Ashish

2013-01-01

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach. PMID:24453868

A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem.

PubMed

Khelifi, Lazhar; Mignotte, Max

2017-08-01

Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

Sensor fusion approaches for EMI and GPR-based subsurface threat identification

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

Z-Pinch fusion-based nuclear propulsion

NASA Astrophysics Data System (ADS)

Miernik, J.; Statham, G.; Fabisinski, L.; Maples, C. D.; Adams, R.; Polsgrove, T.; Fincher, S.; Cassibry, J.; Cortez, R.; Turner, M.; Percy, T.

2013-02-01

Fusion-based nuclear propulsion has the potential to enable fast interplanetary transportation. Due to the great distances between the planets of our solar system and the harmful radiation environment of interplanetary space, high specific impulse (Isp) propulsion in vehicles with high payload mass fractions must be developed to provide practical and safe vehicles for human space flight missions. The Z-Pinch dense plasma focus method is a Magneto-Inertial Fusion (MIF) approach that may potentially lead to a small, low cost fusion reactor/engine assembly [1]. Recent advancements in experimental and theoretical understanding of this concept suggest favorable scaling of fusion power output yield [2]. The magnetic field resulting from the large current compresses the plasma to fusion conditions, and this process can be pulsed over short timescales (10-6 s). This type of plasma formation is widely used in the field of Nuclear Weapons Effects testing in the defense industry, as well as in fusion energy research. A Z-Pinch propulsion concept was designed for a vehicle based on a previous fusion vehicle study called "Human Outer Planet Exploration" (HOPE), which used Magnetized Target Fusion (MTF) [3] propulsion. The reference mission is the transport of crew and cargo to Mars and back, with a reusable vehicle. The analysis of the Z-Pinch MIF propulsion system concludes that a 40-fold increase of Isp over chemical propulsion is predicted. An Isp of 19,436 s and thrust of 3812 N s/pulse, along with nearly doubling the predicted payload mass fraction, warrants further development of enabling technologies.

Multi Sensor Fusion Using Fitness Adaptive Differential Evolution

NASA Astrophysics Data System (ADS)

Giri, Ritwik; Ghosh, Arnob; Chowdhury, Aritra; Das, Swagatam

The rising popularity of multi-source, multi-sensor networks supports real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on a modified version of Differential Evolution (DE), called Fitness Adaptive Differential Evolution (FiADE). FiADE treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed approach is formulated to produce good result for the problems that are high-dimensional, highly nonlinear, and random. The proposed approach gives better result in case of optimal allocation of sensors. The performance of the proposed approach is compared with an evolutionary algorithm coordination generalized particle model (C-GPM).

[Anterior lumbar interbody fusion. Indications, technique, advantages and disadvantages].

PubMed

Richter, M; Weidenfeld, M; Uckmann, F P

2015-02-01

Anterior lumbar interbody fusion (ALIF) for lumbar interbody fusion from L2 to the sacrum has been an established technique for decades. The advantages and disadvantages of ALIF compared to posterior interbody fusion techniques are discussed. The operative technique is described in detail. Complications and avoidance strategies are discussed. This article is based on a selective literature search using PubMed and the experience of the authors in this medical field. The advantages of ALIF compared to posterior fusion techniques are the free approach to the anterior disc space without opening of the spinal canal or the neural foramina. This gives the possibility of an extensive anterior release and placement of the largest possible cages without the risk of neural structure damage. The disadvantages of ALIF are the additional anterior approach and the related complications. The most frequent complication is due to damage of vessels. The rate of complications is significantly increased in revision surgery. The ALIF technique meaningfully expands the repertoire of the spinal surgeon especially for the treatment of non-union after interbody fusion, in patients with epidural scar tissue at the index level and spinal infections. Advantages and disadvantages should be considered when evaluating the indications for ALIF.

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

PubMed Central

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-01-01

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes. PMID:26712766

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation.

PubMed

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-12-26

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and 24 fuzzy rules for the robot's movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

PubMed Central

Röbesaat, Jenny; Zhang, Peilin; Abdelaal, Mohamed; Theel, Oliver

2017-01-01

Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter. PMID:28445421

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures.

PubMed

Sjöberg, C; Ahnesjö, A

2013-06-01

Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Ion kinetic effects on the ignition and burn of inertial confinement fusion targets: A multi-scale approach

NASA Astrophysics Data System (ADS)

Peigney, B. E.; Larroche, O.; Tikhonchuk, V.

2014-12-01

In this article, we study the hydrodynamics and burn of the thermonuclear fuel in inertial confinement fusion pellets at the ion kinetic level. The analysis is based on a two-velocity-scale Vlasov-Fokker-Planck kinetic model that is specially tailored to treat fusion products (suprathermal α-particles) in a self-consistent manner with the thermal bulk. The model assumes spherical symmetry in configuration space and axial symmetry in velocity space around the mean flow velocity. A typical hot-spot ignition design is considered. Compared with fluid simulations where a multi-group diffusion scheme is applied to model α transport, the full ion-kinetic approach reveals significant non-local effects on the transport of energetic α-particles. This has a direct impact on hydrodynamic spatial profiles during combustion: the hot spot reactivity is reduced, while the inner dense fuel layers are pre-heated by the escaping α-suprathermal particles, which are transported farther out of the hot spot. We show how the kinetic transport enhancement of fusion products leads to a significant reduction of the fusion yield.

A Markov game theoretic data fusion approach for cyber situational awareness

NASA Astrophysics Data System (ADS)

Shen, Dan; Chen, Genshe; Cruz, Jose B., Jr.; Haynes, Leonard; Kruger, Martin; Blasch, Erik

2007-04-01

This paper proposes an innovative data-fusion/ data-mining game theoretic situation awareness and impact assessment approach for cyber network defense. Alerts generated by Intrusion Detection Sensors (IDSs) or Intrusion Prevention Sensors (IPSs) are fed into the data refinement (Level 0) and object assessment (L1) data fusion components. High-level situation/threat assessment (L2/L3) data fusion based on Markov game model and Hierarchical Entity Aggregation (HEA) are proposed to refine the primitive prediction generated by adaptive feature/pattern recognition and capture new unknown features. A Markov (Stochastic) game method is used to estimate the belief of each possible cyber attack pattern. Game theory captures the nature of cyber conflicts: determination of the attacking-force strategies is tightly coupled to determination of the defense-force strategies and vice versa. Also, Markov game theory deals with uncertainty and incompleteness of available information. A software tool is developed to demonstrate the performance of the high level information fusion for cyber network defense situation and a simulation example shows the enhanced understating of cyber-network defense.

Ion kinetic effects on the ignition and burn of inertial confinement fusion targets: A multi-scale approach

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

Peigney, B. E.; Larroche, O.; Tikhonchuk, V.

2014-12-15

In this article, we study the hydrodynamics and burn of the thermonuclear fuel in inertial confinement fusion pellets at the ion kinetic level. The analysis is based on a two-velocity-scale Vlasov-Fokker-Planck kinetic model that is specially tailored to treat fusion products (suprathermal α-particles) in a self-consistent manner with the thermal bulk. The model assumes spherical symmetry in configuration space and axial symmetry in velocity space around the mean flow velocity. A typical hot-spot ignition design is considered. Compared with fluid simulations where a multi-group diffusion scheme is applied to model α transport, the full ion-kinetic approach reveals significant non-local effectsmore » on the transport of energetic α-particles. This has a direct impact on hydrodynamic spatial profiles during combustion: the hot spot reactivity is reduced, while the inner dense fuel layers are pre-heated by the escaping α-suprathermal particles, which are transported farther out of the hot spot. We show how the kinetic transport enhancement of fusion products leads to a significant reduction of the fusion yield.« less

The High Field Path to Practical Fusion Energy

NASA Astrophysics Data System (ADS)

Mumgaard, Robert; Whyte, D.; Greenwald, M.; Hartwig, Z.; Brunner, D.; Sorbom, B.; Marmar, E.; Minervini, J.; Bonoli, P.; Irby, J.; Labombard, B.; Terry, J.; Vieira, R.; Wukitch, S.

2017-10-01

We propose a faster, lower cost development path for fusion energy enabled by high temperature superconductors, devices at high magnetic field, innovative technologies and modern approaches to technology development. Timeliness, scale, and economic-viability are the drivers for fusion energy to combat climate change and aid economic development. The opportunities provided by high-temperature superconductors, innovative engineering and physics, and new organizational structures identified over the last few years open new possibilities for realizing practical fusion energy that could meet mid-century de-carbonization needs. We discuss re-factoring the fusion energy development path with an emphasis on concrete risk retirement strategies utilizing a modular approach based on the high-field tokamak that leverages the broader tokamak physics understanding of confinement, stability, and operational limits. Elements of this plan include development of high-temperature superconductor magnets, simplified immersion blankets, advanced long-leg divertors, a compact divertor test tokamak, efficient current drive, modular construction, and demountable magnet joints. An R&D plan culminating in the construction of an integrated pilot plant and test facility modeled on the ARC concept is presented.

Techniques of lumbar-sacral spine fusion in spondylosis: systematic literature review and meta-analysis of randomized clinical trials.

PubMed

Umeta, Ricardo S G; Avanzi, Osmar

2011-07-01

Spine fusions can be performed through different techniques and are used to treat a number of vertebral pathologies. However, there seems to be no consensus regarding which technique of fusion is best suited to treat each distinct spinal disease or group of diseases. To study the effectiveness and complications of the different techniques used for spinal fusion in patients with lumbar spondylosis. Systematic literature review and meta-analysis. Randomized clinical studies comparing the most commonly performed surgical techniques for spine fusion in lumbar-sacral spondylosis, as well as those reporting patient outcome were selected. Identify which technique, if any, presents the best clinical, functional, and radiographic outcome. Systematic literature review and meta-analysis based on scientific articles published and indexed to the following databases: PubMed (1966-2009), Cochrane Collaboration-CENTRAL, EMBASE (1980-2009), and LILACS (1982-2009). The general search strategy focused on the surgical treatment of patients with lumbar-sacral spondylosis. Eight studies met the inclusion criteria and were selected with a total of 1,136 patients. Meta-analysis showed that patients who underwent interbody fusion presented a significantly smaller blood loss (p=.001) and a greater rate of bone fusion (p=.02). Patients submitted to fusion using the posterolateral approach had a significantly shorter operative time (p=.007) and less perioperative complications (p=.03). No statistically significant difference was found for the other studied variables (pain, functional impairment, and return to work). The most commonly used techniques for lumbar spine fusion in patients with spondylosis were interbody fusion and posterolateral approach. Both techniques were comparable in final outcome, but the former presented better rates of fusion and the latter the less complications. Copyright © 2011 Elsevier Inc. All rights reserved.

CONFERENCE REPORT: Summary of the 8th IAEA Technical Meeting on Fusion Power Plant Safety

NASA Astrophysics Data System (ADS)

Girard, J. Ph.; Gulden, W.; Kolbasov, B.; Louzeiro-Malaquias, A.-J.; Petti, D.; Rodriguez-Rodrigo, L.

2008-01-01

Reports were presented covering a selection of topics on the safety of fusion power plants. These included a review on licensing studies developed for ITER site preparation surveying common and non-common issues (i.e. site dependent) as lessons to a broader approach for fusion power plant safety. Several fusion power plant models, spanning from accessible technology to more advanced-materials based concepts, were discussed. On the topic related to fusion-specific technology, safety studies were reported on different concepts of breeding blanket modules, tritium handling and auxiliary systems under normal and accident scenarios' operation. The testing of power plant relevant technology in ITER was also assessed in terms of normal operation and accident scenarios, and occupational doses and radioactive releases under these testings have been determined. Other specific safety issues for fusion have also been discussed such as availability and reliability of fusion power plants, dust and tritium inventories and component failure databases. This study reveals that the environmental impact of fusion power plants can be minimized through a proper selection of low activation materials and using recycling technology helping to reduce waste volume and potentially open the route for its reutilization for the nuclear sector or even its clearance into the commercial circuit. Computational codes for fusion safety have been presented in support of the many studies reported. The on-going work on establishing validation approaches aiming at improving the prediction capability of fusion codes has been supported by experimental results and new directions for development have been identified. Fusion standards are not available and fission experience is mostly used as the framework basis for licensing and target design for safe operation and occupational and environmental constraints. It has been argued that fusion can benefit if a specific fusion approach is implemented, in particular for materials selection which will have a large impact on waste disposal and recycling and in the real limits of radiation releases if indexed to the real impact on individuals and the environment given the differences in the types of radiation emitted by tritium when compared with the fission products. Round table sessions resulted in some common recommendations. The discussions also created the awareness of the need for a larger involvement of the IAEA in support of fusion safety standards development.

A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles

PubMed Central

Meng, Xiaoli

2017-01-01

Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization. PMID:28926996

A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles.

PubMed

Meng, Xiaoli; Wang, Heng; Liu, Bingbing

2017-09-18

Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study

PubMed Central

Sappa, Angel D.; Carvajal, Juan A.; Aguilera, Cristhian A.; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X.

2016-01-01

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). PMID:27294938

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.

PubMed

Sappa, Angel D; Carvajal, Juan A; Aguilera, Cristhian A; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X

2016-06-10

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

Fusion Propulsion Z-Pinch Engine Concept

NASA Technical Reports Server (NTRS)

Miernik, J.; Statham, G.; Fabisinski, L.; Maples, C. D.; Adams, R.; Polsgrove, T.; Fincher, S.; Cassibry, J.; Cortez, R.; Turner, M.;

Artificial intelligence (AI)-based relational matching and multimodal medical image fusion: generalized 3D approaches

NASA Astrophysics Data System (ADS)

Vajdic, Stevan M.; Katz, Henry E.; Downing, Andrew R.; Brooks, Michael J.

1994-09-01

A 3D relational image matching/fusion algorithm is introduced. It is implemented in the domain of medical imaging and is based on Artificial Intelligence paradigms--in particular, knowledge base representation and tree search. The 2D reference and target images are selected from 3D sets and segmented into non-touching and non-overlapping regions, using iterative thresholding and/or knowledge about the anatomical shapes of human organs. Selected image region attributes are calculated. Region matches are obtained using a tree search, and the error is minimized by evaluating a `goodness' of matching function based on similarities of region attributes. Once the matched regions are found and the spline geometric transform is applied to regional centers of gravity, images are ready for fusion and visualization into a single 3D image of higher clarity.

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

PubMed Central

Girrbach, Fabian; Hol, Jeroen D.; Bellusci, Giovanni; Diehl, Moritz

2017-01-01

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. PMID:28534857

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach.

PubMed

Girrbach, Fabian; Hol, Jeroen D; Bellusci, Giovanni; Diehl, Moritz

2017-05-19

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.

Cost-effectiveness of lumbar artificial intervertebral disc replacement: driven by the choice of comparator.

PubMed

Parkinson, Bonny; Goodall, Stephen; Thavaneswaran, Prema

2013-09-01

Lower back pain is a common and costly condition in Australia. This paper aims to conduct an economic evaluation of lumbar artificial intervertebral disc replacement (AIDR) compared with lumbar fusion for the treatment of patients suffering from significant axial back pain and/or radicular (nerve root) pain, secondary to disc degeneration or prolapse, who have failed conservative treatment. A cost-effectiveness approach was used to compare costs and benefits of AIDR to five fusion approaches. Resource use was based on Medicare Benefits Schedule claims data and expert opinion. Effectiveness and re-operation rates were based on published randomized controlled trials. The key clinical outcomes considered were narcotic medication discontinuation, achievement of overall clinical success, achievement of Oswestry Disability Index success and quality-adjusted life-years gained. AIDR was estimated to be cost-saving compared with fusion overall ($1600/patient); however, anterior lumbar interbody fusion and posterolateral fusion were less costly by $2155 and $807, respectively. The incremental cost-effectiveness depends on the outcome considered and the comparator. AIDR is potentially a cost-saving treatment for lumbar disc degeneration, although longer-term follow-up data are required to substantiate this claim. The incremental cost-effectiveness depends on the outcome considered and the comparator, and further research is required before any firm conclusions can be drawn. © 2012 The Authors. ANZ Journal of Surgery © 2012 Royal Australasian College of Surgeons.

Label fusion based brain MR image segmentation via a latent selective model

NASA Astrophysics Data System (ADS)

Liu, Gang; Guo, Xiantang; Zhu, Kai; Liao, Hengxu

2018-04-01

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

Modeling and numerical analysis of a magneto-inertial fusion concept with the target created through FRC merging

NASA Astrophysics Data System (ADS)

Li, Chenguang; Yang, Xianjun

2016-10-01

The Magnetized Plasma Fusion Reactor concept is proposed as a magneto-inertial fusion approach based on the target plasma created through the collision merging of two oppositely translating field reversed configuration plasmas, which is then compressed by the imploding liner driven by the pulsed-power driver. The target creation process is described by a two-dimensional magnetohydrodynamics model, resulting in the typical target parameters. The implosion process and the fusion reaction are modeled by a simple zero-dimensional model, taking into account the alpha particle heating and the bremsstrahlung radiation loss. The compression on the target can be 2D cylindrical or 2.4D with the additive axial contraction taken into account. The dynamics of the liner compression and fusion burning are simulated and the optimum fusion gain and the associated target parameters are predicted. The scientific breakeven could be achieved at the optimized conditions.

ANNETTE Project: Contributing to The Nuclearization of Fusion

NASA Astrophysics Data System (ADS)

Ambrosini, W.; Cizelj, L.; Dieguez Porras, P.; Jaspers, R.; Noterdaeme, J.; Scheffer, M.; Schoenfelder, C.

2018-01-01

The ANNETTE Project (Advanced Networking for Nuclear Education and Training and Transfer of Expertise) is well underway, and one of its work packages addresses the design, development and implementation of nuclear fusion training. A systematic approach is used that leads to the development of new training courses, based on identified nuclear competences needs of the work force of (future) fusion reactors and on the current availability of suitable training courses. From interaction with stakeholders involved in the ITER design and construction or the JET D-T campaign, it became clear that the lack of nuclear safety culture awareness already has an impact on current projects. Through the collaboration between the European education networks in fission (ENEN) and fusion (FuseNet) in the ANNETTE project, this project is well positioned to support the development of nuclear competences for ongoing and future fusion projects. Thereby it will make a clear contribution to the realization of fusion energy.

Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications

PubMed Central

Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng

2011-01-01

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible. PMID:22164117

Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications.

PubMed

Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng

2011-01-01

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver's visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible.

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

PubMed

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

Comparison of complications, costs, and length of stay of three different lumbar interbody fusion techniques: an analysis of the Nationwide Inpatient Sample database.

PubMed

Goz, Vadim; Weinreb, Jeffrey H; Schwab, Frank; Lafage, Virginie; Errico, Thomas J

2014-09-01

Lumbar interbody fusion (LIF) techniques have been used for years to treat a number of pathologies of the lower back. These procedures may use an anterior, posterior, or combined surgical approach. Each approach is associated with a unique set of complications, but the exact prevalence of complications associated with each approach remains unclear. To investigate the rates of perioperative complications of anterior lumbar interbody fusion (ALIF), posterior/transforaminal lumbar interbody fusion (P/TLIF), and LIF with a combined anterior-posterior interbody fusion (APF). Retrospective review of national data from a large administrative database. Patients undergoing ALIF, P/TLIF, or APF. Perioperative complications, length of stay (LOS), total costs, and mortality. The Nationwide Inpatient Sample database was queried for patients undergoing ALIF, P/TLIF, or APF between 2001 and 2010 as identified via International Classification of Diseases, ninth revision codes. Univariate analyses were carried out comparing the three cohorts in terms of the outcomes of interest. Multivariate analysis for primary outcomes was carried out adjusting for overall comorbidity burden, race, gender, age, and length of fusion. National estimates of annual total number of procedures were calculated based on the provided discharge weights. Geographic distribution of the three cohorts was also investigated. An estimated total of 923,038 LIFs were performed between 2001 and 2010 in the United States. Posterior/transforaminal lumbar interbody fusions accounted for 79% to 86% of total LIFs between 2001 and 2010, ALIFs for 10% to 15%, and APF decreased from 10% in 2002 to less than 1% in 2010. On average, P/TLIF patients were oldest (54.55 years), followed by combined approach (47.23 years) and ALIF (46.94 years) patients (p<.0001). Anterior lumbar interbody fusion, P/TLIF, and combined surgical costs were $75,872, $65,894, and $92,249, respectively (p<.0001). Patients in the P/TLIF cohort had the greatest number of comorbidities, having the highest prevalence for 10 of 17 comorbidities investigated. Anterior-posterior interbody fusion group was associated with the greatest number of complications, having the highest incidence of 12 of the 16 complications investigated. These data help to define the perioperative risks for several LIF approaches. Comparison of outcomes showed that a combined approach is more expensive and associated with greater LOS, whereas ALIF is associated with the highest postoperative mortality. These trends should be taken into consideration during surgical planning to improve clinical outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation.

PubMed

Torrado-Carvajal, Angel; Herraiz, Joaquin L; Hernandez-Tamames, Juan A; San Jose-Estepar, Raul; Eryaman, Yigitcan; Rozenholc, Yves; Adalsteinsson, Elfar; Wald, Lawrence L; Malpica, Norberto

2016-04-01

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. © 2015 Wiley Periodicals, Inc.

Dynamic approach to description of entrance channel effects in angular distributions of fission fragments

NASA Astrophysics Data System (ADS)

Eremenko, D. O.; Drozdov, V. A.; Fotina, O. V.; Platonov, S. Yu.; Yuminov, O. A.

2016-07-01

Background: It is well known that the anomalous behavior of angular anisotropies of fission fragments at sub- and near-barrier energies is associated with a memory of conditions in the entrance channel of the heavy-ion reactions, particularly, deformations and spins of colliding nuclei that determine the initial distributions for the components of the total angular momentum over the symmetry axis of the fissioning system and the beam axis. Purpose: We develop a new dynamic approach, which allows the description of the memory effects in the fission fragment angular distributions and provides new information on fusion and fission dynamics. Methods: The approach is based on the dynamic model of the fission fragment angular distributions which takes into account stochastic aspects of nuclear fission and thermal fluctuations for the tilting mode that is characterized by the projection of the total angular momentum onto the symmetry axis of the fissioning system. Another base of our approach is the quantum mechanical method to calculate the initial distributions over the components of the total angular momentum of the nuclear system immediately following complete fusion. Results: A method is suggested for calculating the initial distributions of the total angular momentum projection onto the symmetry axis for the nuclear systems formed in the reactions of complete fusion of deformed nuclei with spins. The angular distributions of fission fragments for the 16O+232Th,12C+235,236,238, and 13C+235U reactions have been analyzed within the dynamic approach over a range of sub- and above-barrier energies. The analysis allowed us to determine the relaxation time for the tilting mode and the fraction of fission events occurring in times not larger than the relaxation time for the tilting mode. Conclusions: It is shown that the memory effects play an important role in the formation of the angular distributions of fission fragments for the reactions induced by heavy ions. The approach developed for analysis of the effects is a suitable tool to get insight into the complete fusion-fission dynamics, in particular, to investigate the mechanism of the complete fusion and fission time scale.

High-Energy Space Propulsion Based on Magnetized Target Fusion

NASA Technical Reports Server (NTRS)

Thio, Y. C. F.; Landrum, D. B.; Freeze, B.; Kirkpatrick, R. C.; Gerrish, H.; Schmidt, G. R.

1999-01-01

Magnetized target fusion is an approach in which a magnetized target plasma is compressed inertially by an imploding material wall. A high energy plasma liner may be used to produce the required implosion. The plasma liner is formed by the merging of a number of high momentum plasma jets converging towards the center of a sphere where two compact toroids have been introduced. Preliminary 3-D hydrodynamics modeling results using the SPHINX code of Los Alamos National Laboratory have been very encouraging and confirm earlier theoretical expectations. The concept appears ready for experimental exploration and plans for doing so are being pursued. In this talk, we explore conceptually how this innovative fusion approach could be packaged for space propulsion for interplanetary travel. We discuss the generally generic components of a baseline propulsion concept including the fusion engine, high velocity plasma accelerators, generators of compact toroids using conical theta pinches, magnetic nozzle, neutron absorption blanket, tritium reprocessing system, shock absorber, magnetohydrodynamic generator, capacitor pulsed power system, thermal management system, and micrometeorite shields.

Framework for 2D-3D image fusion of infrared thermography with preoperative MRI.

PubMed

Hoffmann, Nico; Weidner, Florian; Urban, Peter; Meyer, Tobias; Schnabel, Christian; Radev, Yordan; Schackert, Gabriele; Petersohn, Uwe; Koch, Edmund; Gumhold, Stefan; Steiner, Gerald; Kirsch, Matthias

2017-11-27

Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.

Lumbar interbody fusion: techniques, indications and comparison of interbody fusion options including PLIF, TLIF, MI-TLIF, OLIF/ATP, LLIF and ALIF

PubMed Central

Phan, Kevin; Malham, Greg; Seex, Kevin; Rao, Prashanth J.

2015-01-01

Degenerative disc and facet joint disease of the lumbar spine is common in the ageing population, and is one of the most frequent causes of disability. Lumbar spondylosis may result in mechanical back pain, radicular and claudicant symptoms, reduced mobility and poor quality of life. Surgical interbody fusion of degenerative levels is an effective treatment option to stabilize the painful motion segment, and may provide indirect decompression of the neural elements, restore lordosis and correct deformity. The surgical options for interbody fusion of the lumbar spine include: posterior lumbar interbody fusion (PLIF), transforaminal lumbar interbody fusion (TLIF), minimally invasive transforaminal lumbar interbody fusion (MI-TLIF), oblique lumbar interbody fusion/anterior to psoas (OLIF/ATP), lateral lumbar interbody fusion (LLIF) and anterior lumbar interbody fusion (ALIF). The indications may include: discogenic/facetogenic low back pain, neurogenic claudication, radiculopathy due to foraminal stenosis, lumbar degenerative spinal deformity including symptomatic spondylolisthesis and degenerative scoliosis. In general, traditional posterior approaches are frequently used with acceptable fusion rates and low complication rates, however they are limited by thecal sac and nerve root retraction, along with iatrogenic injury to the paraspinal musculature and disruption of the posterior tension band. Minimally invasive (MIS) posterior approaches have evolved in an attempt to reduce approach related complications. Anterior approaches avoid the spinal canal, cauda equina and nerve roots, however have issues with approach related abdominal and vascular complications. In addition, lateral and OLIF techniques have potential risks to the lumbar plexus and psoas muscle. The present study aims firstly to comprehensively review the available literature and evidence for different lumbar interbody fusion (LIF) techniques. Secondly, we propose a set of recommendations and guidelines for the indications for interbody fusion options. Thirdly, this article provides a description of each approach, and illustrates the potential benefits and disadvantages of each technique with reference to indication and spine level performed. PMID:27683674

Measuring situational awareness and resolving inherent high-level fusion obstacles

NASA Astrophysics Data System (ADS)

Sudit, Moises; Stotz, Adam; Holender, Michael; Tagliaferri, William; Canarelli, Kathie

2006-04-01

Information Fusion Engine for Real-time Decision Making (INFERD) is a tool that was developed to supplement current graph matching techniques in Information Fusion models. Based on sensory data and a priori models, INFERD dynamically generates, evolves, and evaluates hypothesis on the current state of the environment. The a priori models developed are hierarchical in nature lending them to a multi-level Information Fusion process whose primary output provides a situational awareness of the environment of interest in the context of the models running. In this paper we look at INFERD's multi-level fusion approach and provide insight on the inherent problems such as fragmentation in the approach and the research being undertaken to mitigate those deficiencies. Due to the large variance of data in disparate environments, the awareness of situations in those environments can be drastically different. To accommodate this, the INFERD framework provides support for plug-and-play fusion modules which can be developed specifically for domains of interest. However, because the models running in INFERD are graph based, some default measurements can be provided and will be discussed in the paper. Among these are a Depth measurement to determine how much danger is presented by the action taking place, a Breadth measurement to gain information regarding the scale of an attack that is currently happening, and finally a Reliability measure to tell the user the credibility of a particular hypothesis. All of these results will be demonstrated in the Cyber domain where recent research has shown to be an area that is welldefined and bounded, so that new models and algorithms can be developed and evaluated.

Multimodal biometric system using rank-level fusion approach.

PubMed

Monwar, Md Maruf; Gavrilova, Marina L

2009-08-01

In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher's linear discriminant methods for individual matchers (face, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues.

PubMed

Gunawardena, Harsha P; O'Brien, Jonathon; Wrobel, John A; Xie, Ling; Davies, Sherri R; Li, Shunqiang; Ellis, Matthew J; Qaqish, Bahjat F; Chen, Xian

2016-02-01

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to "rescue" the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Three-Dimensional Road Network by Fusion of Polarimetric and Interferometric SAR Data

NASA Technical Reports Server (NTRS)

Gamba, P.; Houshmand, B.

1998-01-01

In this paper a fuzzy classification procedure is applied to polarimetric radar measurements, and street pixels are detected. These data are successively grouped into consistent roads by means of a dynamic programming approach based on the fuzzy membership function values. Further fusion of the 2D road network extracted and 3D TOPSAR measurements provides a powerful way to analyze urban infrastructures.

Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

Design and implementation of PAVEMON: A GIS web-based pavement monitoring system based on large amounts of heterogeneous sensors data

NASA Astrophysics Data System (ADS)

Shahini Shamsabadi, Salar

A web-based PAVEment MONitoring system, PAVEMON, is a GIS oriented platform for accommodating, representing, and leveraging data from a multi-modal mobile sensor system. Stated sensor system consists of acoustic, optical, electromagnetic, and GPS sensors and is capable of producing as much as 1 Terabyte of data per day. Multi-channel raw sensor data (microphone, accelerometer, tire pressure sensor, video) and processed results (road profile, crack density, international roughness index, micro texture depth, etc.) are outputs of this sensor system. By correlating the sensor measurements and positioning data collected in tight time synchronization, PAVEMON attaches a spatial component to all the datasets. These spatially indexed outputs are placed into an Oracle database which integrates seamlessly with PAVEMON's web-based system. The web-based system of PAVEMON consists of two major modules: 1) a GIS module for visualizing and spatial analysis of pavement condition information layers, and 2) a decision-support module for managing maintenance and repair (Mℝ) activities and predicting future budget needs. PAVEMON weaves together sensor data with third-party climate and traffic information from the National Oceanic and Atmospheric Administration (NOAA) and Long Term Pavement Performance (LTPP) databases for an organized data driven approach to conduct pavement management activities. PAVEMON deals with heterogeneous and redundant observations by fusing them for jointly-derived higher-confidence results. A prominent example of the fusion algorithms developed within PAVEMON is a data fusion algorithm used for estimating the overall pavement conditions in terms of ASTM's Pavement Condition Index (PCI). PAVEMON predicts PCI by undertaking a statistical fusion approach and selecting a subset of all the sensor measurements. Other fusion algorithms include noise-removal algorithms to remove false negatives in the sensor data in addition to fusion algorithms developed for identifying features on the road. PAVEMON offers an ideal research and monitoring platform for rapid, intelligent and comprehensive evaluation of tomorrow's transportation infrastructure based on up-to-date data from heterogeneous sensor systems.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Fusion method of SAR and optical images for urban object extraction

NASA Astrophysics Data System (ADS)

Jia, Yonghong; Blum, Rick S.; Li, Fangfang

2007-11-01

A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR texture is extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high pass details extracted from the available Pan image by means of the á trous wavelet decomposition. Then, high pass details modulated with the texture is applied to obtain the fusion product by HPFM (High pass Filter-based Modulation) fusion method. A set of image data including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the experiment. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, and the proposed approach is effective for image interpret and classification.

Proposed evaluation framework for assessing operator performance with multisensor displays

NASA Technical Reports Server (NTRS)

Foyle, David C.

1992-01-01

Despite aggressive work on the development of sensor fusion algorithms and techniques, no formal evaluation procedures have been proposed. Based on existing integration models in the literature, an evaluation framework is developed to assess an operator's ability to use multisensor, or sensor fusion, displays. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The operator's performance with the sensor fusion display can be compared to the models' predictions based on the operator's performance when viewing the original sensor displays prior to fusion. This allows for the determination as to when a sensor fusion system leads to: 1) poorer performance than one of the original sensor displays (clearly an undesirable system in which the fused sensor system causes some distortion or interference); 2) better performance than with either single sensor system alone, but at a sub-optimal (compared to the model predictions) level; 3) optimal performance (compared to model predictions); or, 4) super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays. An experiment demonstrating the usefulness of the proposed evaluation framework is discussed.

Sensitivity of the fusion cross section to the density dependence of the symmetry energy

NASA Astrophysics Data System (ADS)

Reinhard, P.-G.; Umar, A. S.; Stevenson, P. D.; Piekarewicz, J.; Oberacker, V. E.; Maruhn, J. A.

2016-04-01

Background: The study of the nuclear equation of state (EOS) and the behavior of nuclear matter under extreme conditions is crucial to our understanding of many nuclear and astrophysical phenomena. Nuclear reactions serve as one of the means for studying the EOS. Purpose: It is the aim of this paper to discuss the impact of nuclear fusion on the EOS. This is a timely subject given the expected availability of increasingly exotic beams at rare isotope facilities [A. B. Balantekin et al., Mod. Phys. Lett. A 29, 1430010 (2014), 10.1142/S0217732314300109]. In practice, we focus on 48Ca+48Ca fusion. Method: We employ three different approaches to calculate fusion cross sections for a set of energy density functionals with systematically varying nuclear matter properties. Fusion calculations are performed using frozen densities, using a dynamic microscopic method based on density-constrained time-dependent Hartree-Fock (DC-TDHF) approach, as well as direct TDHF study of above barrier cross sections. For these studies, we employ a family of Skyrme parametrizations with systematically varied nuclear matter properties. Results: The folding-potential model provides a reasonable first estimate of cross sections. DC-TDHF, which includes dynamical polarization, reduces the fusion barriers and delivers much better cross sections. Full TDHF near the barrier agrees nicely with DC-TDHF. Most of the Skyrme forces which we used deliver, on the average, fusion cross sections in good agreement with the data. Trying to read off a trend in the results, we find a slight preference for forces which deliver a slope of symmetry energy of L ≈50 MeV that corresponds to a neutron-skin thickness of 48Ca of Rskin=(0.180 -0.210 ) fm. Conclusions: Fusion reactions in the barrier and sub-barrier region can be a tool to study the EOS and the neutron skin of nuclei. The success of the approach will depend on reduced experimental uncertainties of fusion data as well as the development of fusion theories that closely couple to the microscopic structure and dynamics.

Calculation of Formation and Decay of Heavy Compound Nuclei

NASA Astrophysics Data System (ADS)

Cherepanov, E. A.

2001-04-01

The report describes a method for calculating fusion and decay probabilities in reactions leading to the production of transfermium elements. The competition between quasi-fission and fussion is described on the basis of the Dinuclear System Concept (DNSC). The both competition between fusion and quasi-fission and statistical decay of heavy highly fissionable excited compound nuclei is described in an approach based on the Monte-Carlo method.

Multiscale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks.

PubMed

Ellmauthaler, Andreas; Pagliari, Carla L; da Silva, Eduardo A B

2013-03-01

Multiscale transforms are among the most popular techniques in the field of pixel-level image fusion. However, the fusion performance of these methods often deteriorates for images derived from different sensor modalities. In this paper, we demonstrate that for such images, results can be improved using a novel undecimated wavelet transform (UWT)-based fusion scheme, which splits the image decomposition process into two successive filtering operations using spectral factorization of the analysis filters. The actual fusion takes place after convolution with the first filter pair. Its significantly smaller support size leads to the minimization of the unwanted spreading of coefficient values around overlapping image singularities. This usually complicates the feature selection process and may lead to the introduction of reconstruction errors in the fused image. Moreover, we will show that the nonsubsampled nature of the UWT allows the design of nonorthogonal filter banks, which are more robust to artifacts introduced during fusion, additionally improving the obtained results. The combination of these techniques leads to a fusion framework, which provides clear advantages over traditional multiscale fusion approaches, independent of the underlying fusion rule, and reduces unwanted side effects such as ringing artifacts in the fused reconstruction.

Visual affective classification by combining visual and text features.

PubMed

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task.

Visual affective classification by combining visual and text features

PubMed Central

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task. PMID:28850566

Anisotropy of the angular distribution of fission fragments in heavy-ion fusion-fission reactions: The influence of the level-density parameter and the neck thickness

NASA Astrophysics Data System (ADS)

Naderi, D.; Pahlavani, M. R.; Alavi, S. A.

2013-05-01

Using the Langevin dynamical approach, the neutron multiplicity and the anisotropy of angular distribution of fission fragments in heavy ion fusion-fission reactions were calculated. We applied one- and two-dimensional Langevin equations to study the decay of a hot excited compound nucleus. The influence of the level-density parameter on neutron multiplicity and anisotropy of angular distribution of fission fragments was investigated. We used the level-density parameter based on the liquid drop model with two different values of the Bartel approach and Pomorska approach. Our calculations show that the anisotropy and neutron multiplicity are affected by level-density parameter and neck thickness. The calculations were performed on the 16O+208Pb and 20Ne+209Bi reactions. Obtained results in the case of the two-dimensional Langevin with a level-density parameter based on Bartel and co-workers approach are in better agreement with experimental data.

Focus measure method based on the modulus of the gradient of the color planes for digital microscopy

NASA Astrophysics Data System (ADS)

Hurtado-Pérez, Román; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso; Aguilar-Valdez, J. Félix; Ortega-Mendoza, Gabriel

2018-02-01

The modulus of the gradient of the color planes (MGC) is implemented to transform multichannel information to a grayscale image. This digital technique is used in two applications: (a) focus measurements during autofocusing (AF) process and (b) extending the depth of field (EDoF) by means of multifocus image fusion. In the first case, the MGC procedure is based on an edge detection technique and is implemented in over 15 focus metrics that are typically handled in digital microscopy. The MGC approach is tested on color images of histological sections for the selection of in-focus images. An appealing attribute of all the AF metrics working in the MGC space is their monotonic behavior even up to a magnification of 100×. An advantage of the MGC method is its computational simplicity and inherent parallelism. In the second application, a multifocus image fusion algorithm based on the MGC approach has been implemented on graphics processing units (GPUs). The resulting fused images are evaluated using a nonreference image quality metric. The proposed fusion method reveals a high-quality image independently of faulty illumination during the image acquisition. Finally, the three-dimensional visualization of the in-focus image is shown.

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

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Production of Hev b5 as a fluorescent biotin-binding tripartite fusion protein in insect cells.

PubMed

Nordlund, Henri R; Laitinen, Olli H; Uotila, Sanna T H; Kulmala, Minna; Kalkkinen, Nisse; Kulomaa, Markku S

2005-10-14

The presented green fluorescent protein and streptavidin core-based tripartite fusion system provides a simple and efficient way for the production of proteins fused to it in insect cells. This fusion protein forms a unique tag, which serves as a multipurpose device enabling easy optimization of production, one-step purification via streptavidin-biotin interaction, and visualization of the fusion protein during downstream processing and in applications. In the present study, we demonstrate the successful production, purification, and detection of a natural rubber latex allergen Hev b5 with this system. We also describe the production of another NRL allergen with the system, Hev b1, which formed large aggregates and gave small yields in purification. The aggregates were detected at early steps by microscopical inspection of the infected insect cells producing this protein. Therefore, this fusion system can also be utilized as a fast indicator of the solubility of the expressed fusion proteins and may therefore be extremely useful in high-throughput expression approaches.

Residue-level resolution of alphavirus envelope protein interactions in pH-dependent fusion.

PubMed

Zeng, Xiancheng; Mukhopadhyay, Suchetana; Brooks, Charles L

2015-02-17

Alphavirus envelope proteins, organized as trimers of E2-E1 heterodimers on the surface of the pathogenic alphavirus, mediate the low pH-triggered fusion of viral and endosomal membranes in human cells. The lack of specific treatment for alphaviral infections motivates our exploration of potential antiviral approaches by inhibiting one or more fusion steps in the common endocytic viral entry pathway. In this work, we performed constant pH molecular dynamics based on an atomic model of the alphavirus envelope with icosahedral symmetry. We have identified pH-sensitive residues that cause the largest shifts in thermodynamic driving forces under neutral and acidic pH conditions for various fusion steps. A series of conserved interdomain His residues is identified to be responsible for the pH-dependent conformational changes in the fusion process, and ligand binding sites in their vicinity are anticipated to be potential drug targets aimed at inhibiting viral infections.

Designing Image Operators for MRI-PET Image Fusion of the Brain

NASA Astrophysics Data System (ADS)

Márquez, Jorge; Gastélum, Alfonso; Padilla, Miguel A.

2006-09-01

Our goal is to obtain images combining in a useful and precise way the information from 3D volumes of medical imaging sets. We address two modalities combining anatomy (Magnetic Resonance Imaging or MRI) and functional information (Positron Emission Tomography or PET). Commercial imaging software offers image fusion tools based on fixed blending or color-channel combination of two modalities, and color Look-Up Tables (LUTs), without considering the anatomical and functional character of the image features. We used a sensible approach for image fusion taking advantage mainly from the HSL (Hue, Saturation and Luminosity) color space, in order to enhance the fusion results. We further tested operators for gradient and contour extraction to enhance anatomical details, plus other spatial-domain filters for functional features corresponding to wide point-spread-function responses in PET images. A set of image-fusion operators was formulated and tested on PET and MRI acquisitions.

An epidemic model for biological data fusion in ad hoc sensor networks

NASA Astrophysics Data System (ADS)

Chang, K. C.; Kotari, Vikas

2009-05-01

Bio terrorism can be a very refined and a catastrophic approach of attacking a nation. This requires the development of a complete architecture dedicatedly designed for this purpose which includes but is not limited to Sensing/Detection, Tracking and Fusion, Communication, and others. In this paper we focus on one such architecture and evaluate its performance. Various sensors for this specific purpose have been studied. The accent has been on use of Distributed systems such as ad-hoc networks and on application of epidemic data fusion algorithms to better manage the bio threat data. The emphasis has been on understanding the performance characteristics of these algorithms under diversified real time scenarios which are implemented through extensive JAVA based simulations. Through comparative studies on communication and fusion the performance of channel filter algorithm for the purpose of biological sensor data fusion are validated.

Facility Monitoring: A Qualitative Theory for Sensor Fusion

NASA Technical Reports Server (NTRS)

Figueroa, Fernando

2001-01-01

Data fusion and sensor management approaches have largely been implemented with centralized and hierarchical architectures. Numerical and statistical methods are the most common data fusion methods found in these systems. Given the proliferation and low cost of processing power, there is now an emphasis on designing distributed and decentralized systems. These systems use analytical/quantitative techniques or qualitative reasoning methods for date fusion.Based on other work by the author, a sensor may be treated as a highly autonomous (decentralized) unit. Each highly autonomous sensor (HAS) is capable of extracting qualitative behaviours from its data. For example, it detects spikes, disturbances, noise levels, off-limit excursions, step changes, drift, and other typical measured trends. In this context, this paper describes a distributed sensor fusion paradigm and theory where each sensor in the system is a HAS. Hence, given the reach qualitative information from each HAS, a paradigm and formal definitions are given so that sensors and processes can reason and make decisions at the qualitative level. This approach to sensor fusion makes it possible the implementation of intuitive (effective) methods to monitor, diagnose, and compensate processes/systems and their sensors. This paradigm facilitates a balanced distribution of intelligence (code and/or hardware) to the sensor level, the process/system level, and a higher controller level. The primary application of interest is in intelligent health management of rocket engine test stands.

A Smartphone-Based Driver Safety Monitoring System Using Data Fusion

PubMed Central

Lee, Boon-Giin; Chung, Wan-Young

2012-01-01

This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility. The safety monitoring process involves the fusion of attributes gathered from different sensors, including video, electrocardiography, photoplethysmography, temperature, and a three-axis accelerometer, that are assigned as input variables to an inference analysis framework. A Fuzzy Bayesian framework is designed to indicate the driver’s capability level and is updated continuously in real-time. The sensory data are transmitted via Bluetooth communication to the smartphone device. A fake incoming call warning service alerts the driver if his or her safety level is suspiciously compromised. Realistic testing of the system demonstrates the practical benefits of multiple features and their fusion in providing a more authentic and effective driver safety monitoring. PMID:23247416

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

PubMed Central

Wang, Zhenzhu; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm. PMID:28421125

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning.

PubMed

Zhou, Wei; Wu, Chengdong; Chen, Dali; Wang, Zhenzhu; Yi, Yugen; Du, Wenyou

2017-01-01

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

Multi-PSF fusion in image restoration of range-gated systems

NASA Astrophysics Data System (ADS)

Wang, Canjin; Sun, Tao; Wang, Tingfeng; Miao, Xikui; Wang, Rui

2018-07-01

For the task of image restoration, an accurate estimation of degrading PSF/kernel is the premise of recovering a visually superior image. The imaging process of range-gated imaging system in atmosphere associates with lots of factors, such as back scattering, background radiation, diffraction limit and the vibration of the platform. On one hand, due to the difficulty of constructing models for all factors, the kernels from physical-model based methods are not strictly accurate and practical. On the other hand, there are few strong edges in images, which brings significant errors to most of image-feature-based methods. Since different methods focus on different formation factors of the kernel, their results often complement each other. Therefore, we propose an approach which combines physical model with image features. With an fusion strategy using GCRF (Gaussian Conditional Random Fields) framework, we get a final kernel which is closer to the actual one. Aiming at the problem that ground-truth image is difficult to obtain, we then propose a semi data-driven fusion method in which different data sets are used to train fusion parameters. Finally, a semi blind restoration strategy based on EM (Expectation Maximization) and RL (Richardson-Lucy) algorithm is proposed. Our methods not only models how the lasers transfer in the atmosphere and imaging in the ICCD (Intensified CCD) plane, but also quantifies other unknown degraded factors using image-based methods, revealing how multiple kernel elements interact with each other. The experimental results demonstrate that our method achieves better performance than state-of-the-art restoration approaches.

Towards Omni-Tomography—Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography

PubMed Central

Wang, Ge; Zhang, Jie; Gao, Hao; Weir, Victor; Yu, Hengyong; Cong, Wenxiang; Xu, Xiaochen; Shen, Haiou; Bennett, James; Furth, Mark; Wang, Yue; Vannier, Michael

2012-01-01

We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose “omni-tomography”, a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine. PMID:22768108

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases.

PubMed

Tulay, Emine Elif; Metin, Barış; Tarhan, Nevzat; Arıkan, Mehmet Kemal

2018-06-01

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling.

PubMed

Huda, Shamsul; Yearwood, John; Togneri, Roberto

2009-02-01

This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM).

A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts

NASA Astrophysics Data System (ADS)

Hannachi, Ammar; Kohler, Sophie; Lallement, Alex; Hirsch, Ernest

2015-04-01

3D modeling of scene contents takes an increasing importance for many computer vision based applications. In particular, industrial applications of computer vision require efficient tools for the computation of this 3D information. Routinely, stereo-vision is a powerful technique to obtain the 3D outline of imaged objects from the corresponding 2D images. As a consequence, this approach provides only a poor and partial description of the scene contents. On another hand, for structured light based reconstruction techniques, 3D surfaces of imaged objects can often be computed with high accuracy. However, the resulting active range data in this case lacks to provide data enabling to characterize the object edges. Thus, in order to benefit from the positive points of various acquisition techniques, we introduce in this paper promising approaches, enabling to compute complete 3D reconstruction based on the cooperation of two complementary acquisition and processing techniques, in our case stereoscopic and structured light based methods, providing two 3D data sets describing respectively the outlines and surfaces of the imaged objects. We present, accordingly, the principles of three fusion techniques and their comparison based on evaluation criterions related to the nature of the workpiece and also the type of the tackled application. The proposed fusion methods are relying on geometric characteristics of the workpiece, which favour the quality of the registration. Further, the results obtained demonstrate that the developed approaches are well adapted for 3D modeling of manufactured parts including free-form surfaces and, consequently quality control applications using these 3D reconstructions.

Systematic investigations of deep sub-barrier fusion reactions using an adiabatic approach

NASA Astrophysics Data System (ADS)

Ichikawa, Takatoshi

2015-12-01

Background: At extremely low incident energies, unexpected decreases in fusion cross sections, compared to the standard coupled-channels (CC) calculations, have been observed in a wide range of fusion reactions. These significant reductions of the fusion cross sections are often referred to as the fusion hindrance. However, the physical origin of the fusion hindrance is still unclear. Purpose: To describe the fusion hindrance based on an adiabatic approach, I propose a novel extension of the standard CC model by introducing a damping factor that describes a smooth transition from sudden to adiabatic processes, that is, the transition from the separated two-body to the united dinuclear system. I demonstrate the performance of this model by systematically investigating various deep sub-barrier fusion reactions. Method: I extend the standard CC model by introducing a damping factor into the coupling matrix elements in the standard CC model. This avoids double counting of the CC effects, when two colliding nuclei overlap one another. I adopt the Yukawa-plus-exponential (YPE) model as a basic heavy ion-ion potential, which is advantageous for a unified description of the one- and two-body potentials. For the purpose of these systematic investigations, I approximate the one-body potential with a third-order polynomial function based on the YPE model. Results: Calculated fusion cross sections for the medium-heavy mass systems of 64Ni+64Ni , 58Ni+58Ni , and 58Ni+54Fe , the medium-light mass systems of 40Ca+40Ca , 48Ca+48Ca , and 24Mg+30Si , and the mass-asymmetric systems of 48Ca+96Zr and 16O+208Pb are consistent with the experimental data. The astrophysical S factor and logarithmic derivative representations of these are also in good agreement with the experimental data. The values obtained for the individual radius and diffuseness parameters in the damping factor, which reproduce the fusion cross sections well, are nearly equal to the average value for all the systems. Conclusions: Since the results calculated with the damping factor are in excellent agreement with the experimental data in all systems, I conclude that a coordinate-dependent coupling strength is responsible for the fusion hindrance. In all systems, the potential energies at the touching point VTouch strongly correlate with the incident threshold energies for which the fusion hindrance starts to emerge, except for the medium-light mass systems.

Complications with axial presacral lumbar interbody fusion: A 5-year postmarketing surveillance experience

PubMed Central

Gundanna, Mukund I.; Miller, Larry E.; Block, Jon E.

2011-01-01

Background Open and minimally invasive lumbar fusion procedures have inherent procedural risks, with posterior and transforaminal approaches resulting in significant soft-tissue injury and the anterior approach endangering organs and major blood vessels. An alternative lumbar fusion technique uses a small paracoccygeal incision and a presacral approach to the L5-S1 intervertebral space, which avoids critical structures and may result in a favorable safety profile versus open and other minimally invasive fusion techniques. The purpose of this study was to evaluate complications associated with axial interbody lumbar fusion procedures using the Axial Lumbar Interbody Fusion (AxiaLIF) System (TranS1, Wilmington, North Carolina) in the postmarketing period. Methods Between March 2005 and March 2010, 9,152 patients underwent interbody fusion with the AxiaLIF System through an axial presacral approach. A single-level L5-S1 fusion was performed in 8,034 patients (88%), and a 2-level (L4-S1) fusion was used in 1,118 (12%). A predefined database was designed to record device- or procedure-related complaints via spontaneous reporting. The complications that were recorded included bowel injury, superficial wound and systemic infections, transient intraoperative hypotension, migration, subsidence, presacral hematoma, sacral fracture, vascular injury, nerve injury, and ureter injury. Results Complications were reported in 120 of 9,152 patients (1.3%). The most commonly reported complications were bowel injury (n = 59, 0.6%) and transient intraoperative hypotension (n = 20, 0.2%). The overall complication rate was similar between single-level (n = 102, 1.3%) and 2-level (n = 18, 1.6%) fusion procedures, with no significant differences noted for any single complication. Conclusions The 5-year postmarketing surveillance experience with the AxiaLIF System suggests that axial interbody lumbar fusion through the presacral approach is associated with a low incidence of complications. The overall complication rates observed in our evaluation compare favorably with those reported in trials of open and minimally invasive lumbar fusion surgery. PMID:25802673

Correlative Microscopy Combining Secondary Ion Mass Spectrometry and Electron Microscopy: Comparison of Intensity-Hue-Saturation and Laplacian Pyramid Methods for Image Fusion.

PubMed

Vollnhals, Florian; Audinot, Jean-Nicolas; Wirtz, Tom; Mercier-Bonin, Muriel; Fourquaux, Isabelle; Schroeppel, Birgit; Kraushaar, Udo; Lev-Ram, Varda; Ellisman, Mark H; Eswara, Santhana

2017-10-17

Correlative microscopy combining various imaging modalities offers powerful insights into obtaining a comprehensive understanding of physical, chemical, and biological phenomena. In this article, we investigate two approaches for image fusion in the context of combining the inherently lower-resolution chemical images obtained using secondary ion mass spectrometry (SIMS) with the high-resolution ultrastructural images obtained using electron microscopy (EM). We evaluate the image fusion methods with three different case studies selected to broadly represent the typical samples in life science research: (i) histology (unlabeled tissue), (ii) nanotoxicology, and (iii) metabolism (isotopically labeled tissue). We show that the intensity-hue-saturation fusion method often applied for EM-sharpening can result in serious image artifacts, especially in cases where different contrast mechanisms interplay. Here, we introduce and demonstrate Laplacian pyramid fusion as a powerful and more robust alternative method for image fusion. Both physical and technical aspects of correlative image overlay and image fusion specific to SIMS-based correlative microscopy are discussed in detail alongside the advantages, limitations, and the potential artifacts. Quantitative metrics to evaluate the results of image fusion are also discussed.

Comparison of atlas-based techniques for whole-body bone segmentation.

PubMed

Arabi, Hossein; Zaidi, Habib

2017-02-01

We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC= 0.65 ± 0.05) compared to non-weighted IA (DSC= 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC= 0.56 ± 0.05) and Hofmann's methods (DSC= 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue.

PubMed

Bougrini, Madiha; Tahri, Khalid; Haddi, Zouhair; El Bari, Nezha; Llobet, Eduard; Jaffrezic-Renault, Nicole; Bouchikhi, Benachir

2014-12-01

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage day's discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor. Copyright © 2014 Elsevier B.V. All rights reserved.

Application of Magnetized Target Fusion to High-Energy Space Propulsion

NASA Technical Reports Server (NTRS)

Thio, Y. C. F.; Schmidt, G. R.; Kirkpatrick, R. C.; Rodgers, Stephen L. (Technical Monitor)

2001-01-01

Most fusion propulsion concepts that have been investigated in the past employ some form of inertial or magnetic confinement. Although the prospective performance of these concepts is excellent, the fusion processes on which these concepts are based still require considerable development before they can be seriously considered for actual applications. Furthermore, these processes are encumbered by the need for sophisticated plasma and power handling systems that are generally quite inefficient and have historically resulted in large, massive spacecraft designs. Here we present a comparatively new approach, Magnetized Target Fusion (MTF), which offers a nearer-term avenue for realizing the tremendous performance benefits of fusion propulsion'. The key advantage of MTF is its less demanding requirements for driver energy and power processing. Additional features include: 1) very low system masses and volumes, 2) high gain and relatively low waste heat, 3) substantial utilization of energy from product neutrons, 4) efficient, low peak-power drivers based on existing pulsed power technology, and 5) very high Isp, specific power and thrust. MTF overcomes many of the problems associated with traditional fusion techniques, thus making it particularly attractive for space applications. Isp greater than 50,000 seconds and specific powers greater than 50 kilowatts/kilogram appear feasible using relatively near-term pulse power and plasma gun technology.

Design of a multisensor data fusion system for target detection

NASA Astrophysics Data System (ADS)

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

1993-09-01

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

The da Vinci robotic surgical assisted anterior lumbar interbody fusion: technical development and case report.

PubMed

Beutler, William J; Peppelman, Walter C; DiMarco, Luciano A

2013-02-15

Technique development to use the da Vince Robotic Surgical System for anterior lumbar interbody fusion at L5-S1 is detailed. A case report is also presented. To evaluate and develop the da Vinci robotic assisted laparoscopic anterior lumbar stand-alone interbody fusion procedure. Anterior lumbar interbody fusion is a common procedure associated with potential morbidity related to the surgical approach. The da Vinci robot provides intra-abdominal dissection and visualization advantages compared with the traditional open and laparoscopic approach. The surgical techniques for approach to the anterior lumbar spine using the da Vinci robot were developed and modified progressively beginning with operative models followed by placement of an interbody fusion cage in the living porcine model. Development continued to progress with placement of fusion cage in a human cadaver, completed first in the laboratory setting and then in the operating room. Finally, the first patient with fusion completed using the da Vinci robot-assisted approach is presented. The anterior transperitoneal approach to the lumbar spine is accomplished with enhanced visualization and dissection capability, with maintenance of pneumoperitoneum using the da Vinci robot. Blood loss is minimal. The visualization inside the disc space and surrounding structures was considered better than current open and laparoscopic techniques. The da Vinci robot Surgical System technique continues to develop and is now described for the transperitoneal approach to the anterior lumbar spine. 4.

The kinetic stabilizer: a route to simpler tandem mirror systems

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

Post, R F

2001-02-02

As we enter the new millennium there is a growing urgency to address the issue of finding long-range solutions to the world's energy needs. Fusion offers such a solution, provided economically viable means can be found to extract useful energy from fusion reactions. While the magnetic confinement approach to fusion has a long and productive history, to date the mainline approaches to magnetic confinement, namely closed systems such as the tokamak, appear to many as being too large and complex to be acceptable economically, despite the impressive progress that has made toward the achievement of fusion-relevant confinement parameters. Thus theremore » is a growing feeling that it is imperative to search for new and simpler approaches to magnetic fusion, ones that might lead to smaller and more economically attractive fusion power plants.« less

Stalk model of membrane fusion: solution of energy crisis.

PubMed Central

Kozlovsky, Yonathan; Kozlov, Michael M

2002-01-01

Membrane fusion proceeds via formation of intermediate nonbilayer structures. The stalk model of fusion intermediate is commonly recognized to account for the major phenomenology of the fusion process. However, in its current form, the stalk model poses a challenge. On one hand, it is able to describe qualitatively the modulation of the fusion reaction by the lipid composition of the membranes. On the other, it predicts very large values of the stalk energy, so that the related energy barrier for fusion cannot be overcome by membranes within a biologically reasonable span of time. We suggest a new structure for the fusion stalk, which resolves the energy crisis of the model. Our approach is based on a combined deformation of the stalk membrane including bending of the membrane surface and tilt of the hydrocarbon chains of lipid molecules. We demonstrate that the energy of the fusion stalk is a few times smaller than those predicted previously and the stalks are feasible in real systems. We account quantitatively for the experimental results on dependence of the fusion reaction on the lipid composition of different membrane monolayers. We analyze the dependence of the stalk energy on the distance between the fusing membranes and provide the experimentally testable predictions for the structural features of the stalk intermediates. PMID:11806930

A Time-Space Domain Information Fusion Method for Specific Emitter Identification Based on Dempster-Shafer Evidence Theory.

PubMed

Jiang, Wen; Cao, Ying; Yang, Lin; He, Zichang

2017-08-28

Specific emitter identification plays an important role in contemporary military affairs. However, most of the existing specific emitter identification methods haven't taken into account the processing of uncertain information. Therefore, this paper proposes a time-space domain information fusion method based on Dempster-Shafer evidence theory, which has the ability to deal with uncertain information in the process of specific emitter identification. In this paper, radars will generate a group of evidence respectively based on the information they obtained, and our main task is to fuse the multiple groups of evidence to get a reasonable result. Within the framework of recursive centralized fusion model, the proposed method incorporates a correlation coefficient, which measures the relevance between evidence and a quantum mechanical approach, which is based on the parameters of radar itself. The simulation results of an illustrative example demonstrate that the proposed method can effectively deal with uncertain information and get a reasonable recognition result.

Feature-based fusion of medical imaging data.

PubMed

Calhoun, Vince D; Adali, Tülay

2009-09-01

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.

Age Estimation Based on Children's Voice: A Fuzzy-Based Decision Fusion Strategy

PubMed Central

Ting, Hua-Nong

2014-01-01

Automatic estimation of a speaker's age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker's age is presented. The method features a “divide and conquer” strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier's learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier's outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier's outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker's age from various speech sources. PMID:25006595

Scale Estimation and Correction of the Monocular Simultaneous Localization and Mapping (SLAM) Based on Fusion of 1D Laser Range Finder and Vision Data.

PubMed

Zhang, Zhuang; Zhao, Rujin; Liu, Enhai; Yan, Kun; Ma, Yuebo

2018-06-15

This article presents a new sensor fusion method for visual simultaneous localization and mapping (SLAM) through integration of a monocular camera and a 1D-laser range finder. Such as a fusion method provides the scale estimation and drift correction and it is not limited by volume, e.g., the stereo camera is constrained by the baseline and overcomes the limited depth range problem associated with SLAM for RGBD cameras. We first present the analytical feasibility for estimating the absolute scale through the fusion of 1D distance information and image information. Next, the analytical derivation of the laser-vision fusion is described in detail based on the local dense reconstruction of the image sequences. We also correct the scale drift of the monocular SLAM using the laser distance information which is independent of the drift error. Finally, application of this approach to both indoor and outdoor scenes is verified by the Technical University of Munich dataset of RGBD and self-collected data. We compare the effects of the scale estimation and drift correction of the proposed method with the SLAM for a monocular camera and a RGBD camera.

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

Shi, Xin, E-mail: xinshih86029@gmail.com; Zhao, Xiangmo, E-mail: xinshih86029@gmail.com; Hui, Fei, E-mail: xinshih86029@gmail.com

Clock synchronization in wireless sensor networks (WSNs) has been studied extensively in recent years and many protocols are put forward based on the point of statistical signal processing, which is an effective way to optimize accuracy. However, the accuracy derived from the statistical data can be improved mainly by sufficient packets exchange, which will consume the limited power resources greatly. In this paper, a reliable clock estimation using linear weighted fusion based on pairwise broadcast synchronization is proposed to optimize sync accuracy without expending additional sync packets. As a contribution, a linear weighted fusion scheme for multiple clock deviations ismore » constructed with the collaborative sensing of clock timestamp. And the fusion weight is defined by the covariance of sync errors for different clock deviations. Extensive simulation results show that the proposed approach can achieve better performance in terms of sync overhead and sync accuracy.« less

Energy-Based Tissue Fusion for Sutureless Closure: Applications, Mechanisms, and Potential for Functional Recovery.

PubMed

Kramer, Eric A; Rentschler, Mark E

2018-06-04

As minimally invasive surgical techniques progress, the demand for efficient, reliable methods for vascular ligation and tissue closure becomes pronounced. The surgical advantages of energy-based vessel sealing exceed those of traditional, compression-based ligatures in procedures sensitive to duration, foreign bodies, and recovery time alike. Although the use of energy-based devices to seal or transect vasculature and connective tissue bundles is widespread, the breadth of heating strategies and energy dosimetry used across devices underscores an uncertainty as to the molecular nature of the sealing mechanism and induced tissue effect. Furthermore, energy-based techniques exhibit promise for the closure and functional repair of soft and connective tissues in the nervous, enteral, and dermal tissue domains. A constitutive theory of molecular bonding forces that arise in response to supraphysiological temperatures is required in order to optimize and progress the use of energy-based tissue fusion. While rapid tissue bonding has been suggested to arise from dehydration, dipole interactions, molecular cross-links, or the coagulation of cellular proteins, long-term functional tissue repair across fusion boundaries requires that the reaction to thermal damage be tailored to catalyze the onset of biological healing and remodeling. In this review, we compile and contrast findings from published thermal fusion research in an effort to encourage a molecular approach to characterization of the prevalent and promising energy-based tissue bond.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

PubMed Central

Latysheva, Natasha S.; Babu, M. Madan

2016-01-01

Abstract Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different—yet highly complementary and symbiotic—approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation. PMID:27105842

Effect of Surgical Approach on Pulmonary Function in Adolescent Idiopathic Scoliosis Patients: A Systemic Review and Meta-analysis.

PubMed

Lee, Andy C H; Feger, Mark A; Singla, Anuj; Abel, Mark F

2016-11-15

Systemic review and meta-analysis. To analyze the effect of spinal fusion and instrumentation for adolescent idiopathic scoliosis (AIS) on absolute pulmonary function test (PFTs). Pulmonary function is correlated with severity of deformity in AIS patients and studies that have analyzed the effect of spinal fusion and instrumentation on PFTs for AIS have reported inconsistent results. There is a need to analyze the effect of spinal fusion on PFTs with stratification by surgical approach. Our analysis included 22 studies. Cohen's d effect sizes were calculated for absolute PFT outcome measures with 95% confidence intervals (CI). Meta-analyses were performed at each postoperative time frame for six homogeneous surgical approaches: (i) combined anterior release and posterior fusion with instrumentation; (ii) combined video assisted anterior release and posterior fusion with instrumentation without thoracoplasty; (iii) posterior fusion with instrumentation without thoracoplasty; (iv) anterior fusion with instrumentation and without thoracoplasty; (v) video assisted anterior fusion with instrumentation without thoracoplasty; and (vi) any scoliosis surgery with additional thoracoplasty. Anterior spinal fusion with instrumentation, any scoliosis surgery with concomitant thoracoplasty, or video-assisted anterior fusion with instrumentation for AIS had similar absolute PFTs at their 2 year postoperative follow up compared with their preoperative PFTs (effect sizes ranging from -0.2-0.2 with all CI crossing "0"). Posterior spinal fusion with instrumentation (with or without an anterior release) demonstrated small to moderate increases in PFTs 2 years postoperatively (effect sizes ranging from 0.35-0.65 with all CI not crossing "0"). Anterior fusion with instrumentation, regardless of the approach, and any scoliosis surgery with concomitant thoracoplasty do not lead to significant change in pulmonary functions 2 year after surgery. Posterior spinal fusion with instrumentation (with or without an anterior release) resulted in small to moderate increases in PFTs. N/A.

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

PubMed Central

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-01-01

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE. PMID:27447635

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

PubMed

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-07-19

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.

Adaptation of Decoy Fusion Strategy for Existing Multi-Stage Search Workflows

NASA Astrophysics Data System (ADS)

Ivanov, Mark V.; Levitsky, Lev I.; Gorshkov, Mikhail V.

2016-09-01

A number of proteomic database search engines implement multi-stage strategies aiming at increasing the sensitivity of proteome analysis. These approaches often employ a subset of the original database for the secondary stage of analysis. However, if target-decoy approach (TDA) is used for false discovery rate (FDR) estimation, the multi-stage strategies may violate the underlying assumption of TDA that false matches are distributed uniformly across the target and decoy databases. This violation occurs if the numbers of target and decoy proteins selected for the second search are not equal. Here, we propose a method of decoy database generation based on the previously reported decoy fusion strategy. This method allows unbiased TDA-based FDR estimation in multi-stage searches and can be easily integrated into existing workflows utilizing popular search engines and post-search algorithms.

Probabilistic sparse matching for robust 3D/3D fusion in minimally invasive surgery.

PubMed

Neumann, Dominik; Grbic, Sasa; John, Matthias; Navab, Nassir; Hornegger, Joachim; Ionasec, Razvan

2015-01-01

Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.

Fc-fusion proteins and FcRn: structural insights for longer-lasting and more effective therapeutics

PubMed Central

Rath, Timo; Baker, Kristi; Dumont, Jennifer A.; Peters, Robert T.; Jiang, Haiyan; Qiao, Shuo-Wang; Lencer, Wayne I.; Pierce, Glenn F.; Blumberg, Richard S.

2016-01-01

Nearly 350 IgG-based therapeutics are approved for clinical use or are under development for many diseases lacking adequate treatment options. These include molecularly engineered biologicals comprising the IgG Fc-domain fused to various effector molecules (so-called Fc-fusion proteins) that confer the advantages of IgG, including binding to the neonatal Fc receptor (FcRn) to facilitate in vivo stability, and the therapeutic benefit of the specific effector functions. Advances in IgG structure-function relationships and an understanding of FcRn biology have provided therapeutic opportunities for previously unapproachable diseases. This article discusses approved Fc-fusion therapeutics, novel Fc-fusion proteins and FcRn-dependent delivery approaches in development, and how engineering of the FcRn–Fc interaction can generate longer-lasting and more effective therapeutics. PMID:24156398

Status of fusion research and implications for D/He-3 systems

NASA Technical Reports Server (NTRS)

Miley, George H.

1988-01-01

World wide programs in both magnetic confinement and inertial confinement fusion research have made steady progress towards the experimental demonstration of energy breakeven. However, after breakeven is achieved, considerable time and effort must still be expended to develop a usable power plant. The main program described is focused on Deuterium-Tritium devices. In magnetic confinement, three of the most promising high beta approaches with a reasonable experimental data base are the Field Reversed Configuration, the high field tokamak, and the dense Z-pinch. The situation is less clear in inertial confinement where the first step requires an experimental demonstration of D/T spark ignition. It appears that fusion research has reached a point in time where an R and D plan to develop a D/He-3 fusion reactor can be laid out with some confidence of success.

Modeling and analysis of tritium dynamics in a DT fusion fuel cycle

NASA Astrophysics Data System (ADS)

Kuan, William

1998-11-01

A number of crucial design issues have a profound effect on the dynamics of the tritium fuel cycle in a DT fusion reactor, where the development of appropriate solutions to these issues is of particular importance to the introduction of fusion as a commercial system. Such tritium-related issues can be classified according to their operational, safety, and economic impact to the operation of the reactor during its lifetime. Given such key design issues inherent in next generation fusion devices using the DT fuel cycle development of appropriate models can then lead to optimized designs of the fusion fuel cycle for different types of DT fusion reactors. In this work, two different types of modeling approaches are developed and their application to solving key tritium issues presented. For the first approach, time-dependent inventories, concentrations, and flow rates characterizing the main subsystems of the fuel cycle are simulated with a new dynamic modular model of a fusion reactor's fuel cycle, named X-TRUFFLES (X-Windows TRitiUm Fusion Fuel cycLE dynamic Simulation). The complex dynamic behavior of the recycled fuel within each of the modeled subsystems is investigated using this new integrated model for different reactor scenarios and design approaches. Results for a proposed fuel cycle design taking into account current technologies are presented, including sensitivity studies. Ways to minimize the tritium inventory are also assessed by examining various design options that could be used to minimize local and global tritium inventories. The second modeling approach involves an analytical model to be used for the calculation of the required tritium breeding ratio, i.e., a primary design issue which relates directly to the feasibility and economics of DT fusion systems. A time-integrated global tritium balance scheme is developed and appropriate analytical expressions are derived for tritium self-sufficiency relevant parameters. The easy exploration of the large parameter space of the fusion fuel cycle can thus be conducted as opposed to previous modeling approaches. Future guidance for R&D (research and development) in fusion nuclear technology is discussed in view of possible routes to take in reducing the tritium breeding requirements of DT fusion reactors.

Strategies for enhancing bioluminescent bacterial sensor performance by promoter region manipulation

PubMed Central

Bilic, Benny; Belkin, Shimshon

2010-01-01

Genetically engineered microbial reporter strains are based upon the fusion of an inducible sensing element upstream of a reporting element, so that the construct emits a dose-dependent signal when exposed to the inducing compound(s) or stress factor(s). In this communication1 we described several general approaches undertaken in order to enhance the sensing performance of such promoter::reporter fusions. Significant improvements in detection sensitivity, response kinetics and signal intensity were achieved by modi fication of the length of the promoter-containing DNA fragment, by random or site-directed mutagenesis and by promoter duplication. The general nature of these genetics manipulations makes them applicable to other types of promoter::reporter fusions. PMID:21326942

Relevance of advanced nuclear fusion research: Breakthroughs and obstructions

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

Coppi, Bruno, E-mail: coppi@mit.edu

2016-03-25

An in depth understanding of the collective modes that can be excited in a wide range of high-energy plasmas is necessary to advance nuclear fusion research in parallel with other fields that include space and astrophysics in particular. Important achievements are shown to have resulted from implementing programs based on this reality, maintaining a tight connection with different areas of investigations. This involves the undertaking of a plurality of experimental approaches aimed at understanding the physics of fusion burning plasmas. At present, the most advanced among these is the Ignitor experiment involving international cooperation, that is designed to investigate burningmore » plasma regimes near ignition for the first time.« less

Numerically Simulating Collisions of Plastic and Foam Laser-Driven Foils

NASA Astrophysics Data System (ADS)

Zalesak, S. T.; Velikovich, A. L.; Schmitt, A. J.; Aglitskiy, Y.; Metzler, N.

2007-11-01

Interest in experiments on colliding planar foils has recently been stimulated by (a) the Impact Fast Ignition approach to laser fusion [1], and (b) the approach to a high-repetition rate ignition facility based on direct drive with the KrF laser [2]. Simulating the evolution of perturbations to such foils can be a numerical challenge, especially if the initial perturbation amplitudes are small. We discuss the numerical issues involved in such simulations, describe their benchmarking against recently-developed analytic results, and present simulations of such experiments on NRL's Nike laser. [1] M. Murakami et al., Nucl. Fusion 46, 99 (2006) [2] S. P. Obenschain et al., Phys. Plasmas 13, 056320 (2006).

New approach to information fusion for Lipschitz classifiers ensembles: Application in multi-channel C-OTDR-monitoring systems

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

Timofeev, Andrey V.; Egorov, Dmitry V.

This paper presents new results concerning selection of an optimal information fusion formula for an ensemble of Lipschitz classifiers. The goal of information fusion is to create an integral classificatory which could provide better generalization ability of the ensemble while achieving a practically acceptable level of effectiveness. The problem of information fusion is very relevant for data processing in multi-channel C-OTDR-monitoring systems. In this case we have to effectively classify targeted events which appear in the vicinity of the monitored object. Solution of this problem is based on usage of an ensemble of Lipschitz classifiers each of which corresponds tomore » a respective channel. We suggest a brand new method for information fusion in case of ensemble of Lipschitz classifiers. This method is called “The Weighing of Inversely as Lipschitz Constants” (WILC). Results of WILC-method practical usage in multichannel C-OTDR monitoring systems are presented.« less

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation.

PubMed

Bousdar Ahmed, Dina; Munoz Diaz, Estefania

2017-11-03

Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs). However, most of them use a single inertial measurement unit (IMU) attached to the pedestrian's body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of wearable-based INSs. The latter are based on a pocket-mounted IMU and a foot-mounted IMU. The loosely coupled fusion combines the output of the two INSs not only when these outputs are least erroneous, but also automatically favoring the best output. This approach is named smart update. The main challenge is determining the quality of the heading estimation of each INS, which changes every time. In order to address this, a novel concept to determine the quality of the heading estimation is presented. This concept is subject to a patent application. The results show that the position error rate of the loosely coupled fusion is 10 cm/s better than either the foot INS's or pocket INS's error rate in 95% of the cases.

Fusion of GEDI, ICESAT2 & NISAR data for above ground biomass mapping in Sonoma County, California

NASA Astrophysics Data System (ADS)

Duncanson, L.; Simard, M.; Thomas, N. M.; Neuenschwander, A. L.; Hancock, S.; Armston, J.; Dubayah, R.; Hofton, M. A.; Huang, W.; Tang, H.; Marselis, S.; Fatoyinbo, T.

2017-12-01

Several upcoming NASA missions will collect data sensitive to forest structure (GEDI, ICESAT-2 & NISAR). The LiDAR and SAR data collected by these missions will be used in coming years to map forest aboveground biomass at various resolutions. This research focuses on developing and testing multi-sensor data fusion approaches in advance of these missions. Here, we present the first case study of a CMS-16 grant with results from Sonoma County, California. We simulate lidar and SAR datasets from GEDI, ICESAT-2 and NISAR using airborne discrete return lidar and UAVSAR data, respectively. GEDI and ICESAT-2 signals are simulated from high point density discrete return lidar that was acquired over the entire county in 2014 through a previous CMS project (Dubayah & Hurtt, CMS-13). NISAR is simulated from L-band UAVSAR data collected in 2014. These simulations are empirically related to 300 field plots of aboveground biomass as well as a 30m biomass map produced from the 2014 airborne lidar data. We model biomass independently for each simulated mission dataset and then test two fusion methods for County-wide mapping 1) a pixel based approach and 2) an object oriented approach. In the pixel-based approach, GEDI and ICESAT-2 biomass models are calibrated over field plots and applied in orbital simulations for a 2-year period of the GEDI and ICESAT-2 missions. These simulated samples are then used to calibrate UAVSAR data to produce a 0.25 ha map. In the object oriented approach, the GEDI and ICESAT-2 data are identical to the pixel-based approach, but calibrate image objects of similar L-band backscatter rather than uniform pixels. The results of this research demonstrate the estimated ability for each of these three missions to independently map biomass in a temperate, high biomass system, as well as the potential improvement expected through combining mission datasets.

Development of next generation tempered and ODS reduced activation ferritic/martensitic steels for fusion energy applications

DOE PAGES

Zinkle, S. J.; Boutard, J. L.; Hoelzer, D. T.; ...

2017-06-09

Reduced activation ferritic/martensitic steels are currently the most technologically mature option for the structural material of proposed fusion energy reactors. Advanced next-generation higher performance steels offer the opportunity for improvements in fusion reactor operational lifetime and reliability, superior neutron radiation damage resistance, higher thermodynamic efficiency, and reduced construction costs. The two main strategies for developing improved steels for fusion energy applications are based on (1) an evolutionary pathway using computational thermodynamics modelling and modified thermomechanical treatments (TMT) to produce higher performance reduced activation ferritic/martensitic (RAFM) steels and (2) a higher risk, potentially higher payoff approach based on powder metallurgy techniquesmore » to produce very high strength oxide dispersion strengthened (ODS) steels capable of operation to very high temperatures and with potentially very high resistance to fusion neutron-induced property degradation. The current development status of these next-generation high performance steels is summarized, and research and development challenges for the successful development of these materials are outlined. In conclusion, material properties including temperature-dependent uniaxial yield strengths, tensile elongations, high-temperature thermal creep, Charpy impact ductile to brittle transient temperature (DBTT) and fracture toughness behaviour, and neutron irradiation-induced low-temperature hardening and embrittlement and intermediate-temperature volumetric void swelling (including effects associated with fusion-relevant helium and hydrogen generation) are described for research heats of the new steels.« less

A label field fusion bayesian model and its penalized maximum rand estimator for image segmentation.

PubMed

Mignotte, Max

2010-06-01

This paper presents a novel segmentation approach based on a Markov random field (MRF) fusion model which aims at combining several segmentation results associated with simpler clustering models in order to achieve a more reliable and accurate segmentation result. The proposed fusion model is derived from the recently introduced probabilistic Rand measure for comparing one segmentation result to one or more manual segmentations of the same image. This non-parametric measure allows us to easily derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Concretely, this Gibbs energy model encodes the set of binary constraints, in terms of pairs of pixel labels, provided by each segmentation results to be fused. Combined with a prior distribution, this energy-based Gibbs model also allows for definition of an interesting penalized maximum probabilistic rand estimator with which the fusion of simple, quickly estimated, segmentation results appears as an interesting alternative to complex segmentation models existing in the literature. This fusion framework has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.

Development of next generation tempered and ODS reduced activation ferritic/martensitic steels for fusion energy applications

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

Zinkle, S. J.; Boutard, J. L.; Hoelzer, D. T.

Reduced activation ferritic/martensitic steels are currently the most technologically mature option for the structural material of proposed fusion energy reactors. Advanced next-generation higher performance steels offer the opportunity for improvements in fusion reactor operational lifetime and reliability, superior neutron radiation damage resistance, higher thermodynamic efficiency, and reduced construction costs. The two main strategies for developing improved steels for fusion energy applications are based on (1) an evolutionary pathway using computational thermodynamics modelling and modified thermomechanical treatments (TMT) to produce higher performance reduced activation ferritic/martensitic (RAFM) steels and (2) a higher risk, potentially higher payoff approach based on powder metallurgy techniquesmore » to produce very high strength oxide dispersion strengthened (ODS) steels capable of operation to very high temperatures and with potentially very high resistance to fusion neutron-induced property degradation. The current development status of these next-generation high performance steels is summarized, and research and development challenges for the successful development of these materials are outlined. In conclusion, material properties including temperature-dependent uniaxial yield strengths, tensile elongations, high-temperature thermal creep, Charpy impact ductile to brittle transient temperature (DBTT) and fracture toughness behaviour, and neutron irradiation-induced low-temperature hardening and embrittlement and intermediate-temperature volumetric void swelling (including effects associated with fusion-relevant helium and hydrogen generation) are described for research heats of the new steels.« less

Development of next generation tempered and ODS reduced activation ferritic/martensitic steels for fusion energy applications

NASA Astrophysics Data System (ADS)

Zinkle, S. J.; Boutard, J. L.; Hoelzer, D. T.; Kimura, A.; Lindau, R.; Odette, G. R.; Rieth, M.; Tan, L.; Tanigawa, H.

2017-09-01

Reduced activation ferritic/martensitic steels are currently the most technologically mature option for the structural material of proposed fusion energy reactors. Advanced next-generation higher performance steels offer the opportunity for improvements in fusion reactor operational lifetime and reliability, superior neutron radiation damage resistance, higher thermodynamic efficiency, and reduced construction costs. The two main strategies for developing improved steels for fusion energy applications are based on (1) an evolutionary pathway using computational thermodynamics modelling and modified thermomechanical treatments (TMT) to produce higher performance reduced activation ferritic/martensitic (RAFM) steels and (2) a higher risk, potentially higher payoff approach based on powder metallurgy techniques to produce very high strength oxide dispersion strengthened (ODS) steels capable of operation to very high temperatures and with potentially very high resistance to fusion neutron-induced property degradation. The current development status of these next-generation high performance steels is summarized, and research and development challenges for the successful development of these materials are outlined. Material properties including temperature-dependent uniaxial yield strengths, tensile elongations, high-temperature thermal creep, Charpy impact ductile to brittle transient temperature (DBTT) and fracture toughness behaviour, and neutron irradiation-induced low-temperature hardening and embrittlement and intermediate-temperature volumetric void swelling (including effects associated with fusion-relevant helium and hydrogen generation) are described for research heats of the new steels.

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents.

PubMed

Li, Peilin; Lee, Sang-Heon; Hsu, Hung-Yao; Park, Jae-Sam

2017-01-13

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents

PubMed Central

Li, Peilin; Lee, Sang-Heon; Hsu, Hung-Yao; Park, Jae-Sam

2017-01-01

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions. PMID:28098797

Defense Small Business Innovation Research Program (SBIR) Abstracts of Phase 1 Awards 1983.

DTIC Science & Technology

1984-04-06

STRATEGY, THE INTERPLAY BETWEEN ELECTROMAGNETIC EMISSION CON- TROL AND FLEET OPERATION. THE TECHNICAL APPROACH IS BASED ON AN ANALYSIS OF EMCON...THEM AND A BOTTOM UP APPROACH . THE REQUIREMENTS AND ARCHITECTURAL ASPECTS WILL BE EXPLORED FROM THE MORE ENCOMPASSING PERSPECTIVE OF THE TOTAL...AN AI APPROACH TO INFORMATION FUSION INCLUDING KNOW- LEDGE ORGANIZATION, HYPOTHESIS REPRESENTATIVES, DOMAIN KNOWLEDGE RE- PRESENTATION, HYPOTHESIS

Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

NASA Astrophysics Data System (ADS)

Fortunati, Valerio; Verhaart, René F.; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; van Walsum, Theo

2015-08-01

A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck. Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available. The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used. Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing

PubMed Central

Weirather, Jason L.; Afshar, Pegah Tootoonchi; Clark, Tyson A.; Tseng, Elizabeth; Powers, Linda S.; Underwood, Jason G.; Zabner, Joseph; Korlach, Jonas; Wong, Wing Hung; Au, Kin Fai

2015-01-01

We developed an innovative hybrid sequencing approach, IDP-fusion, to detect fusion genes, determine fusion sites and identify and quantify fusion isoforms. IDP-fusion is the first method to study gene fusion events by integrating Third Generation Sequencing long reads and Second Generation Sequencing short reads. We applied IDP-fusion to PacBio data and Illumina data from the MCF-7 breast cancer cells. Compared with the existing tools, IDP-fusion detects fusion genes at higher precision and a very low false positive rate. The results show that IDP-fusion will be useful for unraveling the complexity of multiple fusion splices and fusion isoforms within tumorigenesis-relevant fusion genes. PMID:26040699

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

Distributed multimodal data fusion for large scale wireless sensor networks

NASA Astrophysics Data System (ADS)

Ertin, Emre

2006-05-01

Sensor network technology has enabled new surveillance systems where sensor nodes equipped with processing and communication capabilities can collaboratively detect, classify and track targets of interest over a large surveillance area. In this paper we study distributed fusion of multimodal sensor data for extracting target information from a large scale sensor network. Optimal tracking, classification, and reporting of threat events require joint consideration of multiple sensor modalities. Multiple sensor modalities improve tracking by reducing the uncertainty in the track estimates as well as resolving track-sensor data association problems. Our approach to solving the fusion problem with large number of multimodal sensors is construction of likelihood maps. The likelihood maps provide a summary data for the solution of the detection, tracking and classification problem. The likelihood map presents the sensory information in an easy format for the decision makers to interpret and is suitable with fusion of spatial prior information such as maps, imaging data from stand-off imaging sensors. We follow a statistical approach to combine sensor data at different levels of uncertainty and resolution. The likelihood map transforms each sensor data stream to a spatio-temporal likelihood map ideally suitable for fusion with imaging sensor outputs and prior geographic information about the scene. We also discuss distributed computation of the likelihood map using a gossip based algorithm and present simulation results.

Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

NASA Astrophysics Data System (ADS)

Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

2017-12-01

In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

Joint interpretation of geophysical data using Image Fusion techniques

NASA Astrophysics Data System (ADS)

Karamitrou, A.; Tsokas, G.; Petrou, M.

2013-12-01

Joint interpretation of geophysical data produced from different methods is a challenging area of research in a wide range of applications. In this work we apply several image fusion approaches to combine maps of electrical resistivity, electromagnetic conductivity, vertical gradient of the magnetic field, magnetic susceptibility, and ground penetrating radar reflections, in order to detect archaeological relics. We utilize data gathered from Arkansas University, with the support of the U.S. Department of Defense, through the Strategic Environmental Research and Development Program (SERDP-CS1263). The area of investigation is the Army City, situated in Riley Country of Kansas, USA. The depth of the relics is estimated about 30 cm from the surface, yet the surface indications of its existence are limited. We initially register the images from the different methods to correct from random offsets due to the use of hand-held devices during the measurement procedure. Next, we apply four different image fusion approaches to create combined images, using fusion with mean values, wavelet decomposition, curvelet transform, and curvelet transform enhancing the images along specific angles. We create seven combinations of pairs between the available geophysical datasets. The combinations are such that for every pair at least one high-resolution method (resistivity or magnetic gradiometry) is included. Our results indicate that in almost every case the method of mean values produces satisfactory fused images that corporate the majority of the features of the initial images. However, the contrast of the final image is reduced, and in some cases the averaging process nearly eliminated features that are fade in the original images. Wavelet based fusion outputs also good results, providing additional control in selecting the feature wavelength. Curvelet based fusion is proved the most effective method in most of the cases. The ability of curvelet domain to unfold the image in terms of space, wavenumber, and orientation, provides important advantages compared with the rest of the methods by allowing the incorporation of a-priori information about the orientation of the potential targets.

Multisource data fusion for documenting archaeological sites

NASA Astrophysics Data System (ADS)

Knyaz, Vladimir; Chibunichev, Alexander; Zhuravlev, Denis

2017-10-01

The quality of archaeological sites documenting is of great importance for cultural heritage preserving and investigating. The progress in developing new techniques and systems for data acquisition and processing creates an excellent basis for achieving a new quality of archaeological sites documenting and visualization. archaeological data has some specific features which have to be taken into account when acquiring, processing and managing. First of all, it is a needed to gather as full as possible information about findings providing no loss of information and no damage to artifacts. Remote sensing technologies are the most adequate and powerful means which satisfy this requirement. An approach to archaeological data acquiring and fusion based on remote sensing is proposed. It combines a set of photogrammetric techniques for obtaining geometrical and visual information at different scales and detailing and a pipeline for archaeological data documenting, structuring, fusion, and analysis. The proposed approach is applied for documenting of Bosporus archaeological expedition of Russian State Historical Museum.

A pretargeted nanoparticle system for tumor cell labeling

PubMed Central

Gunn, Jonathan; Park, Steven I.; Veiseh, Omid; Press, Oliver W.; Zhang, Miqin

2011-01-01

Nanoparticle-based cancer diagnostics and therapeutics can be significantly enhanced by selective tissue localization, but the strategy can be complicated by the requirement of a targeting ligand conjugated on nanoparticles, that is specific to only one or a limited few types of neoplastic cells, necessitating the development of multiple nanoparticle systems for different diseases. Here, we present a new nanoparticle system that capitalizes on a targeting pretreatment strategy, where a circulating fusion protein (FP) selectively prelabels the targeted cellular epitope, and a biotinylated iron oxide nanoparticle serves as a secondary label that binds to the FP on the target cell. This approach enables a single nanoparticle formulation to be used with any one of existing fusion proteins to bind a variety of target cells. We demonstrated this approach with two fusion proteins against two model cancer cell lines: lymphoma (Ramos) and leukemia (Jurkat), which showed 72.2% and 91.1% positive labeling, respectively. Notably, TEM analysis showed that a large nanoparticle population was endocytosed via attachment to the non-internalizing CD20 epitope. PMID:21107453

A pretargeted nanoparticle system for tumor cell labeling.

PubMed

Gunn, Jonathan; Park, Steven I; Veiseh, Omid; Press, Oliver W; Zhang, Miqin

2011-03-01

Nanoparticle-based cancer diagnostics and therapeutics can be significantly enhanced by selective tissue localization, but the strategy can be complicated by the requirement of a targeting ligand conjugated on nanoparticles, that is specific to only one or a limited few types of neoplastic cells, necessitating the development of multiple nanoparticle systems for different diseases. Here, we present a new nanoparticle system that capitalizes on a targeting pretreatment strategy, where a circulating fusion protein (FP) selectively prelabels the targeted cellular epitope, and a biotinylated iron oxide nanoparticle serves as a secondary label that binds to the FP on the target cell. This approach enables a single nanoparticle formulation to be used with any one of existing fusion proteins to bind a variety of target cells. We demonstrated this approach with two fusion proteins against two model cancer cell lines: lymphoma (Ramos) and leukemia (Jurkat), which showed 72.2% and 91.1% positive labeling, respectively. Notably, TEM analysis showed that a large nanoparticle population was endocytosed via attachment to the non-internalizing CD20 epitope.

An infrared-visible image fusion scheme based on NSCT and compressed sensing

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Maldague, Xavier

2015-05-01

Image fusion, as a research hot point nowadays in the field of infrared computer vision, has been developed utilizing different varieties of methods. Traditional image fusion algorithms are inclined to bring problems, such as data storage shortage and computational complexity increase, etc. Compressed sensing (CS) uses sparse sampling without knowing the priori knowledge and greatly reconstructs the image, which reduces the cost and complexity of image processing. In this paper, an advanced compressed sensing image fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. NSCT provides better sparsity than the wavelet transform in image representation. Throughout the NSCT decomposition, the low-frequency and high-frequency coefficients can be obtained respectively. For the fusion processing of low-frequency coefficients of infrared and visible images , the adaptive regional energy weighting rule is utilized. Thus only the high-frequency coefficients are specially measured. Here we use sparse representation and random projection to obtain the required values of high-frequency coefficients, afterwards, the coefficients of each image block can be fused via the absolute maximum selection rule and/or the regional standard deviation rule. In the reconstruction of the compressive sampling results, a gradient-based iterative algorithm and the total variation (TV) method are employed to recover the high-frequency coefficients. Eventually, the fused image is recovered by inverse NSCT. Both the visual effects and the numerical computation results after experiments indicate that the presented approach achieves much higher quality of image fusion, accelerates the calculations, enhances various targets and extracts more useful information.

Reducing parametric backscattering by polarization rotation

DOE PAGES

Barth, Ido; Fisch, Nathaniel J.

2016-10-01

When a laser passes through underdense plasmas, Raman and Brillouin Backscattering can reflect a substantial portion of the incident laser energy. This is a major loss mechanism, for example, in employing lasers in inertial confinement fusion. But, by slow rotation of the incident linear polarization, the overall reflectivity can be reduced significantly. Particle in cell simulations show that, for parameters similar to those of indirect drive fusion experiments, polarization rotation reduces the reflectivity by a factor of 5. A general, fluid-model based analytical estimation for the reflectivity reduction agrees with simulations. However, in identifying the source of the backscatter reduction,more » it is difficult to disentangle the rotating polarization from the frequency separation based approach used to engineer the beam's polarization. Though the backscatter reduction arises similarly to other approaches that employ frequency separation, in the case here, the intensity remains constant in time.« less

Enhanced EDX images by fusion of multimodal SEM images using pansharpening techniques.

PubMed

Franchi, G; Angulo, J; Moreaud, M; Sorbier, L

2018-01-01

The goal of this paper is to explore the potential interest of image fusion in the context of multimodal scanning electron microscope (SEM) imaging. In particular, we aim at merging the backscattered electron images that usually have a high spatial resolution but do not provide enough discriminative information to physically classify the nature of the sample, with energy-dispersive X-ray spectroscopy (EDX) images that have discriminative information but a lower spatial resolution. The produced images are named enhanced EDX. To achieve this goal, we have compared the results obtained with classical pansharpening techniques for image fusion with an original approach tailored for multimodal SEM fusion of information. Quantitative assessment is obtained by means of two SEM images and a simulated dataset produced by a software based on PENELOPE. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

Cost Modeling and Design of Field-Reversed Configuration Fusion Power Plants

NASA Astrophysics Data System (ADS)

Kirtley, David; Slough, John; Helion Team

2017-10-01

The Inductively Driven Liner (IDL) fusion concept uses the magnetically driven implosion of thin (0.5-1 mm) Aluminum hoops to magnetically compress a merged Field-Reversed Configuration (FRC) plasma to fusion conditions. Both the driver and the target have been studied experimentally and theoretically by researchers at Helion Energy, MSNW, and the University of Washington, demonstrating compression fields greater than 100 T and suitable fusion targets. In the presented study, a notional power plant facility using this approach will be described. In addition, a full cost study based on the LLNL Z-IFE and HYLIFE-II studies, the ARIES Tokamak concept, and RAND power plant studies will be described. Finally, the expected capital costs, development requirements, and LCOE for 50 and 500 MW power plants will be given. This analysis includes core FRC plant scaling, metallic liner recycling, radiation shielding, operations, and facilities capital requirements.

VEGFR2-targeted fusion antibody improved NK cell-mediated immunosurveillance against K562 cells.

PubMed

Ren, Xueyan; Xie, Wei; Wang, Youfu; Xu, Menghuai; Liu, Fang; Tang, Mingying; Li, Chenchen; Wang, Min; Zhang, Juan

2016-08-01

MHC class I polypeptide-related sequence A (MICA), which is normally expressed on cancer cells, activates NK cells via NK group 2-member D pathway. However, some cancer cells escape NK-mediated immune surveillance by shedding membrane MICA causing immune suppression. To address this issue, we designed an antibody-MICA fusion targeting tumor-specific antigen (vascular endothelial growth factor receptor 2, VEGFR2) based on our patented antibody (mAb04) against VEGFR2. In vitro results demonstrate that the fusion antibody retains both the antineoplastic and the immunomodulatory activity of mAb04. Further, we revealed that it enhanced NK-mediated immunosurveillance against K562 cells through increasing degranulation and cytokine production of NK cells. The overall data suggest our new fusion protein provides a promising approach for cancer-targeted immunotherapy and has prospects for potential application of chronic myeloid leukemia.

Generic Stellarator-like Magnetic Fusion Reactor

NASA Astrophysics Data System (ADS)

Sheffield, John; Spong, Donald

2015-11-01

The Generic Magnetic Fusion Reactor paper, published in 1985, has been updated, reflecting the improved science and technology base in the magnetic fusion program. Key changes beyond inflation are driven by important benchmark numbers for technologies and costs from ITER construction, and the use of a more conservative neutron wall flux and fluence in modern fusion reactor designs. In this paper the generic approach is applied to a catalyzed D-D stellarator-like reactor. It is shown that an interesting power plant might be possible if the following parameters could be achieved for a reference reactor: R/ < a > ~ 4 , confinement factor, fren = 0.9-1.15, < β > ~ 8 . 0 -11.5 %, Zeff ~ 1.45 plus a relativistic temperature correction, fraction of fast ions lost ~ 0.07, Bm ~ 14-16 T, and R ~ 18-24 m. J. Sheffield was supported under ORNL subcontract 4000088999 with the University of Tennessee.

Is mindfulness-based therapy an effective intervention for obsessive-intrusive thoughts: a case series.

PubMed

Wilkinson-Tough, Megan; Bocci, Laura; Thorne, Kirsty; Herlihy, Jane

2010-01-01

Despite the efficacy of cognitive-behavioural interventions in improving the experience of obsessions and compulsions, some people do not benefit from this approach. The present research uses a case series design to establish whether mindfulness-based therapy could benefit those experiencing obsessive-intrusive thoughts by targeting thought-action fusion and thought suppression. Three participants received a relaxation control intervention followed by a six-session mindfulness-based intervention which emphasized daily practice. Following therapy all participants demonstrated reductions in Yale-Brown Obsessive-Compulsive Scale scores to below clinical levels, with two participants maintaining this at follow-up. Qualitative analysis of post-therapy feedback suggested that mindfulness skills such as observation, awareness and acceptance were seen as helpful in managing thought-action fusion and suppression. Despite being limited by small participant numbers, these results suggest that mindfulness may be beneficial to some people experiencing intrusive unwanted thoughts and that further research could establish the possible efficacy of this approach in larger samples. Copyright (c) 2009 John Wiley & Sons, Ltd.

Studies of Ebola Virus Glycoprotein-Mediated Entry and Fusion by Using Pseudotyped Human Immunodeficiency Virus Type 1 Virions: Involvement of Cytoskeletal Proteins and Enhancement by Tumor Necrosis Factor Alpha

PubMed Central

Yonezawa, Akihito; Cavrois, Marielle; Greene, Warner C.

2005-01-01

The Ebola filoviruses are aggressive pathogens that cause severe and often lethal hemorrhagic fever syndromes in humans and nonhuman primates. To date, no effective therapies have been identified. To analyze the entry and fusion properties of Ebola virus, we adapted a human immunodeficiency virus type 1 (HIV-1) virion-based fusion assay by substituting Ebola virus glycoprotein (GP) for the HIV-1 envelope. Fusion was detected by cleavage of the fluorogenic substrate CCF2 by β-lactamase-Vpr incorporated into virions and released as a result of virion fusion. Entry and fusion induced by the Ebola virus GP occurred with much slower kinetics than with vesicular stomatitis virus G protein (VSV-G) and were blocked by depletion of membrane cholesterol and by inhibition of vesicular acidification with bafilomycin A1. These properties confirmed earlier studies and validated the assay for exploring other properties of Ebola virus GP-mediated entry and fusion. Entry and fusion of Ebola virus GP pseudotypes, but not VSV-G or HIV-1 Env pseudotypes, were impaired in the presence of the microtubule-disrupting agent nocodazole but were enhanced in the presence of the microtubule-stabilizing agent paclitaxel (Taxol). Agents that impaired microfilament function, including cytochalasin B, cytochalasin D, latrunculin A, and jasplakinolide, also inhibited Ebola virus GP-mediated entry and fusion. Together, these findings suggest that both microtubules and microfilaments may play a role in the effective trafficking of vesicles containing Ebola virions from the cell surface to the appropriate acidified vesicular compartment where fusion occurs. In terms of Ebola virus GP-mediated entry and fusion to various target cells, primary macrophages proved highly sensitive, while monocytes from the same donors displayed greatly reduced levels of entry and fusion. We further observed that tumor necrosis factor alpha, which is released by Ebola virus-infected monocytes/macrophages, enhanced Ebola virus GP-mediated entry and fusion to human umbilical vein endothelial cells. Thus, Ebola virus infection of one target cell may induce biological changes that facilitate infection of secondary target cells that play a key role in filovirus pathogenesis. Finally, these studies indicate that pseudotyping in the HIV-1 virion-based fusion assay may be a valuable approach to the study of entry and fusion properties mediated through the envelopes of other viral pathogens. PMID:15613320

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.

PubMed

Min, Jianliang; Wang, Ping; Hu, Jianfeng

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Comparison of interbody fusion approaches for disabling low back pain.

PubMed

Hacker, R J

1997-03-15

This is a study comparing two groups of patients surgically treated for disabling low back pain. One group was treated with lumbar anteroposterior fusion (360 degrees fusion), the other with posterior lumbar interbody fusion and an interbody fixation device. To determine which approach provided the best and most cost-effective outcome using similar patient selection criteria. Others have shown that certain patients with disabling low back pain benefit from lumbar fusion. Although rarely reported, the costs of different surgical treatments appear to vary significantly, whereas the patient outcome may vary little. Since 1991, 75 patients have been treated Starting in 1993, posterior lumbar interbody fusion BAK was offered to patients as an alternative to 360 degrees fusion. The treating surgeon reviewed the cases. The interbody fixation device used (BAK; Spine-Tech, Inc., Minneapolis, MN) was part of a Food and Drug Administration study. Patient selection criteria included examination, response to conservative therapy, imaging, psychological profile, and discography. North American Spine Society outcome questionnaires, BAK investigation data radiographs, chart entries, billing records and patient interviews were the basis for assessment. Age, sex compensable injury history and history of previous surgery were similar. Operative time; blood loss, hospitalization time, and total costs were significantly different. There was a quicker return to work and closure of workers compensation claims for the posterior lumbar interbody fusion-BAK group. Patient satisfaction was comparable at last follow-up. Posterior lumbar interbody fusion-BAK achieves equal patient satisfaction but fiscally surpasses the 360 degrees fusion approach. Today's environment of regulated medical practice requires the surgeon to consider cost effectiveness when performing fusion for low back pain.

Magnetized Target Fusion Driven by Plasma Liners

NASA Technical Reports Server (NTRS)

Thio, Y. C. Francis; Kirkpatrick, Ronald C.; Knapp, Charles E.; Rodgers, Stephen L. (Technical Monitor)

2002-01-01

Magnetized target fusion is an emerging, relatively unexplored approach to fusion for electrical power and propulsion application. The physical principles of the concept are founded upon both inertial confinement fusion (ICF) and magnetic confinement fusion (MCF). It attempts to combine the favorable attributes of both these orthogonal approaches to fusion, but at the same time, avoiding the extreme technical challenges of both by exploiting a fusion regime intermediate between them. It uses a material liner to compress, heat and contain the fusion reacting plasma (the target plasma) mentally. By doing so, the fusion burn could be made to occur at plasma densities as high as six orders of magnitude higher than conventional MCF such as tokamak, thus leading to an approximately three orders of magnitude reduction in the plasma energy required for ignition. It also uses a transient magnetic field, compressed to extremely high intensity (100's T to 1000T) in the target plasma, to slow down the heat transport to the liner and to increase the energy deposition of charged-particle fusion products. This has several compounding beneficial effects. It leads to longer energy confinement time compared with conventional ICF without magnetized target, and thus permits the use of much lower plasma density to produce reasonable burn-up fraction. The compounding effects of lower plasma density and the magneto-insulation of the target lead to greatly reduced compressional heating power on the target. The increased energy deposition rate of charged-particle fusion products also helps to lower the energy threshold required for ignition and increasing the burn-up fraction. The reduction in ignition energy and the compressional power compound to lead to reduced system size, mass and R&D cost. It is a fusion approach that has an affordable R&D pathway, and appears attractive for propulsion application in the nearer term.

A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

PubMed Central

Adali, Tülay; Yu, Qingbao; Calhoun, Vince D.

2011-01-01

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data. PMID:22108139

Fusion of magnetometer and gradiometer sensors of MEG in the presence of multiplicative error.

PubMed

Mohseni, Hamid R; Woolrich, Mark W; Kringelbach, Morten L; Luckhoo, Henry; Smith, Penny Probert; Aziz, Tipu Z

2012-07-01

Novel neuroimaging techniques have provided unprecedented information on the structure and function of the living human brain. Multimodal fusion of data from different sensors promises to radically improve this understanding, yet optimal methods have not been developed. Here, we demonstrate a novel method for combining multichannel signals. We show how this method can be used to fuse signals from the magnetometer and gradiometer sensors used in magnetoencephalography (MEG), and through extensive experiments using simulation, head phantom and real MEG data, show that it is both robust and accurate. This new approach works by assuming that the lead fields have multiplicative error. The criterion to estimate the error is given within a spatial filter framework such that the estimated power is minimized in the worst case scenario. The method is compared to, and found better than, existing approaches. The closed-form solution and the conditions under which the multiplicative error can be optimally estimated are provided. This novel approach can also be employed for multimodal fusion of other multichannel signals such as MEG and EEG. Although the multiplicative error is estimated based on beamforming, other methods for source analysis can equally be used after the lead-field modification.

AxiaLIF system: minimally invasive device for presacral lumbar interbody spinal fusion

PubMed Central

Rapp, Steven M; Miller, Larry E; Block, Jon E

2011-01-01

Lumbar fusion is commonly performed to alleviate chronic low back and leg pain secondary to disc degeneration, spondylolisthesis with or without concomitant lumbar spinal stenosis, or chronic lumbar instability. However, the risk of iatrogenic injury during traditional anterior, posterior, and transforaminal open fusion surgery is significant. The axial lumbar interbody fusion (AxiaLIF) system is a minimally invasive fusion device that accesses the lumbar (L4–S1) intervertebral disc spaces via a reproducible presacral approach that avoids critical neurovascular and musculoligamentous structures. Since the AxiaLIF system received marketing clearance from the US Food and Drug Administration in 2004, clinical studies of this device have reported high fusion rates without implant subsidence, significant improvements in pain and function, and low complication rates. This paper describes the design and approach of this lumbar fusion system, details the indications for use, and summarizes the clinical experience with the AxiaLIF system to date. PMID:22915939

AxiaLIF system: minimally invasive device for presacral lumbar interbody spinal fusion.

PubMed

Rapp, Steven M; Miller, Larry E; Block, Jon E

2011-01-01

Lumbar fusion is commonly performed to alleviate chronic low back and leg pain secondary to disc degeneration, spondylolisthesis with or without concomitant lumbar spinal stenosis, or chronic lumbar instability. However, the risk of iatrogenic injury during traditional anterior, posterior, and transforaminal open fusion surgery is significant. The axial lumbar interbody fusion (AxiaLIF) system is a minimally invasive fusion device that accesses the lumbar (L4-S1) intervertebral disc spaces via a reproducible presacral approach that avoids critical neurovascular and musculoligamentous structures. Since the AxiaLIF system received marketing clearance from the US Food and Drug Administration in 2004, clinical studies of this device have reported high fusion rates without implant subsidence, significant improvements in pain and function, and low complication rates. This paper describes the design and approach of this lumbar fusion system, details the indications for use, and summarizes the clinical experience with the AxiaLIF system to date.

Efficiency and Accuracy in Thermal Simulation of Powder Bed Fusion of Bulk Metallic Glass

NASA Astrophysics Data System (ADS)

Lindwall, J.; Malmelöv, A.; Lundbäck, A.; Lindgren, L.-E.

2018-05-01

Additive manufacturing by powder bed fusion processes can be utilized to create bulk metallic glass as the process yields considerably high cooling rates. However, there is a risk that reheated material set in layers may become devitrified, i.e., crystallize. Therefore, it is advantageous to simulate the process to fully comprehend it and design it to avoid the aforementioned risk. However, a detailed simulation is computationally demanding. It is necessary to increase the computational speed while maintaining accuracy of the computed temperature field in critical regions. The current study evaluates a few approaches based on temporal reduction to achieve this. It is found that the evaluated approaches save a lot of time and accurately predict the temperature history.

Creation of Dystrophin Expressing Chimeric Cells of Myoblast Origin as a Novel Stem Cell Based Therapy for Duchenne Muscular Dystrophy.

PubMed

Siemionow, M; Cwykiel, J; Heydemann, A; Garcia-Martinez, J; Siemionow, K; Szilagyi, E

2018-04-01

Over the past decade different stem cell (SC) based approaches were tested to treat Duchenne Muscular Dystrophy (DMD), a lethal X-linked disorder caused by mutations in dystrophin gene. Despite research efforts, there is no curative therapy for DMD. Allogeneic SC therapies aim to restore dystrophin in the affected muscles; however, they are challenged by rejection and limited engraftment. Thus, there is a need to develop new more efficacious SC therapies. Chimeric Cells (CC), created via ex vivo fusion of donor and recipient cells, represent a promising therapeutic option for tissue regeneration and Vascularized Composite Allotransplantation (VCA) due to tolerogenic properties that eliminate the need for lifelong immunosuppression. This proof of concept study tested feasibility of myoblast fusion for Dystrophin Expressing. Chimeric Cell (DEC) therapy through in vitro characterization and in vivo assessment of engraftment, survival, and efficacy in the mdx mouse model of DMD. Murine DEC were created via ex vivo fusion of normal (snj) and dystrophin-deficient (mdx) myoblasts using polyethylene glycol. Efficacy of myoblast fusion was confirmed by flow cytometry and dystrophin immunostaining, while proliferative and myogenic differentiation capacity of DEC were assessed in vitro. Therapeutic effect after DEC transplant (0.5 × 10 6 ) into the gastrocnemius muscle (GM) of mdx mice was assessed by muscle functional tests. At 30 days post-transplant dystrophin expression in GM of injected mdx mice increased to 37.27 ± 12.1% and correlated with improvement of muscle strength and function. Our study confirmed feasibility and efficacy of DEC therapy and represents a novel SC based approach for treatment of muscular dystrophies.

Forecasting Chronic Diseases Using Data Fusion.

PubMed

Acar, Evrim; Gürdeniz, Gözde; Savorani, Francesco; Hansen, Louise; Olsen, Anja; Tjønneland, Anne; Dragsted, Lars Ove; Bro, Rasmus

2017-07-07

Data fusion, that is, extracting information through the fusion of complementary data sets, is a topic of great interest in metabolomics because analytical platforms such as liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy commonly used for chemical profiling of biofluids provide complementary information. In this study, with a goal of forecasting acute coronary syndrome (ACS), breast cancer, and colon cancer, we jointly analyzed LC-MS, NMR measurements of plasma samples, and the metadata corresponding to the lifestyle of participants. We used supervised data fusion based on multiple kernel learning and exploited the linearity of the models to identify significant metabolites/features for the separation of healthy referents and the cases developing a disease. We demonstrated that (i) fusing LC-MS, NMR, and metadata provided better separation of ACS cases and referents compared with individual data sets, (ii) NMR data performed the best in terms of forecasting breast cancer, while fusion degraded the performance, and (iii) neither the individual data sets nor their fusion performed well for colon cancer. Furthermore, we showed the strengths and limitations of the fusion models by discussing their performance in terms of capturing known biomarkers for smoking and coffee. While fusion may improve performance in terms of separating certain conditions by jointly analyzing metabolomics and metadata sets, it is not necessarily always the best approach as in the case of breast cancer.

Unraveling a Three-Step Spatiotemporal Mechanism of Triggering of Receptor-Induced Nipah Virus Fusion and Cell Entry

PubMed Central

Liu, Qian; Stone, Jacquelyn A.; Bradel-Tretheway, Birgit; Dabundo, Jeffrey; Benavides Montano, Javier A.; Santos-Montanez, Jennifer; Biering, Scott B.; Nicola, Anthony V.; Iorio, Ronald M.; Lu, Xiaonan; Aguilar, Hector C.

2013-01-01

Membrane fusion is essential for entry of the biomedically-important paramyxoviruses into their host cells (viral-cell fusion), and for syncytia formation (cell-cell fusion), often induced by paramyxoviral infections [e.g. those of the deadly Nipah virus (NiV)]. For most paramyxoviruses, membrane fusion requires two viral glycoproteins. Upon receptor binding, the attachment glycoprotein (HN/H/G) triggers the fusion glycoprotein (F) to undergo conformational changes that merge viral and/or cell membranes. However, a significant knowledge gap remains on how HN/H/G couples cell receptor binding to F-triggering. Via interdisciplinary approaches we report the first comprehensive mechanism of NiV membrane fusion triggering, involving three spatiotemporally sequential cell receptor-induced conformational steps in NiV-G: two in the head and one in the stalk. Interestingly, a headless NiV-G mutant was able to trigger NiV-F, and the two head conformational steps were required for the exposure of the stalk domain. Moreover, the headless NiV-G prematurely triggered NiV-F on virions, indicating that the NiV-G head prevents premature triggering of NiV-F on virions by concealing a F-triggering stalk domain until the correct time and place: receptor-binding. Based on these and recent paramyxovirus findings, we present a comprehensive and fundamentally conserved mechanistic model of paramyxovirus membrane fusion triggering and cell entry. PMID:24278018

Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing.

PubMed

Weirather, Jason L; Afshar, Pegah Tootoonchi; Clark, Tyson A; Tseng, Elizabeth; Powers, Linda S; Underwood, Jason G; Zabner, Joseph; Korlach, Jonas; Wong, Wing Hung; Au, Kin Fai

2015-10-15

We developed an innovative hybrid sequencing approach, IDP-fusion, to detect fusion genes, determine fusion sites and identify and quantify fusion isoforms. IDP-fusion is the first method to study gene fusion events by integrating Third Generation Sequencing long reads and Second Generation Sequencing short reads. We applied IDP-fusion to PacBio data and Illumina data from the MCF-7 breast cancer cells. Compared with the existing tools, IDP-fusion detects fusion genes at higher precision and a very low false positive rate. The results show that IDP-fusion will be useful for unraveling the complexity of multiple fusion splices and fusion isoforms within tumorigenesis-relevant fusion genes. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Support Vector Machine-Based Gender Identification Using Speech Signal

NASA Astrophysics Data System (ADS)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Can dendritic cells improve whole cancer cell vaccines based on immunogenically killed cancer cells?

PubMed Central

Cicchelero, Laetitia; Denies, Sofie; Devriendt, Bert; de Rooster, Hilde; Sanders, Niek N

2015-01-01

Immunogenic cell death (ICD) offers interesting opportunities in cancer cell (CC) vaccine manufacture, as it increases the immunogenicity of the dead CC. Furthermore, fusion of CCs with dendritic cells (DCs) is considered a superior method for generating whole CC vaccines. Therefore, in this work, we determined in naive mice whether immunogenically killed CCs per se (CC vaccine) elicit an antitumoral immune response different from the response observed when immunogenically killed CCs are associated with DCs through fusion (fusion vaccine) or through co-incubation (co-incubation vaccine). After tumor inoculation, the type of immune response in the prophylactically vaccinated mice differed between the groups. In more detail, fusion vaccines elicited a humoral anticancer response, whereas the co-incubation and CC vaccine mainly induced a cellular response. Despite these differences, all three approaches offered a prophylactic protection against tumor development in the murine mammary carcinoma model. In summary, it can be concluded that whole CC vaccines based on immunogenically killed CCs may not necessarily require association with DCs to elicit a protective anticancer immune response. If this finding can be endorsed in other cancer models, the manufacture of CC vaccines would greatly benefit from this new insight, as production of DC-based vaccines is laborious, time-consuming and expensive. PMID:26587315

Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter

PubMed Central

Chowdhury, Amor; Sarjaš, Andrej

2016-01-01

The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation. PMID:27649197

Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter.

PubMed

Chowdhury, Amor; Sarjaš, Andrej

2016-09-15

The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles

PubMed Central

de Ponte Müller, Fabian

2017-01-01

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability. PMID:28146129

Joint modality fusion and temporal context exploitation for semantic video analysis

NASA Astrophysics Data System (ADS)

Papadopoulos, Georgios Th; Mezaris, Vasileios; Kompatsiaris, Ioannis; Strintzis, Michael G.

2011-12-01

In this paper, a multi-modal context-aware approach to semantic video analysis is presented. Overall, the examined video sequence is initially segmented into shots and for every resulting shot appropriate color, motion and audio features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing an initial association of each shot with the semantic classes that are of interest separately for each modality. Subsequently, a graphical modeling-based approach is proposed for jointly performing modality fusion and temporal context exploitation. Novelties of this work include the combined use of contextual information and multi-modal fusion, and the development of a new representation for providing motion distribution information to HMMs. Specifically, an integrated Bayesian Network is introduced for simultaneously performing information fusion of the individual modality analysis results and exploitation of temporal context, contrary to the usual practice of performing each task separately. Contextual information is in the form of temporal relations among the supported classes. Additionally, a new computationally efficient method for providing motion energy distribution-related information to HMMs, which supports the incorporation of motion characteristics from previous frames to the currently examined one, is presented. The final outcome of this overall video analysis framework is the association of a semantic class with every shot. Experimental results as well as comparative evaluation from the application of the proposed approach to four datasets belonging to the domains of tennis, news and volleyball broadcast video are presented.

Introduction to Nuclear Fusion Power and the Design of Fusion Reactors. An Issue-Oriented Module.

ERIC Educational Resources Information Center

Fillo, J. A.

This three-part module focuses on the principles of nuclear fusion and on the likely nature and components of a controlled-fusion power reactor. The physical conditions for a net energy release from fusion and two approaches (magnetic and inertial confinement) which are being developed to achieve this goal are described. Safety issues associated…

Computer-guided percutaneous interbody fixation and fusion of the L5-S1 disc: a 2-year prospective study.

PubMed

Mac Millan, Michael

2005-02-01

The clinical outcomes of lumbar fusion are diminished by the complications associated with the surgical approach. Posterior approaches cause segmental muscular necrosis and anterior approaches risk visceral and vascular injury. This report details a two-year prospective study of a percutaneous method which avoids the major problems associated with existing approaches. Seventeen patients underwent percutaneous, trans-sacral fusion and fixation of L5-S1 with the assistance of computer guidance. Each patient was followed for a minimum of two years post surgery. SF-36 questionnaires and radiographs were obtained preoperatively and at two years post-operatively. Fusion was assessed with post-operative radiographs and/or CT scan. Ninety-three percent of the people fused as judged by plain AP films, Ferguson's view radiographs, and/or CT scans at the two year follow-up. Prospective health and functional SF-36 scores showed significant improvement from the preoperative to the postoperative period. There were no significant complications related to the approach or to the placement of the implants. Percutaneous fusion of the lumbosacral spine appears safe and provides excellent clinical results with a minimal amount of associated tissue trauma.

Formulating Spatially Varying Performance in the Statistical Fusion Framework

PubMed Central

Landman, Bennett A.

2012-01-01

To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. PMID:22438513

Magneto-Inertial Fusion

DOE PAGES

Wurden, G. A.; Hsu, S. C.; Intrator, T. P.; ...

2015-11-17

In this community white paper, we describe an approach to achieving fusion which employs a hybrid of elements from the traditional magnetic and inertial fusion concepts, called magneto-inertial fusion (MIF). Furthermore, the status of MIF research in North America at multiple institutions is summarized including recent progress, research opportunities, and future plans.

Ecto-Fc MS identifies ligand-receptor interactions through extracellular domain Fc fusion protein baits and shotgun proteomic analysis

PubMed Central

Savas, Jeffrey N.; De Wit, Joris; Comoletti, Davide; Zemla, Roland; Ghosh, Anirvan

2015-01-01

Ligand-receptor interactions represent essential biological triggers which regulate many diverse and important cellular processes. We have developed a discovery-based proteomic biochemical protocol which couples affinity purification with multidimensional liquid chromatographic tandem mass spectrometry (LCLC-MS/MS) and bioinformatic analysis. Compared to previous approaches, our analysis increases sensitivity, shortens analysis duration, and boosts comprehensiveness. In this protocol, receptor extracellular domains are fused with the Fc region of IgG to generate fusion proteins that are purified from transfected HEK293T cells. These “ecto-Fcs” are coupled to protein A beads and serve as baits for binding assays with prey proteins extracted from rodent brain. After capture, the affinity purified proteins are digested into peptides and comprehensively analyzed by LCLC-MS/MS with ion trap mass spectrometers. In four working days, this protocol can generate shortlists of candidate ligand-receptor protein-protein interactions. Our “Ecto-Fc MS” approach outperforms antibody-based approaches and provides a reproducible and robust framework to identify extracellular ligand – receptor interactions. PMID:25101821

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

PubMed

Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

2018-01-24

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Advanced algorithms for distributed fusion

NASA Astrophysics Data System (ADS)

Gelfand, A.; Smith, C.; Colony, M.; Bowman, C.; Pei, R.; Huynh, T.; Brown, C.

2008-03-01

The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an environment, centralized fusion approaches will have limited application due to the constraints of real-time communications networks and computational resources. To overcome these limitations, we are developing a formalized architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created and managed information network. This network will support the incorporation and utilization of low level tracking information within the Army Distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.

Game theory-based visual tracking approach focusing on color and texture features.

PubMed

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Chen, Chuanhua; Wang, Xin

2017-07-20

It is difficult for a single-feature tracking algorithm to achieve strong robustness under a complex environment. To solve this problem, we proposed a multifeature fusion tracking algorithm that is based on game theory. By focusing on color and texture features as two gamers, this algorithm accomplishes tracking by using a mean shift iterative formula to search for the Nash equilibrium of the game. The contribution of different features is always keeping the state of optical balance, so that the algorithm can fully take advantage of feature fusion. According to the experiment results, this algorithm proves to possess good performance, especially under the condition of scene variation, target occlusion, and similar interference.

Local SIMPLE multi-atlas-based segmentation applied to lung lobe detection on chest CT

NASA Astrophysics Data System (ADS)

Agarwal, M.; Hendriks, E. A.; Stoel, B. C.; Bakker, M. E.; Reiber, J. H. C.; Staring, M.

2012-02-01

For multi atlas-based segmentation approaches, a segmentation fusion scheme which considers local performance measures may be more accurate than a method which uses a global performance measure. We improve upon an existing segmentation fusion method called SIMPLE and extend it to be localized and suitable for multi-labeled segmentations. We demonstrate the algorithm performance on 23 CT scans of COPD patients using a leave-one- out experiment. Our algorithm performs significantly better (p < 0.01) than majority voting, STAPLE, and SIMPLE, with a median overlap of the fissure of 0.45, 0.48, 0.55 and 0.6 for majority voting, STAPLE, SIMPLE, and the proposed algorithm, respectively.

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

NASA Astrophysics Data System (ADS)

Liu, Yan; Yu, Feihong

2014-09-01

Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.

A plasmid toolkit for cloning chimeric cDNAs encoding customized fusion proteins into any Gateway destination expression vector

PubMed Central

2013-01-01

Background Valuable clone collections encoding the complete ORFeomes for some model organisms have been constructed following the completion of their genome sequencing projects. These libraries are based on Gateway cloning technology, which facilitates the study of protein function by simplifying the subcloning of open reading frames (ORF) into any suitable destination vector. The expression of proteins of interest as fusions with functional modules is a frequent approach in their initial functional characterization. A limited number of Gateway destination expression vectors allow the construction of fusion proteins from ORFeome-derived sequences, but they are restricted to the possibilities offered by their inbuilt functional modules and their pre-defined model organism-specificity. Thus, the availability of cloning systems that overcome these limitations would be highly advantageous. Results We present a versatile cloning toolkit for constructing fully-customizable three-part fusion proteins based on the MultiSite Gateway cloning system. The fusion protein components are encoded in the three plasmids integral to the kit. These can recombine with any purposely-engineered destination vector that uses a heterologous promoter external to the Gateway cassette, leading to the in-frame cloning of an ORF of interest flanked by two functional modules. In contrast to previous systems, a third part becomes available for peptide-encoding as it no longer needs to contain a promoter, resulting in an increased number of possible fusion combinations. We have constructed the kit’s component plasmids and demonstrate its functionality by providing proof-of-principle data on the expression of prototype fluorescent fusions in transiently-transfected cells. Conclusions We have developed a toolkit for creating fusion proteins with customized N- and C-term modules from Gateway entry clones encoding ORFs of interest. Importantly, our method allows entry clones obtained from ORFeome collections to be used without prior modifications. Using this technology, any existing Gateway destination expression vector with its model-specific properties could be easily adapted for expressing fusion proteins. PMID:23957834

A plasmid toolkit for cloning chimeric cDNAs encoding customized fusion proteins into any Gateway destination expression vector.

PubMed

Buj, Raquel; Iglesias, Noa; Planas, Anna M; Santalucía, Tomàs

2013-08-20

Valuable clone collections encoding the complete ORFeomes for some model organisms have been constructed following the completion of their genome sequencing projects. These libraries are based on Gateway cloning technology, which facilitates the study of protein function by simplifying the subcloning of open reading frames (ORF) into any suitable destination vector. The expression of proteins of interest as fusions with functional modules is a frequent approach in their initial functional characterization. A limited number of Gateway destination expression vectors allow the construction of fusion proteins from ORFeome-derived sequences, but they are restricted to the possibilities offered by their inbuilt functional modules and their pre-defined model organism-specificity. Thus, the availability of cloning systems that overcome these limitations would be highly advantageous. We present a versatile cloning toolkit for constructing fully-customizable three-part fusion proteins based on the MultiSite Gateway cloning system. The fusion protein components are encoded in the three plasmids integral to the kit. These can recombine with any purposely-engineered destination vector that uses a heterologous promoter external to the Gateway cassette, leading to the in-frame cloning of an ORF of interest flanked by two functional modules. In contrast to previous systems, a third part becomes available for peptide-encoding as it no longer needs to contain a promoter, resulting in an increased number of possible fusion combinations. We have constructed the kit's component plasmids and demonstrate its functionality by providing proof-of-principle data on the expression of prototype fluorescent fusions in transiently-transfected cells. We have developed a toolkit for creating fusion proteins with customized N- and C-term modules from Gateway entry clones encoding ORFs of interest. Importantly, our method allows entry clones obtained from ORFeome collections to be used without prior modifications. Using this technology, any existing Gateway destination expression vector with its model-specific properties could be easily adapted for expressing fusion proteins.

Hemagglutinin-Mediated Membrane Fusion: A Biophysical Perspective.

PubMed

Boonstra, Sander; Blijleven, Jelle S; Roos, Wouter H; Onck, Patrick R; van der Giessen, Erik; van Oijen, Antoine M

2018-05-20

Influenza hemagglutinin (HA) is a viral membrane protein responsible for the initial steps of the entry of influenza virus into the host cell. It mediates binding of the virus particle to the host-cell membrane and catalyzes fusion of the viral membrane with that of the host. HA is therefore a major target in the development of antiviral strategies. The fusion of two membranes involves high activation barriers and proceeds through several intermediate states. Here, we provide a biophysical description of the membrane fusion process, relating its kinetic and thermodynamic properties to the large conformational changes taking place in HA and placing these in the context of multiple HA proteins working together to mediate fusion. Furthermore, we highlight the role of novel single-particle experiments and computational approaches in understanding the fusion process and their complementarity with other biophysical approaches.

Data fusion for CD metrology: heterogeneous hybridization of scatterometry, CDSEM, and AFM data

NASA Astrophysics Data System (ADS)

Hazart, J.; Chesneau, N.; Evin, G.; Largent, A.; Derville, A.; Thérèse, R.; Bos, S.; Bouyssou, R.; Dezauzier, C.; Foucher, J.

2014-04-01

The manufacturing of next generation semiconductor devices forces metrology tool providers for an exceptional effort in order to meet the requirements for precision, accuracy and throughput stated in the ITRS. In the past years hybrid metrology (based on data fusion theories) has been investigated as a new methodology for advanced metrology [1][2][3]. This paper provides a new point of view of data fusion for metrology through some experiments and simulations. The techniques are presented concretely in terms of equations to be solved. The first point of view is High Level Fusion which is the use of simple numbers with their associated uncertainty postprocessed by tools. In this paper, it is divided into two stages: one for calibration to reach accuracy, the second to reach precision thanks to Bayesian Fusion. From our perspective, the first stage is mandatory before applying the second stage which is commonly presented [1]. However a reference metrology system is necessary for this fusion. So, precision can be improved if and only if the tools to be fused are perfectly matched at least for some parameters. We provide a methodology similar to a multidimensional TMU able to perform this matching exercise. It is demonstrated on a 28 nm node backend lithography case. The second point of view is Deep Level Fusion which works on the contrary with raw data and their combination. In the approach presented here, the analysis of each raw data is based on a parametric model and connections between the parameters of each tool. In order to allow OCD/SEM Deep Level Fusion, a SEM Compact Model derived from [4] has been developed and compared to AFM. As far as we know, this is the first time such techniques have been coupled at Deep Level. A numerical study on the case of a simple stack for lithography is performed. We show strict equivalence of Deep Level Fusion and High Level Fusion when tools are sensitive and models are perfect. When one of the tools can be considered as a reference and the second is biased, High Level Fusion is far superior to standard Deep Level Fusion. Otherwise, only the second stage of High Level Fusion is possible (Bayesian Fusion) and do not provide substantial advantage. Finally, when OCD is equipped with methods for bias detection [5], Deep Level Fusion outclasses the two-stage High Level Fusion and will benefit to the industry for most advanced nodes production.

Integration of language and sensor information

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Weijers, Bertus

2003-04-01

The talk describes the development of basic technologies of intelligent systems fusing data from multiple domains and leading to automated computational techniques for understanding data contents. Understanding involves inferring appropriate decisions and recommending proper actions, which in turn requires fusion of data and knowledge about objects, situations, and actions. Data might include sensory data, verbal reports, intelligence intercepts, or public records, whereas knowledge ought to encompass the whole range of objects, situations, people and their behavior, and knowledge of languages. In the past, a fundamental difficulty in combining knowledge with data was the combinatorial complexity of computations, too many combinations of data and knowledge pieces had to be evaluated. Recent progress in understanding of natural intelligent systems, including the human mind, leads to the development of neurophysiologically motivated architectures for solving these challenging problems, in particular the role of emotional neural signals in overcoming combinatorial complexity of old logic-based approaches. Whereas past approaches based on logic tended to identify logic with language and thinking, recent studies in cognitive linguistics have led to appreciation of more complicated nature of linguistic models. Little is known about the details of the brain mechanisms integrating language and thinking. Understanding and fusion of linguistic information with sensory data represent a novel challenging aspect of the development of integrated fusion systems. The presentation will describe a non-combinatorial approach to this problem and outline techniques that can be used for fusing diverse and uncertain knowledge with sensory and linguistic data.

Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation

PubMed Central

Munoz Diaz, Estefania

2017-01-01

Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs). However, most of them use a single inertial measurement unit (IMU) attached to the pedestrian’s body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of wearable-based INSs. The latter are based on a pocket-mounted IMU and a foot-mounted IMU. The loosely coupled fusion combines the output of the two INSs not only when these outputs are least erroneous, but also automatically favoring the best output. This approach is named smart update. The main challenge is determining the quality of the heading estimation of each INS, which changes every time. In order to address this, a novel concept to determine the quality of the heading estimation is presented. This concept is subject to a patent application. The results show that the position error rate of the loosely coupled fusion is 10 cm/s better than either the foot INS’s or pocket INS’s error rate in 95% of the cases. PMID:29099807

Noncontact Sleep Study by Multi-Modal Sensor Fusion.

PubMed

Chung, Ku-Young; Song, Kwangsub; Shin, Kangsoo; Sohn, Jinho; Cho, Seok Hyun; Chang, Joon-Hyuk

2017-07-21

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Noncontact Sleep Study by Multi-Modal Sensor Fusion

PubMed Central

Chung, Ku-young; Song, Kwangsub; Shin, Kangsoo; Sohn, Jinho; Cho, Seok Hyun; Chang, Joon-Hyuk

2017-01-01

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner. PMID:28753994

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

PubMed Central

Zhou, Deyun; Zhuang, Miaoyan; Fang, Xueyi; Xie, Chunhe

2017-01-01

As an important tool of information fusion, Dempster–Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster–Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster’s combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method. PMID:28927017

A weighted optimization approach to time-of-flight sensor fusion.

PubMed

Schwarz, Sebastian; Sjostrom, Marten; Olsson, Roger

2014-01-01

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes.

PubMed

Molina, Iñigo; Martinez, Estibaliz; Morillo, Carmen; Velasco, Jesus; Jara, Alvaro

2016-09-30

In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal "invariant features" is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a "change map", which can be accomplished by means of the CDI's informational content. For this purpose, information metrics such as the Shannon Entropy and "Specific Information" have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf's) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

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.

Summary of human social, cultural, behavioral (HSCB) modeling for information fusion panel discussion

NASA Astrophysics Data System (ADS)

Blasch, Erik; Salerno, John; Kadar, Ivan; Yang, Shanchieh J.; Fenstermacher, Laurie; Endsley, Mica; Grewe, Lynne

2013-05-01

During the SPIE 2012 conference, panelists convened to discuss "Real world issues and challenges in Human Social/Cultural/Behavioral modeling with Applications to Information Fusion." Each panelist presented their current trends and issues. The panel had agreement on advanced situation modeling, working with users for situation awareness and sense-making, and HSCB context modeling in focusing research activities. Each panelist added different perspectives based on the domain of interest such as physical, cyber, and social attacks from which estimates and projections can be forecasted. Also, additional techniques were addressed such as interest graphs, network modeling, and variable length Markov Models. This paper summarizes the panelists discussions to highlight the common themes and the related contrasting approaches to the domains in which HSCB applies to information fusion applications.

Model-theoretic framework for sensor data fusion

NASA Astrophysics Data System (ADS)

Zavoleas, Kyriakos P.; Kokar, Mieczyslaw M.

1993-09-01

The main goal of our research in sensory data fusion (SDF) is the development of a systematic approach (a methodology) to designing systems for interpreting sensory information and for reasoning about the situation based upon this information and upon available data bases and knowledge bases. To achieve such a goal, two kinds of subgoals have been set: (1) develop a theoretical framework in which rational design/implementation decisions can be made, and (2) design a prototype SDF system along the lines of the framework. Our initial design of the framework has been described in our previous papers. In this paper we concentrate on the model-theoretic aspects of this framework. We postulate that data are embedded in data models, and information processing mechanisms are embedded in model operators. The paper is devoted to analyzing the classes of model operators and their significance in SDF. We investigate transformation abstraction and fusion operators. A prototype SDF system, fusing data from range and intensity sensors, is presented, exemplifying the structures introduced. Our framework is justified by the fact that it provides modularity, traceability of information flow, and a basis for a specification language for SDF.

Robust fusion-based processing for military polarimetric imaging systems

NASA Astrophysics Data System (ADS)

Hickman, Duncan L.; Smith, Moira I.; Kim, Kyung Su; Choi, Hyun-Jin

2017-05-01

Polarisation information within a scene can be exploited in military systems to give enhanced automatic target detection and recognition (ATD/R) performance. However, the performance gain achieved is highly dependent on factors such as the geometry, viewing conditions, and the surface finish of the target. Such performance sensitivities are highly undesirable in many tactical military systems where operational conditions can vary significantly and rapidly during a mission. Within this paper, a range of processing architectures and fusion methods is considered in terms of their practical viability and operational robustness for systems requiring ATD/R. It is shown that polarisation information can give useful performance gains but, to retained system robustness, the introduction of polarimetric processing should be done in such a way as to not compromise other discriminatory scene information in the spectral and spatial domains. The analysis concludes that polarimetric data can be effectively integrated with conventional intensity-based ATD/R by either adapting the ATD/R processing function based on the scene polarisation or else by detection-level fusion. Both of these approaches avoid the introduction of processing bottlenecks and limit the impact of processing on system latency.

Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

NASA Astrophysics Data System (ADS)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing

2014-12-01

Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.

A biometric identification system based on eigenpalm and eigenfinger features.

PubMed

Ribaric, Slobodan; Fratric, Ivan

2005-11-01

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

COLA: Optimizing Stream Processing Applications via Graph Partitioning

NASA Astrophysics Data System (ADS)

Khandekar, Rohit; Hildrum, Kirsten; Parekh, Sujay; Rajan, Deepak; Wolf, Joel; Wu, Kun-Lung; Andrade, Henrique; Gedik, Buğra

In this paper, we describe an optimization scheme for fusing compile-time operators into reasonably-sized run-time software units called processing elements (PEs). Such PEs are the basic deployable units in System S, a highly scalable distributed stream processing middleware system. Finding a high quality fusion significantly benefits the performance of streaming jobs. In order to maximize throughput, our solution approach attempts to minimize the processing cost associated with inter-PE stream traffic while simultaneously balancing load across the processing hosts. Our algorithm computes a hierarchical partitioning of the operator graph based on a minimum-ratio cut subroutine. We also incorporate several fusion constraints in order to support real-world System S jobs. We experimentally compare our algorithm with several other reasonable alternative schemes, highlighting the effectiveness of our approach.

New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2017-07-01

This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.

An Approach to Automated Fusion System Design and Adaptation

PubMed Central

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-01-01

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. PMID:28300762

An Approach to Automated Fusion System Design and Adaptation.

PubMed

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-03-16

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

Green fluorescence protein-based content-mixing assay of SNARE-driven membrane fusion

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

Heo, Paul; Kong, Byoungjae; Jung, Young-Hun

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins mediate intracellular membrane fusion by forming a ternary SNARE complex. A minimalist approach utilizing proteoliposomes with reconstituted SNARE proteins yielded a wealth of information pinpointing the molecular mechanism of SNARE-mediated fusion and its regulation by accessory proteins. Two important attributes of a membrane fusion are lipid-mixing and the formation of an aqueous passage between apposing membranes. These two attributes are typically observed by using various fluorescent dyes. Currently available in vitro assay systems for observing fusion pore opening have several weaknesses such as cargo-bleeding, incomplete removal of unencapsulated dyes, and inadequate information regardingmore » the size of the fusion pore, limiting measurements of the final stage of membrane fusion. In the present study, we used a biotinylated green fluorescence protein and streptavidin conjugated with Dylight 594 (DyStrp) as a Föster resonance energy transfer (FRET) donor and acceptor, respectively. This FRET pair encapsulated in each v-vesicle containing synaptobrevin and t-vesicle containing a binary acceptor complex of syntaxin 1a and synaptosomal-associated protein 25 revealed the opening of a large fusion pore of more than 5 nm, without the unwanted signals from unencapsulated dyes or leakage. This system enabled determination of the stoichiometry of the merging vesicles because the FRET efficiency of the FRET pair depended on the molar ratio between dyes. Here, we report a robust and informative assay for SNARE-mediated fusion pore opening. - Highlights: • SNARE proteins drive membrane fusion and open a pore for cargo release. • Biotinylated GFP and DyStrp was used as the reporter pair of fusion pore opening. • Procedure for efficient SNARE reconstitution and reporter encapsulation was established. • The FRET pair reported opening of a large fusion pore bigger than 5 nm. • The assay was robust and provided information of stoichiometry of vesicle fusion.« less

Magneto-hydrodynamically stable axisymmetric mirrorsa)

NASA Astrophysics Data System (ADS)

Ryutov, D. D.; Berk, H. L.; Cohen, B. I.; Molvik, A. W.; Simonen, T. C.

2011-09-01

Making axisymmetric mirrors magnetohydrodynamically (MHD) stable opens up exciting opportunities for using mirror devices as neutron sources, fusion-fission hybrids, and pure-fusion reactors. This is also of interest from a general physics standpoint (as it seemingly contradicts well-established criteria of curvature-driven instabilities). The axial symmetry allows for much simpler and more reliable designs of mirror-based fusion facilities than the well-known quadrupole mirror configurations. In this tutorial, after a summary of classical results, several techniques for achieving MHD stabilization of the axisymmetric mirrors are considered, in particular: (1) employing the favorable field-line curvature in the end tanks; (2) using the line-tying effect; (3) controlling the radial potential distribution; (4) imposing a divertor configuration on the solenoidal magnetic field; and (5) affecting the plasma dynamics by the ponderomotive force. Some illuminative theoretical approaches for understanding axisymmetric mirror stability are described. The applicability of the various stabilization techniques to axisymmetric mirrors as neutron sources, hybrids, and pure-fusion reactors are discussed; and the constraints on the plasma parameters are formulated.

Femtosecond spectroscopy probes the folding quality of antibody fragments expressed as GFP fusions in the cytoplasm

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

Didier, P.; Weiss, E.; Sibler, A.-P.

2008-02-22

Time-resolved femtosecond spectroscopy can improve the application of green fluorescent proteins (GFPs) as protein-folding reporters. The study of ultrafast excited-state dynamics (ESD) of GFP fused to single chain variable fragment (scFv) antibody fragments, allowed us to define and measure an empirical parameter that only depends on the folding quality (FQ) of the fusion. This method has been applied to the analysis of genetic fusions expressed in the bacterial cytoplasm and allowed us to distinguish folded and thus functional antibody fragments (high FQ) with respect to misfolded antibody fragments. Moreover, these findings were strongly correlated to the behavior of the samemore » scFvs expressed in animal cells. This method is based on the sensitivity of the ESD to the modifications in the tertiary structure of the GFP induced by the aggregation state of the fusion partner. This approach may be applicable to the study of the FQ of polypeptides over-expressed under reducing conditions.« less

In vitro comparison of endplate preparation between four mini-open interbody fusion approaches.

PubMed

Tatsumi, Robert; Lee, Yu-Po; Khajavi, Kaveh; Taylor, William; Chen, Foster; Bae, Hyun

2015-04-01

Discectomy and endplate preparation are important steps in interbody fusion for ensuring sufficient arthrodesis. While modern less-invasive approaches for lumbar interbody fusion have gained in popularity, concerns exist regarding their ability to allow for adequate disc space and endplate preparation. Thus, the purpose of this study was to quantitatively and qualitatively evaluate and compare disc space and endplate preparation achieved with four less-invasive approaches for lumbar interbody fusion in cadaveric spines. A total of 24 disc spaces (48 endplates) from L2 to L5 were prepared in eight cadaveric torsos using mini-open anterior lumbar interbody fusion (mini-ALIF), minimally invasive posterior lumbar interbody fusion (MAS PLIF), minimally invasive transforaminal lumbar interbody fusion (MAS TLIF) or minimally invasive lateral, transpsoas interbody fusion (XLIF) on two specimens each, for a total of six levels and 12 endplates prepared per procedure type. Following complete discectomy and endplate preparation, spines were excised and split axially at the interbody disc spaces. Endplates were digitally photographed and evaluated using image analysis software. Area of endplate preparation was measured and qualitative evaluation was also performed to grade the quality of preparation. The XLIF approach resulted in the greatest relative area of endplate preparation (58.3 %) while mini-ALIF resulted in the lowest at 35.0 %. Overall, there were no differences in percentage of preparation between cranial and caudal endplates, though this was significantly different in the XLIF group (65 vs 52 %, respectively). ALL damage was observed in 3 MAS TLIF levels. Percentage of endplate that was deemed to have complete disc removal was highest in XLIF group with 90 % compared to 65 % in MAS TLIF group, 43 % in MAS PLIF, and 40 % in mini-ALIF group. Endplate damage area was highest in the MAS TLIF group at 48 % and lowest in XLIF group at 4 %. These results demonstrate that adequate endplate preparation for interbody fusion can be achieved utilizing various minimally invasive approach techniques (mini-ALIF, MAS TLIF, MAS PLIF, XLIF), however, XLIF appears to provide a greater area of and more complete endplate preparation.

Fusion of Geophysical Images in the Study of Archaeological Sites

NASA Astrophysics Data System (ADS)

Karamitrou, A. A.; Petrou, M.; Tsokas, G. N.

2011-12-01

This paper presents results from different fusion techniques between geophysical images from different modalities in order to combine them into one image with higher information content than the two original images independently. The resultant image will be useful for the detection and mapping of buried archaeological relics. The examined archaeological area is situated in Kampana site (NE Greece) near the ancient theater of Maronia city. Archaeological excavations revealed an ancient theater, an aristocratic house and the temple of the ancient Greek God Dionysus. Numerous ceramic objects found in the broader area indicated the probability of the existence of buried urban structure. In order to accurately locate and map the latter, geophysical measurements performed with the use of the magnetic method (vertical gradient of the magnetic field) and of the electrical method (apparent resistivity). We performed a semi-stochastic pixel based registration method between the geophysical images in order to fine register them by correcting their local spatial offsets produced by the use of hand held devices. After this procedure we applied to the registered images three different fusion approaches. Image fusion is a relatively new technique that not only allows integration of different information sources, but also takes advantage of the spatial and spectral resolution as well as the orientation characteristics of each image. We have used three different fusion techniques, fusion with mean values, with wavelets by enhancing selected frequency bands and curvelets giving emphasis at specific bands and angles (according the expecting orientation of the relics). In all three cases the fused images gave significantly better results than each of the original geophysical images separately. The comparison of the results of the three different approaches showed that the fusion with the use of curvelets, giving emphasis at the features' orientation, seems to give the best fused image. In the resultant image appear clear linear and ellipsoid features corresponding to potential archaeological relics.

A novel framework of tissue membrane systems for image fusion.

PubMed

Zhang, Zulin; Yi, Xinzhong; Peng, Hong

2014-01-01

This paper proposes a tissue membrane system-based framework to deal with the optimal image fusion problem. A spatial domain fusion algorithm is given, and a tissue membrane system of multiple cells is used as its computing framework. Based on the multicellular structure and inherent communication mechanism of the tissue membrane system, an improved velocity-position model is developed. The performance of the fusion framework is studied with comparison of several traditional fusion methods as well as genetic algorithm (GA)-based and differential evolution (DE)-based spatial domain fusion methods. Experimental results show that the proposed fusion framework is superior or comparable to the other methods and can be efficiently used for image fusion.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

PubMed Central

Min, Jianliang; Wang, Ping

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver. PMID:29220351

Minimally invasive trans-sacral approach to L5-S1 interbody fusion: Preliminary results from 1 center and review of the literature.

PubMed

Bradley, W Daniel; Hisey, Michael S; Verma-Kurvari, Sunita; Ohnmeiss, Donna D

2012-01-01

Lumbar interbody fusion has long been used for the treatment of painful degenerative spinal conditions. The anterior approach is not feasible in some patients, and the posterior approach is associated with a risk of neural complications and possibly muscle injury. A trans-sacral technique was developed that allows access to the L5-S1 disc space. The purposes of this study were to investigate the clinical outcome of trans-sacral interbody fusion in a consecutive series of patients from 1 center and to perform a comprehensive review of the literature on this procedure. A literature search using PubMed was performed to identify articles published on trans-sacral axial lumbar interbody fusion (AxiaLIF). Articles reviewed included biomechanical testing, feasibility of the technique, and clinical results. The data from our center were collected retrospectively from charts for the consecutive series, beginning with the first case, of all patients undergoing fusion using the AxiaLIF technique. In most cases, posterior instrumentation was also used. A total of 41 patients with at least 6 months' follow-up were included (mean follow-up, 22.2 months). The primary clinical outcome measures were visual analog scales separately assessing back and leg pain and the Oswestry Disability Index. Radiographic assessment of fusion was also performed. In the group of 28 patients undergoing single-level AxiaLIF combined with posterior fusion, the visual analog scale scores assessing back and leg pain and mean Oswestry Disability Index scores improved significantly (P < .01). In the remaining 13 patients, back pain improved significantly with a trend for improvement in leg pain. Reoperation occurred in 19.5% of patients; in half of these, reoperation was not related to the anterior procedure. A review of the literature found that the AxiaLIF technique was similar to other fusion techniques with respect to biomechanical properties and produced acceptable clinical outcomes, although results varied among studies. The AxiaLIF approach allows access to the L5-S1 interspace without violating the annulus or longitudinal ligaments and with minimal risk to dorsal neural elements. It may be a viable alternative to other approaches to interbody fusion at the L5-S1 level. It is important that the patients be selected carefully and surgeons are familiar with the presacral anatomy and the surgical approach.

Optic disk localization by a robust fusion method

NASA Astrophysics Data System (ADS)

Zhang, Jielin; Yin, Fengshou; Wong, Damon W. K.; Liu, Jiang; Baskaran, Mani; Cheng, Ching-Yu; Wong, Tien Yin

2013-02-01

The optic disk localization plays an important role in developing computer-aided diagnosis (CAD) systems for ocular diseases such as glaucoma, diabetic retinopathy and age-related macula degeneration. In this paper, we propose an intelligent fusion of methods for the localization of the optic disk in retinal fundus images. Three different approaches are developed to detect the location of the optic disk separately. The first method is the maximum vessel crossing method, which finds the region with the most number of blood vessel crossing points. The second one is the multichannel thresholding method, targeting the area with the highest intensity. The final method searches the vertical and horizontal region-of-interest separately on the basis of blood vessel structure and neighborhood entropy profile. Finally, these three methods are combined using an intelligent fusion method to improve the overall accuracy. The proposed algorithm was tested on the STARE database and the ORIGAlight database, each consisting of images with various pathologies. The preliminary result on the STARE database can achieve 81.5%, while a higher result of 99% can be obtained for the ORIGAlight database. The proposed method outperforms each individual approach and state-of-the-art method which utilizes an intensity-based approach. The result demonstrates a high potential for this method to be used in retinal CAD systems.

Perspectives on Lunar Helium-3

NASA Astrophysics Data System (ADS)

Schmitt, Harrison H.

1999-01-01

Global demand for energy will likely increase by a factor of six or eight by the mid-point of the 21st Century due to a combination of population increase, new energy intensive technologies, and aspirations for improved standards of living in the less-developed world (1). Lunar helium-3 (3He), with a resource base in the Tranquillitatis titanium-rich lunar maria (2,3) of at least 10,000 tonnes (4), represents one potential energy source to meet this rapidly escalating demand. The energy equivalent value of 3He delivered to operating fusion power plants on Earth would be about 3 billion per tonne relative to today's coal which supplies most of the approximately 90 billion domestic electrical power market (5). These numbers illustrate the magnitude of the business opportunity. The results from the Lunar Prospector neutron spectrometer (6) suggests that 3He also may be concentrated at the lunar poles along with solar wind hydrogen (7). Mining, extraction, processing, and transportation of helium to Earth requires new innovations in engineering but no known new engineering concepts (1). By-products of lunar 3He extraction, largely hydrogen, oxygen, and water, have large potential markets in space and ultimately will add to the economic attractiveness of this business opportunity (5). Inertial electrostatic confinement (IEC) fusion technology appears to be the most attractive and least capital intensive approach to terrestrial fusion power plants (8). Heavy lift launch costs comprise the largest cost uncertainty facing initial business planning, however, many factors, particularly long term production contracts, promise to lower these costs into the range of 1-2000 per kilogram versus about 70,000 per kilogram fully burdened for the Apollo Saturn V rocket (1). A private enterprise approach to developing lunar 3He and terrestrial IEC fusion power would be the most expeditious means of realizing this unique opportunity (9). In spite of the large, long-term potential return on investment, access to capital markets for a lunar 3He and terrestrial fusion power business will require a near-term return on investment, based on early applications of IEC fusion technology (10).

A Multi-Level Decision Fusion Strategy for Condition Based Maintenance of Composite Structures

PubMed Central

Sharif Khodaei, Zahra; Aliabadi, M.H.

2016-01-01

In this work, a multi-level decision fusion strategy is proposed which weighs the Value of Information (VoI) against the intended functions of a Structural Health Monitoring (SHM) system. This paper presents a multi-level approach for three different maintenance strategies in which the performance of the SHM systems is evaluated against its intended functions. Level 1 diagnosis results in damage existence with minimum sensors covering a large area by finding the maximum energy difference for the guided waves propagating in pristine structure and the post-impact state; Level 2 diagnosis provides damage detection and approximate localization using an approach based on Electro-Mechanical Impedance (EMI) measures, while Level 3 characterizes damage (exact location and size) in addition to its detection by utilising a Weighted Energy Arrival Method (WEAM). The proposed multi-level strategy is verified and validated experimentally by detection of Barely Visible Impact Damage (BVID) on a curved composite fuselage panel. PMID:28773910

Prm3p is a pheromone-induced peripheral nuclear envelope protein required for yeast nuclear fusion.

PubMed

Shen, Shu; Tobery, Cynthia E; Rose, Mark D

2009-05-01

Nuclear membrane fusion is the last step in the mating pathway of the yeast Saccharomyces cerevisiae. We adapted a bioinformatics approach to identify putative pheromone-induced membrane proteins potentially required for nuclear membrane fusion. One protein, Prm3p, was found to be required for nuclear membrane fusion; disruption of PRM3 caused a strong bilateral defect, in which nuclear congression was completed but fusion did not occur. Prm3p was localized to the nuclear envelope in pheromone-responding cells, with significant colocalization with the spindle pole body in zygotes. A previous report, using a truncated protein, claimed that Prm3p is localized to the inner nuclear envelope. Based on biochemistry, immunoelectron microscopy and live cell microscopy, we find that functional Prm3p is a peripheral membrane protein exposed on the cytoplasmic face of the outer nuclear envelope. In support of this, mutations in a putative nuclear localization sequence had no effect on full-length protein function or localization. In contrast, point mutations and deletions in the highly conserved hydrophobic carboxy-terminal domain disrupted both protein function and localization. Genetic analysis, colocalization, and biochemical experiments indicate that Prm3p interacts directly with Kar5p, suggesting that nuclear membrane fusion is mediated by a protein complex.

Magnetized target fusion: An ultra high energy approach in an unexplored parameter space

NASA Astrophysics Data System (ADS)

Lindemuth, I. R.

Magnetized target fusion is a concept that may lead to practical fusion applications in a variety of settings. However, the crucial first step is to demonstrate that it works as advertised. Among the possibilities for doing this is an ultrahigh energy approach to magnetized target fusion, one powered by explosive pulsed power generators that have become available for application to thermonuclear fusion research. In a collaborative effort between Los Alamos and the All-Russian Scientific Institute for Experimental Physics (VNIIEF) a very powerful helical generator with explosive power switching has been used to produce an energetic magnetized plasma. Several diagnostics have been fielded to ascertain the properties of this plasma. We are intensively studying the results of the experiments and calculationally analyzing the performance of this experiment.

The effects of local insulin application to lumbar spinal fusions in a rat model.

PubMed

Koerner, John D; Yalamanchili, Praveen; Munoz, William; Uko, Linda; Chaudhary, Saad B; Lin, Sheldon S; Vives, Michael J

2013-01-01

The rates of pseudoarthrosis after a single-level spinal fusion have been reported up to 35%, and the agents that increase the rate of fusion have an important role in decreasing pseudoarthrosis after spinal fusion. Previous studies have analyzed the effects of local insulin application to an autograft in a rat segmental defect model. Defects treated with a time-released insulin implant had significantly more new bone formation and greater quality of bone compared with controls based on histology and histomorphometry. A time-released insulin implant may have similar effects when applied in a lumbar spinal fusion model. This study analyzes the effects of a local time-released insulin implant applied to the fusion bed in a rat posterolateral lumbar spinal fusion model. Our hypothesis was twofold: first, a time-released insulin implant applied to the autograft bed in a rat posterolateral lumbar fusion will increase the rate of successful fusion and second, will alter the local environment of the fusion site by increasing the levels of local growth factors. Animal model (Institutional Animal Care and Use Committee approved) using 40 adult male Sprague-Dawley rats. Forty skeletally mature Sprague-Dawley rats weighing approximately 500 g each underwent posterolateral intertransverse lumbar fusions with iliac crest autograft from L4 to L5 using a Wiltse-type approach. After exposure of the transverse processes and high-speed burr decortication, a Linplant (Linshin Canada, Inc., ON, Canada) consisting of 95% microrecrystalized palmitic acid and 5% bovine insulin (experimental group) or a sham implant consisting of only palmitic acid (control group) was implanted on the fusion bed with iliac crest autograft. As per the manufacturer, the Linplant has a release rate of 2 U/day for a minimum of 40 days. The transverse processes and autograft beds of 10 animals from the experimental and 10 from the control group were harvested at Day 4 and analyzed for growth factors. The remaining 20 spines were harvested at 8 weeks and underwent a radiographic examination, manual palpation, and microcomputed tomographic (micro-CT) examination. One of the 8-week control animals died on postoperative Day 1, likely due to anesthesia. In the groups sacrificed at Day 4, there was a significant increase in insulinlike growth factor-I (IGF-I) in the insulin treatment group compared with the controls (0.185 vs. 0.129; p=.001). No significant differences were demonstrated in the levels of transforming growth factor beta-1, platelet-derived growth factor-AB, and vascular endothelial growth factor between the groups (p=.461, .452, and .767 respectively). Based on the radiographs, 1 of 9 controls had a solid bilateral fusion mass, 2 of 9 had unilateral fusion mass, 3 of 9 had small fusion mass bilaterally, and 3 of 9 had graft resorption. The treatment group had solid bilateral fusion mass in 6 of 10 and unilateral fusion mass in 4 of 10, whereas a small bilateral fusion mass and graft resorption were not observed. The difference between the groups was significant (p=.0067). Based on manual palpation, only 1 of 9 controls was considered fused, 4 of 9 were partially fused, and 4 of 9 were not fused. In the treatment group, there were 6 of 10 fusions, 3 of 10 partial fusions, and 1 of 10 were not fused. The difference between the groups was significant (p=.0084). Based on the micro-CT, the mean bone volume of the control group was 126.7 mm(3) and 203.8 mm(3) in the insulin treatment group. The difference between the groups was significant (p=.0007). This study demonstrates the potential role of a time-released insulin implant as a bone graft enhancer using a rat posterolateral intertransverse lumbar fusion model. The insulin-treatment group had significantly higher fusion rates based on the radiographs and manual palpation and had significantly higher levels of IGF-I and significantly more bone volume on micro-CT. Copyright © 2013 Elsevier Inc. All rights reserved.

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2015-10-21

The identification of disease genes from human genome is of great importance to improve diagnosis and treatment of disease. Several machine learning methods have been introduced to identify disease genes. However, these methods mostly differ in the prior knowledge used to construct the feature vector for each instance (gene), the ways of selecting negative data (non-disease genes) where there is no investigational approach to find them and the classification methods used to make the final decision. In this work, a novel Sequence-based fusion method (SFM) is proposed to identify disease genes. In this regard, unlike existing methods, instead of using a noisy and incomplete prior-knowledge, the amino acid sequence of the proteins which is universal data has been carried out to present the genes (proteins) into four different feature vectors. To select more likely negative data from candidate genes, the intersection set of four negative sets which are generated using distance approach is considered. Then, Decision Tree (C4.5) has been applied as a fusion method to combine the results of four independent state-of the-art predictors based on support vector machine (SVM) algorithm, and to make the final decision. The experimental results of the proposed method have been evaluated by some standard measures. The results indicate the precision, recall and F-measure of 82.6%, 85.6% and 84, respectively. These results confirm the efficiency and validity of the proposed method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Direct-laser metal writing of surface acoustic wave transducers for integrated-optic spatial light modulators in lithium niobate

NASA Astrophysics Data System (ADS)

Datta, Bianca C.; Savidis, Nickolaos; Moebius, Michael; Jolly, Sundeep; Mazur, Eric; Bove, V. Michael

2017-02-01

Recently, the fabrication of high-resolution silver nanostructures using a femtosecond laser-based direct write process in a gelatin matrix was reported. The application of direct metal writing towards feature development has also been explored with direct metal fusion, in which metal is fused onto the surface of the substrate via a femtosecond laser process. In this paper, we present a comparative study of gelatin matrix and metal fusion approaches for directly laser-written fabrication of surface acoustic wave transducers on a lithium niobate substrate for application in integrated optic spatial light modulators.

Multisensor fusion for 3D target tracking using track-before-detect particle filter

NASA Astrophysics Data System (ADS)

Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.

2015-05-01

This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.

Alternative approaches to fusion. [reactor design and reactor physics for Tokamak fusion reactors

NASA Technical Reports Server (NTRS)

Roth, R. J.

1976-01-01

The limitations of the Tokamak fusion reactor concept are discussed and various other fusion reactor concepts are considered that employ the containment of thermonuclear plasmas by magnetic fields (i.e., stellarators). Progress made in the containment of plasmas in toroidal devices is reported. Reactor design concepts are illustrated. The possibility of using fusion reactors as a power source in interplanetary space travel and electric power plants is briefly examined.

Mapping Forest Height in Gabon Using UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Lidar Fusion

NASA Astrophysics Data System (ADS)

Simard, M.; Denbina, M. W.

2017-12-01

Using data collected by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and Land, Vegetation, and Ice Sensor (LVIS) lidar, we have estimated forest canopy height for a number of study areas in the country of Gabon using a new machine learning data fusion approach. Using multi-baseline polarimetric synthetic aperture radar interferometry (PolInSAR) data collected by UAVSAR, forest heights can be estimated using the random volume over ground model. In the case of multi-baseline UAVSAR data consisting of many repeat passes with spatially separated flight tracks, we can estimate different forest height values for each different image pair, or baseline. In order to choose the best forest height estimate for each pixel, the baselines must be selected or ranked, taking care to avoid baselines with unsuitable spatial separation, or severe temporal decorrelation effects. The current baseline selection algorithms in the literature use basic quality metrics derived from the PolInSAR data which are not necessarily indicative of the true height accuracy in all cases. We have developed a new data fusion technique which treats PolInSAR baseline selection as a supervised classification problem, where the classifier is trained using a sparse sampling of lidar data within the PolInSAR coverage area. The classifier uses a large variety of PolInSAR-derived features as input, including radar backscatter as well as features based on the PolInSAR coherence region shape and the PolInSAR complex coherences. The resulting data fusion method produces forest height estimates which are more accurate than a purely radar-based approach, while having a larger coverage area than the input lidar training data, combining some of the strengths of each sensor. The technique demonstrates the strong potential for forest canopy height and above-ground biomass mapping using fusion of PolInSAR with data from future spaceborne lidar missions such as the upcoming Global Ecosystems Dynamics Investigation (GEDI) lidar.

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

PubMed

Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar

2017-11-27

The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.

General fusion approaches for the age determination of latent fingerprint traces: results for 2D and 3D binary pixel feature fusion

NASA Astrophysics Data System (ADS)

Merkel, Ronny; Gruhn, Stefan; Dittmann, Jana; Vielhauer, Claus; Bräutigam, Anja

2012-03-01

Determining the age of latent fingerprint traces found at crime scenes is an unresolved research issue since decades. Solving this issue could provide criminal investigators with the specific time a fingerprint trace was left on a surface, and therefore would enable them to link potential suspects to the time a crime took place as well as to reconstruct the sequence of events or eliminate irrelevant fingerprints to ensure privacy constraints. Transferring imaging techniques from different application areas, such as 3D image acquisition, surface measurement and chemical analysis to the domain of lifting latent biometric fingerprint traces is an upcoming trend in forensics. Such non-destructive sensor devices might help to solve the challenge of determining the age of a latent fingerprint trace, since it provides the opportunity to create time series and process them using pattern recognition techniques and statistical methods on digitized 2D, 3D and chemical data, rather than classical, contact-based capturing techniques, which alter the fingerprint trace and therefore make continuous scans impossible. In prior work, we have suggested to use a feature called binary pixel, which is a novel approach in the working field of fingerprint age determination. The feature uses a Chromatic White Light (CWL) image sensor to continuously scan a fingerprint trace over time and retrieves a characteristic logarithmic aging tendency for 2D-intensity as well as 3D-topographic images from the sensor. In this paper, we propose to combine such two characteristic aging features with other 2D and 3D features from the domains of surface measurement, microscopy, photography and spectroscopy, to achieve an increase in accuracy and reliability of a potential future age determination scheme. Discussing the feasibility of such variety of sensor devices and possible aging features, we propose a general fusion approach, which might combine promising features to a joint age determination scheme in future. We furthermore demonstrate the feasibility of the introduced approach by exemplary fusing the binary pixel features based on 2D-intensity and 3D-topographic images of the mentioned CWL sensor. We conclude that a formula based age determination approach requires very precise image data, which cannot be achieved at the moment, whereas a machine learning based classification approach seems to be feasible, if an adequate amount of features can be provided.

Image fusion via nonlocal sparse K-SVD dictionary learning.

PubMed

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Tracking by Identification Using Computer Vision and Radio

PubMed Central

Mandeljc, Rok; Kovačič, Stanislav; Kristan, Matej; Perš, Janez

2013-01-01

We present a novel system for detection, localization and tracking of multiple people, which fuses a multi-view computer vision approach with a radio-based localization system. The proposed fusion combines the best of both worlds, excellent computer-vision-based localization, and strong identity information provided by the radio system, and is therefore able to perform tracking by identification, which makes it impervious to propagated identity switches. We present comprehensive methodology for evaluation of systems that perform person localization in world coordinate system and use it to evaluate the proposed system as well as its components. Experimental results on a challenging indoor dataset, which involves multiple people walking around a realistically cluttered room, confirm that proposed fusion of both systems significantly outperforms its individual components. Compared to the radio-based system, it achieves better localization results, while at the same time it successfully prevents propagation of identity switches that occur in pure computer-vision-based tracking. PMID:23262485

Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

NASA Astrophysics Data System (ADS)

Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.

2016-05-01

A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.

Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

NASA Astrophysics Data System (ADS)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Chen, Ying

2014-05-01

A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

Multimodal biometric approach for cancelable face template generation

NASA Astrophysics Data System (ADS)

Paul, Padma Polash; Gavrilova, Marina

2012-06-01

Due to the rapid growth of biometric technology, template protection becomes crucial to secure integrity of the biometric security system and prevent unauthorized access. Cancelable biometrics is emerging as one of the best solutions to secure the biometric identification and verification system. We present a novel technique for robust cancelable template generation algorithm that takes advantage of the multimodal biometric using feature level fusion. Feature level fusion of different facial features is applied to generate the cancelable template. A proposed algorithm based on the multi-fold random projection and fuzzy communication scheme is used for this purpose. In cancelable template generation, one of the main difficulties is keeping interclass variance of the feature. We have found that interclass variations of the features that are lost during multi fold random projection can be recovered using fusion of different feature subsets and projecting in a new feature domain. Applying the multimodal technique in feature level, we enhance the interclass variability hence improving the performance of the system. We have tested the system for classifier fusion for different feature subset and different cancelable template fusion. Experiments have shown that cancelable template improves the performance of the biometric system compared with the original template.

Game-theoretic homological sensor resource management for SSA

NASA Astrophysics Data System (ADS)

Chin, Sang Peter

2009-05-01

We present a game-theoretic approach to Level 2/3/4 fusion for the purpose of Space Situational Awareness (SSA) along with prototypical SW implementation of this approach to demonstrate its effectiveness for possible future space operations. Our approach is based upon innovative techniques that we are developing to solve dynamic games and Nperson cooperative/non-cooperative games, as well as a new emerging homological sensing algorithms which we apply to control disparate network of space sensors in order to gain better SSA.

A flexible and economical barcoding approach for highly multiplexed amplicon sequencing of diverse target genes

PubMed Central

Herbold, Craig W.; Pelikan, Claus; Kuzyk, Orest; Hausmann, Bela; Angel, Roey; Berry, David; Loy, Alexander

2015-01-01

High throughput sequencing of phylogenetic and functional gene amplicons provides tremendous insight into the structure and functional potential of complex microbial communities. Here, we introduce a highly adaptable and economical PCR approach to barcoding and pooling libraries of numerous target genes. In this approach, we replace gene- and sequencing platform-specific fusion primers with general, interchangeable barcoding primers, enabling nearly limitless customized barcode-primer combinations. Compared to barcoding with long fusion primers, our multiple-target gene approach is more economical because it overall requires lower number of primers and is based on short primers with generally lower synthesis and purification costs. To highlight our approach, we pooled over 900 different small-subunit rRNA and functional gene amplicon libraries obtained from various environmental or host-associated microbial community samples into a single, paired-end Illumina MiSeq run. Although the amplicon regions ranged in size from approximately 290 to 720 bp, we found no significant systematic sequencing bias related to amplicon length or gene target. Our results indicate that this flexible multiplexing approach produces large, diverse, and high quality sets of amplicon sequence data for modern studies in microbial ecology. PMID:26236305

Functional Basis of Microorganism Classification.

PubMed

Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana

2015-08-01

Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.

Functional Basis of Microorganism Classification

PubMed Central

Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana

2015-01-01

Correctly identifying nearest “neighbors” of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent. PMID:26317871

50 years of fusion research

NASA Astrophysics Data System (ADS)

Meade, Dale

2010-01-01

Fusion energy research began in the early 1950s as scientists worked to harness the awesome power of the atom for peaceful purposes. There was early optimism for a quick solution for fusion energy as there had been for fission. However, this was soon tempered by reality as the difficulty of producing and confining fusion fuel at temperatures of 100 million °C in the laboratory was appreciated. Fusion research has followed two main paths—inertial confinement fusion and magnetic confinement fusion. Over the past 50 years, there has been remarkable progress with both approaches, and now each has a solid technical foundation that has led to the construction of major facilities that are aimed at demonstrating fusion energy producing plasmas.

Embedding the results of focussed Bayesian fusion into a global context

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael

2014-05-01

Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

Fusion reactions initiated by laser-accelerated particle beams in a laser-produced plasma.

PubMed

Labaune, C; Baccou, C; Depierreux, S; Goyon, C; Loisel, G; Yahia, V; Rafelski, J

2013-01-01

The advent of high-intensity-pulsed laser technology enables the generation of extreme states of matter under conditions that are far from thermal equilibrium. This in turn could enable different approaches to generating energy from nuclear fusion. Relaxing the equilibrium requirement could widen the range of isotopes used in fusion fuels permitting cleaner and less hazardous reactions that do not produce high-energy neutrons. Here we propose and implement a means to drive fusion reactions between protons and boron-11 nuclei by colliding a laser-accelerated proton beam with a laser-generated boron plasma. We report proton-boron reaction rates that are orders of magnitude higher than those reported previously. Beyond fusion, our approach demonstrates a new means for exploring low-energy nuclear reactions such as those that occur in astrophysical plasmas and related environments.

Region-based multifocus image fusion for the precise acquisition of Pap smear images.

PubMed

Tello-Mijares, Santiago; Bescós, Jesús

2018-05-01

A multifocus image fusion method to obtain a single focused image from a sequence of microscopic high-magnification Papanicolau source (Pap smear) images is presented. These images, captured each in a different position of the microscope lens, frequently show partially focused cells or parts of cells, which makes them unpractical for the direct application of image analysis techniques. The proposed method obtains a focused image with a high preservation of original pixels information while achieving a negligible visibility of the fusion artifacts. The method starts by identifying the best-focused image of the sequence; then, it performs a mean-shift segmentation over this image; the focus level of the segmented regions is evaluated in all the images of the sequence, and best-focused regions are merged in a single combined image; finally, this image is processed with an adaptive artifact removal process. The combination of a region-oriented approach, instead of block-based approaches, and a minimum modification of the value of focused pixels in the original images achieve a highly contrasted image with no visible artifacts, which makes this method especially convenient for the medical imaging domain. The proposed method is compared with several state-of-the-art alternatives over a representative dataset. The experimental results show that our proposal obtains the best and more stable quality indicators. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Multiview 3-D Echocardiography Fusion with Breath-Hold Position Tracking Using an Optical Tracking System.

PubMed

Punithakumar, Kumaradevan; Hareendranathan, Abhilash R; McNulty, Alexander; Biamonte, Marina; He, Allen; Noga, Michelle; Boulanger, Pierre; Becher, Harald

2016-08-01

Recent advances in echocardiography allow real-time 3-D dynamic image acquisition of the heart. However, one of the major limitations of 3-D echocardiography is the limited field of view, which results in an acquisition insufficient to cover the whole geometry of the heart. This study proposes the novel approach of fusing multiple 3-D echocardiography images using an optical tracking system that incorporates breath-hold position tracking to infer that the heart remains at the same position during different acquisitions. In six healthy male volunteers, 18 pairs of apical/parasternal 3-D ultrasound data sets were acquired during a single breath-hold as well as in subsequent breath-holds. The proposed method yielded a field of view improvement of 35.4 ± 12.5%. To improve the quality of the fused image, a wavelet-based fusion algorithm was developed that computes pixelwise likelihood values for overlapping voxels from multiple image views. The proposed wavelet-based fusion approach yielded significant improvement in contrast (66.46 ± 21.68%), contrast-to-noise ratio (49.92 ± 28.71%), signal-to-noise ratio (57.59 ± 47.85%) and feature count (13.06 ± 7.44%) in comparison to individual views. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Accuracy of implementing principles of fusion imaging in the follow up and surveillance of complex aneurysm repair.

PubMed

Martin-Gonzalez, Teresa; Penney, Graeme; Chong, Debra; Davis, Meryl; Mastracci, Tara M

2018-05-01

Fusion imaging is standard for the endovascular treatment of complex aortic aneurysms, but its role in follow up has not been explored. A critical issue is renal function deterioration over time. Renal volume has been used as a marker of renal impairment; however, it is not reproducible and remains a complex and resource-intensive procedure. The aim of this study is to determine the accuracy of a fusion-based software to automatically calculate the renal volume changes during follow up. In this study, computerized tomography (CT) scans of 16 patients who underwent complex aortic endovascular repair were analysed. Preoperative, 1-month and 1-year follow-up CT scans have been analysed using a conventional approach of semi-automatic segmentation, and a second approach with automatic segmentation. For each kidney and at each time point the percentage of change in renal volume was calculated using both techniques. After review, volume assessment was feasible for all CT scans. For the left kidney, the intraclass correlation coefficient (ICC) was 0.794 and 0.877 at 1 month and 1 year, respectively. For the right side, the ICC was 0.817 at 1 month and 0.966 at 1 year. The automated technique reliably detected a decrease in renal volume for the eight patients with occluded renal arteries during follow up. This is the first report of a fusion-based algorithm to detect changes in renal volume during postoperative surveillance using an automated process. Using this technique, the standardized assessment of renal volume could be implemented with greater ease and reproducibility and serve as a warning of potential renal impairment.

Shock ignition: a new approach to high gain inertial confinement fusion on the national ignition facility.

PubMed

Perkins, L J; Betti, R; LaFortune, K N; Williams, W H

2009-07-24

Shock ignition, an alternative concept for igniting thermonuclear fuel, is explored as a new approach to high gain, inertial confinement fusion targets for the National Ignition Facility (NIF). Results indicate thermonuclear yields of approximately 120-250 MJ may be possible with laser drive energies of 1-1.6 MJ, while gains of approximately 50 may still be achievable at only approximately 0.2 MJ drive energy. The scaling of NIF energy gain with laser energy is found to be G approximately 126E (MJ);{0.510}. This offers the potential for high-gain targets that may lead to smaller, more economic fusion power reactors and a cheaper fusion energy development path.

Influenza Virus-Mediated Membrane Fusion: Determinants of Hemagglutinin Fusogenic Activity and Experimental Approaches for Assessing Virus Fusion

PubMed Central

Hamilton, Brian S.; Whittaker, Gary R.; Daniel, Susan

2012-01-01

Hemagglutinin (HA) is the viral protein that facilitates the entry of influenza viruses into host cells. This protein controls two critical aspects of entry: virus binding and membrane fusion. In order for HA to carry out these functions, it must first undergo a priming step, proteolytic cleavage, which renders it fusion competent. Membrane fusion commences from inside the endosome after a drop in lumenal pH and an ensuing conformational change in HA that leads to the hemifusion of the outer membrane leaflets of the virus and endosome, the formation of a stalk between them, followed by pore formation. Thus, the fusion machinery is an excellent target for antiviral compounds, especially those that target the conserved stem region of the protein. However, traditional ensemble fusion assays provide a somewhat limited ability to directly quantify fusion partly due to the inherent averaging of individual fusion events resulting from experimental constraints. Inspired by the gains achieved by single molecule experiments and analysis of stochastic events, recently-developed individual virion imaging techniques and analysis of single fusion events has provided critical information about individual virion behavior, discriminated intermediate fusion steps within a single virion, and allowed the study of the overall population dynamics without the loss of discrete, individual information. In this article, we first start by reviewing the determinants of HA fusogenic activity and the viral entry process, highlight some open questions, and then describe the experimental approaches for assaying fusion that will be useful in developing the most effective therapies in the future. PMID:22852045

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

PubMed Central

Molina, Iñigo; Martinez, Estibaliz; Morillo, Carmen; Velasco, Jesus; Jara, Alvaro

2016-01-01

In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal “invariant features” is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a “change map”, which can be accomplished by means of the CDI’s informational content. For this purpose, information metrics such as the Shannon Entropy and “Specific Information” have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf’s) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances. PMID:27706048

Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring.

PubMed

Hoog Antink, Christoph; Schulz, Florian; Leonhardt, Steffen; Walter, Marian

2017-12-25

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR S is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.

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

NASA Technical Reports Server (NTRS)

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

1997-01-01

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

Construction of hybrid peptide synthetases by module and domain fusions

PubMed Central

Mootz, Henning D.; Schwarzer, Dirk; Marahiel, Mohamed A.

2000-01-01

Nonribosomal peptide synthetases are modular enzymes that assemble peptides of diverse structures and important biological activities. Their modular organization provides a great potential for the rational design of novel compounds by recombination of the biosynthetic genes. Here we describe the extension of a dimodular system to trimodular ones based on whole-module fusion. The recombinant hybrid enzymes were purified to monitor product assembly in vitro. We started from the first two modules of tyrocidine synthetase, which catalyze the formation of the dipeptide dPhe-Pro, to construct such hybrid systems. Fusion of the second, proline-specific module with the ninth and tenth modules of the tyrocidine synthetases, specific for ornithine and leucine, respectively, resulted in dimodular hybrid enzymes exhibiting the combined substrate specificities. The thioesterase domain was fused to the terminal module. Upon incubation of these dimodular enzymes with the first tyrocidine module, TycA, incorporating dPhe, the predicted tripeptides dPhe-Pro-Orn and dPhe-Pro-Leu were obtained at rates of 0.15 min-1 and 2.1 min-1. The internal thioesterase domain was necessary and sufficient to release the products from the hybrid enzymes and thereby facilitate a catalytic turnover. Our approach of whole-module fusion is based on an improved definition of the fusion sites and overcomes the recently discovered editing function of the intrinsic condensation domains. The stepwise construction of hybrid peptide synthetases from catalytic subunits reinforces the inherent potential for the synthesis of novel, designed peptides. PMID:10811885

Construction of hybrid peptide synthetases by module and domain fusions.

PubMed

Mootz, H D; Schwarzer, D; Marahiel, M A

2000-05-23

Nonribosomal peptide synthetases are modular enzymes that assemble peptides of diverse structures and important biological activities. Their modular organization provides a great potential for the rational design of novel compounds by recombination of the biosynthetic genes. Here we describe the extension of a dimodular system to trimodular ones based on whole-module fusion. The recombinant hybrid enzymes were purified to monitor product assembly in vitro. We started from the first two modules of tyrocidine synthetase, which catalyze the formation of the dipeptide dPhe-Pro, to construct such hybrid systems. Fusion of the second, proline-specific module with the ninth and tenth modules of the tyrocidine synthetases, specific for ornithine and leucine, respectively, resulted in dimodular hybrid enzymes exhibiting the combined substrate specificities. The thioesterase domain was fused to the terminal module. Upon incubation of these dimodular enzymes with the first tyrocidine module, TycA, incorporating dPhe, the predicted tripeptides dPhe-Pro-Orn and dPhe-Pro-Leu were obtained at rates of 0.15 min(-1) and 2.1 min(-1). The internal thioesterase domain was necessary and sufficient to release the products from the hybrid enzymes and thereby facilitate a catalytic turnover. Our approach of whole-module fusion is based on an improved definition of the fusion sites and overcomes the recently discovered editing function of the intrinsic condensation domains. The stepwise construction of hybrid peptide synthetases from catalytic subunits reinforces the inherent potential for the synthesis of novel, designed peptides.

Improving the recognition of fingerprint biometric system using enhanced image fusion

NASA Astrophysics Data System (ADS)

Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

2010-04-01

Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Investigating trends in water use over the Choptank River watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

Development of the Scale for "Convergence Thinking" in Engineering

ERIC Educational Resources Information Center

Park, Sungmi

2016-01-01

Purpose: The purpose of this paper is to define the concept of "convergence thinking" as a trading zone for knowledge fusion in the engineering field, and develops its measuring scale. Design/ Methodology/Approach: Based on results from literature review, this study clarifies a theoretical ground for "convergence thinking."…

Graduate Training: Evidence from FUSION Projects in Ireland

ERIC Educational Resources Information Center

Hegarty, Cecilia; Johnston, Janet

2008-01-01

Purpose: This paper aims to explore graduate training through SME-based project work. The views and behaviours of graduates are examined along with the perceptions of the SMEs and academic partner institutions charged with training graduates. Design/methodology/approach: The data are largely qualitative and derived from the experiences of…

Anterior lumbar fusion with titanium threaded and mesh interbody cages.

PubMed

Rauzzino, M J; Shaffrey, C I; Nockels, R P; Wiggins, G C; Rock, J; Wagner, J

1999-12-15

The authors report their experience with 42 patients in whom anterior lumbar fusion was performed using titanium cages as a versatile adjunct to treat a wide variety of spinal deformity and pathological conditions. These conditions included congenital, degenerative, iatrogenic, infectious, traumatic, and malignant disorders of the thoracolumbar spine. Fusion rates and complications are compared with data previously reported in the literature. Between July 1996 and July 1999 the senior authors (C.I.S., R.P.N., and M.J.R.) treated 42 patients by means of a transabdominal extraperitoneal (13 cases) or an anterolateral extraperitoneal approach (29 cases), 51 vertebral levels were fused using titanium cages packed with autologous bone. All vertebrectomies (27 cases) were reconstructed using a Miami Moss titanium mesh cage and Kaneda instrumentation. Interbody fusion (15 cases) was performed with either the BAK titanium threaded interbody cage (in 13 patients) or a Miami Moss titanium mesh cage (in two patients). The average follow-up period was 14.3 months. Seventeen patients had sustained a thoracolumbar burst fracture, 12 patients presented with degenerative spinal disorders, six with metastatic tumor, four with spinal deformity (one congenital and three iatrogenic), and three patients presented with spinal infections. In five patients anterior lumbar interbody fusion (ALIF) was supplemented with posterior segmental fixation at the time of the initial procedure. Of the 51 vertebral levels treated, solid arthrodesis was achieved in 49, a 96% fusion rate. One case of pseudarthrosis occurred in the group treated with BAK cages; the diagnosis was made based on the patient's continued mechanical back pain after undergoing L4-5 ALIF. The patient was treated with supplemental posterior fixation, and successful fusion occurred uneventfully with resolution of her back pain. In the group in which vertebrectomy was performed there was one case of fusion failure in a patient with metastatic breast cancer who had undergone an L-3 corpectomy with placement of a mesh cage. Although her back pain was immediately resolved, she died of systemic disease 3 months after surgery and before fusion could occur. Complications related to the anterior approach included two vascular injuries (two left common iliac vein lacerations); one injury to the sympathetic plexus; one case of superficial phlebitis; two cases of prolonged ileus (greater than 48 hours postoperatively); one anterior femoral cutaneous nerve palsy; and one superficial wound infection. No deaths were directly related to the surgical procedure. There were no cases of dural laceration and no nerve root injury. There were no cases of deep venous thrombosis, pulmonary embolus, retrograde ejaculation, abdominal hernia, bowel or ureteral injury, or deep wound infection. Fusion-related complications included an iliac crest hematoma and prolonged donor-site pain in one patient. There were no complications related to placement or migration of the cages, but there was one case of screw fracture of the Kaneda device that did not require revision. The authors conclude that anterior lumbar fusion performed using titanium interbody or mesh cages, packed with autologous bone, is an effective, safe method to achieve fusion in a wide variety of pathological conditions of the thoracolumbar spine. The fusion rate of 96% compares favorably with results reported in the literature. The complication rate mirrors the low morbidity rate associated with the anterior approach. A detailed study of clinical outcomes is in progress. Patient selection and strategies for avoiding complication are discussed.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

PubMed

Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga

2016-07-15

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Fusion Gain Analysis of the Inductively Driven Liner Compression Based Fusion

NASA Astrophysics Data System (ADS)

Shimazu, Akihisa; Slough, John

2016-10-01

An analytical analysis of the fusion gain expected in the inductively driven liner compression (IDLC) based fusion is conducted to identify the fusion gain scaling at various operating conditions. The fusion based on the IDLC is a magneto-inertial fusion concept, where a Field-Reversed Configuration (FRC) plasmoid is compressed via the inductively-driven metal liner to drive the FRC to fusion conditions. In the past, an approximate scaling law for the expected fusion gain for the IDLC based fusion was obtained under the key assumptions of (1) D-T fuel at 5-40 keV, (2) adiabatic scaling laws for the FRC dynamics, (3) FRC energy dominated by the pressure balance with the edge magnetic field at the peak compression, and (4) the liner dwell time being liner final diameter divided by the peak liner velocity. In this study, various assumptions made in the previous derivation is relaxed to study the change in the fusion gain scaling from the previous result of G ml1 / 2 El11 / 8 , where ml is the liner mass and El is the peak liner kinetic energy. The implication from the modified fusion gain scaling on the performance of the IDLC fusion reactor system is also explored.

Comparison of the Recently proposed Super Marx Generator Approach to Thermonuclear Ignition with the DT Laser Fusion-Fission Hybrid Concept (LIFE) by the Lawrence Livermore National Laboratory.

NASA Astrophysics Data System (ADS)

Winterberg, Friedwardt

2009-05-01

The recently proposed Super Marx pure deuterium micro-detonation ignition concept [1] is compared to the Lawrence Livermore National Ignition Facility (NIF) laser DT fusion-fission hybrid concept (LIFE) [2]. A typical example of the LIFE concept is a fusion gain 30, and a fission gain of 10, making up for a total gain of 300, with about 10 times more energy released into fission as compared to fusion. This means a substantial release of fission products, as in fusion-less pure fission reactors. In the Super Marx approach for the ignition of a pure deuterium micro-detonation gains of the same magnitude can in theory be reached. If the theoretical prediction can be supported by more elaborate calculations, the Super Marx approach is likely to make lasers obsolete as a means for the ignition of thermonuclear micro-explosions. [1] ``Ignition of a Deuterium Micro-Detonation with a Gigavolt Super Marx Generator,'' Winterberg, F., Journal of Fusion Energy, Springer, 2008. http://www.springerlink.com/content/r2j046177j331241/fulltext.pdf. [2] ``LIFE: Clean Energy from Nuclear Waste,'' https://lasers.llnl.gov/missions/energy&_slash;for&_slash;the&_slash;future/life/

Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

PubMed Central

Daza, Iván G.; Bergasa, Luis M.; Bronte, Sebastián; Yebes, J. Javier; Almazán, Javier; Arroyo, Roberto

2014-01-01

This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study. PMID:24412904

Ghost detection and removal based on super-pixel grouping in exposure fusion

NASA Astrophysics Data System (ADS)

Jiang, Shenyu; Xu, Zhihai; Li, Qi; Chen, Yueting; Feng, Huajun

2014-09-01

A novel multi-exposure images fusion method for dynamic scenes is proposed. The commonly used techniques for high dynamic range (HDR) imaging are based on the combination of multiple differently exposed images of the same scene. The drawback of these methods is that ghosting artifacts will be introduced into the final HDR image if the scene is not static. In this paper, a super-pixel grouping based method is proposed to detect the ghost in the image sequences. We introduce the zero mean normalized cross correlation (ZNCC) as a measure of similarity between a given exposure image and the reference. The calculation of ZNCC is implemented in super-pixel level, and the super-pixels which have low correlation with the reference are excluded by adjusting the weight maps for fusion. Without any prior information on camera response function or exposure settings, the proposed method generates low dynamic range (LDR) images which can be shown on conventional display devices directly with details preserving and ghost effects reduced. Experimental results show that the proposed method generates high quality images which have less ghost artifacts and provide a better visual quality than previous approaches.

Data fusion and photometric restoration

NASA Astrophysics Data System (ADS)

Pirzkal, Norbert; Hook, Richard N.

2001-11-01

The current generation of 8-10m optical ground-based telescopes have a symbiotic relationship with space telescopes. For direct imaging in the optical the former can collect photons relatively cheaply but the latter can still achieve, even in the era of adaptive optics, significantly higher spatial resolution, point-spread function stability and astrometric fidelity over fields of a few arcminutes. The large archives of HST imaging already in place, when combined with the ease of access to ground-based data afforded by the virtual observatory currently under development, will make space-ground data fusion a powerful tool for the future. We describe a photometric image restoration method that we have developed which allows the efficient and accurate use of high-resolution space imaging of crowded fields to extract high quality photometry from very crowded ground-based images. We illustrate the method using HST and ESO VLT/FORS imaging of a globular cluster and demonstrate quantitatively the photometric measurements quality that can achieved using the data fusion approach instead of just using data from just one telescope. This method can handle most of the common difficulties encountered when attempting this problem such as determining the geometric mapping to the requisite precision, deriving the PSF and the background.

Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT

NASA Astrophysics Data System (ADS)

Liu, Qingyi; Mohy-ud-Din, Hassan; Boutagy, Nabil E.; Jiang, Mingyan; Ren, Silin; Stendahl, John C.; Sinusas, Albert J.; Liu, Chi

2017-05-01

Anatomical-based partial volume correction (PVC) has been shown to improve image quality and quantitative accuracy in cardiac SPECT/CT. However, this method requires manual segmentation of various organs from contrast-enhanced computed tomography angiography (CTA) data. In order to achieve fully automatic CTA segmentation for clinical translation, we investigated the most common multi-atlas segmentation methods. We also modified the multi-atlas segmentation method by introducing a novel label fusion algorithm for multiple organ segmentation to eliminate overlap and gap voxels. To evaluate our proposed automatic segmentation, eight canine 99mTc-labeled red blood cell SPECT/CT datasets that incorporated PVC were analyzed, using the leave-one-out approach. The Dice similarity coefficient of each organ was computed. Compared to the conventional label fusion method, our proposed label fusion method effectively eliminated gaps and overlaps and improved the CTA segmentation accuracy. The anatomical-based PVC of cardiac SPECT images with automatic multi-atlas segmentation provided consistent image quality and quantitative estimation of intramyocardial blood volume, as compared to those derived using manual segmentation. In conclusion, our proposed automatic multi-atlas segmentation method of CTAs is feasible, practical, and facilitates anatomical-based PVC of cardiac SPECT/CT images.

Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals.

PubMed

Verma, Gyanendra K; Tiwary, Uma Shanker

2014-11-15

The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for 'Depressing' with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach. Copyright © 2013 Elsevier Inc. All rights reserved.

Minimally invasive surgery: lateral approach interbody fusion: results and review.

PubMed

Youssef, Jim A; McAfee, Paul C; Patty, Catherine A; Raley, Erin; DeBauche, Spencer; Shucosky, Erin; Chotikul, Liana

2010-12-15

A retrospective review of patients treated at 2 institutions with anterior lumbar interbody fusion using a minimally invasive lateral retroperitoneal approach, and review of literature. To analyze the outcomes from historical literature and from a retrospectively compiled database of patients having undergone anterior interbody fusions performed through a lateral approach. A paucity of published literature exists describing outcomes following lateral approach fusion surgery. Patients treated with extreme lateral interbody fusion (XLIF) were identified through retrospective chart review. Treatment variables included operating room (OR) time, estimated blood loss (EBL), length of hospital stay (LOS), complications, and fusion rate. A literature review, using the National Center for Biotechnology Information databases PubMed/MEDLINE and Google Scholar, yielded 14 peer-reviewed articles reporting outcomes scoring, complications, fusion status, long-term follow-up, and radiographic assessments related to XLIF. Published XLIF results were summarized and evaluated with current study data. A total of 84 XLIF patients were included in the current cohort analysis. OR time, EBL, and length of hospital stay averaged 199 minutes, 155 mL, and 2.6 days, respectively, and perioperative and postoperative complication rates were 2.4% and 6.1%. Mean follow-up was 15.7 months. Sixty-eight patients showed evidence of solid arthrodesis and no subsidence on computed tomography and flexion/extension radiographs. Results were within the ranges of those in the literature. Literature review identified reports of significant improvements in clinical outcomes scores, radiographic measures, and cost effectiveness. Current data corroborates and contributes to the existing body of literature describing XLIF outcomes. Procedures are generally performed with short OR times, minimal EBL, and few complications. Patients recover quickly, requiring minimal hospital stay, although transient hip/thigh pain and/or weakness is common. Long-term outcomes are generally favorable, with maintained improvements in patient-reported pain and function scores as well as radiographic parameters, including high rates of fusion.

An overview of the development of the first wall and other principal components of a laser fusion power plant

NASA Astrophysics Data System (ADS)

Sethian, John D.; Raffray, A. Rene; Latkowski, Jeffery; Blanchard, James P.; Snead, Lance; Renk, Timothy J.; Sharafat, Shahram

2005-12-01

This paper introduces the JNM Special Issue on the development of a first wall for the reaction chamber in a laser fusion power plant. In this approach to fusion energy a spherical target is injected into a large chamber and heated to fusion burn by an array of lasers. The target emissions are absorbed by the wall and encapsulating blanket, and the resulting heat converted into electricity. The bulk of the energy deposited in the first wall is in the form of X-rays (1.0-100 keV) and ions (0.1-4 MeV). In order to have a practical power plant, the first wall must be resistant to these emissions and suffer virtually no erosion on each shot. A wall candidate based on tungsten armor bonded to a low activation ferritic steel substrate has been chosen as the initial system to be studied. The choice was based on the vast experience with these materials in a nuclear environment and the ability to address most of the key remaining issues with existing facilities. This overview paper is divided into three parts. The first part summarizes the current state of the development of laser fusion energy. The second part introduces the tungsten armored ferritic steel concept, the three critical development issues (thermo-mechanical fatigue, helium retention, and bonding) and the research to address them. Based on progress to date the latter two appear to be resolvable, but the former remains a challenge. Complete details are presented in the companion papers in this JNM Special Issue. The third part discusses other factors that must be considered in the design of the first wall, including compatibility with blanket concepts, radiological concerns, and structural considerations.

Fusion of classifiers for REIS-based detection of suspicious breast lesions

NASA Astrophysics Data System (ADS)

Lederman, Dror; Wang, Xingwei; Zheng, Bin; Sumkin, Jules H.; Tublin, Mitchell; Gur, David

2011-03-01

After developing a multi-probe resonance-frequency electrical impedance spectroscopy (REIS) system aimed at detecting women with breast abnormalities that may indicate a developing breast cancer, we have been conducting a prospective clinical study to explore the feasibility of applying this REIS system to classify younger women (< 50 years old) into two groups of "higher-than-average risk" and "average risk" of having or developing breast cancer. The system comprises one central probe placed in contact with the nipple, and six additional probes uniformly distributed along an outside circle to be placed in contact with six points on the outer breast skin surface. In this preliminary study, we selected an initial set of 174 examinations on participants that have completed REIS examinations and have clinical status verification. Among these, 66 examinations were recommended for biopsy due to findings of a highly suspicious breast lesion ("positives"), and 108 were determined as negative during imaging based procedures ("negatives"). A set of REIS-based features, extracted using a mirror-matched approach, was computed and fed into five machine learning classifiers. A genetic algorithm was used to select an optimal subset of features for each of the five classifiers. Three fusion rules, namely sum rule, weighted sum rule and weighted median rule, were used to combine the results of the classifiers. Performance evaluation was performed using a leave-one-case-out cross-validation method. The results indicated that REIS may provide a new technology to identify younger women with higher than average risk of having or developing breast cancer. Furthermore, it was shown that fusion rule, such as a weighted median fusion rule and a weighted sum fusion rule may improve performance as compared with the highest performing single classifier.

A generic screening platform for inhibitors of virus induced cell fusion using cellular electrical impedance

PubMed Central

Watterson, Daniel; Robinson, Jodie; Chappell, Keith J.; Butler, Mark S.; Edwards, David J.; Fry, Scott R.; Bermingham, Imogen M.; Cooper, Matthew A.; Young, Paul R.

2016-01-01

Fusion of the viral envelope with host cell membranes is an essential step in the life cycle of all enveloped viruses. Despite such a clear target for antiviral drug development, few anti-fusion drugs have progressed to market. One significant hurdle is the absence of a generic, high-throughput, reproducible fusion assay. Here we report that real time, label-free measurement of cellular electrical impedance can quantify cell-cell fusion mediated by either individually expressed recombinant viral fusion proteins, or native virus infection. We validated this approach for all three classes of viral fusion and demonstrated utility in quantifying fusion inhibition using antibodies and small molecule inhibitors specific for dengue virus and respiratory syncytial virus. PMID:26976324

The application of machine learning in multi sensor data fusion for activity recognition in mobile device space

NASA Astrophysics Data System (ADS)

Marhoubi, Asmaa H.; Saravi, Sara; Edirisinghe, Eran A.

2015-05-01

The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device's behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.

Paramyxovirus fusion: Real-time measurement of parainfluenza virus 5 virus-cell fusion

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

Connolly, Sarah A.; Lamb, Robert A.

2006-11-25

Although cell-cell fusion assays are useful surrogate methods for studying virus fusion, differences between cell-cell and virus-cell fusion exist. To examine paramyxovirus fusion in real time, we labeled viruses with fluorescent lipid probes and monitored virus-cell fusion by fluorimetry. Two parainfluenza virus 5 (PIV5) isolates (W3A and SER) and PIV5 containing mutations within the fusion protein (F) were studied. Fusion was specific and temperature-dependent. Compared to many low pH-dependent viruses, the kinetics of PIV5 fusion was slow, approaching completion within several minutes. As predicted from cell-cell fusion assays, virus containing an F protein with an extended cytoplasmic tail (rSV5 F551)more » had reduced fusion compared to wild-type virus (W3A). In contrast, virus-cell fusion for SER occurred at near wild-type levels, despite the fact that this isolate exhibits a severely reduced cell-cell fusion phenotype. These results support the notion that virus-cell and cell-cell fusion have significant differences.« less

Fusion yield: Guderley model and Tsallis statistics

NASA Astrophysics Data System (ADS)

Haubold, H. J.; Kumar, D.

2011-02-01

The reaction rate probability integral is extended from Maxwell-Boltzmann approach to a more general approach by using the pathway model introduced by Mathai in 2005 (A pathway to matrix-variate gamma and normal densities. Linear Algebr. Appl. 396, 317-328). The extended thermonuclear reaction rate is obtained in the closed form via a Meijer's G-function and the so-obtained G-function is represented as a solution of a homogeneous linear differential equation. A physical model for the hydrodynamical process in a fusion plasma-compressed and laser-driven spherical shock wave is used for evaluating the fusion energy integral by integrating the extended thermonuclear reaction rate integral over the temperature. The result obtained is compared with the standard fusion yield obtained by Haubold and John in 1981 (Analytical representation of the thermonuclear reaction rate and fusion energy production in a spherical plasma shock wave. Plasma Phys. 23, 399-411). An interpretation for the pathway parameter is also given.

Evaluation of performance of select fusion experiments and projected reactors

NASA Technical Reports Server (NTRS)

Miley, G. H.

1978-01-01

The performance of NASA Lewis fusion experiments (SUMMA and Bumpy Torus) is compared with other experiments and that necessary for a power reactor. Key parameters cited are gain (fusion power/input power) and the time average fusion power, both of which may be more significant for real fusion reactors than the commonly used Lawson parameter. The NASA devices are over 10 orders of magnitude below the required powerplant values in both gain and time average power. The best experiments elsewhere are also as much as 4 to 5 orders of magnitude low. However, the NASA experiments compare favorably with other alternate approaches that have received less funding than the mainline experiments. The steady-state character and efficiency of plasma heating are strong advantages of the NASA approach. The problem, though, is to move ahead to experiments of sufficient size to advance in gain and average power parameters.

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

NASA Astrophysics Data System (ADS)

Fan, Lei

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems.

Track classification within wireless sensor network

NASA Astrophysics Data System (ADS)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

An Extension to Deng's Entropy in the Open World Assumption with an Application in Sensor Data Fusion.

PubMed

Tang, Yongchuan; Zhou, Deyun; Chan, Felix T S

2018-06-11

Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed world where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the open world by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the open world assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed world wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few open issues still exist in the current work: the necessary properties for a belief entropy in the open world assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty.

On the Heating of Ions in Noncylindrical Z-Pinches

NASA Astrophysics Data System (ADS)

Svirsky, E. B.

2018-01-01

The method proposed here for analyzing processes in a hot plasma of noncylindrical Z-pinches is based on separation of the group of high-energy ions into a special fraction. Such ions constitute an insignificant fraction ( 10%) of the total volume of the Z-pinch plasma, but these ions contribute the most to the formation of conditions in which the pinch becomes a source of nuclear fusion products and X-ray radiation. The method allows a quite correct approach to obtaining quantitative estimates of the plasma parameters, the nuclear fusion energy yield, and the features of neutron fluxes in experiments with Z-pinches.

Green frequency-doubled laser-beam propagation in high-temperature hohlraum plasmas.

PubMed

Niemann, C; Berger, R L; Divol, L; Froula, D H; Jones, O; Kirkwood, R K; Meezan, N; Moody, J D; Ross, J; Sorce, C; Suter, L J; Glenzer, S H

2008-02-01

We demonstrate propagation and small backscatter losses of a frequency-doubled (2omega) laser beam interacting with inertial confinement fusion hohlraum plasmas. The electron temperature of 3.3 keV, approximately a factor of 2 higher than achieved in previous experiments with open geometry targets, approaches plasma conditions of high-fusion yield hohlraums. In this new temperature regime, we measure 2omega laser-beam transmission approaching 80% with simultaneous backscattering losses of less than 10%. These findings suggest that good laser coupling into fusion hohlraums using 2omega light is possible.

BIM and IoT: A Synopsis from GIS Perspective

NASA Astrophysics Data System (ADS)

Isikdag, U.

2015-10-01

Internet-of-Things (IoT) focuses on enabling communication between all devices, things that are existent in real life or that are virtual. Building Information Models (BIMs) and Building Information Modelling is a hype that has been the buzzword of the construction industry for last 15 years. BIMs emerged as a result of a push by the software companies, to tackle the problems of inefficient information exchange between different software and to enable true interoperability. In BIM approach most up-to-date an accurate models of a building are stored in shared central databases during the design and the construction of a project and at post-construction stages. GIS based city monitoring / city management applications require the fusion of information acquired from multiple resources, BIMs, City Models and Sensors. This paper focuses on providing a method for facilitating the GIS based fusion of information residing in digital building "Models" and information acquired from the city objects i.e. "Things". Once this information fusion is accomplished, many fields ranging from Emergency Response, Urban Surveillance, Urban Monitoring to Smart Buildings will have potential benefits.

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

PubMed

Deng, Xinyang; Jiang, Wen

2017-09-12

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

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

PubMed Central

Deng, Xinyang

2017-01-01

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

Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment

NASA Astrophysics Data System (ADS)

David, S.; Visvikis, D.; Roux, C.; Hatt, M.

2011-09-01

In positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications.

A Method of Detections' Fusion for GNSS Anti-Spoofing.

PubMed

Tao, Huiqi; Li, Hong; Lu, Mingquan

2016-12-19

The spoofing attack is one of the security threats of systems depending on the Global Navigation Satellite System (GNSS). There have been many GNSS spoofing detection methods, and each of them focuses on a characteristic of the GNSS signal or a measurement that the receiver has obtained. The method based on a single detector is insufficient against spoofing attacks in some scenarios. How to fuse multiple detections together is a problem that concerns the performance of GNSS anti-spoofing. Scholars have put forward a model to fuse different detection results based on the Dempster-Shafer theory (DST) of evidence combination. However, there are some problems in the application. The main challenge is the valuation of the belief function, which is a key issue in DST. This paper proposes a practical method of detections' fusion based on an approach to assign the belief function for spoofing detections. The frame of discernment is simplified, and the hard decision of hypothesis testing is replaced by the soft decision; then, the belief functions for some detections can be evaluated. The method is discussed in detail, and a performance evaluation is provided, as well. Detections' fusion reduces false alarms of detection and makes the result more reliable. Experimental results based on public test datasets demonstrate the performance of the proposed method.

3D pose estimation and motion analysis of the articulated human hand-forearm limb in an industrial production environment

NASA Astrophysics Data System (ADS)

Hahn, Markus; Barrois, Björn; Krüger, Lars; Wöhler, Christian; Sagerer, Gerhard; Kummert, Franz

2010-09-01

This study introduces an approach to model-based 3D pose estimation and instantaneous motion analysis of the human hand-forearm limb in the application context of safe human-robot interaction. 3D pose estimation is performed using two approaches: The Multiocular Contracting Curve Density (MOCCD) algorithm is a top-down technique based on pixel statistics around a contour model projected into the images from several cameras. The Iterative Closest Point (ICP) algorithm is a bottom-up approach which uses a motion-attributed 3D point cloud to estimate the object pose. Due to their orthogonal properties, a fusion of these algorithms is shown to be favorable. The fusion is performed by a weighted combination of the extracted pose parameters in an iterative manner. The analysis of object motion is based on the pose estimation result and the motion-attributed 3D points belonging to the hand-forearm limb using an extended constraint-line approach which does not rely on any temporal filtering. A further refinement is obtained using the Shape Flow algorithm, a temporal extension of the MOCCD approach, which estimates the temporal pose derivative based on the current and the two preceding images, corresponding to temporal filtering with a short response time of two or at most three frames. Combining the results of the two motion estimation stages provides information about the instantaneous motion properties of the object. Experimental investigations are performed on real-world image sequences displaying several test persons performing different working actions typically occurring in an industrial production scenario. In all example scenes, the background is cluttered, and the test persons wear various kinds of clothes. For evaluation, independently obtained ground truth data are used. [Figure not available: see fulltext.

Considerations on the construction of a Powder Bed Fusion platform for Additive Manufacturing

NASA Astrophysics Data System (ADS)

Andersen, Sebastian Aagaard; Nielsen, Karl-Emil; Pedersen, David Bue; Nielsen, Jakob Skov

As the demand for moulds and other tools becomes increasingly specific and complex, an additive manufacturing approach to production is making its way to the industry through laser based consolidation of metal powder particles by a method known as powder bed fusion. This paper concerns a variety of design choices facilitating the development of an experimental powder bed fusion machine tool, capable of manufacturing metal parts with strength matching that of conventional manufactured parts and a complexity surpassing that of subtractive processes. To understand the different mechanisms acting within such an experimental machine tool, a fully open and customizable rig is constructed. Emphasizing modularity in the rig, allows alternation of lasers, scanner systems, optical elements, powder deposition, layer height, temperature, atmosphere, and powder type. Through a custom-made software platform, control of the process is achieved, which extends into a graphical user interface, easing adjustment of process parameters and the job file generation.

Site-directed antibody immobilization using a protein A-gold binding domain fusion protein for enhanced SPR immunosensing.

PubMed

de Juan-Franco, Elena; Caruz, Antonio; Pedrajas, J R; Lechuga, Laura M

2013-04-07

We have implemented a novel strategy for the oriented immobilization of antibodies onto a gold surface based on the use of a fusion protein, the protein A-gold binding domain (PAG). PAG consists of a gold binding peptide (GBP) coupled to the immunoglobulin-binding domains of staphylococcal protein A. This fusion protein provides an easy and fast oriented immobilization of antibodies preserving its native structure, while leaving the antigen binding sites (Fab) freely exposed. Using this immobilization strategy, we have demonstrated the performance of the immunosensing of the human Growth Hormone by SPR. A limit of detection of 90 ng mL(-1) was obtained with an inter-chip variability lower than 7%. The comparison of this method with other strategies for the direct immobilization of antibodies over gold surfaces has showed the enhanced sensitivity provided by the PAG approach.

Interactive Plasma Physics Education Using Data from Fusion Experiments

NASA Astrophysics Data System (ADS)

Calderon, Brisa; Davis, Bill; Zwicker, Andrew

2010-11-01

The Internet Plasma Physics Education Experience (IPPEX) website was created in 1996 to give users access to data from plasma and fusion experiments. Interactive material on electricity, magnetism, matter, and energy was presented to generate interest and prepare users to understand data from a fusion experiment. Initially, users were allowed to analyze real-time and archival data from the Tokamak Fusion Test Reactor (TFTR) experiment. IPPEX won numerous awards for its novel approach of allowing users to participate in ongoing research. However, the latest revisions of IPPEX were in 2001 and the interactive material is no longer functional on modern browsers. Also, access to real-time data was lost when TFTR was shut down. The interactive material on IPPEX is being rewritten in ActionScript3.0, and real-time and archival data from the National Spherical Tokamak Experiment (NSTX) will be made available to users. New tools like EFIT animations, fast cameras, and plots of important plasma parameters will be included along with an existing Java-based ``virtual tokamak.'' Screenshots from the upgraded website and future directions will be presented.

Structural Transition and Antibody Binding of EBOV GP and ZIKV E Proteins from Pre-Fusion to Fusion-Initiation State.

PubMed

Lappala, Anna; Nishima, Wataru; Miner, Jacob; Fenimore, Paul; Fischer, Will; Hraber, Peter; Zhang, Ming; McMahon, Benjamin; Tung, Chang-Shung

2018-05-10

Membrane fusion proteins are responsible for viral entry into host cells—a crucial first step in viral infection. These proteins undergo large conformational changes from pre-fusion to fusion-initiation structures, and, despite differences in viral genomes and disease etiology, many fusion proteins are arranged as trimers. Structural information for both pre-fusion and fusion-initiation states is critical for understanding virus neutralization by the host immune system. In the case of Ebola virus glycoprotein (EBOV GP) and Zika virus envelope protein (ZIKV E), pre-fusion state structures have been identified experimentally, but only partial structures of fusion-initiation states have been described. While the fusion-initiation structure is in an energetically unfavorable state that is difficult to solve experimentally, the existing structural information combined with computational approaches enabled the modeling of fusion-initiation state structures of both proteins. These structural models provide an improved understanding of four different neutralizing antibodies in the prevention of viral host entry.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

PubMed

Liu, Xian; Xu, Yuan; Li, Shanshan; Wang, Yulan; Peng, Jianlong; Luo, Cheng; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

2014-01-01

Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities. With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Combining elements of information fusion and knowledge-based systems to support situation analysis

NASA Astrophysics Data System (ADS)

Roy, Jean

2006-04-01

Situation awareness has emerged as an important concept in military and public security environments. Situation analysis is defined as a process, the examination of a situation, its elements, and their relations, to provide and maintain a product, i.e., a state of situation awareness for the decision maker(s). It is well established that information fusion, defined as the process of utilizing one or more information sources over time to assemble a representation of aspects of interest in an environment, is a key enabler to meeting the demanding requirements of situation analysis. However, although information fusion is important, developing and adopting a knowledge-centric view of situation analysis should provide a more holistic perspective of this process. This is based on the notion that awareness ultimately has to do with having knowledge of something. Moreover, not all of the situation elements and relationships of interest are directly observable. Those aspects of interest that cannot be observed must be inferred, i.e., derived as a conclusion from facts or premises, or by reasoning from evidence. This paper discusses aspects of knowledge, and how it can be acquired from experts, formally represented and stored in knowledge bases to be exploited by computer programs, and validated. Knowledge engineering is reviewed, with emphasis given to cognitive and ontological engineering. Facets of reasoning are discussed, along with inferencing methods that can be used in computer applications. Finally, combining elements of information fusion and knowledge-based systems, an overall approach and framework for the building of situation analysis support systems is presented.

Enhanced disease characterization through multi network functional normalization in fMRI.

PubMed

Çetin, Mustafa S; Khullar, Siddharth; Damaraju, Eswar; Michael, Andrew M; Baum, Stefi A; Calhoun, Vince D

2015-01-01

Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

Distributed ISAR Subimage Fusion of Nonuniform Rotating Target Based on Matching Fourier Transform.

PubMed

Li, Yuanyuan; Fu, Yaowen; Zhang, Wenpeng

2018-06-04

In real applications, the image quality of the conventional monostatic Inverse Synthetic Aperture Radar (ISAR) for the maneuvering target is subject to the strong fluctuation of Radar Cross Section (RCS), as the target aspect varies enormously. Meanwhile, the maneuvering target introduces nonuniform rotation after translation motion compensation which degrades the imaging performance of the conventional Fourier Transform (FT)-based method in the cross-range dimension. In this paper, a method which combines the distributed ISAR technique and the Matching Fourier Transform (MFT) is proposed to overcome these problems. Firstly, according to the characteristics of the distributed ISAR, the multiple channel echoes of the nonuniform rotation target from different observation angles can be acquired. Then, by applying the MFT to the echo of each channel, the defocused problem of nonuniform rotation target which is inevitable by using the FT-based imaging method can be avoided. Finally, after preprocessing, scaling and rotation of all subimages, the noncoherent fusion image containing all the RCS information in all channels can be obtained. The accumulation coefficients of all subimages are calculated adaptively according to the their image qualities. Simulation and experimental data are used to validate the effectiveness of the proposed approach, and fusion image with improved recognizability can be obtained. Therefore, by using the distributed ISAR technique and MFT, subimages of high-maneuvering target from different observation angles can be obtained. Meanwhile, by employing the adaptive subimage fusion method, the RCS fluctuation can be alleviated and more recognizable final image can be obtained.

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

NASA Technical Reports Server (NTRS)

Li, Winston; Leung, Henry; Kwan, Chiman; Linnell, Bruce R.

2005-01-01

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Nonsubsampled rotated complex wavelet transform (NSRCxWT) for medical image fusion related to clinical aspects in neurocysticercosis.

PubMed

Chavan, Satishkumar S; Mahajan, Abhishek; Talbar, Sanjay N; Desai, Subhash; Thakur, Meenakshi; D'cruz, Anil

2017-02-01

Neurocysticercosis (NCC) is a parasite infection caused by the tapeworm Taenia solium in its larvae stage which affects the central nervous system of the human body (a definite host). It results in the formation of multiple lesions in the brain at different locations during its various stages. During diagnosis of such symptomatic patients, these lesions can be better visualized using a feature based fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). This paper presents a novel approach to Multimodality Medical Image Fusion (MMIF) used for the analysis of the lesions for the diagnostic purpose and post treatment review of NCC. The MMIF presented here is a technique of combining CT and MRI data of the same patient into a new slice using a Nonsubsampled Rotated Complex Wavelet Transform (NSRCxWT). The forward NSRCxWT is applied on both the source modalities separately to extract the complementary and the edge related features. These features are then combined to form a composite spectral plane using average and maximum value selection fusion rules. The inverse transformation on this composite plane results into a new, visually better, and enriched fused image. The proposed technique is tested on the pilot study data sets of patients infected with NCC. The quality of these fused images is measured using objective and subjective evaluation metrics. Objective evaluation is performed by estimating the fusion parameters like entropy, fusion factor, image quality index, edge quality measure, mean structural similarity index measure, etc. The fused images are also evaluated for their visual quality using subjective analysis with the help of three expert radiologists. The experimental results on 43 image data sets of 17 patients are promising and superior when compared with the state of the art wavelet based fusion algorithms. The proposed algorithm can be a part of computer-aided detection and diagnosis (CADD) system which assists the radiologists in clinical practices. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Study of Feature Combination for Vehicle Detection Based on Image Processing

PubMed Central

2014-01-01

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification. PMID:24672299

Homeland security application of the Army Soft Target Exploitation and Fusion (STEF) system

NASA Astrophysics Data System (ADS)

Antony, Richard T.; Karakowski, Joseph A.

2010-04-01

A fusion system that accommodates both text-based extracted information along with more conventional sensor-derived input has been developed and demonstrated in a terrorist attack scenario as part of the Empire Challenge (EC) 09 Exercise. Although the fusion system was developed to support Army military analysts, the system, based on a set of foundational fusion principles, has direct applicability to department of homeland security (DHS) & defense, law enforcement, and other applications. Several novel fusion technologies and applications were demonstrated in EC09. One such technology is location normalization that accommodates both fuzzy semantic expressions such as behind Library A, across the street from the market place, as well as traditional spatial representations. Additionally, the fusion system provides a range of fusion products not supported by traditional fusion algorithms. Many of these additional capabilities have direct applicability to DHS. A formal test of the fusion system was performed during the EC09 exercise. The system demonstrated that it was able to (1) automatically form tracks, (2) help analysts visualize behavior of individuals over time, (3) link key individuals based on both explicit message-based information as well as discovered (fusion-derived) implicit relationships, and (4) suggest possible individuals of interest based on their association with High Value Individuals (HVI) and user-defined key locations.

Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.

PubMed

Dong, Pei; Guo, Yangrong; Gao, Yue; Liang, Peipeng; Shi, Yonghong; Wang, Qian; Shen, Dinggang; Wu, Guorong

2016-10-01

Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First , we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second , besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third , since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.

The role of imaging based prostate biopsy morphology in a data fusion paradigm for transducing prognostic predictions

NASA Astrophysics Data System (ADS)

Khan, Faisal M.; Kulikowski, Casimir A.

2016-03-01

A major focus area for precision medicine is in managing the treatment of newly diagnosed prostate cancer patients. For patients with a positive biopsy, clinicians aim to develop an individualized treatment plan based on a mechanistic understanding of the disease factors unique to each patient. Recently, there has been a movement towards a multi-modal view of the cancer through the fusion of quantitative information from multiple sources, imaging and otherwise. Simultaneously, there have been significant advances in machine learning methods for medical prognostics which integrate a multitude of predictive factors to develop an individualized risk assessment and prognosis for patients. An emerging area of research is in semi-supervised approaches which transduce the appropriate survival time for censored patients. In this work, we apply a novel semi-supervised approach for support vector regression to predict the prognosis for newly diagnosed prostate cancer patients. We integrate clinical characteristics of a patient's disease with imaging derived metrics for biomarker expression as well as glandular and nuclear morphology. In particular, our goal was to explore the performance of nuclear and glandular architecture within the transduction algorithm and assess their predictive power when compared with the Gleason score manually assigned by a pathologist. Our analysis in a multi-institutional cohort of 1027 patients indicates that not only do glandular and morphometric characteristics improve the predictive power of the semi-supervised transduction algorithm; they perform better when the pathological Gleason is absent. This work represents one of the first assessments of quantitative prostate biopsy architecture versus the Gleason grade in the context of a data fusion paradigm which leverages a semi-supervised approach for risk prognosis.

Nuclear astrophysics at Gran Sasso Laboratory: the LUNA experiment

NASA Astrophysics Data System (ADS)

Cavanna, Francesca

2018-05-01

LUNA is an experimental approach for the study of nuclear fusion reactions based on an underground accelerator laboratory. Aim of the experiment is the direct measurement of the cross section of nuclear reactions relevant for stellar and primordial nucleosynthesis. In the following the latest results and the future goals will be presented.

Robust model-based 3d/3D fusion using sparse matching for minimally invasive surgery.

PubMed

Neumann, Dominik; Grbic, Sasa; John, Matthias; Navab, Nassir; Hornegger, Joachim; Ionasec, Razvan

2013-01-01

Classical surgery is being disrupted by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm CT and C-arm fluoroscopy are routinely used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel sparse matching approach for fusing high quality pre-operative CT and non-contrasted, non-gated intra-operative C-arm CT by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the pre-operative CT and mapped to the intra-operative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments demonstrate that our model-based fusion approach has an average execution time of 2.9 s, while the accuracy lies within expert user confidence intervals.

Structural and biological mimicry of protein surface recognition by [alpha/beta]-peptide foldamers

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

Horne, W. Seth; Johnson, Lisa M.; Ketas, Thomas J.

Unnatural oligomers that can mimic protein surfaces offer a potentially useful strategy for blocking biomedically important protein-protein interactions. Here we evaluate an approach based on combining {alpha}- and {beta}-amino acid residues in the context of a polypeptide sequence from the HIV protein gp41, which represents an excellent testbed because of the wealth of available structural and biological information. We show that {alpha}/{beta}-peptides can mimic structural and functional properties of a critical gp41 subunit. Physical studies in solution, crystallographic data, and results from cell-fusion and virus-infectivity assays collectively indicate that the gp41-mimetic {alpha}/{beta}-peptides effectively block HIV-cell fusion via a mechanism comparablemore » to that of gp41-derived {alpha}-peptides. An optimized {alpha}/{beta}-peptide is far less susceptible to proteolytic degradation than is an analogous {alpha}-peptide. Our findings show how a two-stage design approach, in which sequence-based {alpha} {yields} {beta} replacements are followed by site-specific backbone rigidification, can lead to physical and biological mimicry of a natural biorecognition process.« less

DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco P.; Pasquariello, Guido

2018-03-01

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

Development of Electron Beam Pumped KrF Lasers for Fusion Energy

DTIC Science & Technology

2008-01-01

Direct drive with krypton fluoride (KrF) lasers is an attractive approach to inertial fusion energy (IFE): KrF lasers have outstanding beam spatial...attractive power plant [3]. In view of these advances, several world-wide programs are underway to develop KrF lasers for fusion energy . These include

Project Icarus: Nuclear Fusion Propulsion Concept Comparison

NASA Astrophysics Data System (ADS)

Stanic, M.

Project Icarus will use nuclear fusion as the primary propulsion, since achieving breakeven is imminent within the next decade. Therefore, fusion technology provides confidence in further development and fairly high technological maturity by the time the Icarus mission would be plausible. Currently there are numerous (over 2 dozen) different fusion approaches that are simultaneously being developed around the World and it is difficult to predict which of the concepts is going to be the most successful one. This study tried to estimate current technological maturity and possible technological extrapolation of fusion approaches for which appropriate data could be found. Figures of merit that were assessed include: current technological state, mass and volume estimates, possible gain values, main advantages and disadvantages of the concept and an attempt to extrapolate current technological state for the next decade or two. Analysis suggests that Magnetic Confinement Fusion (MCF) concepts are not likely to deliver sufficient performance due to size, mass, gain and large technological barriers of the concept. However, ICF and PJMIF did show potential for delivering necessary performance, assuming appropriate techno- logical advances. This paper is a submission of the Project Icarus Study Group.

Differences in 3D vs. 2D analysis in lumbar spinal fusion simulations.

PubMed

Hsu, Hung-Wei; Bashkuev, Maxim; Pumberger, Matthias; Schmidt, Hendrik

2018-04-27

Lumbar interbody fusion is currently the gold standard in treating patients with disc degeneration or segmental instability. Despite it having been used for several decades, the non-union rate remains high. A failed fusion is frequently attributed to an inadequate mechanical environment after instrumentation. Finite element (FE) models can provide insights into the mechanics of the fusion process. Previous fusion simulations using FE models showed that the geometries and material of the cage can greatly influence the fusion outcome. However, these studies used axisymmetric models which lacked realistic spinal geometries. Therefore, different modeling approaches were evaluated to understand the bone-formation process. Three FE models of the lumbar motion segment (L4-L5) were developed: 2D, Sym-3D and Nonsym-3D. The fusion process based on existing mechano-regulation algorithms using the FE simulations to evaluate the mechanical environment was then integrated into these models. In addition, the influence of different lordotic angles (5, 10 and 15°) was investigated. The volume of newly formed bone, the axial stiffness of the whole segment and bone distribution inside and surrounding the cage were evaluated. In contrast to the Nonsym-3D, the 2D and Sym-3D models predicted excessive bone formation prior to bridging (peak values with 36 and 9% higher than in equilibrium, respectively). The 3D models predicted a more uniform bone distribution compared to the 2D model. The current results demonstrate the crucial role of the realistic 3D geometry of the lumbar motion segment in predicting bone formation after lumbar spinal fusion. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Symmetry based assembly of a 2 dimensional protein lattice

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

Poulos, Sandra; Agah, Sayeh; Jallah, Nikardi

2017-04-18

The design of proteins that self-assemble into higher order architectures is of great interest due to their potential application in nanotechnology. Specifically, the self-assembly of proteins into ordered lattices is of special interest to the field of structural biology. Here we designed a 2 dimensional (2D) protein lattice using a fusion of a tandem repeat of three TelSAM domains (TTT) to the Ferric uptake regulator (FUR) domain. We determined the structure of the designed (TTT-FUR) fusion protein to 2.3 Å by X-ray crystallographic methods. In agreement with the design, a 2D lattice composed of TelSAM fibers interdigitated by the FURmore » domain was observed. As expected, the fusion of a tandem repeat of three TelSAM domains formed 21 screw axis, and the self-assembly of the ordered oligomer was under pH control. We demonstrated that the fusion of TTT to a domain having a 2-fold symmetry, such as the FUR domain, can produce an ordered 2D lattice. The TTT-FUR system combines features from the rotational symmetry matching approach with the oligomer driven crystallization method. This TTT-FUR fusion was amenable to X-ray crystallographic methods, and is a promising crystallization chaperone.« less

A novel framework for command and control of networked sensor systems

NASA Astrophysics Data System (ADS)

Chen, Genshe; Tian, Zhi; Shen, Dan; Blasch, Erik; Pham, Khanh

2007-04-01

In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigation for future military operations by graphical game theory and advanced knowledge information fusion infrastructures. The problem is approached by representing distributed sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information superiority-shared, accurate, and timely situational awareness upon which advanced automated management aids for IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach.

TU-AB-202-11: Tumor Segmentation by Fusion of Multi-Tracer PET Images Using Copula Based Statistical Methods

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

Lapuyade-Lahorgue, J; Ruan, S; Li, H

Purpose: Multi-tracer PET imaging is getting more attention in radiotherapy by providing additional tumor volume information such as glucose and oxygenation. However, automatic PET-based tumor segmentation is still a very challenging problem. We propose a statistical fusion approach to joint segment the sub-area of tumors from the two tracers FDG and FMISO PET images. Methods: Non-standardized Gamma distributions are convenient to model intensity distributions in PET. As a serious correlation exists in multi-tracer PET images, we proposed a new fusion method based on copula which is capable to represent dependency between different tracers. The Hidden Markov Field (HMF) model ismore » used to represent spatial relationship between PET image voxels and statistical dynamics of intensities for each modality. Real PET images of five patients with FDG and FMISO are used to evaluate quantitatively and qualitatively our method. A comparison between individual and multi-tracer segmentations was conducted to show advantages of the proposed fusion method. Results: The segmentation results show that fusion with Gaussian copula can receive high Dice coefficient of 0.84 compared to that of 0.54 and 0.3 of monomodal segmentation results based on individual segmentation of FDG and FMISO PET images. In addition, high correlation coefficients (0.75 to 0.91) for the Gaussian copula for all five testing patients indicates the dependency between tumor regions in the multi-tracer PET images. Conclusion: This study shows that using multi-tracer PET imaging can efficiently improve the segmentation of tumor region where hypoxia and glucidic consumption are present at the same time. Introduction of copulas for modeling the dependency between two tracers can simultaneously take into account information from both tracers and deal with two pathological phenomena. Future work will be to consider other families of copula such as spherical and archimedian copulas, and to eliminate partial volume effect by considering dependency between neighboring voxels.« less

Dicentric breakage at telomere fusions

PubMed Central

Pobiega, Sabrina; Marcand, Stéphane

2010-01-01

Nonhomologous end-joining (NHEJ) inhibition at telomeres ensures that native chromosome ends do not fuse together. But the occurrence and consequences of rare telomere fusions are not well understood. It is notably unclear whether a telomere fusion could be processed to restore telomere ends. Here we address the behavior of individual dicentrics formed by telomere fusion in the yeast Saccharomyces cerevisiae. Our approach was to first stabilize and amplify fusions between two chromosomes by temporarily inactivating one centromere. Next we analyzed dicentric breakage following centromere reactivation. Unexpectedly, dicentrics often break at the telomere fusions during progression through mitosis, a process that restores the parental chromosomes. This unforeseen result suggests a rescue pathway able to process telomere fusions and to back up NHEJ inhibition at telomeres. PMID:20360388

Remote Sensing-Based, 5-m, Vegetation Distributions, Kougarok Study Site, Seward Peninsula, Alaska, ca. 2009 - 2016

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

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5~m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. This data was developed using the fusion of hyperspectral, multispectral, and terrain datasets. The current data is located in the Kougarok watershed but we plan to expand this over the Seward Peninsula.

Multiview echocardiography fusion using an electromagnetic tracking system.

PubMed

Punithakumar, Kumaradevan; Hareendranathan, Abhilash R; Paakkanen, Riitta; Khan, Nehan; Noga, Michelle; Boulanger, Pierre; Becher, Harald

2016-08-01

Three-dimensional ultrasound is an emerging modality for the assessment of complex cardiac anatomy and function. The advantages of this modality include lack of ionizing radiation, portability, low cost, and high temporal resolution. Major limitations include limited field-of-view, reliance on frequently limited acoustic windows, and poor signal to noise ratio. This study proposes a novel approach to combine multiple views into a single image using an electromagnetic tracking system in order to improve the field-of-view. The novel method has several advantages: 1) it does not rely on image information for alignment, and therefore, the method does not require image overlap; 2) the alignment accuracy of the proposed approach is not affected by any poor image quality as in the case of image registration based approaches; 3) in contrast to previous optical tracking based system, the proposed approach does not suffer from line-of-sight limitation; and 4) it does not require any initial calibration. In this pilot project, we were able to show that using a heart phantom, our method can fuse multiple echocardiographic images and improve the field-of view. Quantitative evaluations showed that the proposed method yielded a nearly optimal alignment of image data sets in three-dimensional space. The proposed method demonstrates the electromagnetic system can be used for the fusion of multiple echocardiography images with a seamless integration of sensors to the transducer.

Online energy management strategy of fuel cell hybrid electric vehicles based on data fusion approach

NASA Astrophysics Data System (ADS)

Zhou, Daming; Al-Durra, Ahmed; Gao, Fei; Ravey, Alexandre; Matraji, Imad; Godoy Simões, Marcelo

2017-10-01

Energy management strategy plays a key role for Fuel Cell Hybrid Electric Vehicles (FCHEVs), it directly affects the efficiency and performance of energy storages in FCHEVs. For example, by using a suitable energy distribution controller, the fuel cell system can be maintained in a high efficiency region and thus saving hydrogen consumption. In this paper, an energy management strategy for online driving cycles is proposed based on a combination of the parameters from three offline optimized fuzzy logic controllers using data fusion approach. The fuzzy logic controllers are respectively optimized for three typical driving scenarios: highway, suburban and city in offline. To classify patterns of online driving cycles, a Probabilistic Support Vector Machine (PSVM) is used to provide probabilistic classification results. Based on the classification results of the online driving cycle, the parameters of each offline optimized fuzzy logic controllers are then fused using Dempster-Shafer (DS) evidence theory, in order to calculate the final parameters for the online fuzzy logic controller. Three experimental validations using Hardware-In-the-Loop (HIL) platform with different-sized FCHEVs have been performed. Experimental comparison results show that, the proposed PSVM-DS based online controller can achieve a relatively stable operation and a higher efficiency of fuel cell system in real driving cycles.

Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images

PubMed Central

Zhu, Xiaolin; Gao, Feng; Chou, Bryan; Li, Jiang; Shen, Yuzhong; Koperski, Krzysztof; Marchisio, Giovanni

2018-01-01

Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images. PMID:29614745

Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images.

PubMed

Kwan, Chiman; Zhu, Xiaolin; Gao, Feng; Chou, Bryan; Perez, Daniel; Li, Jiang; Shen, Yuzhong; Koperski, Krzysztof; Marchisio, Giovanni

2018-03-31

Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images.

Value Driven Information Processing and Fusion

DTIC Science & Technology

2016-03-01

consensus approach allows a decentralized approach to achieve the optimal error exponent of the centralized counterpart, a conclusion that is signifi...SECURITY CLASSIFICATION OF: The objective of the project is to develop a general framework for value driven decentralized information processing...including: optimal data reduction in a network setting for decentralized inference with quantization constraint; interactive fusion that allows queries and

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-02-06

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

Anatomy of the psoas muscle and lumbar plexus with respect to the surgical approach for lateral transpsoas interbody fusion.

PubMed

Kepler, Christopher K; Bogner, Eric A; Herzog, Richard J; Huang, Russel C

2011-04-01

Lateral transpsoas interbody fusion (LTIF) is a minimally invasive technique that permits interbody fusion utilizing cages placed via a direct lateral retroperitoneal approach. We sought to describe the locations of relevant neurovascular structures based on MRI with respect to this novel surgical approach. We retrospectively reviewed consecutive lumbosacral spine MRI scans in 43 skeletally mature adults. MRI scans were independently reviewed by two readers to identify the location of the psoas muscle, lumbar plexus, femoral nerve, inferior vena cava and right iliac vein. Structures potentially at risk for injury were identified by: a distance from the anterior aspect of the adjacent vertebral bodies of <20 mm, representing the minimum retraction necessary for cage placement, and extension of vascular structures posterior to the anterior vertebral body, requiring anterior retraction. The percentage of patients with neurovascular structures at risk for left-sided approaches was 2.3% at L1-2, 7.0% at L2-3, 4.7% at L3-4 and 20.9% at L4-5. For right-sided approaches, this rose to 7.0% at L1-2, 7.0% at L2-3, 9.3% at L3-4 and 44.2% at L4-5, largely because of the relatively posterior right-sided vasculature. A relationship between the position of psoas muscle and lumbar plexus is described which allows use of the psoas position as a proxy for lumbar plexus position to identify patients who may be at risk, particularly at the L4-5 level. Further study will establish the clinical relevance of these measurements and the ability of neurovascular structures to be retracted without significant injury.

Performance Investigation of Proteomic Identification by HCD/CID Fragmentations in Combination with High/Low-Resolution Detectors on a Tribrid, High-Field Orbitrap Instrument

PubMed Central

Shen, Shichen; Sheng, Quanhu; Shyr, Yu; Qu, Jun

2016-01-01

The recently-introduced Orbitrap Fusion mass spectrometry permits various types of MS2 acquisition methods. To date, these different MS2 strategies and the optimal data interpretation approach for each have not been adequately evaluated. This study comprehensively investigated the four MS2 strategies: HCD-OT (higher-energy-collisional-dissociation with Orbitrap detection), HCD-IT (HCD with ion trap, IT), CID-IT (collision-induced-dissociation with IT) and CID-OT on Orbitrap Fusion. To achieve extensive comparison and identify the optimal data interpretation method for each technique, several search engines (SEQUEST and Mascot) and post-processing methods (score-based, PeptideProphet, and Percolator) were assessed for all techniques for the analysis of a human cell proteome. It was found that divergent conclusions could be made from the same dataset when different data interpretation approaches were used and therefore requiring a relatively fair comparison among techniques. Percolator was chosen for comparison of techniques because it performs the best among all search engines and MS2 strategies. For the analysis of human cell proteome using individual MS2 strategies, the highest number of identifications was achieved by HCD-OT, followed by HCD-IT and CID-IT. Based on these results, we concluded that a relatively fair platform for data interpretation is necessary to avoid divergent conclusions from the same dataset, and HCD-OT and HCD-IT may be preferable for protein/peptide identification using Orbitrap Fusion. PMID:27472422

Palmprint authentication using multiple classifiers

NASA Astrophysics Data System (ADS)

Kumar, Ajay; Zhang, David

2004-08-01

This paper investigates the performance improvement for palmprint authentication using multiple classifiers. The proposed methods on personal authentication using palmprints can be divided into three categories; appearance- , line -, and texture-based. A combination of these approaches can be used to achieve higher performance. We propose to simultaneously extract palmprint features from PCA, Line detectors and Gabor-filters and combine their corresponding matching scores. This paper also investigates the comparative performance of simple combination rules and the hybrid fusion strategy to achieve performance improvement. Our experimental results on the database of 100 users demonstrate the usefulness of such approach over those based on individual classifiers.

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness

PubMed Central

Calhoun, Vince D; Sui, Jing

2016-01-01

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness. PMID:27347565

Socioeconomic and regional differences in the treatment of cervical spondylotic myelopathy

PubMed Central

Palejwala, Sheri K.; Rughani, Anand I.; Lemole, G. Michael; Dumont, Travis M.

2017-01-01

Background: Cervical spondylotic myelopathy (CSM) is the leading cause of spinal cord dysfunction in the world. Surgical treatment is both medically and economically advantageous, and can be achieved through multiple approaches, with or without fusion. We used the Nationwide Inpatient Sample (NIS) database to better elucidate regional and socioeconomic variances in the treatment of CSM. Methods: The NIS database was queried for elective admissions with a primary diagnosis of CSM (ICD-9 721.1). This was evaluated for patients who also carried a diagnosis of anterior (ICD-9 81.02) or posterior cervical fusion (ICD-9 81.03), posterior cervical laminectomy (ICD 03.09), or a combination. We then investigated variances including regional trends and disparities according to hospital and insurance types. Results: During 2002–2012, 50605 patients were electively admitted with a diagnosis of CSM. Anterior fusions were more common in Midwestern states and in nonteaching hospitals. Fusion procedures were used more frequently than other treatments in private hospitals and with private insurance. Median hospital charges were also expectedly higher for fusion procedures and combined surgical approaches. Combined approaches were found to be significantly greater in patients with concurrent diagnoses of ossification of the posterior longitudinal ligament (OPLL) and CSM. Ultimately, there has been an increased utilization of fusion procedures versus nonfusion treatments, over the past decade, for patients with cervical myelopathy. Conclusions: Fusion surgery is being increasingly used for the treatment of CSM. Expensive procedures are being performed more frequently in both private hospitals and for those with private insurance, whereas the most economical procedure, posterior cervical laminectomy, was underutilized. PMID:28607826

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness.

PubMed

Calhoun, Vince D; Sui, Jing

2016-05-01

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.

Embedded security system for multi-modal surveillance in a railway carriage

NASA Astrophysics Data System (ADS)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

A Novel Dual Expression Platform for High Throughput Functional Screening of Phage Libraries in Product like Format.

PubMed

Xiao, Xiaodong; Chen, Yan; Mugabe, Sheila; Gao, Changshou; Tkaczyk, Christine; Mazor, Yariv; Pavlik, Peter; Wu, Herren; Dall'Acqua, William; Chowdhury, Partha Sarathi

2015-01-01

High throughput screenings of single chain Fv (scFv) antibody phage display libraries are currently done as soluble scFvs produced in E.coli. Due to endotoxin contaminations from bacterial cells these preparations cannot be reliably used in mammalian cell based assays. The monovalent nature and lack of Fc in soluble scFvs prevent functional assays that are dependent on target cross linking and/or Fc functions. A convenient approach is to convert scFvs into scFv.Fc fusion proteins and express them in mammalian cell lines for screening. This approach is low throughput and is only taken after primary screening of monovalent scFvs that are expressed in bacteria. There is no platform at present that combines the benefits of both bacterial and mammalian expression system for screening phage library output. We have, therefore, developed a novel dual expression vector, called pSplice, which can be used to express scFv.Fc fusion proteins both in E.coli and mammalian cell lines. The hallmark of the vector is an engineered intron which houses the bacterial promoter and signal peptide for expression and secretion of scFv.Fc in E.coli. When the vector is transfected into a mammalian cell line, the intron is efficiently spliced out resulting in a functional operon for expression and secretion of the scFv.Fc fusion protein into the culture medium. By applying basic knowledge of mammalian introns and splisosome, we designed this vector to enable screening of phage libraries in a product like format. Like IgG, the scFv.Fc fusion protein is bi-valent for the antigen and possesses Fc effector functions. Expression in E.coli maintains the speed of the bacterial expression platform and is used to triage clones based on binding and other assays that are not sensitive to endotoxin. Triaged clones are then expressed in a mammalian cell line without the need for any additional cloning steps. Conditioned media from the mammalian cell line containing the fusion proteins are then used for different types of cell based assays. Thus this system retains the speed of the current screening system for phage libraries and adds additional functionality to it.

confFuse: High-Confidence Fusion Gene Detection across Tumor Entities.

PubMed

Huang, Zhiqin; Jones, David T W; Wu, Yonghe; Lichter, Peter; Zapatka, Marc

2017-01-01

Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant. Results: confFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7%). Validation of 18 novel, high-confidence fusions detected in three breast tumor samples resulted in a 100% validation rate. Conclusions: confFuse is a novel downstream filtering method that allows selection of highly reliable fusion gene candidates for further downstream analysis and experimental validations. confFuse is available at https://github.com/Zhiqin-HUANG/confFuse.

Transmembrane Domains of Highly Pathogenic Viral Fusion Proteins Exhibit Trimeric Association In Vitro

PubMed Central

Webb, Stacy R.; Smith, Stacy E.; Fried, Michael G.

2018-01-01

ABSTRACT Enveloped viruses require viral fusion proteins to promote fusion of the viral envelope with a target cell membrane. To drive fusion, these proteins undergo large conformational changes that must occur at the right place and at the right time. Understanding the elements which control the stability of the prefusion state and the initiation of conformational changes is key to understanding the function of these important proteins. The construction of mutations in the fusion protein transmembrane domains (TMDs) or the replacement of these domains with lipid anchors has implicated the TMD in the fusion process. However, the structural and molecular details of the role of the TMD in these fusion events remain unclear. Previously, we demonstrated that isolated paramyxovirus fusion protein TMDs associate in a monomer-trimer equilibrium, using sedimentation equilibrium analytical ultracentrifugation. Using a similar approach, the work presented here indicates that trimeric interactions also occur between the fusion protein TMDs of Ebola virus, influenza virus, severe acute respiratory syndrome coronavirus (SARS CoV), and rabies virus. Our results suggest that TM-TM interactions are important in the fusion protein function of diverse viral families. IMPORTANCE Many important human pathogens are enveloped viruses that utilize membrane-bound glycoproteins to mediate viral entry. Factors that contribute to the stability of these glycoproteins have been identified in the ectodomain of several viral fusion proteins, including residues within the soluble ectodomain. Although it is often thought to simply act as an anchor, the transmembrane domain of viral fusion proteins has been implicated in protein stability and function as well. Here, using a biophysical approach, we demonstrated that the fusion protein transmembrane domains of several deadly pathogens—Ebola virus, influenza virus, SARS CoV, and rabies virus—self-associate. This observation across various viral families suggests that transmembrane domain interactions may be broadly relevant and serve as a new target for therapeutic development. PMID:29669880

Membrane Fusion Involved in Neurotransmission: Glimpse from Electron Microscope and Molecular Simulation

PubMed Central

Yang, Zhiwei; Gou, Lu; Chen, Shuyu; Li, Na; Zhang, Shengli; Zhang, Lei

2017-01-01

Membrane fusion is one of the most fundamental physiological processes in eukaryotes for triggering the fusion of lipid and content, as well as the neurotransmission. However, the architecture features of neurotransmitter release machinery and interdependent mechanism of synaptic membrane fusion have not been extensively studied. This review article expounds the neuronal membrane fusion processes, discusses the fundamental steps in all fusion reactions (membrane aggregation, membrane association, lipid rearrangement and lipid and content mixing) and the probable mechanism coupling to the delivery of neurotransmitters. Subsequently, this work summarizes the research on the fusion process in synaptic transmission, using electron microscopy (EM) and molecular simulation approaches. Finally, we propose the future outlook for more exciting applications of membrane fusion involved in synaptic transmission, with the aid of stochastic optical reconstruction microscopy (STORM), cryo-EM (cryo-EM), and molecular simulations. PMID:28638320

Intelligent Information Fusion in the Aviation Domain: A Semantic-Web based Approach

NASA Technical Reports Server (NTRS)

Ashish, Naveen; Goforth, Andre

2005-01-01

Information fusion from multiple sources is a critical requirement for System Wide Information Management in the National Airspace (NAS). NASA and the FAA envision creating an "integrated pool" of information originally coming from different sources, which users, intelligent agents and NAS decision support tools can tap into. In this paper we present the results of our initial investigations into the requirements and prototype development of such an integrated information pool for the NAS. We have attempted to ascertain key requirements for such an integrated pool based on a survey of DSS tools that will benefit from this integrated pool. We then advocate key technologies from computer science research areas such as the semantic web, information integration, and intelligent agents that we believe are well suited to achieving the envisioned system wide information management capabilities.

Adaptive multisensor fusion for planetary exploration rovers

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Textual and visual content-based anti-phishing: a Bayesian approach.

PubMed

Zhang, Haijun; Liu, Gang; Chow, Tommy W S; Liu, Wenyin

2011-10-01

A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases. © 2011 IEEE

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

PubMed Central

Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach.

PubMed

Liang, Fan; Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach.

PubMed

Peng, Changhui; Guiot, Joel; Wu, Haibin; Jiang, Hong; Luo, Yiqi

2011-05-01

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e., palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services. © 2011 Blackwell Publishing Ltd/CNRS.

Active inclusion bodies of acid phosphatase PhoC: aggregation induced by GFP fusion and activities modulated by linker flexibility

PubMed Central

2013-01-01

Background Biologically active inclusion bodies (IBs) have gained much attention in recent years. Fusion with IB-inducing partner has been shown to be an efficient strategy for generating active IBs. To make full use of the advantages of active IBs, one of the key issues will be to improve the activity yield of IBs when expressed in cells, which would need more choices on IB-inducing fusion partners and approaches for engineering IBs. Green fluorescent protein (GFP) has been reported to aggregate when overexpressed, but GFP fusion has not been considered as an IB-inducing approach for these fusion proteins so far. In addition, the role of linker in fusion proteins has been shown to be important for protein characteristics, yet impact of linker on active IBs has never been reported. Results Here we report that by fusing GFP and acid phosphatase PhoC via a linker region, the resultant PhoC-GFPs were expressed largely as IBs. These IBs show high levels of specific fluorescence and specific PhoC activities (phosphatase and phosphotransferase), and can account for up to over 80% of the total PhoC activities in the cells. We further demonstrated that the aggregation of GFP moiety in the fusion protein plays an essential role in the formation of PhoC-GFP IBs. In addition, PhoC-GFP IBs with linkers of different flexibility were found to exhibit different levels of activities and ratios in the cells, suggesting that the linker region can be utilized to manipulate the characteristics of active IBs. Conclusions Our results show that active IBs of PhoC can be generated by GFP fusion, demonstrating for the first time the potential of GFP fusion to induce active IB formation of another soluble protein. We also show that the linker sequence in PhoC-GFP fusion proteins plays an important role on the regulation of IB characteristics, providing an alternative and important approach for engineering of active IBs with the goal of obtaining high activity yield of IBs. PMID:23497261

A color fusion method of infrared and low-light-level images based on visual perception

NASA Astrophysics Data System (ADS)

Han, Jing; Yan, Minmin; Zhang, Yi; Bai, Lianfa

2014-11-01

The color fusion images can be obtained through the fusion of infrared and low-light-level images, which will contain both the information of the two. The fusion images can help observers to understand the multichannel images comprehensively. However, simple fusion may lose the target information due to inconspicuous targets in long-distance infrared and low-light-level images; and if targets extraction is adopted blindly, the perception of the scene information will be affected seriously. To solve this problem, a new fusion method based on visual perception is proposed in this paper. The extraction of the visual targets ("what" information) and parallel processing mechanism are applied in traditional color fusion methods. The infrared and low-light-level color fusion images are achieved based on efficient typical targets learning. Experimental results show the effectiveness of the proposed method. The fusion images achieved by our algorithm can not only improve the detection rate of targets, but also get rich natural information of the scenes.

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

Approach to interpret images produced by new generations of multidetector CT scanners in post-operative spine.

PubMed

Zeitoun, Rania; Hussein, Manar

2017-11-01

To reach a practical approach to interpret MDCT findings in post-operative spine cases and to change the false belief of CT failure in the setting of instruments secondary to related artefacts. We performed observational retrospective analysis of premier, early and late MDCT scans in 68 post-operative spine patients, with emphasis on instruments related complications and osseous fusion status. We used a grading system for assessment of osseous fusion in 35 patients and we further analysed the findings in failure of fusion, grade (D). We observed a variety of instruments related complications (mostly screws medially penetrating the pedicle) and osseous fusion status in late scans. We graded 11 interbody and 14 posterolateral levels as osseous fusion failure, showing additional instruments related complications, end plates erosive changes, adjacent segments spondylosis and malalignment. Modern MDCT scanners provide high quality images and are strongly recommended in assessment of the instruments and status of osseous fusion. In post-operative imaging of the spine, it is essential to be aware for what you are looking for, in relevance to the date of surgery. Advances in knowledge: Modern MDCT scanners allow assessment of instruments position and integrity and osseous fusion status in post-operative spine. We propose a helpful algorithm to simplify interpreting post-operative spine imaging.

Fusion of imaging and nonimaging data for surveillance aircraft

NASA Astrophysics Data System (ADS)

Shahbazian, Elisa; Gagnon, Langis; Duquet, Jean Remi; Macieszczak, Maciej; Valin, Pierre

1997-06-01

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for airborne surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an identification friend or foe (IFF) system, an electronic support measures (ESM) system, a spotlight synthetic aperture radar (SSAR), a forward looking infra-red (FLIR) sensor and a link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (1) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (2) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (3) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as multi-sensor data fusion (MSDF), situation and threat assessment (STA) and resource management (RM).

Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events.

PubMed

Botwey, Ransford Henry; Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G

2014-01-01

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Annexin-directed β-glucuronidase for the targeted treatment of solid tumors.

PubMed

Guillen, Katrin P; Ruben, Eliza A; Virani, Needa; Harrison, Roger G

2017-02-01

Enzyme prodrug therapy has the potential to remedy the lack of selectivity associated with the systemic administration of chemotherapy. However, most current systems are immunogenic and constrained to a monotherapeutic approach. We developed a new class of fusion proteins centered about the human enzyme β-glucuronidase (βG), capable of converting several innocuous prodrugs into chemotherapeutics. We targeted βG to phosphatidylserine on tumor cells, tumor vasculature and metastases via annexin A1/A5. Phosphatidylserine shows promise as a universal marker for solid tumors and allows for tumor type-independent targeting. To create fusion proteins, human annexin A1/A5 was genetically fused to the activity-enhancing 16a3 mutant of human βG, expressed in chemically defined, fed-batch suspension culture, and chromatographically purified. All fusion constructs achieved >95% purity with yields up to 740 μg/l. Fusion proteins displayed cancer selective cell-surface binding with cell line-dependent binding stability. One fusion protein in combination with the prodrug SN-38 glucuronide was as effective as the drug SN-38 on Panc-1 pancreatic cancer cells and HAAE-1 endothelial cells, and demonstrated efficacy against MCF-7 breast cancer cells. βG fusion proteins effectively enable localized combination therapy that can be tailored to each patient via prodrug selection, with promising clinical potential based on their near fully human design. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development of an inducible platform for intercellular protein delivery.

PubMed

Siller, Richard; Dufour, Eric; Lycke, Max; Wilmut, Ian; Jung, Yong-Wook; Park, In Hyun; Sullivan, Gareth J

2017-04-30

A challenge to protein based therapies is the ability to produce biologically active proteins and their ensured delivery. Various approaches have been utilised including fusion of protein transduction domains with a protein or biomolecule of interest. A compounding issue is lack of specificity, efficiency and indeed whether the protein fusions are actually translocated into the cell and not merely an artefact of the fixation process. Here we present a novel platform, allowing the inducible export and uptake of a protein of interest. The system utilises a combination of the Tetracyline repressor system, combined with a fusion protein containing the N-terminal signal peptide from human chorionic gonadotropin beta-subunit, and a C-terminal poly-arginine domain for efficient uptake by target cells. This novel platform was validated using enhanced green fluorescent protein as the gene of interest. Doxycycline efficiently induced expression of the fusion protein. The human chorionic gonadotropin beta-subunit facilitated the export of the fusion protein into the cell culture media. Finally, the fusion protein was able to efficiently enter into neighbouring cells (target cells), mediated by the poly-arginine cell penetrating peptide. Importantly we have addressed the issue of whether the observed uptake is an artefact of the fixation process or indeed genuine translocation. In addition this platform provides a number of potential applications in diverse areas such as stem cell biology, immune therapy and cancer targeting therapies. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of elemental content off rocks by laser ablation inductively coupled plasma mass spectrometry

USGS Publications Warehouse

Lichte, F.E.

1995-01-01

A new method of analysis for rocks and soils is presented using laser ablation inductively coupled plasma mass spectrometry. It is based on a lithium borate fusion and the free-running mode of a Nd/YAG laser. An Ar/N2 sample gas improves sensitivity 7 ?? for most elements. Sixty-three elements are characterized for the fusion, and 49 elements can be quantified. Internal standards and isotopic spikes ensure accurate results. Limits of detection are 0.01 ??g/g for many trace elements. Accuracy approaches 5% for all elements. A new quality assurance procedure is presented that uses fundamental parameters to test relative response factors for the calibration.

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

PubMed

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

Multi-focus image fusion using a guided-filter-based difference image.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Yang, Tingwu

2016-03-20

The aim of multi-focus image fusion technology is to integrate different partially focused images into one all-focused image. To realize this goal, a new multi-focus image fusion method based on a guided filter is proposed and an efficient salient feature extraction method is presented in this paper. Furthermore, feature extraction is primarily the main objective of the present work. Based on salient feature extraction, the guided filter is first used to acquire the smoothing image containing the most sharpness regions. To obtain the initial fusion map, we compose a mixed focus measure by combining the variance of image intensities and the energy of the image gradient together. Then, the initial fusion map is further processed by a morphological filter to obtain a good reprocessed fusion map. Lastly, the final fusion map is determined via the reprocessed fusion map and is optimized by a guided filter. Experimental results demonstrate that the proposed method does markedly improve the fusion performance compared to previous fusion methods and can be competitive with or even outperform state-of-the-art fusion methods in terms of both subjective visual effects and objective quality metrics.

FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer.

PubMed

Panigrahi, Priyabrata; Jere, Abhay; Anamika, Krishanpal

2018-01-01

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

A model for explaining fusion suppression using classical trajectory method

NASA Astrophysics Data System (ADS)

Phookan, C. K.; Kalita, K.

2015-01-01

We adopt a semi-classical approach for explanation of projectile breakup and above barrier fusion suppression for the reactions 6Li+152Sm and 6Li+144Sm. The cut-off impact parameter for fusion is determined by employing quantum mechanical ideas. Within this cut-off impact parameter for fusion, the fraction of projectiles undergoing breakup is determined using the method of classical trajectory in two-dimensions. For obtaining the initial conditions of the equations of motion, a simplified model of the 6Li nucleus has been proposed. We introduce a simple formula for explanation of fusion suppression. We find excellent agreement between the experimental and calculated fusion cross section. A slight modification of the above formula for fusion suppression is also proposed for a three-dimensional model.

Neural network-based multiple robot simultaneous localization and mapping.

PubMed

Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

2011-12-01

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

Contactless and pose invariant biometric identification using hand surface.

PubMed

Kanhangad, Vivek; Kumar, Ajay; Zhang, David

2011-05-01

This paper presents a novel approach for hand matching that achieves significantly improved performance even in the presence of large hand pose variations. The proposed method utilizes a 3-D digitizer to simultaneously acquire intensity and range images of the user's hand presented to the system in an arbitrary pose. The approach involves determination of the orientation of the hand in 3-D space followed by pose normalization of the acquired 3-D and 2-D hand images. Multimodal (2-D as well as 3-D) palmprint and hand geometry features, which are simultaneously extracted from the user's pose normalized textured 3-D hand, are used for matching. Individual matching scores are then combined using a new dynamic fusion strategy. Our experimental results on the database of 114 subjects with significant pose variations yielded encouraging results. Consistent (across various hand features considered) performance improvement achieved with the pose correction demonstrates the usefulness of the proposed approach for hand based biometric systems with unconstrained and contact-free imaging. The experimental results also suggest that the dynamic fusion approach employed in this work helps to achieve performance improvement of 60% (in terms of EER) over the case when matching scores are combined using the weighted sum rule.

Hypothesis: spring-loaded boomerang mechanism of influenza hemagglutinin-mediated membrane fusion.

PubMed

Tamm, Lukas K

2003-07-11

Substantial progress has been made in recent years to augment the current understanding of structures and interactions that promote viral membrane fusion. This progress is reviewed with a particular emphasis on recently determined structures of viral fusion domains and their interactions with lipid membranes. The results from the different structural and thermodynamic experimental approaches are synthesized into a new proposed mechanism, termed the "spring-loaded boomerang" mechanism of membrane fusion, which is presented here as a hypothesis.

New High Gain Target Design for a Laser Fusion Power Plant

DTIC Science & Technology

2000-06-07

target with a minimum energy gain, about 100. Demonstration of ignition or low gain is only important for fusion energy if it leads into a target concept...nonlinear saturation of these instabilities. Our approach is to try to avoid them. 4. A Development Path to Fusion Energy The laser and target concept...on the exact date required to develop fusion energy , it would be worthwhile for a power plant development program to provide enough time and funds

Enhanced image fusion using directional contrast rules in fuzzy transform domain.

PubMed

Nandal, Amita; Rosales, Hamurabi Gamboa

2016-01-01

In this paper a novel image fusion algorithm based on directional contrast in fuzzy transform (FTR) domain is proposed. Input images to be fused are first divided into several non-overlapping blocks. The components of these sub-blocks are fused using directional contrast based fuzzy fusion rule in FTR domain. The fused sub-blocks are then transformed into original size blocks using inverse-FTR. Further, these inverse transformed blocks are fused according to select maximum based fusion rule for reconstructing the final fused image. The proposed fusion algorithm is both visually and quantitatively compared with other standard and recent fusion algorithms. Experimental results demonstrate that the proposed method generates better results than the other methods.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Performing label-fusion-based segmentation using multiple automatically generated templates.

PubMed

Chakravarty, M Mallar; Steadman, Patrick; van Eede, Matthijs C; Calcott, Rebecca D; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D Louis; Lerch, Jason P

2013-10-01

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Copyright © 2012 Wiley Periodicals, Inc.

Deformable registration of x-ray to MRI for post-implant dosimetry in prostate brachytherapy

NASA Astrophysics Data System (ADS)

Park, Seyoun; Song, Danny Y.; Lee, Junghoon

2016-03-01

Post-implant dosimetric assessment in prostate brachytherapy is typically performed using CT as the standard imaging modality. However, poor soft tissue contrast in CT causes significant variability in target contouring, resulting in incorrect dose calculations for organs of interest. CT-MR fusion-based approach has been advocated taking advantage of the complementary capabilities of CT (seed identification) and MRI (soft tissue visibility), and has proved to provide more accurate dosimetry calculations. However, seed segmentation in CT requires manual review, and the accuracy is limited by the reconstructed voxel resolution. In addition, CT deposits considerable amount of radiation to the patient. In this paper, we propose an X-ray and MRI based post-implant dosimetry approach. Implanted seeds are localized using three X-ray images by solving a combinatorial optimization problem, and the identified seeds are registered to MR images by an intensity-based points-to-volume registration. We pre-process the MR images using geometric and Gaussian filtering. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine transformation and local deformable registration. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. We tested our algorithm on six patient data sets, achieving registration error of (1.2+/-0.8) mm in < 30 sec. Our proposed approach has the potential to be a fast and cost-effective solution for post-implant dosimetry with equivalent accuracy as the CT-MR fusion-based approach.

Future Directions for Fusion Propulsion Research at NASA

NASA Technical Reports Server (NTRS)

Adams, Robert B.; Cassibry, Jason T.

2005-01-01

Fusion propulsion is inevitable if the human race remains dedicated to exploration of the solar system. There are fundamental reasons why fusion surpasses more traditional approaches to routine crewed missions to Mars, crewed missions to the outer planets, and deep space high speed robotic missions, assuming that reduced trip times, increased payloads, and higher available power are desired. A recent series of informal discussions were held among members from government, academia, and industry concerning fusion propulsion. We compiled a sufficient set of arguments for utilizing fusion in space. .If the U.S. is to lead the effort and produce a working system in a reasonable amount of time, NASA must take the initiative, relying on, but not waiting for, DOE guidance. Arguments for fusion propulsion are presented, along with fusion enabled mission examples, fusion technology trade space, and a proposed outline for future efforts.

Product development using process monitoring and NDE data fusion

NASA Astrophysics Data System (ADS)

Peterson, Todd; Bossi, Richard H.

1998-03-01

Composite process/product development relies on both process monitoring information and nondestructive evaluation measurements for determining application suitability. In the past these activities have been performed and analyzed independently. Our present approach is to present the process monitoring and NDE data together in a data fusion workstation. This methodology leads to final product acceptance based on a combined process monitoring and NDE criteria. The data fusion work station combines process parameter and NDE data in a single workspace enabling all the data to be used in the acceptance/rejection decision process. An example application is the induction welding process, a unique joining method for assembling primary composite structure, that offers significant cost and weight advantages over traditional fasted structure. The determination of the required time, temperature and pressure conditions used in the process to achieve a complete weld is being aided by the use of ultrasonic inspection techniques. Full waveform ultrasonic inspection data is employed to evaluate the quality of spar cap to skin fit, an essential element of the welding process, and is processed to find a parameter that can be used for weld acceptance. Certification of the completed weld incorporates the data fusion methodology.

Combining multiple ChIP-seq peak detection systems using combinatorial fusion.

PubMed

Schweikert, Christina; Brown, Stuart; Tang, Zuojian; Smith, Phillip R; Hsu, D Frank

2012-01-01

Due to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator. We define two methods to merge and rescore the regions of two peak detection systems and analyze the performance based on average precision and coverage of transcription start sites. The results indicate that ChIP-seq peak detection can be improved by fusion using score or rank combination. Our method of combination and fusion analysis would provide a means for generic assessment of available technologies and systems and assist researchers in choosing an appropriate system (or fusion method) for analyzing ChIP-seq data. This analysis offers an alternate approach for increasing true positive rates, while decreasing false positive rates and hence improving the ChIP-seq peak identification process.

Nonlinear Burn Control and Operating Point Optimization in ITER

NASA Astrophysics Data System (ADS)

Boyer, Mark; Schuster, Eugenio

2013-10-01

Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).

The effect of minimally invasive posterior cervical approaches versus open anterior approaches on neck pain and disability

PubMed Central

Steinberg, Jeffrey A.; German, John W.

2012-01-01

Background The choice of surgical approach to the cervical spine may have an influence on patient outcome, particularly with respect to future neck pain and disability. Some surgeons suggest that patients with myelopathy or radiculopathy and significant axial pain should be treated with an anterior interbody fusion because a posterior decompression alone may exacerbate the patients’ neck pain. To date, the effect of a minimally invasive posterior cervical decompression approach (miPCD) on neck pain has not been compared with that of an anterior cervical diskectomy or corpectomy with interbody fusion (ACF). Methods A retrospective review was undertaken of 63 patients undergoing either an miPCD (n = 35) or ACF (n = 28) for treatment of myelopathy or radiculopathy who had achieved a minimum of 6 months’ follow-up. Clinical outcomes were assessed by a patient-derived neck visual analog scale (VAS) score and the neck disability index (NDI). Outcomes were analyzed by use of (1) a threshold in which outcomes were classified as success (NDI < 40, VAS score < 4.0) or failure (NDI > 40, VAS score > 4.0) and (2) perioperative change in which outcomes were classified as success (ΔNDI ≥ – 15, ΔVAS score ≥ – 2.0) or failure (ΔNDI < – 15, ΔVAS score < –2.0). Groups were compared by use of χ2 tests with significance taken at P < .05. Results At last follow-up, the percentages of patients classified as successful using the perioperative change criteria were as follows: 42% for miPCD group versus 63% for ACF group based on neck VAS score (P = not significant [NS]) and 33% for miPCD group versus 50% for ACF group based on NDI (P < .05). At last follow-up, the percentages of patients classified as successful using the threshold criteria were as follows: 71% for miPCD group versus 82% for ACF group based on neck VAS score (P = NS) and 69% for miPCD group versus 68% for ACF group based on NDI (P = NS). Conclusions In this small retrospective analysis, miPCD was associated with similar neck pain and disability to ACF. Given the avoidance of cervical instrumentation and interbody fusion in the miPCD group, these results suggest that further comparative effectiveness study is warranted. PMID:25694872

Evaluation of parallel reduction strategies for fusion of sensory information from a robot team

NASA Astrophysics Data System (ADS)

Lyons, Damian M.; Leroy, Joseph

2015-05-01

The advantage of using a team of robots to search or to map an area is that by navigating the robots to different parts of the area, searching or mapping can be completed more quickly. A crucial aspect of the problem is the combination, or fusion, of data from team members to generate an integrated model of the search/mapping area. In prior work we looked at the issue of removing mutual robots views from an integrated point cloud model built from laser and stereo sensors, leading to a cleaner and more accurate model. This paper addresses a further challenge: Even with mutual views removed, the stereo data from a team of robots can quickly swamp a WiFi connection. This paper proposes and evaluates a communication and fusion approach based on the parallel reduction operation, where data is combined in a series of steps of increasing subsets of the team. Eight different strategies for selecting the subsets are evaluated for bandwidth requirements using three robot missions, each carried out with teams of four Pioneer 3-AT robots. Our results indicate that selecting groups to combine based on similar pose but distant location yields the best results.

Fusion of waveform events and radionuclide detections with the help of atmospheric transport modelling

NASA Astrophysics Data System (ADS)

Krysta, Monika; Kushida, Noriyuki; Kotselko, Yuriy; Carter, Jerry

2016-04-01

Possibilities of associating information from four pillars constituting CTBT monitoring and verification regime, namely seismic, infrasound, hydracoustic and radionuclide networks, have been explored by the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) for a long time. Based on a concept of overlying waveform events with the geographical regions constituting possible sources of the detected radionuclides, interactive and non-interactive tools were built in the past. Based on the same concept, a design of a prototype of a Fused Event Bulletin was proposed recently. One of the key design elements of the proposed approach is the ability to access fusion results from either the radionuclide or from the waveform technologies products, which are available on different time scales and through various different automatic and interactive products. To accommodate various time scales a dynamic product evolving while the results of the different technologies are being processed and compiled is envisioned. The product would be available through the Secure Web Portal (SWP). In this presentation we describe implementation of the data fusion functionality in the test framework of the SWP. In addition, we address possible refinements to the already implemented concepts.

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

The Integration of SMOS Soil Moisture in a Consistent Soil Moisture Climate Record

NASA Astrophysics Data System (ADS)

de Jeu, Richard; Kerr, Yann; Wigneron, Jean Pierre; Rodriguez-Fernandez, Nemesio; Al-Yaari, Amen; van der Schalie, Robin; Dolman, Han; Drusch, Matthias; Mecklenburg, Susanne

2015-04-01

Recently, a study funded by the European Space Agency (ESA) was set up to provide guidelines for the development of a global soil moisture climate record with a special emphasis on the integration of SMOS. Three different data fusion approaches were designed and implemented on 10 year passive microwave data (2003-2013) from two different satellite sensors; the ESA Soil Moisture Ocean Salinity Mission (SMOS) and the NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E). The AMSR-E data covered the period from January 2003 until Oct 2011 and SMOS data covered the period from June 2010 until the end of 2013. The fusion approaches included a neural network approach (Rodriguez-Fernandez et al., this conference session HS6.4), a regression approach (Wigneron et al., 2004), and an approach based on the baseline algorithm of ESAs current Climate Change Initiative soil moisture program, the Land Parameter Retrieval Model (Van der Schalie et al., this conference session HS6.4). With this presentation we will show the first results from this study including a description of the different approaches and the validation activities using both globally covered modeled datasets and ground observations from the international soil moisture network. The statistical validation analyses will give us information on the temporal and spatial performance of the three different approaches. Based on these results we will then discuss the next steps towards a seamless integration of SMOS in a consistent soil moisture climate record. References Wigneron J.-P., J.-C. Calvet, P. de Rosnay, Y. Kerr, P. Waldteufel, K. Saleh, M. J. Escorihuela, A. Kruszewski, 'Soil Moisture Retrievals from Bi-Angular L-band Passive Microwave Observations', IEEE Trans. Geosc. Remote Sens. Let., vol 1, no. 4, 277-281, 2004.

Z-Pinch Pulsed Plasma Propulsion Technology Development

NASA Technical Reports Server (NTRS)

Polsgrove, Tara; Adams, Robert B.; Fabisinski, Leo; Fincher, Sharon; Maples, C. Dauphne; Miernik, Janie; Percy, Tom; Statham, Geoff; Turner, Matt; Cassibry, Jason;

Multi-focus image fusion based on area-based standard deviation in dual tree contourlet transform domain

NASA Astrophysics Data System (ADS)

Dong, Min; Dong, Chenghui; Guo, Miao; Wang, Zhe; Mu, Xiaomin

2018-04-01

Multiresolution-based methods, such as wavelet and Contourlet are usually used to image fusion. This work presents a new image fusion frame-work by utilizing area-based standard deviation in dual tree Contourlet trans-form domain. Firstly, the pre-registered source images are decomposed with dual tree Contourlet transform; low-pass and high-pass coefficients are obtained. Then, the low-pass bands are fused with weighted average based on area standard deviation rather than the simple "averaging" rule. While the high-pass bands are merged with the "max-absolute' fusion rule. Finally, the modified low-pass and high-pass coefficients are used to reconstruct the final fused image. The major advantage of the proposed fusion method over conventional fusion is the approximately shift invariance and multidirectional selectivity of dual tree Contourlet transform. The proposed method is compared with wavelet- , Contourletbased methods and other the state-of-the art methods on common used multi focus images. Experiments demonstrate that the proposed fusion framework is feasible and effective, and it performs better in both subjective and objective evaluation.

Fusion peptides from oncogenic chimeric proteins as putative specific biomarkers of cancer.

PubMed

Conlon, Kevin P; Basrur, Venkatesha; Rolland, Delphine; Wolfe, Thomas; Nesvizhskii, Alexey I; MacCoss, Michael J; Lim, Megan S; Elenitoba-Johnson, Kojo S J

2013-10-01

Chromosomal translocations encoding chimeric fusion proteins constitute one of the most common mechanisms underlying oncogenic transformation in human cancer. Fusion peptides resulting from such oncogenic chimeric fusions, though unique to specific cancer subtypes, are unexplored as cancer biomarkers. Here we show, using an approach termed fusion peptide multiple reaction monitoring mass spectrometry, the direct identification of different cancer-specific fusion peptides arising from protein chimeras that are generated from the juxtaposition of heterologous genes fused by recurrent chromosomal translocations. Using fusion peptide multiple reaction monitoring mass spectrometry in a clinically relevant scenario, we demonstrate the specific, sensitive, and unambiguous detection of a specific diagnostic fusion peptide in clinical samples of anaplastic large cell lymphoma, but not in a diverse array of benign lymph nodes or other forms of primary malignant lymphomas and cancer-derived cell lines. Our studies highlight the utility of fusion peptides as cancer biomarkers and carry broad implications for the use of protein biomarkers in cancer detection and monitoring.

Multifocus watermarking approach based on discrete cosine transform.

PubMed

Waheed, Safa Riyadh; Alkawaz, Mohammed Hazim; Rehman, Amjad; Almazyad, Abdulaziz S; Saba, Tanzila

2016-05-01

Image fusion process consolidates data and information from various images of same sight into a solitary image. Each of the source images might speak to a fractional perspective of the scene, and contains both "pertinent" and "immaterial" information. In this study, a new image fusion method is proposed utilizing the Discrete Cosine Transform (DCT) to join the source image into a solitary minimized image containing more exact depiction of the sight than any of the individual source images. In addition, the fused image comes out with most ideal quality image without bending appearance or loss of data. DCT algorithm is considered efficient in image fusion. The proposed scheme is performed in five steps: (1) RGB colour image (input image) is split into three channels R, G, and B for source images. (2) DCT algorithm is applied to each channel (R, G, and B). (3) The variance values are computed for the corresponding 8 × 8 blocks of each channel. (4) Each block of R of source images is compared with each other based on the variance value and then the block with maximum variance value is selected to be the block in the new image. This process is repeated for all channels of source images. (5) Inverse discrete cosine transform is applied on each fused channel to convert coefficient values to pixel values, and then combined all the channels to generate the fused image. The proposed technique can potentially solve the problem of unwanted side effects such as blurring or blocking artifacts by reducing the quality of the subsequent image in image fusion process. The proposed approach is evaluated using three measurement units: the average of Q(abf), standard deviation, and peak Signal Noise Rate. The experimental results of this proposed technique have shown good results as compared with older techniques. © 2016 Wiley Periodicals, Inc.

Joint Data Management for MOVINT Data-to-Decision Making

DTIC Science & Technology

2011-07-01

flux tensor , aligned motion history images, and related approaches have been shown to be versatile approaches [12, 16, 17, 18]. Scaling these...methods include voting , neural networks, fuzzy logic, neuro-dynamic programming, support vector machines, Bayesian and Dempster-Shafer methods. One way...Information Fusion, 2010. [16] F. Bunyak, K. Palaniappan, S. K. Nath, G. Seetharaman, “Flux tensor constrained geodesic active contours with sensor fusion

Perspectives for the high field approach in fusion research and advances within the Ignitor Program

NASA Astrophysics Data System (ADS)

Coppi, B.; Airoldi, A.; Albanese, R.; Ambrosino, G.; Belforte, G.; Boggio-Sella, E.; Cardinali, A.; Cenacchi, G.; Conti, F.; Costa, E.; D'Amico, A.; Detragiache, P.; De Tommasi, G.; DeVellis, A.; Faelli, G.; Ferraris, P.; Frattolillo, A.; Giammanco, F.; Grasso, G.; Lazzaretti, M.; Mantovani, S.; Merriman, L.; Migliori, S.; Napoli, R.; Perona, A.; Pierattini, S.; Pironti, A.; Ramogida, G.; Rubinacci, G.; Sassi, M.; Sestero, A.; Spillantini, S.; Tavani, M.; Tumino, A.; Villone, F.; Zucchi, L.

2015-05-01

The Ignitor Program maintains the objective of approaching D-T ignition conditions by incorporating systematical advances made with relevant high field magnet technology and with experiments on high density well confined plasmas in the present machine design. An additional objective is that of charting the development of the high field line of experiments that goes from the Alcator machine to the ignitor device. The rationale for this class of experiments, aimed at producing poloidal fields with the highest possible values (compatible with proven safety factors of known plasma instabilities) is given. On the basis of the favourable properties of high density plasmas produced systematically by this line of machines, the envisioned future for the line, based on novel high field superconducting magnets, includes the possibility of investigating more advanced fusion burn conditions than those of the D-T plasmas for which Ignitor is designed. Considering that a detailed machine design has been carried out (Coppi et al 2013 Nucl. Fusion 53 104013), the advances made in different areas of the physics and technology that are relevant to the Ignitor project are reported. These are included within the following sections of the present paper: main components issues, assembly and welding procedures; robotics criteria; non-linear feedback control; simulations with three-dimensional structures and disruption studies; ICRH and dedicated diagnostics systems; anomalous transport processes including self-organization for fusion burning regimes and the zero-dimensional model; tridimensional structures of the thermonuclear instability and control provisions; superconducting components of the present machine; envisioned experiments with high field superconducting magnets.

Nighttime images fusion based on Laplacian pyramid

NASA Astrophysics Data System (ADS)

Wu, Cong; Zhan, Jinhao; Jin, Jicheng

2018-02-01

This paper expounds method of the average weighted fusion, image pyramid fusion, the wavelet transform and apply these methods on the fusion of multiple exposures nighttime images. Through calculating information entropy and cross entropy of fusion images, we can evaluate the effect of different fusion. Experiments showed that Laplacian pyramid image fusion algorithm is suitable for processing nighttime images fusion, it can reduce the halo while preserving image details.

Neyman-Pearson biometric score fusion as an extension of the sum rule

NASA Astrophysics Data System (ADS)

Hube, Jens Peter

2007-04-01

We define the biometric performance invariance under strictly monotonic functions on match scores as normalization symmetry. We use this symmetry to clarify the essential difference between the standard score-level fusion approaches of sum rule and Neyman-Pearson. We then express Neyman-Pearson fusion assuming match scores defined using false acceptance rates on a logarithmic scale. We show that by stating Neyman-Pearson in this form, it reduces to sum rule fusion for ROC curves with logarithmic slope. We also introduce a one parameter model of biometric performance and use it to express Neyman-Pearson fusion as a weighted sum rule.

Applicability of common measures in multifocus image fusion comparison

NASA Astrophysics Data System (ADS)

Vajgl, Marek

2017-11-01

Image fusion is an image processing area aimed at fusion of multiple input images to achieve an output image somehow better then each of the input ones. In the case of "multifocus fusion", input images are capturing the same scene differing ina focus distance. The aim is to obtain an image, which is sharp in all its areas. The are several different approaches and methods used to solve this problem. However, it is common question which one is the best. This work describes a research covering the field of common measures with a question, if some of them can be used as a quality measure of the fusion result evaluation.

Single-stage transforaminal decompression, debridement, interbody fusion, and posterior instrumentation for lumbosacral brucellosis.

PubMed

Abulizi, Yakefu; Liang, Wei-Dong; Muheremu, Aikeremujiang; Maimaiti, Maierdan; Sheng, Wei-Bin

2017-07-14

Spinal brucellosis is a less commonly reported infectious spinal pathology. There are few reports regarding the surgical treatment of spinal brucellosis in existing literature. This retrospective study was conducted to determine the effectiveness of single-stage transforaminal decompression, debridement, interbody fusion, and posterior instrumentation for lumbosacral spinal brucellosis. From February 2012 to April 2015, 32 consecutive patients (19 males and 13 females, mean age 53.7 ± 8.7) with lumbosacral brucellosis treated by transforaminal decompression, debridement, interbody fusion, and posterior instrumentation were enrolled. Medical records, imaging studies, laboratory data were collected and summarized. Surgical outcomes were evaluated based on visual analogue scale (VAS), Oswestry Disability Index (ODI) and Japanese Orthopaedic Association (JOA) scale. The changes in C-reactive protein (CRP) levels, erythrocyte sedimentation rate (ESR), clinical symptoms and complications were investigated. Graft fusion was evaluated using Bridwell grading criteria. The mean follow-up period was 24.9 ± 8.2 months. Back pain and radiating leg pain was relieved significantly in all patients after operation. No implant failures were observed in any patients. Wound infection was observed in two patients and sinus formation was observed in one patient. Solid bony fusion was achieved in 30 patients and the fusion rate was 93.8%. The levels of ESR and CRP were returned to normal by the end of three months' follow-up. VAS and ODI scores were significantly improved (P < 0.05). According to JOA score, surgical improvement was excellent in 22 cases (68.8%), good in 9 cases (28.1%), moderate in 1 case (3.1%) at the last follow-up. Single-stage transforaminal decompression, debridement, interbody fusion, and posterior instrumentation is an effective and safe approach for lumbosacral brucellosis.

Intense fusion neutron sources

NASA Astrophysics Data System (ADS)

Kuteev, B. V.; Goncharov, P. R.; Sergeev, V. Yu.; Khripunov, V. I.

2010-04-01

The review describes physical principles underlying efficient production of free neutrons, up-to-date possibilities and prospects of creating fission and fusion neutron sources with intensities of 1015-1021 neutrons/s, and schemes of production and application of neutrons in fusion-fission hybrid systems. The physical processes and parameters of high-temperature plasmas are considered at which optimal conditions for producing the largest number of fusion neutrons in systems with magnetic and inertial plasma confinement are achieved. The proposed plasma methods for neutron production are compared with other methods based on fusion reactions in nonplasma media, fission reactions, spallation, and muon catalysis. At present, intense neutron fluxes are mainly used in nanotechnology, biotechnology, material science, and military and fundamental research. In the near future (10-20 years), it will be possible to apply high-power neutron sources in fusion-fission hybrid systems for producing hydrogen, electric power, and technological heat, as well as for manufacturing synthetic nuclear fuel and closing the nuclear fuel cycle. Neutron sources with intensities approaching 1020 neutrons/s may radically change the structure of power industry and considerably influence the fundamental and applied science and innovation technologies. Along with utilizing the energy produced in fusion reactions, the achievement of such high neutron intensities may stimulate wide application of subcritical fast nuclear reactors controlled by neutron sources. Superpower neutron sources will allow one to solve many problems of neutron diagnostics, monitor nano-and biological objects, and carry out radiation testing and modification of volumetric properties of materials at the industrial level. Such sources will considerably (up to 100 times) improve the accuracy of neutron physics experiments and will provide a better understanding of the structure of matter, including that of the neutron itself.

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

PubMed Central

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-01-01

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs. PMID:25587878

Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks.

PubMed

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-11-26

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

Hydrophobin fusion of an influenza virus hemagglutinin allows high transient expression in Nicotiana benthamiana, easy purification and immune response with neutralizing activity.

PubMed

Jacquet, Nicolas; Navarre, Catherine; Desmecht, Daniel; Boutry, Marc

2014-01-01

The expression of recombinant hemagglutinin in plants is a promising alternative to the current egg-based production system for the influenza vaccines. Protein-stabilizing fusion partners have been developed to overcome the low production yields and the high downstream process costs associated with the plant expression system. In this context, we tested the fusion of hydrophobin I to the hemagglutinin ectodomain of the influenza A (H1N1)pdm09 virus controlled by the hybrid En2PMA4 transcriptional promoter to rapidly produce high levels of recombinant antigen by transient expression in agro-infiltrated Nicotiana benthamiana leaves. The fusion increased the expression level by a factor of ∼ 2.5 compared to the unfused protein allowing a high accumulation level of 8.6% of the total soluble proteins. Hemagglutinin was located in ER-derived protein bodies and was successfully purified by combining an aqueous-two phase partition system and a salting out step. Hydrophobin interactions allowed the formation of high molecular weight hemagglutinin structures, while unfused proteins were produced as monomers. Purified protein was shown to be biologically active and to induce neutralizing antibodies after mice immunization. Hydrophobin fusion to influenza hemagglutinin might therefore be a promising approach for rapid, easy, and low cost production of seasonal or pandemic influenza vaccines in plants.

Gene fusion analysis in the battle against the African endemic sleeping sickness.

PubMed

Trimpalis, Philip; Koumandou, Vassiliki Lila; Pliakou, Evangelia; Anagnou, Nicholas P; Kossida, Sophia

2013-01-01

The protozoan Trypanosoma brucei causes African Trypanosomiasis or sleeping sickness in humans, which can be lethal if untreated. Most available pharmacological treatments for the disease have severe side-effects. The purpose of this analysis was to detect novel protein-protein interactions (PPIs), vital for the parasite, which could lead to the development of drugs against this disease to block the specific interactions. In this work, the Domain Fusion Analysis (Rosetta Stone method) was used to identify novel PPIs, by comparing T. brucei to 19 organisms covering all major lineages of the tree of life. Overall, 49 possible protein-protein interactions were detected, and classified based on (a) statistical significance (BLAST e-value, domain length etc.), (b) their involvement in crucial metabolic pathways, and (c) their evolutionary history, particularly focusing on whether a protein pair is split in T. brucei and fused in the human host. We also evaluated fusion events including hypothetical proteins, and suggest a possible molecular function or involvement in a certain biological process. This work has produced valuable results which could be further studied through structural biology or other experimental approaches so as to validate the protein-protein interactions proposed here. The evolutionary analysis of the proteins involved showed that, gene fusion or gene fission events can happen in all organisms, while some protein domains are more prone to fusion and fission events and present complex evolutionary patterns.

Accelerator based fusion reactor

NASA Astrophysics Data System (ADS)

Liu, Keh-Fei; Chao, Alexander Wu

2017-08-01

A feasibility study of fusion reactors based on accelerators is carried out. We consider a novel scheme where a beam from the accelerator hits the target plasma on the resonance of the fusion reaction and establish characteristic criteria for a workable reactor. We consider the reactions d+t\\to n+α,d+{{}3}{{H}\\text{e}}\\to p+α , and p+{{}11}B\\to 3α in this study. The critical temperature of the plasma is determined from overcoming the stopping power of the beam with the fusion energy gain. The needed plasma lifetime is determined from the width of the resonance, the beam velocity and the plasma density. We estimate the critical beam flux by balancing the energy of fusion production against the plasma thermo-energy and the loss due to stopping power for the case of an inert plasma. The product of critical flux and plasma lifetime is independent of plasma density and has a weak dependence on temperature. Even though the critical temperatures for these reactions are lower than those for the thermonuclear reactors, the critical flux is in the range of {{10}22}-{{10}24}~\\text{c}{{\\text{m}}-2}~{{\\text{s}}-1} for the plasma density {ρt}={{10}15}~\\text{c}{{\\text{m}}-3} in the case of an inert plasma. Several approaches to control the growth of the two-stream instability are discussed. We have also considered several scenarios for practical implementation which will require further studies. Finally, we consider the case where the injected beam at the resonance energy maintains the plasma temperature and prolongs its lifetime to reach a steady state. The equations for power balance and particle number conservation are given for this case.

Regional Distribution of Forest Height and Biomass from Multisensor Data Fusion

NASA Technical Reports Server (NTRS)

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

2010-01-01

Elevation data acquired from radar interferometry at C-band from SRTM are used in data fusion techniques to estimate regional scale forest height and aboveground live biomass (AGLB) over the state of Maine. Two fusion techniques have been developed to perform post-processing and parameter estimations from four data sets: 1 arc sec National Elevation Data (NED), SRTM derived elevation (30 m), Landsat Enhanced Thematic Mapper (ETM) bands (30 m), derived vegetation index (VI) and NLCD2001 land cover map. The first fusion algorithm corrects for missing or erroneous NED data using an iterative interpolation approach and produces distribution of scattering phase centers from SRTM-NED in three dominant forest types of evergreen conifers, deciduous, and mixed stands. The second fusion technique integrates the USDA Forest Service, Forest Inventory and Analysis (FIA) ground-based plot data to develop an algorithm to transform the scattering phase centers into mean forest height and aboveground biomass. Height estimates over evergreen (R2 = 0.86, P < 0.001; RMSE = 1.1 m) and mixed forests (R2 = 0.93, P < 0.001, RMSE = 0.8 m) produced the best results. Estimates over deciduous forests were less accurate because of the winter acquisition of SRTM data and loss of scattering phase center from tree ]surface interaction. We used two methods to estimate AGLB; algorithms based on direct estimation from the scattering phase center produced higher precision (R2 = 0.79, RMSE = 25 Mg/ha) than those estimated from forest height (R2 = 0.25, RMSE = 66 Mg/ha). We discuss sources of uncertainty and implications of the results in the context of mapping regional and continental scale forest biomass distribution.

[An improved medical image fusion algorithm and quality evaluation].

PubMed

Chen, Meiling; Tao, Ling; Qian, Zhiyu

2009-08-01

Medical image fusion is of very important value for application in medical image analysis and diagnosis. In this paper, the conventional method of wavelet fusion is improved,so a new algorithm of medical image fusion is presented and the high frequency and low frequency coefficients are studied respectively. When high frequency coefficients are chosen, the regional edge intensities of each sub-image are calculated to realize adaptive fusion. The choice of low frequency coefficient is based on the edges of images, so that the fused image preserves all useful information and appears more distinctly. We apply the conventional and the improved fusion algorithms based on wavelet transform to fuse two images of human body and also evaluate the fusion results through a quality evaluation method. Experimental results show that this algorithm can effectively retain the details of information on original images and enhance their edge and texture features. This new algorithm is better than the conventional fusion algorithm based on wavelet transform.

Modeling of Steady-state Scenarios for the Fusion Nuclear Science Facility, Advanced Tokamak Approach

NASA Astrophysics Data System (ADS)

Garofalo, A. M.; Chan, V. S.; Prater, R.; Smith, S. P.; St. John, H. E.; Meneghini, O.

2013-10-01

A Fusion National Science Facility (FNSF) would complement ITER in addressing the community identified science and technology gaps to a commercially attractive DEMO, including breeding tritium and completing the fuel cycle, qualifying nuclear materials for high fluence, developing suitable materials for the plasma-boundary interface, and demonstrating power extraction. Steady-state plasma operation is highly desirable to address the requirements for fusion nuclear technology testing [1]. The Advanced Tokamak (AT) is a strong candidate for an FNSF as a consequence of its mature physics base, capability to address the key issues with a more compact device, and the direct relevance to an attractive target power plant. Key features of AT are fully noninductive current drive, strong plasma cross section shaping, internal profiles consistent with high bootstrap fraction, and operation at high beta, typically above the free boundary limit, βN > 3 . Work supported by GA IR&D funding, DE-FC02-04ER54698, and DE-FG02-95ER43309.

Fusion energy for space missions in the 21st Century

NASA Technical Reports Server (NTRS)

Schulze, Norman R.

1991-01-01

Future space missions were hypothesized and analyzed and the energy source for their accomplishment investigated. The mission included manned Mars, scientific outposts to and robotic sample return missions from the outer planets and asteroids, as well as fly-by and rendezvous mission with the Oort Cloud and the nearest star, Alpha Centauri. Space system parametric requirements and operational features were established. The energy means for accomplishing the High Energy Space Mission were investigated. Potential energy options which could provide the propulsion and electric power system and operational requirements were reviewed and evaluated. Fusion energy was considered to be the preferred option and was analyzed in depth. Candidate fusion fuels were evaluated based upon the energy output and neutron flux. Reactors exhibiting a highly efficient use of magnetic fields for space use while at the same time offering efficient coupling to an exhaust propellant or to a direct energy convertor for efficient electrical production were examined. Near term approaches were identified.

Fault diagnosis model for power transformers based on information fusion

NASA Astrophysics Data System (ADS)

Dong, Ming; Yan, Zhang; Yang, Li; Judd, Martin D.

2005-07-01

Methods used to assess the insulation status of power transformers before they deteriorate to a critical state include dissolved gas analysis (DGA), partial discharge (PD) detection and transfer function techniques, etc. All of these approaches require experience in order to correctly interpret the observations. Artificial intelligence (AI) is increasingly used to improve interpretation of the individual datasets. However, a satisfactory diagnosis may not be obtained if only one technique is used. For example, the exact location of PD cannot be predicted if only DGA is performed. However, using diverse methods may result in different diagnosis solutions, a problem that is addressed in this paper through the introduction of a fuzzy information infusion model. An inference scheme is proposed that yields consistent conclusions and manages the inherent uncertainty in the various methods. With the aid of information fusion, a framework is established that allows different diagnostic tools to be combined in a systematic way. The application of information fusion technique for insulation diagnostics of transformers is proved promising by means of examples.

Image-based Analysis to Study Plant Infection with Human Pathogens

PubMed Central

Schikora, Marek; Schikora, Adam

2014-01-01

Our growing awareness that contaminated plants, fresh fruits and vegetables are responsible for a significant proportion of food poisoning with pathogenic microorganisms indorses the demand to understand the interactions between plants and human pathogens. Today we understand that those pathogens do not merely survive on or within plants, they actively infect plant organisms by suppressing their immune system. Studies on the infection process and disease development used mainly physiological, genetic, and molecular approaches, and image-based analysis provides yet another method for this toolbox. Employed as an observational tool, it bears the potential for objective and high throughput approaches, and together with other methods it will be very likely a part of data fusion approaches in the near future. PMID:25505501

Deep learning and texture-based semantic label fusion for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

2018-02-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

PubMed

Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

2018-01-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

PubMed

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

2017-07-28

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

A Review of Depth and Normal Fusion Algorithms

PubMed Central

Štolc, Svorad; Pock, Thomas

2018-01-01

Geometric surface information such as depth maps and surface normals can be acquired by various methods such as stereo light fields, shape from shading and photometric stereo techniques. We compare several algorithms which deal with the combination of depth with surface normal information in order to reconstruct a refined depth map. The reasons for performance differences are examined from the perspective of alternative formulations of surface normals for depth reconstruction. We review and analyze methods in a systematic way. Based on our findings, we introduce a new generalized fusion method, which is formulated as a least squares problem and outperforms previous methods in the depth error domain by introducing a novel normal weighting that performs closer to the geodesic distance measure. Furthermore, a novel method is introduced based on Total Generalized Variation (TGV) which further outperforms previous approaches in terms of the geodesic normal distance error and maintains comparable quality in the depth error domain. PMID:29389903

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

PubMed Central

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang

2017-01-01

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. PMID:28788099

Objective quality assessment for multiexposure multifocus image fusion.

PubMed

Hassen, Rania; Wang, Zhou; Salama, Magdy M A

2015-09-01

There has been a growing interest in image fusion technologies, but how to objectively evaluate the quality of fused images has not been fully understood. Here, we propose a method for objective quality assessment of multiexposure multifocus image fusion based on the evaluation of three key factors of fused image quality: 1) contrast preservation; 2) sharpness; and 3) structure preservation. Subjective experiments are conducted to create an image fusion database, based on which, performance evaluation shows that the proposed fusion quality index correlates well with subjective scores, and gives a significant improvement over the existing fusion quality measures.

A new phase-correlation-based iris matching for degraded images.

PubMed

Krichen, Emine; Garcia-Salicetti, Sonia; Dorizzi, Bernadette

2009-08-01

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images. Results on several degraded databases, namely, the CASIA-BIOSECURE and Iris Challenge Evaluation 2005 databases, show the improvement of our method compared to two available reference systems, Masek and Open Source for Iris (OSRIS), in verification mode.

The Fight for Fusion: A Modern Nuclear War.

ERIC Educational Resources Information Center

Rogers, Adam; Sereda, David

1992-01-01

Describes the work of Bogdan Maglich with helium-based fusion and barriers to its development resulting from lack of government support, competition for funding, and political pet projects. Compares tritium-based to helium-based fusion and the potential for nonradioactive nuclear power to supply the world's energy requirements with no negative…

Knee fusion--a new technique using an old Belgian surgical approach and a new intramedullary nail.

PubMed

Alt, V; Seligson, D

2001-02-01

Knee arthrodesis is a useful procedure in difficult cases such as failed total knee arthroplasty, severe articular trauma, bone tumors, and infected knee joints. The most common techniques for knee fusion include external fixation and intramedullary nailing. Küntscher's nail is driven antegrade from the intertrochanteric region into the knee. We describe a new technique for knee arthrodesis using a new intramedullary nail and an old Belgian surgical approach to the knee joint published by Lambotte in 1913. This approach provides excellent exposure for the implantation of the nail by osteotomizing the patella vertically. The nail is implanted using HeyGroves method, whereby the nail is inserted retrograde into the femur and pulled distally anterograde into the tibia. We now use this technique as our standard procedure for knee fusion.

Efficient Knock-in of a Point Mutation in Porcine Fibroblasts Using the CRISPR/Cas9-GMNN Fusion Gene.

PubMed

Gerlach, Max; Kraft, Theresia; Brenner, Bernhard; Petersen, Björn; Niemann, Heiner; Montag, Judith

2018-06-13

During CRISPR/Cas9 mediated genome editing, site-specific double strand breaks are introduced and repaired either unspecific by non-homologous end joining (NHEJ) or sequence dependent by homology directed repair (HDR). Whereas NHEJ-based generation of gene knock-out is widely performed, the HDR-based knock-in of specific mutations remains a bottleneck. Especially in primary cell lines that are essential for the generation of cell culture and animal models of inherited human diseases, knock-in efficacy is insufficient and needs significant improvement. Here, we tested two different approaches to increase the knock-in frequency of a specific point mutation into the MYH7 -gene in porcine fetal fibroblasts. We added a small molecule inhibitor of NHEJ, SCR7 (5,6-bis((E)-benzylideneamino)-2-mercaptopyrimidin-4-ol), during genome editing and screened cell cultures for the point mutation. However, this approach did not yield increased knock-in rates. In an alternative approach, we fused humanized Cas9 (hCas9) to the N-terminal peptide of the Geminin gene ( GMNN ). The fusion protein is degraded in NHEJ-dominated cell cycle phases, which should increase HDR-rates. Using hCas9- GMNN and point mutation-specific real time PCR screening, we found a two-fold increase in genome edited cell cultures. This increase of HDR by hCas9- GMNN provides a promising way to enrich specific knock-in in porcine fibroblast cultures for somatic cloning approaches.

PCLIPS: Parallel CLIPS

NASA Technical Reports Server (NTRS)

Hall, Lawrence O.; Bennett, Bonnie H.; Tello, Ivan

1994-01-01

A parallel version of CLIPS 5.1 has been developed to run on Intel Hypercubes. The user interface is the same as that for CLIPS with some added commands to allow for parallel calls. A complete version of CLIPS runs on each node of the hypercube. The system has been instrumented to display the time spent in the match, recognize, and act cycles on each node. Only rule-level parallelism is supported. Parallel commands enable the assertion and retraction of facts to/from remote nodes working memory. Parallel CLIPS was used to implement a knowledge-based command, control, communications, and intelligence (C(sup 3)I) system to demonstrate the fusion of high-level, disparate sources. We discuss the nature of the information fusion problem, our approach, and implementation. Parallel CLIPS has also be used to run several benchmark parallel knowledge bases such as one to set up a cafeteria. Results show from running Parallel CLIPS with parallel knowledge base partitions indicate that significant speed increases, including superlinear in some cases, are possible.

Fault tolerant multi-sensor fusion based on the information gain

NASA Astrophysics Data System (ADS)

Hage, Joelle Al; El Najjar, Maan E.; Pomorski, Denis

2017-01-01

In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,… In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.

Context-Aware Fusion of RGB and Thermal Imagery for Traffic Monitoring

PubMed Central

Alldieck, Thiemo; Bahnsen, Chris H.; Moeslund, Thomas B.

2016-01-01

In order to enable a robust 24-h monitoring of traffic under changing environmental conditions, it is beneficial to observe the traffic scene using several sensors, preferably from different modalities. To fully benefit from multi-modal sensor output, however, one must fuse the data. This paper introduces a new approach for fusing color RGB and thermal video streams by using not only the information from the videos themselves, but also the available contextual information of a scene. The contextual information is used to judge the quality of a particular modality and guides the fusion of two parallel segmentation pipelines of the RGB and thermal video streams. The potential of the proposed context-aware fusion is demonstrated by extensive tests of quantitative and qualitative characteristics on existing and novel video datasets and benchmarked against competing approaches to multi-modal fusion. PMID:27869730

Direct Drive Fusion Energy Shock Ignition Designs for Sub-MJ Lasers

DTIC Science & Technology

2008-09-01

FUSION ENERGY SHOCK IGNITION DESIGNS FOR SUB-MJ LASERS Andrew J. Schmitt, J. W. Bates, S. P. Obenschain, and S. T. Zalesak Plasma Physics Division, Naval Research Laboratory, Washington DC 20375 andrew.schmitt@nrl.navy.mil D. E. Fyfe LCP&FD, Naval Research Laboratory, Washington DC 20375 R. Betti Fusion Science Center and Laboratory for Laser Energetics, University of Rochester, Rochester NY New approaches in target design have increased the pos- sibility that useful fusion power can be generated with sub-MJ lasers. We have performed many 1D and 2D

A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Wu, M. F.; Sun, Z. C.; Yang, B.; Yu, S. S.

2016-11-01

In order to reduce the “salt and pepper” in pixel-based urban land cover classification and expand the application of fusion of multi-source data in the field of urban remote sensing, WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data were used to improve the classification of urban land cover. An approach of object- oriented hierarchical classification was proposed in our study. The processing of proposed method consisted of two hierarchies. (1) In the first hierarchy, LiDAR Normalized Digital Surface Model (nDSM) image was segmented to objects. The NDVI, Costal Blue and nDSM thresholds were set for extracting building objects. (2) In the second hierarchy, after removing building objects, WorldView-2 fused imagery was obtained by Haze-ratio-based (HR) fusion, and was segmented. A SVM classifier was applied to generate road/parking lot, vegetation and bare soil objects. (3) Trees and grasslands were split based on an nDSM threshold (2.4 meter). The results showed that compared with pixel-based and non-hierarchical object-oriented approach, proposed method provided a better performance of urban land cover classification, the overall accuracy (OA) and overall kappa (OK) improved up to 92.75% and 0.90. Furthermore, proposed method reduced “salt and pepper” in pixel-based classification, improved the extraction accuracy of buildings based on LiDAR nDSM image segmentation, and reduced the confusion between trees and grasslands through setting nDSM threshold.

Applying a new unequally weighted feature fusion method to improve CAD performance of classifying breast lesions

NASA Astrophysics Data System (ADS)

Zargari Khuzani, Abolfazl; Danala, Gopichandh; Heidari, Morteza; Du, Yue; Mashhadi, Najmeh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Higher recall rates are a major challenge in mammography screening. Thus, developing computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role to improve efficacy of mammography screening. Objective of this study is to develop and test a unique image feature fusion framework to improve performance in classifying suspicious mass-like breast lesions depicting on mammograms. The image dataset consists of 302 suspicious masses detected on both craniocaudal and mediolateral-oblique view images. Amongst them, 151 were malignant and 151 were benign. The study consists of following 3 image processing and feature analysis steps. First, an adaptive region growing segmentation algorithm was used to automatically segment mass regions. Second, a set of 70 image features related to spatial and frequency characteristics of mass regions were initially computed. Third, a generalized linear regression model (GLM) based machine learning classifier combined with a bat optimization algorithm was used to optimally fuse the selected image features based on predefined assessment performance index. An area under ROC curve (AUC) with was used as a performance assessment index. Applying CAD scheme to the testing dataset, AUC was 0.75+/-0.04, which was significantly higher than using a single best feature (AUC=0.69+/-0.05) or the classifier with equally weighted features (AUC=0.73+/-0.05). This study demonstrated that comparing to the conventional equal-weighted approach, using an unequal-weighted feature fusion approach had potential to significantly improve accuracy in classifying between malignant and benign breast masses.

Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. PMID:25608211

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

PubMed Central

Liu, Bailing; Zhang, Fumin; Qu, Xinghua

2015-01-01

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

Lipid droplets fusion in adipocyte differentiated 3T3-L1 cells: A Monte Carlo simulation

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

Boschi, Federico, E-mail: federico.boschi@univr.it; Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona; Rizzatti, Vanni

Several human worldwide diseases like obesity, type 2 diabetes, hepatic steatosis, atherosclerosis and other metabolic pathologies are related to the excessive accumulation of lipids in cells. Lipids accumulate in spherical cellular inclusions called lipid droplets (LDs) whose sizes range from fraction to one hundred of micrometers in adipocytes. It has been suggested that LDs can grow in size due to a fusion process by which a larger LD is obtained with spherical shape and volume equal to the sum of the progenitors’ ones. In this study, the size distribution of two populations of LDs was analyzed in immature and maturemore » (5-days differentiated) 3T3-L1 adipocytes (first and second populations, respectively) after Oil Red O staining. A Monte Carlo simulation of interaction between LDs has been developed in order to quantify the size distribution and the number of fusion events needed to obtain the distribution of the second population size starting from the first one. Four models are presented here based on different kinds of interaction: a surface weighted interaction (R2 Model), a volume weighted interaction (R3 Model), a random interaction (Random model) and an interaction related to the place where the LDs are born (Nearest Model). The last two models mimic quite well the behavior found in the experimental data. This work represents a first step in developing numerical simulations of the LDs growth process. Due to the complex phenomena involving LDs (absorption, growth through additional neutral lipid deposition in existing droplets, de novo formation and catabolism) the study focuses on the fusion process. The results suggest that, to obtain the observed size distribution, a number of fusion events comparable with the number of LDs themselves is needed. Moreover the MC approach results a powerful tool for investigating the LDs growth process. Highlights: • We evaluated the role of the fusion process in the synthesis of the lipid droplets. • We compared the size distribution of the lipid droplets in immature and mature cells. • We used the Monte Carlo simulation approach, simulating 10 thousand of fusion events. • Four different interaction models between the lipid droplets were tested. • The best model which mimics the experimental measures was selected.« less

Behavior Knowledge Space-Based Fusion for Copy-Move Forgery Detection.

PubMed

Ferreira, Anselmo; Felipussi, Siovani C; Alfaro, Carlos; Fonseca, Pablo; Vargas-Munoz, John E; Dos Santos, Jefersson A; Rocha, Anderson

2016-07-20

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterwards, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex datasets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature show the effectiveness of the proposed method and its suitability for real-world applications.

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

PubMed Central

Peralta, Emmanuel; Vargas, Héctor; Hermosilla, Gabriel

2018-01-01

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks. PMID:29495338

Reconnaissance blind multi-chess: an experimentation platform for ISR sensor fusion and resource management

NASA Astrophysics Data System (ADS)

Newman, Andrew J.; Richardson, Casey L.; Kain, Sean M.; Stankiewicz, Paul G.; Guseman, Paul R.; Schreurs, Blake A.; Dunne, Jeffrey A.

2016-05-01

This paper introduces the game of reconnaissance blind multi-chess (RBMC) as a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of heterogeneous Intelligence, Surveillance and Reconnaissance (ISR) sensors to maintain situational awareness informing tactical and strategic decision making. The intent is for RBMC to serve as a common reference or challenge problem in fusion and resource management of heterogeneous sensor ensembles across diverse mission areas. We have defined a basic rule set and a framework for creating more complex versions, developed a web-based software realization to serve as an experimentation platform, and developed some initial machine intelligence approaches to playing it.

Definition of Ignition in Inertial Confinement Fusion

NASA Astrophysics Data System (ADS)

Christopherson, A. R.; Betti, R.

2017-10-01

Defining ignition in inertial confinement fusion (ICF) is an unresolved problem. In ICF, a distinction must be made between the ignition of the hot spot and the propagation of the burn wave in the surrounding dense fuel. Burn propagation requires that the hot spot is robustly ignited and the dense shell exhibits enough areal density. Since most of the energy gain comes from burning the dense shell, in a scale of increasing yields, hot-spot ignition comes before high gains. Identifying this transition from hot-spot ignition to burn-wave propagation is key to defining ignition in general terms applicable to all fusion approaches that use solid DT fuel. Ad hoc definitions such as gain = 1 or doubling the temperature are not generally valid. In this work, we show that it is possible to identify the onset of ignition through a unique value of the yield amplification defined as the ratio of the fusion yield including alpha-particle deposition to the fusion yield without alphas. Since the yield amplification is a function of the fractional alpha energy fα =EαEα 2Ehs 2Ehs (a measurable quantity), it appears possible not only to define ignition but also to measure the onset of ignition by the experimental inference of the fractional alpha energy and yield amplification. This material is based upon work supported by the Department of Energy Office of Fusion Energy Services under Award Number DE-FC02-04ER54789 and National Nuclear Security Administration under Award Number DE-NA0001944.

Inertial-confinement fusion with lasers

DOE PAGES

Betti, R.; Hurricane, O. A.

2016-05-03

The quest for controlled fusion energy has been ongoing for over a half century. The demonstration of ignition and energy gain from thermonuclear fuels in the laboratory has been a major goal of fusion research for decades. Thermonuclear ignition is widely considered a milestone in the development of fusion energy, as well as a major scientific achievement with important applications to national security and basic sciences. The U.S. is arguably the world leader in the inertial con fment approach to fusion and has invested in large facilities to pursue it with the objective of establishing the science related to themore » safety and reliability of the stockpile of nuclear weapons. Even though significant progress has been made in recent years, major challenges still remain in the quest for thermonuclear ignition via laser fusion.« less

Inertial-confinement fusion with lasers

NASA Astrophysics Data System (ADS)

Betti, R.; Hurricane, O. A.

2016-05-01

The quest for controlled fusion energy has been ongoing for over a half century. The demonstration of ignition and energy gain from thermonuclear fuels in the laboratory has been a major goal of fusion research for decades. Thermonuclear ignition is widely considered a milestone in the development of fusion energy, as well as a major scientific achievement with important applications in national security and basic sciences. The US is arguably the world leader in the inertial confinement approach to fusion and has invested in large facilities to pursue it, with the objective of establishing the science related to the safety and reliability of the stockpile of nuclear weapons. Although significant progress has been made in recent years, major challenges still remain in the quest for thermonuclear ignition via laser fusion. Here, we review the current state of the art in inertial confinement fusion research and describe the underlying physical principles.

Efficacy of different bone volume expanders for augmenting lumbar fusions.

PubMed

Epstein, Nancy E

2008-01-01

A wide variety of bone volume expanders are being used in performing posterolateral lumbar noninstrumented and instrumented lumbar fusions. This article presents a review of their efficacy based on fusion rates, complications, and outcomes. Lumbar noninstrumented and instrumented fusions frequently use laminar autografts and different bone graft expanders. This review presents the utility of multiple forms/ratios of DBMs containing allografts. It also discusses the efficacy of artificial bone graft substitutes, including HA and B-TCP. Dynamic x-ray and/or CT examinations were used to document fusion in most series. Outcomes were variously assessed using Odom's criteria or different outcome questionnaires (Oswestry Questionnaire, SF-36, Dallas Pain Questionnaire, and/or Low Back Pain Rating Scale). Performing noninstrumented and instrumented lumbar posterolateral fusions resulted in comparable fusion rates in many series. Similar outcomes were also documented based on Odom's criteria or the multiple patient-based questionnaires. However, in some studies, the addition of spinal instrumentation increased the reoperation rate, operative time, blood loss, and cost. Various forms of DBMs, applied in different ratios to autografts, effectively supplemented spinal fusions in animal models and patient series. beta-Tricalcium phosphate, which is used to augment autograft fusions addressing idiopathic scoliosis or lumbar disease, also proved to be effective. Different types of bone volume expanders, including various forms of allograft-based DBMs, and artificial bone graft substitutes (HA and B-TCP) effectively promote posterolateral lumbar noninstrumented and instrumented fusions when added to autografts.

Epidemiologic and Economic Burden Attributable to First Spinal Fusion Surgery: Analysis From an Italian Administrative Database.

PubMed

Cortesi, Paolo A; Assietti, Roberto; Cuzzocrea, Fabrizio; Prestamburgo, Domenico; Pluderi, Mauro; Cozzolino, Paolo; Tito, Patrizia; Vanelli, Roberto; Cecconi, Davide; Borsa, Stefano; Cesana, Giancarlo; Mantovani, Lorenzo G

2017-09-15

Retrospective large population based-study. Assessment of the epidemiologic trends and economic burden of first spinal fusions. No adequate data are available regarding the epidemiology of spinal fusion surgery and its economic impact in Europe. The study population was identified through a data warehouse (DENALI), which matches clinical and economic data of different Healthcare Administrative databases of the Italian Lombardy Region. The study population consisted of all subjects, resident in Lombardy, who, during the period January 2001 to December 2010, underwent spinal fusion surgery (ICD-9-CM codes: 81.04, 81.05, 81.06, 81.07, and 81.08). The first procedure was used as the index event. We estimated the incidence of first spinal fusion surgery, the population and surgery characteristics and the healthcare costs from the National Health Service's perspective. The analysis was performed for the entire population and divided into the main groups of diagnosis. The analysis identified 17,772 [mean age (SD): 54.6 (14.5) years, 55.3% females] spinal fusion surgeries. Almost 67% of the patients suffered from a lumbar degenerative disease. The incidence rate of interventions increased from 11.5 to 18.5 per 100,000 person-year between 2001 and 2006, and was above 20.0 per 100,000 person-year in the last 4 years. The patients' mean age increased during the observational time period from 48.1 to 55.9 years; whereas the median hospital length of stay reported for the index event decreased. The average cost of the spinal fusion surgery increased during the observational period, from &OV0556; 4726 up to &OV0556; 9388. The study showed an increasing incidence of spinal fusion surgery and costs from 2001 to 2010. These results can be used to better understand the epidemiological and economic burden of these interventions, and help to optimize the resources available considering the different clinical approaches accessible today. 4.

Novel fusion proteins for the antigen-specific staining and elimination of B cell receptor-positive cell populations demonstrated by a tetanus toxoid fragment C (TTC) model antigen.

PubMed

Klose, Diana; Saunders, Ute; Barth, Stefan; Fischer, Rainer; Jacobi, Annett Marita; Nachreiner, Thomas

2016-02-17

In an earlier study we developed a unique strategy allowing us to specifically eliminate antigen-specific murine B cells via their distinct B cell receptors using a new class of fusion proteins. In the present work we elaborated our idea to demonstrate the feasibility of specifically addressing and eliminating human memory B cells. The present study reveals efficient adaptation of the general approach to selectively target and eradicate human memory B cells. In order to demonstrate the feasibility we engineered a fusion protein following the principle of recombinant immunotoxins by combining a model antigen (tetanus toxoid fragment C, TTC) for B cell receptor targeting and a truncated version of Pseudomonas aeruginosa exotoxin A (ETA') to induce apoptosis after cellular uptake. The TTC-ETA' fusion protein not only selectively bound to a TTC-reactive murine B cell hybridoma cell line in vitro but also to freshly isolated human memory B cells from immunized donors ex vivo. Specific toxicity was confirmed on an antigen-specific population of human CD27(+) memory B cells. This protein engineering strategy can be used as a generalized platform approach for the construction of therapeutic fusion proteins with disease-relevant antigens as B cell receptor-binding domains, offering a promising approach for the specific depletion of autoreactive B-lymphocytes in B cell-driven autoimmune diseases.

Fast heating of ultrahigh-density plasma as a step towards laser fusion ignition.

PubMed

Kodama, R; Norreys, P A; Mima, K; Dangor, A E; Evans, R G; Fujita, H; Kitagawa, Y; Krushelnick, K; Miyakoshi, T; Miyanaga, N; Norimatsu, T; Rose, S J; Shozaki, T; Shigemori, K; Sunahara, A; Tampo, M; Tanaka, K A; Toyama, Y; Yamanaka, T; Zepf, M

2001-08-23

Modern high-power lasers can generate extreme states of matter that are relevant to astrophysics, equation-of-state studies and fusion energy research. Laser-driven implosions of spherical polymer shells have, for example, achieved an increase in density of 1,000 times relative to the solid state. These densities are large enough to enable controlled fusion, but to achieve energy gain a small volume of compressed fuel (known as the 'spark') must be heated to temperatures of about 108 K (corresponding to thermal energies in excess of 10 keV). In the conventional approach to controlled fusion, the spark is both produced and heated by accurately timed shock waves, but this process requires both precise implosion symmetry and a very large drive energy. In principle, these requirements can be significantly relaxed by performing the compression and fast heating separately; however, this 'fast ignitor' approach also suffers drawbacks, such as propagation losses and deflection of the ultra-intense laser pulse by the plasma surrounding the compressed fuel. Here we employ a new compression geometry that eliminates these problems; we combine production of compressed matter in a laser-driven implosion with picosecond-fast heating by a laser pulse timed to coincide with the peak compression. Our approach therefore permits efficient compression and heating to be carried out simultaneously, providing a route to efficient fusion energy production.

The new approach for infrared target tracking based on the particle filter algorithm

NASA Astrophysics Data System (ADS)

Sun, Hang; Han, Hong-xia

2011-08-01

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring, precision, and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection, the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure, but in order to capture the change of the state space, it need a certain amount of particles to ensure samples is enough, and this number will increase in accompany with dimension and increase exponentially, this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining", we expand the classic Mean Shift tracking framework .Based on the previous perspective, we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis, Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism, used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation, and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value .Last because of the gray and fusion target motion information, this approach also inhibit interference from the background, ultimately improve the stability and the real-time of the target track.