Blob-level active-passive data fusion for Benthic classification

NASA Astrophysics Data System (ADS)

Park, Joong Yong; Kalluri, Hemanth; Mathur, Abhinav; Ramnath, Vinod; Kim, Minsu; Aitken, Jennifer; Tuell, Grady

2012-06-01

We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.

Fusion of Deep Learning and Compressed Domain features for Content Based Image Retrieval.

PubMed

Liu, Peizhong; Guo, Jing-Ming; Wu, Chi-Yi; Cai, Danlin

2017-08-29

This paper presents an effective image retrieval method by combining high-level features from Convolutional Neural Network (CNN) model and low-level features from Dot-Diffused Block Truncation Coding (DDBTC). The low-level features, e.g., texture and color, are constructed by VQ-indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features (DL-TLCF) is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate (APR) and average recall rate (ARR), are employed to examine various datasets. As documented in the experimental results, the proposed schemes can achieve superior performance compared to the state-of-the-art methods with either low- or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.

Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Xu, Ru

2018-02-01

Integrating synthetic aperture radar (SAR) and optical data to improve urban land cover classification has been identified as a promising approach. However, which integration level is the most suitable remains unclear but important to many researchers and engineers. This study aimed to compare different integration levels for providing a scientific reference for a wide range of studies using optical and SAR data. SAR data from TerraSAR-X and ENVISAT ASAR in both WSM and IMP modes were used to be combined with optical data at pixel level, feature level and decision levels using four typical machine learning methods. The experimental results indicated that: 1) feature level that used both the original images and extracted features achieved a significant improvement of up to 10% compared to that using optical data alone; 2) different levels of fusion required different suitable methods depending on the data distribution and data resolution. For instance, support vector machine was the most stable at both the feature and decision levels, while random forest was suitable at the pixel level but not suitable at the decision level. 3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels. This indicated the benefits of using texture features from SAR data when being combined with optical data for land cover classification. Additionally, the research also shown that combining optical and SAR data does not guarantee improvement compared with using single data source for urban land cover classification, depending on the selection of appropriate fusion levels and fusion methods.

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Du, Pei-jun; Chen, Yun-hao; Jones, Simon; Ferwerda, Jelle G.; Chen, Zhi-jun; Zhang, Hua-peng; Tan, Kun; Yin, Zuo-xia

2006-10-01

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Salient region detection by fusing bottom-up and top-down features extracted from a single image.

PubMed

Tian, Huawei; Fang, Yuming; Zhao, Yao; Lin, Weisi; Ni, Rongrong; Zhu, Zhenfeng

2014-10-01

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.

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.

Defect detection of helical gears based on time-frequency analysis and using multi-layer fusion network

NASA Astrophysics Data System (ADS)

Ebrahimi Orimi, H.; Esmaeili, M.; Refahi Oskouei, A.; Mirhadizadehd, S. A.; Tse, P. W.

2017-10-01

Condition monitoring of rotary devices such as helical gears is an issue of great significance in industrial projects. This paper introduces a feature extraction method for gear fault diagnosis using wavelet packet due to its higher frequency resolution. During this investigation, the mother wavelet Daubechies 10 (Db-10) was applied to calculate the coefficient entropy of each frequency band of 5th level (32 frequency bands) as features. In this study, the peak value of the signal entropies was selected as applicable features in order to improve frequency band differentiation and reduce feature vectors' dimension. Feature extraction is followed by the fusion network where four different structured multi-layer perceptron networks are trained to classify the recorded signals (healthy/faulty). The robustness of fusion network outputs is greater compared to perceptron networks. The results provided by the fusion network indicate a classification of 98.88 and 97.95% for healthy and faulty classes, respectively.

A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence.

PubMed

Goshvarpour, Ateke; Goshvarpour, Atefeh

2018-04-30

Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.

Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.

PubMed

Sarkar, Anjan; Banerjee, Anjan; Banerjee, Nilanjan; Brahma, Siddhartha; Kartikeyan, B; Chakraborty, Manab; Majumder, K L

2005-05-01

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.

Infrared and visible image fusion scheme based on NSCT and low-level visual features

NASA Astrophysics Data System (ADS)

Li, Huafeng; Qiu, Hongmei; Yu, Zhengtao; Zhang, Yafei

2016-05-01

Multi-scale transform (MST) is an efficient tool for image fusion. Recently, many fusion methods have been developed based on different MSTs, and they have shown potential application in many fields. In this paper, we propose an effective infrared and visible image fusion scheme in nonsubsampled contourlet transform (NSCT) domain, in which the NSCT is firstly employed to decompose each of the source images into a series of high frequency subbands and one low frequency subband. To improve the fusion performance we designed two new activity measures for fusion of the lowpass subbands and the highpass subbands. These measures are developed based on the fact that the human visual system (HVS) percept the image quality mainly according to its some low-level features. Then, the selection principles of different subbands are presented based on the corresponding activity measures. Finally, the merged subbands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSCT on these merged subbands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.

Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI.

PubMed

Ahmed, Shaheen; Iftekharuddin, Khan M; Vossough, Arastoo

2011-03-01

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain

NASA Astrophysics Data System (ADS)

Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Lee, Shin-Jye; He, Kangjian

2018-01-01

In order to promote the performance of infrared and visual image fusion and provide better visual effects, this paper proposes a hybrid fusion method for infrared and visual image by the combination of discrete stationary wavelet transform (DSWT), discrete cosine transform (DCT) and local spatial frequency (LSF). The proposed method has three key processing steps. Firstly, DSWT is employed to decompose the important features of the source image into a series of sub-images with different levels and spatial frequencies. Secondly, DCT is used to separate the significant details of the sub-images according to the energy of different frequencies. Thirdly, LSF is applied to enhance the regional features of DCT coefficients, and it can be helpful and useful for image feature extraction. Some frequently-used image fusion methods and evaluation metrics are employed to evaluate the validity of the proposed method. The experiments indicate that the proposed method can achieve good fusion effect, and it is more efficient than other conventional image fusion methods.

Cognitive Load Measurement in a Virtual Reality-based Driving System for Autism Intervention.

PubMed

Zhang, Lian; Wade, Joshua; Bian, Dayi; Fan, Jing; Swanson, Amy; Weitlauf, Amy; Warren, Zachary; Sarkar, Nilanjan

2017-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes-feature level fusion, decision level fusion and hybrid level fusion-were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

Serum carcinoembryonic antigen levels before initial treatment are associated with EGFR mutations and EML4- ALK fusion gene in lung adenocarcinoma patients.

PubMed

Wang, Wen-Tao; Li, Yin; Ma, Jie; Chen, Xiao-Bing; Qin, Jian-Jun

2014-01-01

Epidermal growth factor receptor (EGFR) mutations and echinoderm microtubule associated protein like 4-anaplastic lymphoma kinase (EML4-ALK) define specific molecular subsets of lung adenocarcinomas with distinct clinical features. Our purpose was to analyze clinical features and prognostic value of EGFR gene mutations and the EML4-ALK fusion gene in lung adenocarcinoma. EGFR gene mutations and the EML4-ALK fusion gene were detected in 92 lung adenocarcinoma patients in China. Tumor marker levels before first treatment were measured by electrochemiluminescence immunoassay. EGFR mutations were found in 40.2% (37/92) of lung adenocarcinoma patients, being identified at high frequencies in never-smokers (48.3% vs. 26.5% in smokers; P=0.040) and in patients with abnormal serum carcinoembryonic antigen (CEA) levels before the initial treatment (58.3% vs. 28.6%, P=0.004). Multivariate analysis revealed that a higher serum CEA level before the initial treatment was independently associated with EGFR gene mutations (95%CI: 1.476~11.343, P=0.007). We also identified 8 patients who harbored the EML4-ALK fusion gene (8.7%, 8/92). In concordance with previous reports, younger age was a clinical feature for these (P=0.008). Seven of the positive cases were never smokers, and no coexistence with EGFR mutation was discovered. In addition, the frequency of the EML4-ALK fusion gene among patients with a serum CEA concentration below 5 ng/ml seemed to be higher than patients with a concentration over 5 ng/ml (P=0.021). No significant difference was observed for time to progression and overall survival between EML4-ALK-positive group and EML4-ALK-negative group or between patients with and without an EGFR mutation. The serum CEA level before the initial treatment may be helpful in screening population for EGFR mutations or EML4-ALK fusion gene presence in lung adenocarcinoma patients.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

PubMed

Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

2014-07-03

Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

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.

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 Hierarchical Convolutional Neural Network for vesicle fusion event classification.

PubMed

Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke

2017-09-01

Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu

2017-11-01

This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

Multisensor data fusion for IED threat detection

NASA Astrophysics Data System (ADS)

Mees, Wim; Heremans, Roel

2012-10-01

In this paper we present the multi-sensor registration and fusion algorithms that were developed for a force protection research project in order to detect threats against military patrol vehicles. The fusion is performed at object level, using a hierarchical evidence aggregation approach. It first uses expert domain knowledge about the features used to characterize the detected threats, that is implemented in the form of a fuzzy expert system. The next level consists in fusing intra-sensor and inter-sensor information. Here an ordered weighted averaging operator is used. The object level fusion between candidate threats that are detected asynchronously on a moving vehicle by sensors with different imaging geometries, requires an accurate sensor to world coordinate transformation. This image registration will also be discussed in this paper.

Near infrared and visible face recognition based on decision fusion of LBP and DCT features

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-03-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.

Multispectral image fusion for target detection

NASA Astrophysics Data System (ADS)

Leviner, Marom; Maltz, Masha

2009-09-01

Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in an experiment using MSSF against two established methods: Averaging and Principle Components Analysis (PCA), and against its two source bands, visible and infrared. The task that we studied was: target detection in the cluttered environment. MSSF proved superior to the other fusion methods. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.

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.

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.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

NASA Astrophysics Data System (ADS)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Multiscale infrared and visible image fusion using gradient domain guided image filtering

NASA Astrophysics Data System (ADS)

Zhu, Jin; Jin, Weiqi; Li, Li; Han, Zhenghao; Wang, Xia

2018-03-01

For better surveillance with infrared and visible imaging, a novel hybrid multiscale decomposition fusion method using gradient domain guided image filtering (HMSD-GDGF) is proposed in this study. In this method, hybrid multiscale decomposition with guided image filtering and gradient domain guided image filtering of source images are first applied before the weight maps of each scale are obtained using a saliency detection technology and filtering means with three different fusion rules at different scales. The three types of fusion rules are for small-scale detail level, large-scale detail level, and base level. Finally, the target becomes more salient and can be more easily detected in the fusion result, with the detail information of the scene being fully displayed. After analyzing the experimental comparisons with state-of-the-art fusion methods, the HMSD-GDGF method has obvious advantages in fidelity of salient information (including structural similarity, brightness, and contrast), preservation of edge features, and human visual perception. Therefore, visual effects can be improved by using the proposed HMSD-GDGF method.

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Feature level fusion for enhanced geological mapping of ophiolile complex using ASTER and Landsat TM data

NASA Astrophysics Data System (ADS)

Pournamdari, M.; Hashim, M.

2014-02-01

Chromite ore deposit occurrence is related to ophiolite complexes as a part of the oceanic crust and provides a good opportunity for lithological mapping using remote sensing data. The main contribution of this paper is a novel approaches to discriminate different rock units associated with ophiolite complex using the Feature Level Fusion technique on ASTER and Landsat TM satellite data at regional scale. In addition this study has applied spectral transform approaches, consisting of Spectral Angle Mapper (SAM) to distinguish the concentration of high-potential areas of chromite and also for determining the boundary between different rock units. Results indicated both approaches show superior outputs compared to other methods and can produce a geological map for ophiolite complex rock units in the arid and the semi-arid region. The novel technique including feature level fusion and Spectral Angle Mapper (SAM) discriminated ophiolitic rock units and produced detailed geological maps of the study area. As a case study, Sikhoran ophiolite complex located in SE, Iran has been selected for image processing techniques. In conclusion, a suitable approach for lithological mapping of ophiolite complexes is demonstrated, this technique contributes meaningfully towards economic geology in terms of identifying new prospects.

Cognitive Load Measurement in a Virtual Reality-based Driving System for Autism Intervention

PubMed Central

Zhang, Lian; Wade, Joshua; Bian, Dayi; Fan, Jing; Swanson, Amy; Weitlauf, Amy; Warren, Zachary; Sarkar, Nilanjan

2016-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization. PMID:28966730

Study on polarization image methods in turbid medium

NASA Astrophysics Data System (ADS)

Fu, Qiang; Mo, Chunhe; Liu, Boyu; Duan, Jin; Zhang, Su; Zhu, Yong

2014-11-01

Polarization imaging detection technology in addition to the traditional imaging information, also can get polarization multi-dimensional information, thus improve the probability of target detection and recognition.Image fusion in turbid medium target polarization image research, is helpful to obtain high quality images. Based on visible light wavelength of light wavelength of laser polarization imaging, through the rotation Angle of polaroid get corresponding linear polarized light intensity, respectively to obtain the concentration range from 5% to 10% of turbid medium target stocks of polarization parameters, introduces the processing of image fusion technology, main research on access to the polarization of the image by using different polarization image fusion methods for image processing, discusses several kinds of turbid medium has superior performance of polarization image fusion method, and gives the treatment effect and analysis of data tables. Then use pixel level, feature level and decision level fusion algorithm on three levels of information fusion, DOLP polarization image fusion, the results show that: with the increase of the polarization Angle, polarization image will be more and more fuzzy, quality worse and worse. Than a single fused image contrast of the image be improved obviously, the finally analysis on reasons of the increase the image contrast and polarized light.

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators

PubMed Central

Bai, Xiangzhi

2015-01-01

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion. PMID:26184229

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators.

PubMed

Bai, Xiangzhi

2015-07-15

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Zhang, Jian; Li, Wei; Zhao, Dazhe; Huang, Min; Zaiane, Osmar

2017-03-01

The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ 2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ 2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ 2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ 2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ 2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automated target classification in high resolution dual frequency sonar imagery

NASA Astrophysics Data System (ADS)

Aridgides, Tom; Fernández, Manuel

2007-04-01

An improved computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The classified objects of 2 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution dual frequency sonar imagery. Three significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a Box-Cox nonlinear feature LLRT fusion algorithm was developed. The Box-Cox transformation consists of raising the features to a to-be-determined power. Third, a repeated application of a subset feature selection / feature orthogonalization / Volterra feature LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms summing, baseline single-stage Volterra and Box-Cox feature LLRT algorithms, yielding significant improvements over the best single CAD/CAC processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate. Additionally, the robustness of cascaded Volterra feature fusion was demonstrated, by showing that the algorithm yields similar performance with the training and test sets.

Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

PubMed Central

Clerico, Andrea; Tiwari, Abhishek; Gupta, Rishabh; Jayaraman, Srinivasan; Falk, Tiago H.

2018-01-01

The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG). Typically, conventional features extracted from the EEG signal have been used, such as frequency subband powers and/or inter-hemispheric power asymmetry indices. More recently, the coupling between EEG and peripheral physiological signals, such as the galvanic skin response (GSR), have also been proposed. Here, we show the importance of EEG amplitude modulations and propose several new features that measure the amplitude-amplitude cross-frequency coupling per EEG electrode, as well as linear and non-linear connections between multiple electrode pairs. When tested on a publicly available dataset of music video clips tagged with subjective affective ratings, support vector classifiers trained on the proposed features were shown to outperform those trained on conventional benchmark EEG features by as much as 6, 20, 8, and 7% for arousal, valence, dominance and liking, respectively. Moreover, fusion of the proposed features with EEG-GSR coupling features showed to be particularly useful for arousal (feature-level fusion) and liking (decision-level fusion) prediction. Together, these findings show the importance of the proposed features to characterize human affective states during music clip watching. PMID:29367844

Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use.

PubMed

Apostolou, N; Papazoglou, Th; Koutsouris, D

2006-01-01

Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for Matlab platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in Matlab platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.

Feature-fused SSD: fast detection for small objects

NASA Astrophysics Data System (ADS)

Cao, Guimei; Xie, Xuemei; Yang, Wenzhe; Liao, Quan; Shi, Guangming; Wu, Jinjian

2018-04-01

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

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

Facial expression recognition under partial occlusion based on fusion of global and local features

NASA Astrophysics Data System (ADS)

Wang, Xiaohua; Xia, Chen; Hu, Min; Ren, Fuji

2018-04-01

Facial expression recognition under partial occlusion is a challenging research. This paper proposes a novel framework for facial expression recognition under occlusion by fusing the global and local features. In global aspect, first, information entropy are employed to locate the occluded region. Second, principal Component Analysis (PCA) method is adopted to reconstruct the occlusion region of image. After that, a replace strategy is applied to reconstruct image by replacing the occluded region with the corresponding region of the best matched image in training set, Pyramid Weber Local Descriptor (PWLD) feature is then extracted. At last, the outputs of SVM are fitted to the probabilities of the target class by using sigmoid function. For the local aspect, an overlapping block-based method is adopted to extract WLD features, and each block is weighted adaptively by information entropy, Chi-square distance and similar block summation methods are then applied to obtain the probabilities which emotion belongs to. Finally, fusion at the decision level is employed for the data fusion of the global and local features based on Dempster-Shafer theory of evidence. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the effectiveness and fault tolerance of this method.

Fusion of infrared polarization and intensity images based on improved toggle operator

NASA Astrophysics Data System (ADS)

Zhu, Pan; Ding, Lei; Ma, Xiaoqing; Huang, Zhanhua

2018-01-01

Integration of infrared polarization and intensity images has been a new topic in infrared image understanding and interpretation. The abundant infrared details and target from infrared image and the salient edge and shape information from polarization image should be preserved or even enhanced in the fused result. In this paper, a new fusion method is proposed for infrared polarization and intensity images based on the improved multi-scale toggle operator with spatial scale, which can effectively extract the feature information of source images and heavily reduce redundancy among different scale. Firstly, the multi-scale image features of infrared polarization and intensity images are respectively extracted at different scale levels by the improved multi-scale toggle operator. Secondly, the redundancy of the features among different scales is reduced by using spatial scale. Thirdly, the final image features are combined by simply adding all scales of feature images together, and a base image is calculated by performing mean value weighted method on smoothed source images. Finally, the fusion image is obtained by importing the combined image features into the base image with a suitable strategy. Both objective assessment and subjective vision of the experimental results indicate that the proposed method obtains better performance in preserving the details and edge information as well as improving the image contrast.

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).

Infrared and visible fusion face recognition based on NSCT domain

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-01-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

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.

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.

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.

Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient

NASA Astrophysics Data System (ADS)

Ma, Dan; Liu, Jun; Chen, Kai; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

2016-04-01

In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed. FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.

Estimating workload using EEG spectral power and ERPs in the n-back task

NASA Astrophysics Data System (ADS)

Brouwer, Anne-Marie; Hogervorst, Maarten A.; van Erp, Jan B. F.; Heffelaar, Tobias; Zimmerman, Patrick H.; Oostenveld, Robert

2012-08-01

Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.

Understanding fuel magnetization and mix using secondary nuclear reactions in magneto-inertial fusion.

PubMed

Schmit, P F; Knapp, P F; Hansen, S B; Gomez, M R; Hahn, K D; Sinars, D B; Peterson, K J; Slutz, S A; Sefkow, A B; Awe, T J; Harding, E; Jennings, C A; Chandler, G A; Cooper, G W; Cuneo, M E; Geissel, M; Harvey-Thompson, A J; Herrmann, M C; Hess, M H; Johns, O; Lamppa, D C; Martin, M R; McBride, R D; Porter, J L; Robertson, G K; Rochau, G A; Rovang, D C; Ruiz, C L; Savage, M E; Smith, I C; Stygar, W A; Vesey, R A

2014-10-10

Magnetizing the fuel in inertial confinement fusion relaxes ignition requirements by reducing thermal conductivity and changing the physics of burn product confinement. Diagnosing the level of fuel magnetization during burn is critical to understanding target performance in magneto-inertial fusion (MIF) implosions. In pure deuterium fusion plasma, 1.01 MeV tritons are emitted during deuterium-deuterium fusion and can undergo secondary deuterium-tritium reactions before exiting the fuel. Increasing the fuel magnetization elongates the path lengths through the fuel of some of the tritons, enhancing their probability of reaction. Based on this feature, a method to diagnose fuel magnetization using the ratio of overall deuterium-tritium to deuterium-deuterium neutron yields is developed. Analysis of anisotropies in the secondary neutron energy spectra further constrain the measurement. Secondary reactions also are shown to provide an upper bound for the volumetric fuel-pusher mix in MIF. The analysis is applied to recent MIF experiments [M. R. Gomez et al., Phys. Rev. Lett. 113, 155003 (2014)] on the Z Pulsed Power Facility, indicating that significant magnetic confinement of charged burn products was achieved and suggesting a relatively low-mix environment. Both of these are essential features of future ignition-scale MIF designs.

Adaptive fusion of infrared and visible images in dynamic scene

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Man, Hong; Desai, Sachi

2011-11-01

Multiple modalities sensor fusion has been widely employed in various surveillance and military applications. A variety of image fusion techniques including PCA, wavelet, curvelet and HSV has been proposed in recent years to improve human visual perception for object detection. One of the main challenges for visible and infrared image fusion is to automatically determine an optimal fusion strategy for different input scenes along with an acceptable computational cost. This paper, we propose a fast and adaptive feature selection based image fusion method to obtain high a contrast image from visible and infrared sensors for targets detection. At first, fuzzy c-means clustering is applied on the infrared image to highlight possible hotspot regions, which will be considered as potential targets' locations. After that, the region surrounding the target area is segmented as the background regions. Then image fusion is locally applied on the selected target and background regions by computing different linear combination of color components from registered visible and infrared images. After obtaining different fused images, histogram distributions are computed on these local fusion images as the fusion feature set. The variance ratio which is based on Linear Discriminative Analysis (LDA) measure is employed to sort the feature set and the most discriminative one is selected for the whole image fusion. As the feature selection is performed over time, the process will dynamically determine the most suitable feature for the image fusion in different scenes. Experiment is conducted on the OSU Color-Thermal database, and TNO Human Factor dataset. The fusion results indicate that our proposed method achieved a competitive performance compared with other fusion algorithms at a relatively low computational cost.

Intelligent query by humming system based on score level fusion of multiple classifiers

NASA Astrophysics Data System (ADS)

Pyo Nam, Gi; Thu Trang Luong, Thi; Ha Nam, Hyun; Ryoung Park, Kang; Park, Sung-Joo

2011-12-01

Recently, the necessity for content-based music retrieval that can return results even if a user does not know information such as the title or singer has increased. Query-by-humming (QBH) systems have been introduced to address this need, as they allow the user to simply hum snatches of the tune to find the right song. Even though there have been many studies on QBH, few have combined multiple classifiers based on various fusion methods. Here we propose a new QBH system based on the score level fusion of multiple classifiers. This research is novel in the following three respects: three local classifiers [quantized binary (QB) code-based linear scaling (LS), pitch-based dynamic time warping (DTW), and LS] are employed; local maximum and minimum point-based LS and pitch distribution feature-based LS are used as global classifiers; and the combination of local and global classifiers based on the score level fusion by the PRODUCT rule is used to achieve enhanced matching accuracy. Experimental results with the 2006 MIREX QBSH and 2009 MIR-QBSH corpus databases show that the performance of the proposed method is better than that of single classifier and other fusion methods.

Automatic intelligibility classification of sentence-level pathological speech

PubMed Central

Kim, Jangwon; Kumar, Naveen; Tsiartas, Andreas; Li, Ming; Narayanan, Shrikanth S.

2014-01-01

Pathological speech usually refers to the condition of speech distortion resulting from atypicalities in voice and/or in the articulatory mechanisms owing to disease, illness or other physical or biological insult to the production system. Although automatic evaluation of speech intelligibility and quality could come in handy in these scenarios to assist experts in diagnosis and treatment design, the many sources and types of variability often make it a very challenging computational processing problem. In this work we propose novel sentence-level features to capture abnormal variation in the prosodic, voice quality and pronunciation aspects in pathological speech. In addition, we propose a post-classification posterior smoothing scheme which refines the posterior of a test sample based on the posteriors of other test samples. Finally, we perform feature-level fusions and subsystem decision fusion for arriving at a final intelligibility decision. The performances are tested on two pathological speech datasets, the NKI CCRT Speech Corpus (advanced head and neck cancer) and the TORGO database (cerebral palsy or amyotrophic lateral sclerosis), by evaluating classification accuracy without overlapping subjects’ data among training and test partitions. Results show that the feature sets of each of the voice quality subsystem, prosodic subsystem, and pronunciation subsystem, offer significant discriminating power for binary intelligibility classification. We observe that the proposed posterior smoothing in the acoustic space can further reduce classification errors. The smoothed posterior score fusion of subsystems shows the best classification performance (73.5% for unweighted, and 72.8% for weighted, average recalls of the binary classes). PMID:25414544

Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions.

PubMed

Raghavendra, U; Rajendra Acharya, U; Gudigar, Anjan; Hong Tan, Jen; Fujita, Hamido; Hagiwara, Yuki; Molinari, Filippo; Kongmebhol, Pailin; Hoong Ng, Kwan

2017-05-01

Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessment of Data and Knowledge Fusion Strategies for Diagnostics and Prognostics

DTIC Science & Technology

2001-04-05

prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection , and predicting time...or better than the sum of the parts. Specific to health management, this means reduced uncertainty in current condition assessment reduced (improving...achieve time synchronous averaged vibration features. Semmm Amy -U....1A MreN T.g 4 Id F~As- Anomaly DEtection Figure 1 - Fusion Application Areas At a

Classification of weld defect based on information fusion technology for radiographic testing system

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

Jiang, Hongquan; Liang, Zeming, E-mail: heavenlzm@126.com; Gao, Jianmin

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster–Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defectmore » feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.« less

Classification of weld defect based on information fusion technology for radiographic testing system.

PubMed

Jiang, Hongquan; Liang, Zeming; Gao, Jianmin; Dang, Changying

2016-03-01

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster-Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defect feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.

Decision-level fusion of SAR and IR sensor information for automatic target detection

NASA Astrophysics Data System (ADS)

Cho, Young-Rae; Yim, Sung-Hyuk; Cho, Hyun-Woong; Won, Jin-Ju; Song, Woo-Jin; Kim, So-Hyeon

2017-05-01

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

Multilevel depth and image fusion for human activity detection.

PubMed

Ni, Bingbing; Pei, Yong; Moulin, Pierre; Yan, Shuicheng

2013-10-01

Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.

Detection of buried objects by fusing dual-band infrared images

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

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-11-01

We have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, infrared imagery, and ground penetrating radar (GPR), have been used to acquire data on a number of buried mines and mine surrogates. Because the visible wavelength and GPR data are currently incomplete. This paper focuses on the fusion of two-band infrared images. We use feature-level fusion and supervised learning with the probabilistic neural network (PNN) to evaluate detection performance. The novelty of the work lies in the application of advanced target recognition algorithms, the fusion of dual-band infraredmore » images and evaluation of the techniques using two real data sets.« less

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.

Parametric classification of handvein patterns based on texture features

NASA Astrophysics Data System (ADS)

Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

A Comparison of Coarse-Grained and Continuum Models for Membrane Bending in Lipid Bilayer Fusion Pores

PubMed Central

Yoo, Jejoong; Jackson, Meyer B.; Cui, Qiang

2013-01-01

To establish the validity of continuum mechanics models quantitatively for the analysis of membrane remodeling processes, we compare the shape and energies of the membrane fusion pore predicted by coarse-grained (MARTINI) and continuum mechanics models. The results at these distinct levels of resolution give surprisingly consistent descriptions for the shape of the fusion pore, and the deviation between the continuum and coarse-grained models becomes notable only when the radius of curvature approaches the thickness of a monolayer. Although slow relaxation beyond microseconds is observed in different perturbative simulations, the key structural features (e.g., dimension and shape of the fusion pore near the pore center) are consistent among independent simulations. These observations provide solid support for the use of coarse-grained and continuum models in the analysis of membrane remodeling. The combined coarse-grained and continuum analysis confirms the recent prediction of continuum models that the fusion pore is a metastable structure and that its optimal shape is neither toroidal nor catenoidal. Moreover, our results help reveal a new, to our knowledge, bowing feature in which the bilayers close to the pore axis separate more from one another than those at greater distances from the pore axis; bowing helps reduce the curvature and therefore stabilizes the fusion pore structure. The spread of the bilayer deformations over distances of hundreds of nanometers and the substantial reduction in energy of fusion pore formation provided by this spread indicate that membrane fusion can be enhanced by allowing a larger area of membrane to participate and be deformed. PMID:23442963

Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients

NASA Astrophysics Data System (ADS)

Mu, Wei; Qi, Jin; Lu, Hong; Schabath, Matthew; Balagurunathan, Yoganand; Tunali, Ilke; Gillies, Robert James

2018-02-01

Purpose: Investigate the ability of using complementary information provided by the fusion of PET/CT images to predict immunotherapy response in non-small cell lung cancer (NSCLC) patients. Materials and methods: We collected 64 patients diagnosed with primary NSCLC treated with anti PD-1 checkpoint blockade. Using PET/CT images, fused images were created following multiple methodologies, resulting in up to 7 different images for the tumor region. Quantitative image features were extracted from the primary image (PET/CT) and the fused images, which included 195 from primary images and 1235 features from the fusion images. Three clinical characteristics were also analyzed. We then used support vector machine (SVM) classification models to identify discriminant features that predict immunotherapy response at baseline. Results: A SVM built with 87 fusion features and 13 primary PET/CT features on validation dataset had an accuracy and area under the ROC curve (AUROC) of 87.5% and 0.82, respectively, compared to a model built with 113 original PET/CT features on validation dataset 78.12% and 0.68. Conclusion: The fusion features shows better ability to predict immunotherapy response prediction compared to individual image features.

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.

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.

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.

Morphological filtering and multiresolution fusion for mammographic microcalcification detection

NASA Astrophysics Data System (ADS)

Chen, Lulin; Chen, Chang W.; Parker, Kevin J.

1997-04-01

Mammographic images are often of relatively low contrast and poor sharpness with non-stationary background or clutter and are usually corrupted by noise. In this paper, we propose a new method for microcalcification detection using gray scale morphological filtering followed by multiresolution fusion and present a unified general filtering form called the local operating transformation for whitening filtering and adaptive thresholding. The gray scale morphological filters are used to remove all large areas that are considered as non-stationary background or clutter variations, i.e., to prewhiten images. The multiresolution fusion decision is based on matched filter theory. In addition to the normal matched filter, the Laplacian matched filter which is directly related through the wavelet transforms to multiresolution analysis is exploited for microcalcification feature detection. At the multiresolution fusion stage, the region growing techniques are used in each resolution level. The parent-child relations between resolution levels are adopted to make final detection decision. FROC is computed from test on the Nijmegen database.

An effective method for cirrhosis recognition based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Chen, Yameng; Sun, Gengxin; Lei, Yiming; Zhang, Jinpeng

2018-04-01

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

Data fusion strategies for hazard detection and safe site selection for planetary and small body landings

NASA Astrophysics Data System (ADS)

Câmara, F.; Oliveira, J.; Hormigo, T.; Araújo, J.; Ribeiro, R.; Falcão, A.; Gomes, M.; Dubois-Matra, O.; Vijendran, S.

2015-06-01

This paper discusses the design and evaluation of data fusion strategies to perform tiered fusion of several heterogeneous sensors and a priori data. The aim is to increase robustness and performance of hazard detection and avoidance systems, while enabling safe planetary and small body landings anytime, anywhere. The focus is on Mars and asteroid landing mission scenarios and three distinct data fusion algorithms are introduced and compared. The first algorithm consists of a hybrid camera-LIDAR hazard detection and avoidance system, the H2DAS, in which data fusion is performed at both sensor-level data (reconstruction of the point cloud obtained with a scanning LIDAR using the navigation motion states and correcting the image for motion compensation using IMU data), feature-level data (concatenation of multiple digital elevation maps, obtained from consecutive LIDAR images, to achieve higher accuracy and resolution maps while enabling relative positioning) as well as decision-level data (fusing hazard maps from multiple sensors onto a single image space, with a single grid orientation and spacing). The second method presented is a hybrid reasoning fusion, the HRF, in which innovative algorithms replace the decision-level functions of the previous method, by combining three different reasoning engines—a fuzzy reasoning engine, a probabilistic reasoning engine and an evidential reasoning engine—to produce safety maps. Finally, the third method presented is called Intelligent Planetary Site Selection, the IPSIS, an innovative multi-criteria, dynamic decision-level data fusion algorithm that takes into account historical information for the selection of landing sites and a piloting function with a non-exhaustive landing site search capability, i.e., capable of finding local optima by searching a reduced set of global maps. All the discussed data fusion strategies and algorithms have been integrated, verified and validated in a closed-loop simulation environment. Monte Carlo simulation campaigns were performed for the algorithms performance assessment and benchmarking. The simulations results comprise the landing phases of Mars and Phobos landing mission scenarios.

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.

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.

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.

Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

NASA Astrophysics Data System (ADS)

Zhang, Caiyun

2015-06-01

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, Object-based Image Analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetry data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively.

Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma

DOE PAGES

Wu, Lingfei; Wu, Kesheng; Sim, Alex; ...

2016-06-01

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes tomore » detect and track blob-filaments in real time in fusion plasma. Here, on a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.« less

Facial recognition using multisensor images based on localized kernel eigen spaces.

PubMed

Gundimada, Satyanadh; Asari, Vijayan K

2009-06-01

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

PubMed

Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

2016-11-01

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

Multispectral image fusion for illumination-invariant palmprint recognition

PubMed Central

Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. PMID:28558064

Multispectral image fusion for illumination-invariant palmprint recognition.

PubMed

Lu, Longbin; Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

Distinguishing obsessive features and worries: the role of thought-action fusion.

PubMed

Coles, M E; Mennin, D S; Heimberg, R G

2001-08-01

Obsessions are a key feature of obsessive-compulsive disorder (OCD), and chronic worry is the cardinal feature of generalized anxiety disorder (GAD). However, these two cognitive processes are conceptually very similar, and there is a need to determine how they differ. Recent studies have attempted to identify cognitive processes that may be differentially related to obsessive features and worry. In the current study we proposed that (1) obsessive features and worry could be differentiated and that (2) a measure of the cognitive process thought-action fusion would distinguish between obsessive features and worry, being strongly related to obsessive features after controlling for the effects of worry. These hypotheses were supported in a sample of 173 undergraduate students. Thought-action fusion may be a valuable construct in differentiating between obsessive features and worry.

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

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

D Object Classification Based on Thermal and Visible Imagery in Urban Area

NASA Astrophysics Data System (ADS)

Hasani, H.; Samadzadegan, F.

2015-12-01

The spatial distribution of land cover in the urban area especially 3D objects (buildings and trees) is a fundamental dataset for urban planning, ecological research, disaster management, etc. According to recent advances in sensor technologies, several types of remotely sensed data are available from the same area. Data fusion has been widely investigated for integrating different source of data in classification of urban area. Thermal infrared imagery (TIR) contains information on emitted radiation and has unique radiometric properties. However, due to coarse spatial resolution of thermal data, its application has been restricted in urban areas. On the other hand, visible image (VIS) has high spatial resolution and information in visible spectrum. Consequently, there is a complementary relation between thermal and visible imagery in classification of urban area. This paper evaluates the potential of aerial thermal hyperspectral and visible imagery fusion in classification of urban area. In the pre-processing step, thermal imagery is resampled to the spatial resolution of visible image. Then feature level fusion is applied to construct hybrid feature space include visible bands, thermal hyperspectral bands, spatial and texture features and moreover Principle Component Analysis (PCA) transformation is applied to extract PCs. Due to high dimensionality of feature space, dimension reduction method is performed. Finally, Support Vector Machines (SVMs) classify the reduced hybrid feature space. The obtained results show using thermal imagery along with visible imagery, improved the classification accuracy up to 8% respect to visible image classification.

Integration of heterogeneous features for remote sensing scene classification

NASA Astrophysics Data System (ADS)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Oncogenic fusion proteins adopt the insulin-like growth factor signaling pathway.

PubMed

Werner, Haim; Meisel-Sharon, Shilhav; Bruchim, Ilan

2018-02-19

The insulin-like growth factor-1 receptor (IGF1R) has been identified as a potent anti-apoptotic, pro-survival tyrosine kinase-containing receptor. Overexpression of the IGF1R gene constitutes a typical feature of most human cancers. Consistent with these biological roles, cells expressing high levels of IGF1R are expected not to die, a quintessential feature of cancer cells. Tumor specific chromosomal translocations that disrupt the architecture of transcription factors are a common theme in carcinogenesis. Increasing evidence gathered over the past fifteen years demonstrate that this type of genomic rearrangements is common not only among pediatric and hematological malignancies, as classically thought, but may also provide a molecular and cytogenetic foundation for an ever-increasing portion of adult epithelial tumors. In this review article we provide evidence that the mechanism of action of oncogenic fusion proteins associated with both pediatric and adult malignancies involves transactivation of the IGF1R gene, with ensuing increases in IGF1R levels and ligand-mediated receptor phosphorylation. Disrupted transcription factors adopt the IGF1R signaling pathway and elicit their oncogenic activities via activation of this critical regulatory network. Combined targeting of oncogenic fusion proteins along with the IGF1R may constitute a promising therapeutic approach.

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-11-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. Copyright © 2014 Elsevier Inc. All rights reserved.

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-01-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)1, a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. PMID:25042445

A method based on IHS cylindrical transform model for quality assessment of image fusion

NASA Astrophysics Data System (ADS)

Zhu, Xiaokun; Jia, Yonghong

2005-10-01

Image fusion technique has been widely applied to remote sensing image analysis and processing, and methods for quality assessment of image fusion in remote sensing have also become the research issues at home and abroad. Traditional assessment methods combine calculation of quantitative indexes and visual interpretation to compare fused images quantificationally and qualitatively. However, in the existing assessment methods, there are two defects: on one hand, most imdexes lack the theoretic support to compare different fusion methods. On the hand, there is not a uniform preference for most of the quantitative assessment indexes when they are applied to estimate the fusion effects. That is, the spatial resolution and spectral feature could not be analyzed synchronously by these indexes and there is not a general method to unify the spatial and spectral feature assessment. So in this paper, on the basis of the approximate general model of four traditional fusion methods, including Intensity Hue Saturation(IHS) triangle transform fusion, High Pass Filter(HPF) fusion, Principal Component Analysis(PCA) fusion, Wavelet Transform(WT) fusion, a correlation coefficient assessment method based on IHS cylindrical transform is proposed. By experiments, this method can not only get the evaluation results of spatial and spectral features on the basis of uniform preference, but also can acquire the comparison between fusion image sources and fused images, and acquire differences among fusion methods. Compared with the traditional assessment methods, the new methods is more intuitionistic, and in accord with subjective estimation.

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

2015-01-01

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

A comparative study of multi-focus image fusion validation metrics

NASA Astrophysics Data System (ADS)

Giansiracusa, Michael; Lutz, Adam; Messer, Neal; Ezekiel, Soundararajan; Alford, Mark; Blasch, Erik; Bubalo, Adnan; Manno, Michael

2016-05-01

Fusion of visual information from multiple sources is relevant for applications security, transportation, and safety applications. One way that image fusion can be particularly useful is when fusing imagery data from multiple levels of focus. Different focus levels can create different visual qualities for different regions in the imagery, which can provide much more visual information to analysts when fused. Multi-focus image fusion would benefit a user through automation, which requires the evaluation of the fused images to determine whether they have properly fused the focused regions of each image. Many no-reference metrics, such as information theory based, image feature based and structural similarity-based have been developed to accomplish comparisons. However, it is hard to scale an accurate assessment of visual quality which requires the validation of these metrics for different types of applications. In order to do this, human perception based validation methods have been developed, particularly dealing with the use of receiver operating characteristics (ROC) curves and the area under them (AUC). Our study uses these to analyze the effectiveness of no-reference image fusion metrics applied to multi-resolution fusion methods in order to determine which should be used when dealing with multi-focus data. Preliminary results show that the Tsallis, SF, and spatial frequency metrics are consistent with the image quality and peak signal to noise ratio (PSNR).

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

PubMed

Ganasala, Padma; Kumar, Vinod

2016-02-01

Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

Prediction of adverse outcomes of acute coronary syndrome using intelligent fusion of triage information with HUMINT

NASA Astrophysics Data System (ADS)

McCullough, Claire L.; Novobilski, Andrew J.; Fesmire, Francis M.

2006-04-01

Faculty from the University of Tennessee at Chattanooga and the University of Tennessee College of Medicine, Chattanooga Unit, have used data mining techniques and neural networks to examine a set of fourteen features, data items, and HUMINT assessments for 2,148 emergency room patients with symptoms possibly indicative of Acute Coronary Syndrome. Specifically, the authors have generated Bayesian networks describing linkages and causality in the data, and have compared them with neural networks. The data includes objective information routinely collected during triage and the physician's initial case assessment, a HUMINT appraisal. Both the neural network and the Bayesian network were used to fuse the disparate types of information with the goal of forecasting thirty-day adverse patient outcome. This paper presents details of the methods of data fusion including both the data mining techniques and the neural network. Results are compared using Receiver Operating Characteristic curves describing the outcomes of both methods, both using only objective features and including the subjective physician's assessment. While preliminary, the results of this continuing study are significant both from the perspective of potential use of the intelligent fusion of biomedical informatics to aid the physician in prescribing treatment necessary to prevent serious adverse outcome from ACS and as a model of fusion of objective data with subjective HUMINT assessment. Possible future work includes extension of successfully demonstrated intelligent fusion methods to other medical applications, and use of decision level fusion to combine results from data mining and neural net approaches for even more accurate outcome prediction.

A hierarchical classification method for finger knuckle print recognition

NASA Astrophysics Data System (ADS)

Kong, Tao; Yang, Gongping; Yang, Lu

2014-12-01

Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.

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

Clinicopathologic features of patients with non-small cell lung cancer harboring the EML4-ALK fusion gene: a meta-analysis.

PubMed

Wang, Ying; Wang, Shumin; Xu, Shiguang; Qu, Jiaqi; Liu, Bo

2014-01-01

The frequencies of EML4-ALK fusion gene in non-small cell lung cancer (NSCLC) with different clinicopathologic features described by previous studies are inconsistent. The key demographic and pathologic features associated with EML4-ALK fusion gene have not been definitively established. This meta-analysis was conducted to compare the frequency of the EML4-ALK fusion gene in patients with different clinicopathologic features and to identify an enriched population of patients with NSCLC harboring EML4-ALK fusion gene. The Pubmed and Embase databases for all studies on EML4-ALK fusion gene in NSCLC patients were searched up to July 2014. A criteria list and exclusion criteria were established to screen the studies. The frequency of the EML4-ALK fusion gene and the clinicopathologic features, including smoking status, pathologic type, gender, and EGFR status were abstracted. Seventeen articles consisting of 4511 NSCLC cases were included in this meta-analysis. A significant lower EML4-ALK fusion gene positive rate was associated with smokers (pooled OR = 0.40, 95% CI = 0.30-0.54, P<0.00001). A significantly higher EML4-ALK fusion gene positivity rate was associated with adenocarcinomas (pooled OR = 2.53, 95% CI = 1.66-3.86, P<0.0001) and female (pooled OR = 0.61, 95% CI = 0.41-0.90, P = 0.01). We found that a significantly lower EML4-ALK fusion gene positivity rate was associated with EGFR mutation (pooled OR = 0.07, 95% CI = 0.03-0.19, P<0.00001). No publication bias was observed in any meta-analysis (all P value of Egger's test >0.05); however, because of the small sample size, no results were in the meta-analysis regarding EGFR gene status. This meta-analysis revealed that the EML4-ALK fusion gene is highly correlated with a never/light smoking history, female and the pathologic type of adenocarcinoma, and is largely mutually exclusive of EGFR.

Clinicopathologic Features of Patients with Non-Small Cell Lung Cancer Harboring the EML4-ALK Fusion Gene: A Meta-Analysis

PubMed Central

Wang, Shumin; Xu, Shiguang; Qu, Jiaqi

2014-01-01

Background The frequencies of EML4-ALK fusion gene in non-small cell lung cancer (NSCLC) with different clinicopathologic features described by previous studies are inconsistent. The key demographic and pathologic features associated with EML4-ALK fusion gene have not been definitively established. This meta-analysis was conducted to compare the frequency of the EML4-ALK fusion gene in patients with different clinicopathologic features and to identify an enriched population of patients with NSCLC harboring EML4-ALK fusion gene. Methods The Pubmed and Embase databases for all studies on EML4-ALK fusion gene in NSCLC patients were searched up to July 2014. A criteria list and exclusion criteria were established to screen the studies. The frequency of the EML4-ALK fusion gene and the clinicopathologic features, including smoking status, pathologic type, gender, and EGFR status were abstracted. Results Seventeen articles consisting of 4511 NSCLC cases were included in this meta-analysis. A significant lower EML4-ALK fusion gene positive rate was associated with smokers (pooled OR = 0.40, 95% CI = 0.30–0.54, P<0.00001). A significantly higher EML4-ALK fusion gene positivity rate was associated with adenocarcinomas (pooled OR = 2.53, 95% CI = 1.66–3.86, P<0.0001) and female (pooled OR = 0.61, 95% CI = 0.41–0.90, P = 0.01). We found that a significantly lower EML4-ALK fusion gene positivity rate was associated with EGFR mutation (pooled OR = 0.07, 95% CI = 0.03–0.19, P<0.00001). No publication bias was observed in any meta-analysis (all P value of Egger's test >0.05); however, because of the small sample size, no results were in the meta-analysis regarding EGFR gene status. Conclusion This meta-analysis revealed that the EML4-ALK fusion gene is highly correlated with a never/light smoking history, female and the pathologic type of adenocarcinoma, and is largely mutually exclusive of EGFR. PMID:25360721

Multifocus image fusion using phase congruency

NASA Astrophysics Data System (ADS)

Zhan, Kun; Li, Qiaoqiao; Teng, Jicai; Wang, Mingying; Shi, Jinhui

2015-05-01

We address the problem of fusing multifocus images based on the phase congruency (PC). PC provides a sharpness feature of a natural image. The focus measure (FM) is identified as strong PC near a distinctive image feature evaluated by the complex Gabor wavelet. The PC is more robust against noise than other FMs. The fusion image is obtained by a new fusion rule (FR), and the focused region is selected by the FR from one of the input images. Experimental results show that the proposed fusion scheme achieves the fusion performance of the state-of-the-art methods in terms of visual quality and quantitative evaluations.

Multimodal biometric method that combines veins, prints, and shape of a finger

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Kim, Jeong Nyeo

2011-01-01

Multimodal biometrics provides high recognition accuracy and population coverage by using various biometric features. A single finger contains finger veins, fingerprints, and finger geometry features; by using multimodal biometrics, information on these multiple features can be simultaneously obtained in a short time and their fusion can outperform the use of a single feature. This paper proposes a new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features. This research is novel in the following four ways. First, the performances of the finger-vein and fingerprint recognition are improved by using a method based on a local derivative pattern. Second, the accuracy of the finger geometry recognition is greatly increased by combining a Fourier descriptor with principal component analysis. Third, a fuzzy score normalization method is introduced; its performance is better than the conventional Z-score normalization method. Fourth, finger-vein, fingerprint, and finger geometry recognitions are combined by using three support vector machines and a weighted SUM rule. Experimental results showed that the equal error rate of the proposed method was 0.254%, which was lower than those of the other methods.

Detecting Parkinson's disease from sustained phonation and speech signals.

PubMed

Vaiciukynas, Evaldas; Verikas, Antanas; Gelzinis, Adas; Bacauskiene, Marija

2017-01-01

This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson's disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and smart phone (SP) microphones. Additional modalities were obtained by splitting speech recording into voiced and unvoiced parts. Information in each modality is summarized by 18 well-known audio feature sets. Random forest (RF) is used as a machine learning algorithm, both for individual feature sets and for decision-level fusion. Detection performance is measured by the out-of-bag equal error rate (EER) and the cost of log-likelihood-ratio. Essentia audio feature set was the best using the AC speech modality and YAAFE audio feature set was the best using the SP unvoiced modality, achieving EER of 20.30% and 25.57%, respectively. Fusion of all feature sets and modalities resulted in EER of 19.27% for the AC and 23.00% for the SP channel. Non-linear projection of a RF-based proximity matrix into the 2D space enriched medical decision support by visualization.

Multiple feature fusion via covariance matrix for visual tracking

NASA Astrophysics Data System (ADS)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

2018-04-01

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

Detection of EML4-ALK fusion gene and features associated with EGFR mutations in Chinese patients with non-small-cell lung cancer.

PubMed

Wen, Miaomiao; Wang, Xuejiao; Sun, Ying; Xia, Jinghua; Fan, Liangbo; Xing, Hao; Zhang, Zhipei; Li, Xiaofei

2016-01-01

Echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) and epidermal growth factor receptor (EGFR) define specific molecular subsets of lung cancer with distinct clinical features. We aimed at revealing the clinical features of EML4-ALK fusion gene and EGFR mutation in non-small-cell lung cancer (NSCLC). We enrolled 694 Chinese patients with NSCLC for analysis. EML4-ALK fusion gene was analyzed by real-time polymerase chain reaction, and EGFR mutations were analyzed by amplified refractory mutation system. Among the 694 patients, 60 (8.65%) patients had EML4-ALK fusions. In continuity correction χ (2) test analysis, EML4-ALK fusion gene was correlated with sex, age, smoking status, and histology, but no significant association was observed between EML4-ALK fusion gene and clinical stage. A total of 147 (21.18%) patients had EGFR mutations. In concordance with previous reports, EGFR mutation was correlated with age, smoking status, histology, and clinical stage, whereas patient age was not significantly associated with EGFR mutation. Meanwhile, to our surprise, six (0.86%) patients had coexisting EML4-ALK fusions and EGFR mutations. EML4-ALK fusion gene defines a new molecular subset in patients with NSCLC. Six patients who harbored both EML4-ALK fusion genes and EGFR mutations were identified in our study. The EGFR mutations and the EML4-ALK fusion genes are coexistent.

Detection of EML4-ALK fusion gene and features associated with EGFR mutations in Chinese patients with non-small-cell lung cancer

PubMed Central

Wen, Miaomiao; Wang, Xuejiao; Sun, Ying; Xia, Jinghua; Fan, Liangbo; Xing, Hao; Zhang, Zhipei; Li, Xiaofei

2016-01-01

Purpose Echinoderm microtubule-associated protein-like 4–anaplastic lymphoma kinase (EML4-ALK) and epidermal growth factor receptor (EGFR) define specific molecular subsets of lung cancer with distinct clinical features. We aimed at revealing the clinical features of EML4-ALK fusion gene and EGFR mutation in non-small-cell lung cancer (NSCLC). Methods We enrolled 694 Chinese patients with NSCLC for analysis. EML4-ALK fusion gene was analyzed by real-time polymerase chain reaction, and EGFR mutations were analyzed by amplified refractory mutation system. Results Among the 694 patients, 60 (8.65%) patients had EML4-ALK fusions. In continuity correction χ2 test analysis, EML4-ALK fusion gene was correlated with sex, age, smoking status, and histology, but no significant association was observed between EML4-ALK fusion gene and clinical stage. A total of 147 (21.18%) patients had EGFR mutations. In concordance with previous reports, EGFR mutation was correlated with age, smoking status, histology, and clinical stage, whereas patient age was not significantly associated with EGFR mutation. Meanwhile, to our surprise, six (0.86%) patients had coexisting EML4-ALK fusions and EGFR mutations. Conclusion EML4-ALK fusion gene defines a new molecular subset in patients with NSCLC. Six patients who harbored both EML4-ALK fusion genes and EGFR mutations were identified in our study. The EGFR mutations and the EML4-ALK fusion genes are coexistent. PMID:27103824

Integrated Multi-Aperture Sensor and Navigation Fusion

DTIC Science & Technology

2010-02-01

Visio, Springer-Verlag Inc., New York, 2004. [3] R. G. Brown and P. Y. C. Hwang , Introduction to Random Signals and Applied Kalman Filtering, Third...formulate Kalman filter vision/inertial measurement observables for other images without the need to know (or measure) their feature ranges. As compared...Internal Data Fusion Multi-aperture/INS data fusion is formulated in the feature domain using the complementary Kalman filter methodology [3]. In this

Classifying four-category visual objects using multiple ERP components in single-trial ERP.

PubMed

Qin, Yu; Zhan, Yu; Wang, Changming; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan; Wu, Xia; Hu, Bin

2016-08-01

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

Fusion Blanket Coolant Section Criteria, Methodology, and Results

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

DeMuth, J. A.; Meier, W. R.; Jolodosky, A.

2015-10-02

The focus of this LDRD was to explore potential Li alloys that would meet the tritium breeding and blanket cooling requirements but with reduced chemical reactivity, while maintaining the other attractive features of pure Li breeder/coolant. In other fusion approaches (magnetic fusion energy or MFE), 17Li- 83Pb alloy is used leveraging Pb’s ability to maintain high TBR while lowering the levels of lithium in the system. Unfortunately this alloy has a number of potential draw-backs. Due to the high Pb content, this alloy suffers from very high average density, low tritium solubility, low system energy, and produces undesirable activation productsmore » in particular polonium. The criteria considered in the selection of a tritium breeding alloy are described in the following section.« less

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.

PubMed

Liu, Xingbin; Mei, Wenbo; Du, Huiqian

2018-02-13

In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images. Graphical abstract The detailed implementation of the proposed medical image fusion algorithm.

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.

Multispectral Palmprint Recognition Using a Quaternion Matrix

PubMed Central

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%. PMID:22666049

Multispectral palmprint recognition using a quaternion matrix.

PubMed

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.

PubMed

Iakovidis, Dimitris K; Keramidas, Eystratios G; Maroulis, Dimitris

2010-09-01

This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system. Copyright (c) 2010 Elsevier B.V. All rights reserved.

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.

SU-D-304-07: Application of Proton Boron Fusion Reaction to Radiation Therapy

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

Jung, J; Yoon, D; Shin, H

Purpose: we present the introduction of a therapy method using the proton boron fusion reaction. The purpose of this study is to verify the theoretical validity of proton boron fusion therapy using Monte Carlo simulations. Methods: After boron is accumulated in the tumor region, the emitted from outside the body proton can react with the boron in the tumor region. An increase of the proton’s maximum dose level is caused by the boron and only the tumor cell is damaged more critically. In addition, a prompt gamma ray is emitted from the proton boron reaction point. Here we show thatmore » the effectiveness of the proton boron fusion therapy (PBFT) was verified using Monte Carlo simulations. Results: We found that a dramatic increase by more than half of the proton’s maximum dose level was induced by the boron in the tumor region. This increase occurred only when the proton’s maximum dose point was located within the boron uptake region (BUR). In addition, the 719 keV prompt gamma ray peak produced by the proton boron fusion reaction was positively detected. Conclusion: This therapy method features the advantages such as the application of Bragg-peak to the therapy, the accurate targeting of tumor, improved therapy effects, and the monitoring of the therapy region during treatment.« less

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.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

Robotics and local fusion

NASA Astrophysics Data System (ADS)

Emmerman, Philip J.

2005-05-01

Teams of robots or mixed teams of warfighters and robots on reconnaissance and other missions can benefit greatly from a local fusion station. A local fusion station is defined here as a small mobile processor with interfaces to enable the ingestion of multiple heterogeneous sensor data and information streams, including blue force tracking data. These data streams are fused and integrated with contextual information (terrain features, weather, maps, dynamic background features, etc.), and displayed or processed to provide real time situational awareness to the robot controller or to the robots themselves. These blue and red force fusion applications remove redundancies, lessen ambiguities, correlate, aggregate, and integrate sensor information with context such as high resolution terrain. Applications such as safety, team behavior, asset control, training, pattern analysis, etc. can be generated or enhanced by these fusion stations. This local fusion station should also enable the interaction between these local units and a global information world.

Tyrosine kinase fusion genes in pediatric BCR-ABL1-like acute lymphoblastic leukemia

PubMed Central

Boer, Judith M.; Steeghs, Elisabeth M.P.; Marchante, João R.M.; Boeree, Aurélie; Beaudoin, James J.; Berna Beverloo, H.; Kuiper, Roland P.; Escherich, Gabriele; van der Velden, Vincent H.J.; van der Schoot, C. Ellen; de Groot-Kruseman, Hester A.; Pieters, Rob; den Boer, Monique L.

2017-01-01

Approximately 15% of pediatric B cell precursor acute lymphoblastic leukemia (BCP-ALL) is characterized by gene expression similar to that of BCR-ABL1-positive disease and unfavorable prognosis. This BCR-ABL1-like subtype shows a high frequency of B-cell development gene aberrations and tyrosine kinase-activating lesions. To evaluate the clinical significance of tyrosine kinase gene fusions in children with BCP-ALL, we studied the frequency of recently identified tyrosine kinase fusions, associated genetic features, and prognosis in a representative Dutch/German cohort. We identified 14 tyrosine kinase fusions among 77 BCR-ABL1-like cases (18%) and none among 76 non-BCR-ABL1-like B-other cases. Novel exon fusions were identified for RCSD1-ABL2 and TERF2-JAK2. JAK2 mutation was mutually exclusive with tyrosine kinase fusions and only occurred in cases with high CRLF2 expression. The non/late response rate and levels of minimal residual disease in the fusion-positive BCR-ABL1-like group were higher than in the non-BCR-ABL1-like B-others (p<0.01), and also higher, albeit not statistically significant, compared with the fusion-negative BCR-ABL1-like group. The 8-year cumulative incidence of relapse in the fusion-positive BCR-ABL1-like group (35%) was comparable with that in the fusion-negative BCR-ABL1-like group (35%), and worse than in the non-BCR-ABL1-like B-other group (17%, p=0.07). IKZF1 deletions, predominantly other than the dominant-negative isoform and full deletion, co-occurred with tyrosine kinase fusions. This study shows that tyrosine kinase fusion-positive cases are a high-risk subtype of BCP-ALL, which warrants further studies with specific kinase inhibitors to improve outcome. PMID:27894077

Research on HDR image fusion algorithm based on Laplace pyramid weight transform with extreme low-light CMOS

NASA Astrophysics Data System (ADS)

Guan, Wen; Li, Li; Jin, Weiqi; Qiu, Su; Zou, Yan

2015-10-01

Extreme-Low-Light CMOS has been widely applied in the field of night-vision as a new type of solid image sensor. But if the illumination in the scene has drastic changes or the illumination is too strong, Extreme-Low-Light CMOS can't both clearly present the high-light scene and low-light region. According to the partial saturation problem in the field of night-vision, a HDR image fusion algorithm based on the Laplace Pyramid was researched. The overall gray value and the contrast of the low light image is very low. We choose the fusion strategy based on regional average gradient for the top layer of the long exposure image and short exposure image, which has rich brightness and textural features. The remained layers which represent the edge feature information of the target are based on the fusion strategy based on regional energy. In the process of source image reconstruction with Laplacian pyramid image, we compare the fusion results with four kinds of basal images. The algorithm is tested using Matlab and compared with the different fusion strategies. We use information entropy, average gradient and standard deviation these three objective evaluation parameters for the further analysis of the fusion result. Different low illumination environment experiments show that the algorithm in this paper can rapidly get wide dynamic range while keeping high entropy. Through the verification of this algorithm features, there is a further application prospect of the optimized algorithm. Keywords: high dynamic range imaging, image fusion, multi-exposure image, weight coefficient, information fusion, Laplacian pyramid transform.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method

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

Yan, Shiju; Qian, Wei; Guan, Yubao

2016-06-15

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk prediction performance for stage I NSCLS patients by integrating oversampling, feature selection, and score fusion techniques and develop an optimal prediction model. Methods: A dataset involving 94 early stage lung cancer patients was retrospectively assembled, which includes CT images, nine clinical and biological (CB) markers, and outcome of 3-yr disease-free survival (DFS) after surgery. Among the 94 patients, 74 remained DFS and 20 had cancer recurrence. Applying a computer-aided detection scheme, tumors were segmented from the CT images and 35 quantitative image (QI) features were initiallymore » computed. Two normalized Gaussian radial basis function network (RBFN) based classifiers were built based on QI features and CB markers separately. To improve prediction performance, the authors applied a synthetic minority oversampling technique (SMOTE) and a BestFirst based feature selection method to optimize the classifiers and also tested fusion methods to combine QI and CB based prediction results. Results: Using a leave-one-case-out cross-validation (K-fold cross-validation) method, the computed areas under a receiver operating characteristic curve (AUCs) were 0.716 ± 0.071 and 0.642 ± 0.061, when using the QI and CB based classifiers, respectively. By fusion of the scores generated by the two classifiers, AUC significantly increased to 0.859 ± 0.052 (p < 0.05) with an overall prediction accuracy of 89.4%. Conclusions: This study demonstrated the feasibility of improving prediction performance by integrating SMOTE, feature selection, and score fusion techniques. Combining QI features and CB markers and performing SMOTE prior to feature selection in classifier training enabled RBFN based classifier to yield improved prediction accuracy.« less

Diffuse gliomas with FGFR3-TACC3 fusion have characteristic histopathological and molecular features.

PubMed

Bielle, Franck; Di Stefano, Anna-Luisa; Meyronet, David; Picca, Alberto; Villa, Chiara; Bernier, Michèle; Schmitt, Yohann; Giry, Marine; Rousseau, Audrey; Figarella-Branger, Dominique; Maurage, Claude-Alain; Uro-Coste, Emmanuelle; Lasorella, Anna; Iavarone, Antonio; Sanson, Marc; Mokhtari, Karima

2017-10-04

Adult glioblastomas, IDH-wildtype represent a heterogeneous group of diseases. They are resistant to conventional treatment by concomitant radiochemotherapy and carry a dismal prognosis. The discovery of oncogenic gene fusions in these tumors has led to prospective targeted treatments, but identification of these rare alterations in practice is challenging. Here, we report a series of 30 adult diffuse gliomas with an in frame FGFR3-TACC3 oncogenic fusion (n = 27 WHO grade IV and n = 3 WHO grade II) as well as their histological and molecular features. We observed recurrent morphological features (monomorphous ovoid nuclei, nuclear palisading and thin parallel cytoplasmic processes, endocrinoid network of thin capillaries) associated with frequent microcalcifications and desmoplasia. We report a constant immunoreactivity for FGFR3, which is a valuable method for screening for the FGFR3-TACC3 fusion with 100% sensitivity and 92% specificity. We confirmed the associated molecular features (typical genetic alterations of glioblastoma, except the absence of EGFR amplification, and an increased frequency of CDK4 and MDM2 amplifications). FGFR3 immunopositivity is a valuable tool to identify gliomas that are likely to harbor the FGFR3-TACC3 fusion for inclusion in targeted therapeutic trials. © 2017 International Society of Neuropathology.

Primary Renal Hybrid Low-grade Fibromyxoid Sarcoma-Sclerosing Epithelioid Fibrosarcoma: An Unusual Pediatric Case With EWSR1-CREB3L1 Fusion.

PubMed

Mok, Yingting; Pang, Yin Huei; Sanjeev, Jain Sudhanshi; Kuick, Chik Hong; Chang, Kenneth Tou-En

2018-01-01

Low-grade fibromyxoid sarcoma (LGFMS) and sclerosing epithelioid fibrosarcoma (SEF) are rare tumors with distinct sets of morphological features, both characterized by MUC4 immunoreactivity. Tumors exhibiting features of both entities are considered hybrid LGFMS-SEF lesions. While the majority of LGFMS cases are characterized by FUS-CREB3L2 gene fusions, most cases of pure SEF show EWSR1 gene rearrangements. In the largest study of hybrid LGFMS-SEF tumors to date, all cases exhibited FUS rearrangements, a similar genetic profile to LGFMS. We herein describe the clinicopathological features and genetic findings of a case of primary renal hybrid LGFMS-SEF occurring in a 10-year-old child, with disseminated metastases. Fusion gene detection using a next-generation sequencing-based anchored multiplex PCR technique (Archer FusionPlex Sarcoma Panel) was performed on both the primary renal tumor that showed the morphology of a LGFMS, and a cervical metastasis that showed the morphology of SEF. An EWSR1-CREB3L1 gene fusion occurring between exon 11 of EWSR1 and exon 6 of CREB3L1 was present in both the LGFMS and SEF components. This unusual case provides evidence that a subset of hybrid LGFMS-SEF harbor EWSR1-CREB3L1 gene fusions. In this case, these features were associated with an aggressive clinical course, with disease-associated mortality occurring within 12 months of diagnosis.

Development of high-availability ATCA/PCIe data acquisition instrumentation

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

Correia, Miguel; Sousa, Jorge; Batista, Antonio J.N.

2015-07-01

Latest Fusion energy experiments envision a quasi-continuous operation regime. In consequence, the largest experimental devices, currently in development, specify high-availability (HA) requirements for the whole plant infrastructure. HA features enable the whole facility to perform seamlessly in the case of failure of any of its components, coping with the increasing duration of plasma discharges (steady-state) and assuring safety of equipment, people, environment and investment. IPFN developed a control and data acquisition system, aiming for fast control of advanced Fusion devices, which is thus required to provide such HA features. The system is based on in-house developed Advanced Telecommunication Computing Architecturemore » (ATCA) instrumentation modules - IO blades and data switch blades, establishing a PCIe network on the ATCA shelf's back-plane. The data switch communicates to an external host computer through a PCIe data network. At the hardware management level, the system architecture takes advantage of ATCA native redundancy and hot swap specifications to implement fail-over substitution of IO or data switch blades. A redundant host scheme is also supported by the ATCA/PCIe platform. At the software level, PCIe provides implementation of hot plug services, which translate the hardware changes to the corresponding software/operating system devices. The paper presents how the ATCA and PCIe based system can be setup to perform with the desired degree of HA, thus being suitable for advanced Fusion control and data acquisition systems. (authors)« less

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%).

Spherical torus fusion reactor

DOEpatents

Peng, Yueng-Kay M.

1989-04-04

A fusion reactor is provided having a near spherical-shaped plasma with a modest central opening through which straight segments of toroidal field coils extend that carry electrical current for generating a toroidal magnet plasma confinement fields. By retaining only the indispensable components inboard of the plasma torus, principally the cooled toroidal field conductors and in some cases a vacuum containment vessel wall, the fusion reactor features an exceptionally small aspect ratio (typically about 1.5), a naturally elongated plasma cross section without extensive field shaping, requires low strength magnetic containment fields, small size and high beta. These features combine to produce a spherical torus plasma in a unique physics regime which permits compact fusion at low field and modest cost.

Spherical torus fusion reactor

DOEpatents

Peng, Yueng-Kay M.

1989-01-01

A fusion reactor is provided having a near spherical-shaped plasma with a modest central opening through which straight segments of toroidal field coils extend that carry electrical current for generating a toroidal magnet plasma confinement fields. By retaining only the indispensable components inboard of the plasma torus, principally the cooled toroidal field conductors and in some cases a vacuum containment vessel wall, the fusion reactor features an exceptionally small aspect ratio (typically about 1.5), a naturally elongated plasma cross section without extensive field shaping, requires low strength magnetic containment fields, small size and high beta. These features combine to produce a spherical torus plasma in a unique physics regime which permits compact fusion at low field and modest cost.

Adjacent level effects of bi level disc replacement, bi level fusion and disc replacement plus fusion in cervical spine--a finite element based study.

PubMed

Faizan, Ahmad; Goel, Vijay K; Biyani, Ashok; Garfin, Steven R; Bono, Christopher M

2012-03-01

Studies delineating the adjacent level effect of single level disc replacement systems have been reported in literature. The aim of this study was to compare the adjacent level biomechanics of bi-level disc replacement, bi-level fusion and a construct having adjoining level disc replacement and fusion system. In total, biomechanics of four models- intact, bi level disc replacement, bi level fusion and fusion plus disc replacement at adjoining levels- was studied to gain insight into the effects of various instrumentation systems on cranial and caudal adjacent levels using finite element analysis (73.6N+varying moment). The bi-level fusion models are more than twice as stiff as compared to the intact model during flexion-extension, lateral bending and axial rotation. Bi-level disc replacement model required moments lower than intact model (1.5Nm). Fusion plus disc replacement model required moment 10-25% more than intact model, except in extension. Adjacent level motions, facet loads and endplate stresses increased substantially in the bi-level fusion model. On the other hand, adjacent level motions, facet loads and endplate stresses were similar to intact for the bi-level disc replacement model. For the fusion plus disc replacement model, adjacent level motions, facet loads and endplate stresses were closer to intact model rather than the bi-level fusion model, except in extension. Based on our finite element analysis, fusion plus disc replacement procedure has less severe biomechanical effects on adjacent levels when compared to bi-level fusion procedure. Bi-level disc replacement procedure did not have any adverse mechanical effects on adjacent levels. Copyright © 2011 Elsevier Ltd. All rights reserved.

Assessing the Mirror Fusion Propulsion System (MFPS) Concept as Applied to Outer-Solar-System (OSS) Missions

NASA Astrophysics Data System (ADS)

Carpenter, Scott A.; Deveny, Marc E.; Schulze, Norman R.; Gatti, Raymond C.; Peters, Micheal B.

1994-07-01

In this paper, we strive to achieve three goals: (1) to describe a continuous-thrusting space-fusion-propulsion engine called the Mirror Fusion Propulsion System (MFPS), (2) to describe MFPS' ability to accomplish two candidate outer-solar-system (OSS) missions using various levels of advanced technology identified in the laboratory, and (3) to describe some interesting safety features of MFPS that include continuous mission-abort capability, magnetic-field-shielding against solar particle events (SPE), and performance of in-orbit characterization of the target body's natural resources (prior to human landings) using fusion-neutrons, x-rays, and possibly the neutralized thrust beam. The first OSS mission discussed is a mission to the Saturnian system, primarily exploration and resource- characterization driven, with emphasis on minimizing the Earth-to-Saturn and return-trip flight times. The other OSS mission discussed is an economically-driven mission to Uranus, stopping first to perform in-orbit resource characterization of the major moons of Uranus prior to human landing, and then returning to earth with a payload consisting of 3He (removed from the Uranian atmosphere or extracted from the Uranian moons) to be used in a future earth-based fusion-power industry.

Warthin-like Mucoepidermoid Carcinoma: A Combined Study of Fluorescence In Situ Hybridization and Whole-slide Imaging.

PubMed

Ishibashi, Kenichiro; Ito, Yohei; Masaki, Ayako; Fujii, Kana; Beppu, Shintaro; Sakakibara, Takeo; Takino, Hisashi; Takase, Hiroshi; Ijichi, Kei; Shimozato, Kazuo; Inagaki, Hiroshi

2015-11-01

There has been some debate as to whether a subset of metaplastic Warthin tumors (mWTs) harbor the mucoepidermoid carcinoma (MEC)-associated CRTC1-MAML2 fusion. We analyzed 15 tumors originally diagnosed as mWT (mWT-like tumors), 2 of which had concurrent MECs. We looked for the CRTC1/3-MAML2 fusion transcripts and performed immunohistochemistry for p63 and fluorescence in situ hybridization (FISH) for the MAML2 split. To localize MAML2 split-positive cells at the cellular level, whole tumor tissue sections were digitalized (whole-slide imaging [WSI]). The CRTC1-MAML2, but not CRTC3-MAML2 was detected in 5/15 mWT-like tumors. FISH-WSI results showed that all epithelial cells harbored the MAML2 split in fusion-positive mWT-like tumors and were totally negative in fusion-negative mWT-like tumors. A review of the hematoxylin and eosin-stained slides showed that morphology of the "metaplastic" epithelium was virtually indistinguishable between fusion-positive and fusion-negative tumors. However, oncocytic bilayered tumor epithelium, characteristic to typical WT, was always found somewhere in the fusion-negative tumors but not in the fusion-positive tumors. This distinguishing histologic finding enabled 5 pathologists to easily differentiate the 2 tumor groups with 100% accuracy. The age and sex distribution of fusion-positive mWT-like tumor cases was similar to that of fusion-positive MEC cases and significantly different from those of fusion-negative mWT-like tumor and typical WT cases. In addition, only fusion-positive mWT-like tumors possessed concurrent low-grade MECs. In conclusion, a subset of mWT-like tumors were positive for the CRTC1-MAML2 fusion and had many features that are more in accord with MEC than with WT. The term Warthin-like MEC should be considered for fusion-positive mWT-like tumors.

Investigations of image fusion

NASA Astrophysics Data System (ADS)

Zhang, Zhong

1999-12-01

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for the purpose of human visual perception or further image processing tasks. In this thesis, a region-based fusion algorithm using the wavelet transform is proposed. The identification of important features in each image, such as edges and regions of interest, are used to guide the fusion process. The idea of multiscale grouping is also introduced and a generic image fusion framework based on multiscale decomposition is studied. The framework includes all of the existing multiscale-decomposition- based fusion approaches we found in the literature which did not assume a statistical model for the source images. Comparisons indicate that our framework includes some new approaches which outperform the existing approaches for the cases we consider. Registration must precede our fusion algorithms. So we proposed a hybrid scheme which uses both feature-based and intensity-based methods. The idea of robust estimation of optical flow from time- varying images is employed with a coarse-to-fine multi- resolution approach and feature-based registration to overcome some of the limitations of the intensity-based schemes. Experiments show that this approach is robust and efficient. Assessing image fusion performance in a real application is a complicated issue. In this dissertation, a mixture probability density function model is used in conjunction with the Expectation- Maximization algorithm to model histograms of edge intensity. Some new techniques are proposed for estimating the quality of a noisy image of a natural scene. Such quality measures can be used to guide the fusion. Finally, we study fusion of images obtained from several copies of a new type of camera developed for video surveillance. Our techniques increase the capability and reliability of the surveillance system and provide an easy way to obtain 3-D information of objects in the space monitored by the system.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

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

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.« less

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature. PMID:26843260

Development of emergent processing loops as a system of systems concept

NASA Astrophysics Data System (ADS)

Gainey, James C., Jr.; Blasch, Erik P.

1999-03-01

This paper describes an engineering approach toward implementing the current neuroscientific understanding of how the primate brain fuses, or integrates, 'information' in the decision-making process. We describe a System of Systems (SoS) design for improving the overall performance, capabilities, operational robustness, and user confidence in Identification (ID) systems and show how it could be applied to biometrics security. We use the Physio-associative temporal sensor integration algorithm (PATSIA) which is motivated by observed functions and interactions of the thalamus, hippocampus, and cortical structures in the brain. PATSIA utilizes signal theory mathematics to model how the human efficiently perceives and uses information from the environment. The hybrid architecture implements a possible SoS-level description of the Joint Directors of US Laboratories for Fusion Working Group's functional description involving 5 levels of fusion and their associated definitions. This SoS architecture propose dynamic sensor and knowledge-source integration by implementing multiple Emergent Processing Loops for predicting, feature extracting, matching, and Searching both static and dynamic database like MSTAR's PEMS loops. Biologically, this effort demonstrates these objectives by modeling similar processes from the eyes, ears, and somatosensory channels, through the thalamus, and to the cortices as appropriate while using the hippocampus for short-term memory search and storage as necessary. The particular approach demonstrated incorporates commercially available speaker verification and face recognition software and hardware to collect data and extract features to the PATSIA. The PATSIA maximizes the confidence levels for target identification or verification in dynamic situations using a belief filter. The proof of concept described here is easily adaptable and scaleable to other military and nonmilitary sensor fusion applications.

Technology transfer: the key to fusion commercialization

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

Burnett, S.C.

1981-01-01

The paper brings to light some of the reasons why technology transfer is difficult in fusion, examines some of the impediments to the process, and finally looks at a successful example of technology transfer. The paper considers some subjective features of fusion - one might call them the sociology of fusion - that are none the less real and that serve as impediments to technology transfer.

[Detection of ALK, ROS1 and RET fusion genes in non-small cell lung cancer patients and its clinicopathologic correlation].

PubMed

Zhong, Shan; Zhang, Haiping; Bai, Dongyu; Gao, Dehong; Zheng, Jie; Ding, Yi

2015-09-01

To study the prevalence of ALK, ROS1 and RET fusion genes in non-small cell lung cancer (NSCLC), and its correlation with clinicopathologic features. Formalin-fixed and paraffin-embedded tissue sections from samples of 302 patients with NSCLC were screened for ALK, ROS1, RET fusions by real-time polymerase chain reaction (PCR). All of the cases were validated by Sanger DNA sequencing. The relationship between ALK, ROS1, RET fusion genes and clinicopathologic features were analyzed. In the cohort of 302 NSCLC samples, 3.97% (12/302) were found to contain ALK fusion genes, including 3 cases with E13; A20 gene fusion, 3 cases with E6; A20 gene fusion and 3 cases with E20; A20 gene fusion. There was no statistically significant difference in patient's gender, age, smoking history and histologic type. Moreover, in the 302 NSCLC samples studied, 3.97% (12/302) were found to contain ROS1 fusion genes, with CD74-ROS1 fusion identified in 9 cases. There was no statistically significant difference in patients' gender, age, smoking history and histologic type. One non-smoking elderly female patient with pulmonary adenocarcinoma had RET gene fusion. None of the cases studied had concurrent ALK, ROS1 and RET mutations. The ALK, ROS1 and RET fusion gene mutation rates in NSCLC are low, they represent some specific molecular subtypes of NSCLC. Genetic testing has significant meaning to guide clinical targeted therapy.

The biomechanics of a multilevel lumbar spine hybrid using nucleus replacement in conjunction with fusion.

PubMed

Dahl, Michael C; Ellingson, Arin M; Mehta, Hitesh P; Huelman, Justin H; Nuckley, David J

2013-02-01

Degenerative disc disease is commonly a multilevel pathology with varying deterioration severity. The use of fusion on multiple levels can significantly affect functionality and has been linked to persistent adjacent disc degeneration. A hybrid approach of fusion and nucleus replacement (NR) has been suggested as a solution for mildly degenerated yet painful levels adjacent to fusion. To compare the biomechanical metrics of different hybrid implant constructs, hypothesizing that an NR+fusion hybrid would be similar to a single-level fusion and perform more naturally compared with a two-level fusion. A cadaveric in vitro repeated-measures study was performed to evaluate a multilevel lumbar NR+fusion hybrid. Eight cadaveric spines (L3-S1) were tested in a Spine Kinetic Simulator (Instron, Norwood, MA, USA). Pure moments of 8 Nm were applied in flexion/extension, lateral bending, and axial rotation as well as compression loading. Specimens were tested intact; fused (using transforaminal lumbar interbody fusion instrumentation with posterior rods) at L5-S1; with a nuclectomy at L4-L5 including fusion at L5-S1; with NR at L4-L5 including fusion at L5-S1; and finally with a two-level fusion spanning L4-S1. Repeated-measures analysis of variance and corrected t tests were used to statistically compare outcomes. The NR+fusion hybrid and single-level fusion exhibited no statistical differences for range of motion (ROM), stiffness, neutral zone, and intradiscal pressure in all loading directions. Compared with two-level fusion, the hybrid affords the construct 41.9% more ROM on average. Two-level fusion stiffness was statistically higher than all other constructs and resulted in significantly lower ROM in flexion, extension, and lateral bending. The hybrid construct produced approximately half of the L3-L4 adjacent-level pressures as the two-level fusion case while generating similar pressures to the single-level fusion case. These data portend more natural functional outcomes and fewer adjacent disc complications for a multilevel NR+fusion hybrid compared with the classical two-level fusion. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

Real-Time Visual Tracking through Fusion Features

PubMed Central

Ruan, Yang; Wei, Zhenzhong

2016-01-01

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion. PMID:27347951

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

Image fusion algorithm based on energy of Laplacian and PCNN

NASA Astrophysics Data System (ADS)

Li, Meili; Wang, Hongmei; Li, Yanjun; Zhang, Ke

2009-12-01

Owing to the global coupling and pulse synchronization characteristic of pulse coupled neural networks (PCNN), it has been proved to be suitable for image processing and successfully employed in image fusion. However, in almost all the literatures of image processing about PCNN, linking strength of each neuron is assigned the same value which is chosen by experiments. This is not consistent with the human vision system in which the responses to the region with notable features are stronger than that to the region with nonnotable features. It is more reasonable that notable features, rather than the same value, are employed to linking strength of each neuron. As notable feature, energy of Laplacian (EOL) is used to obtain the value of linking strength in PCNN in this paper. Experimental results demonstrate that the proposed algorithm outperforms Laplacian-based, wavelet-based, PCNN -based fusion algorithms.

Weighted fusion of depth and inertial data to improve view invariance for real-time human action recognition

NASA Astrophysics Data System (ADS)

Chen, Chen; Hao, Huiyan; Jafari, Roozbeh; Kehtarnavaz, Nasser

2017-05-01

This paper presents an extension to our previously developed fusion framework [10] involving a depth camera and an inertial sensor in order to improve its view invariance aspect for real-time human action recognition applications. A computationally efficient view estimation based on skeleton joints is considered in order to select the most relevant depth training data when recognizing test samples. Two collaborative representation classifiers, one for depth features and one for inertial features, are appropriately weighted to generate a decision making probability. The experimental results applied to a multi-view human action dataset show that this weighted extension improves the recognition performance by about 5% over equally weighted fusion deployed in our previous fusion framework.

Variable temporo-insular cortex neuroanatomy in primates suggests a bottleneck effect in eastern gorillas

PubMed Central

Barks, Sarah K.; Bauernfeind, Amy L.; Bonar, Christopher J.; Cranfield, Michael R.; de Sousa, Alexandra A.; Erwin, Joseph M.; Hopkins, William D.; Lewandowski, Albert H.; Mudakikwa, Antoine; Phillips, Kimberley A.; Raghanti, Mary Ann; Stimpson, Cheryl D.; Hof, Patrick R.; Zilles, Karl; Sherwood, Chet C.

2013-01-01

In this study, we describe an atypical neuroanatomical feature present in several primate species that involves a fusion between the temporal lobe (often including Heschl’s gyrus in great apes) and the posterior dorsal insula, such that a portion of insular cortex forms an isolated pocket medial to the Sylvian fissure. We assessed the frequency of this fusion in 56 primate species (including apes, Old World monkeys, New World monkeys, and strepsirrhines) using either magnetic resonance images or histological sections. A fusion between temporal cortex and posterior insula was present in 22 species (7 apes, 2 Old World monkeys, 4 New World monkeys, and 9 strepsirrhines). The temporo-insular fusion was observed in most eastern gorilla (Gorilla beringei beringei and G. b. graueri) specimens (62% and 100% of cases, respectively) but less frequently in other great apes and was never found in humans. We further explored the histology of this fusion in eastern gorillas by examining the cyto- and myeloarchitecture within this region, and observed that the degree to which deep cortical layers and white matter are incorporated into the fusion varies among individuals within a species. We suggest that fusion between temporal and insular cortex is an example of a relatively rare neuroanatomical feature that has become more common in eastern gorillas, possibly as the result of a population bottleneck effect. Characterizing the phylogenetic distribution of this morphology highlights a derived feature of these great apes. PMID:23939630

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.

Two Horizons of Fusion

ERIC Educational Resources Information Center

Lo, Mun Ling; Chik, Pakey Pui Man

2016-01-01

In this paper, we aim to differentiate the internal and external horizons of "fusion." "Fusion" in the internal horizon relates to the structure and meaning of the object of learning as experienced by the learner. It clarifies the interrelationships among an object's critical features and aspects. It also illuminates the…

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.

News video story segmentation method using fusion of audio-visual features

NASA Astrophysics Data System (ADS)

Wen, Jun; Wu, Ling-da; Zeng, Pu; Luan, Xi-dao; Xie, Yu-xiang

2007-11-01

News story segmentation is an important aspect for news video analysis. This paper presents a method for news video story segmentation. Different form prior works, which base on visual features transform, the proposed technique uses audio features as baseline and fuses visual features with it to refine the results. At first, it selects silence clips as audio features candidate points, and selects shot boundaries and anchor shots as two kinds of visual features candidate points. Then this paper selects audio feature candidates as cues and develops different fusion method, which effectively using diverse type visual candidates to refine audio candidates, to get story boundaries. Experiment results show that this method has high efficiency and adaptability to different kinds of news video.

Direct fusion of geostationary meteorological satellite visible and infrared images based on thermal physical properties.

PubMed

Han, Lei; Wulie, Buzha; Yang, Yiling; Wang, Hongqing

2015-01-05

This study investigated a novel method of fusing visible (VIS) and infrared (IR) images with the major objective of obtaining higher-resolution IR images. Most existing image fusion methods focus only on visual performance and many fail to consider the thermal physical properties of the IR images, leading to spectral distortion in the fused image. In this study, we use the IR thermal physical property to correct the VIS image directly. Specifically, the Stefan-Boltzmann Law is used as a strong constraint to modulate the VIS image, such that the fused result shows a similar level of regional thermal energy as the original IR image, while preserving the high-resolution structural features from the VIS image. This method is an improvement over our previous study, which required VIS-IR multi-wavelet fusion before the same correction method was applied. The results of experiments show that applying this correction to the VIS image directly without multi-resolution analysis (MRA) processing achieves similar results, but is considerably more computationally efficient, thereby providing a new perspective on VIS and IR image fusion.

Direct Fusion of Geostationary Meteorological Satellite Visible and Infrared Images Based on Thermal Physical Properties

PubMed Central

Han, Lei; Wulie, Buzha; Yang, Yiling; Wang, Hongqing

2015-01-01

This study investigated a novel method of fusing visible (VIS) and infrared (IR) images with the major objective of obtaining higher-resolution IR images. Most existing image fusion methods focus only on visual performance and many fail to consider the thermal physical properties of the IR images, leading to spectral distortion in the fused image. In this study, we use the IR thermal physical property to correct the VIS image directly. Specifically, the Stefan-Boltzmann Law is used as a strong constraint to modulate the VIS image, such that the fused result shows a similar level of regional thermal energy as the original IR image, while preserving the high-resolution structural features from the VIS image. This method is an improvement over our previous study, which required VIS-IR multi-wavelet fusion before the same correction method was applied. The results of experiments show that applying this correction to the VIS image directly without multi-resolution analysis (MRA) processing achieves similar results, but is considerably more computationally efficient, thereby providing a new perspective on VIS and IR image fusion. PMID:25569749

Multitask saliency detection model for synthetic aperture radar (SAR) image and its application in SAR and optical image fusion

NASA Astrophysics Data System (ADS)

Liu, Chunhui; Zhang, Duona; Zhao, Xintao

2018-03-01

Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.

Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

NASA Astrophysics Data System (ADS)

Bigdeli, Behnaz; Pahlavani, Parham

2017-01-01

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

A multimodal biometric authentication system based on 2D and 3D palmprint features

NASA Astrophysics Data System (ADS)

Aggithaya, Vivek K.; Zhang, David; Luo, Nan

2008-03-01

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features. Here, we aim to improve the accuracy and robustness of existing palmprint authentication systems using 3D palmprint features. The proposed system uses an active stereo technique, structured light, to capture 3D image or range data of the palm and a registered intensity image simultaneously. The surface curvature based method is employed to extract features from 3D palmprint and Gabor feature based competitive coding scheme is used for 2D representation. We individually analyze these representations and attempt to combine them with score level fusion technique. Our experiments on a database of 108 subjects achieve significant improvement in performance (Equal Error Rate) with the integration of 3D features as compared to the case when 2D palmprint features alone are employed.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na

2014-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:24982935

Spermatological characters of the pseudophyllidean cestode Bothriocephalus scorpii (Müller, 1776).

PubMed

Levron, Céline; Brunanská, Magdaléna; Poddubnaya, Larisa G

2006-06-01

Spermiogenesis of Bothriocephalus scorpii (Cestoda, Pseudophyllidea) includes an orthogonal development of two flagella, followed by a flagellar rotation and a proximo-distal fusion with the median cytoplasmic process. The fusion occurs at the level of four attachment zones. The presence of dense material in the apical region of the differentiation zone in the early stage of spermiogenesis appears to be a characteristic feature for the Pseudophyllidea. The mature spermatozoon possesses two axonemes of 9+"1" pattern of the Trepaxonemata, nucleus, cortical microtubules, electron-dense granules and crested body. The anterior part of the gamete exhibits a centriole surrounded by electron-dense tubular structures arranged as incomplete spiral. When the crested body disappears, the electron-dense tubular structures are arranged into a ring encircling the axoneme. The electron-dense tubular structures and their arrangement appear to be a specific feature for the clade "Bothriocephalidea". The organization of the posterior extremity of the gamete with the nucleus is described for the first time in the Pseudophyllidea.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng

2018-01-01

Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

PubMed Central

Men, Hong; Shi, Yan; Fu, Songlin; Jiao, Yanan; Qiao, Yu; Liu, Jingjing

2017-01-01

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively. PMID:28753917

Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

PubMed

Oken, Barry S; Orhan, Umut; Roark, Brian; Erdogmus, Deniz; Fowler, Andrew; Mooney, Aimee; Peters, Betts; Miller, Meghan; Fried-Oken, Melanie B

2014-05-01

Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

Cervical Stand-Alone Polyetheretherketone Cage versus Zero-Profile Anchored Spacer in Single-Level Anterior Cervical Discectomy and Fusion : Minimum 2-Year Assessment of Radiographic and Clinical Outcome.

PubMed

Cho, Hyun-Jun; Hur, Junseok W; Lee, Jang-Bo; Han, Jin-Sol; Cho, Tai-Hyoung; Park, Jung-Yul

2015-08-01

We compared the clinical and radiographic outcomes of stand-alone polyetheretherketone (PEEK) cage and Zero-Profile anchored spacer (Zero-P) for single level anterior cervical discectomy and fusion (ACDF). We retrospectively reviewed 121 patients who underwent single level ACDF within 2 years (Jan 2011-Jan 2013) in a single institute. Total 50 patients were included for the analysis who were evaluated more than 2-year follow-up. Twenty-nine patients were allocated to the cage group (m : f=19 : 10) and 21 for Zero-P group (m : f=12 : 9). Clinical (neck disability index, visual analogue scale arm and neck) and radiographic (Cobb angle-segmental and global cervical, disc height, vertebral height) assessments were followed at pre-operative, immediate post-operative, post-3, 6, 12, and 24 month periods. Demographic features and the clinical outcome showed no difference between two groups. The change between final follow-up (24 months) and immediate post-op of Cobb-segmental angle (p=0.027), disc height (p=0.002), vertebral body height (p=0.033) showed statistically better outcome for the Zero-P group than the cage group, respectively. The Zero-Profile anchored spacer has some advantage after cage for maintaining segmental lordosis and lowering subsidence rate after single level anterior cervical discectomy and fusion.

Five major controversial issues about fusion level selection in corrective surgery for adolescent idiopathic scoliosis: a narrative review.

PubMed

Lee, Choon Sung; Hwang, Chang Ju; Lee, Dong-Ho; Cho, Jae Hwan

2017-07-01

Shoulder imbalance, coronal decompensation, and adding-on phenomenon following corrective surgery in patients with adolescent idiopathic scoliosis are known to be related to the fusion level selected. Although many studies have assessed the appropriate selection of the proximal and distal fusion level, no definite conclusions have been drawn thus far. We aimed to assess the problems with fusion level selection for corrective surgery in patients with adolescent idiopathic scoliosis, and to enhance understanding about these problems. This study is a narrative review. We conducted a literature search of fusion level selection in corrective surgery for adolescent idiopathic scoliosis. Accordingly, we selected and reviewed five debatable topics related to fusion level selection: (1) selective thoracic fusion; (2) selective thoracolumbar-lumbar (TL-L) fusion; (3) adding-on phenomenon; (4) distal fusion level selection for major TL-L curves; and (5) proximal fusion level selection and shoulder imbalance. Selective fusion can be chosen in specific curve types, although there is a risk of coronal decompensation or adding-on phenomenon. Generally, wider indications for selective fusions are usually associated with more frequent complications. Despite the determination of several indications for selective fusion to avoid such complications, no clear guidelines have been established. Although authors have suggested various criteria to prevent the adding-on phenomenon, no consensus has been reached on the appropriate selection of lower instrumented vertebra. The fusion level selection for major TL-L curves primarily focuses on whether distal fusion can terminate at L3, a topic that remains unclear. Furthermore, because of the presence of several related factors and complications, proximal level selection and shoulder imbalance has been constantly debated and remains controversial from its etiology to its prevention. Although several difficult problems in the diagnosis and treatment of adolescent idiopathic scoliosis have been resolved by understanding its mechanism and via technical advancement, no definite guideline for fusion level selection has been established. A review of five major controversial issues about fusion level selection could provide better understanding of adolescent idiopathic scoliosis. We believe that a thorough validation study of the abovementioned controversial issues can help address them. Copyright © 2017 Elsevier Inc. All rights reserved.

From data to information and knowledge for geospatial applications

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B.; Yoon, T.

2006-12-01

An ever-increasing number of airborne and spaceborne data-acquisition missions with various sensors produce a glut of data. Sensory data rarely contains information in a explicit form such that an application can directly use it. The processing and analyzing of data constitutes a real bottleneck; therefore, automating the processes of gaining useful information and knowledge from the raw data is of paramount interest. This presentation is concerned with the transition from data to information and knowledge. With data we refer to the sensor output and we notice that data provide very rarely direct answers for applications. For example, a pixel in a digital image or a laser point from a LIDAR system (data) have no direct relationship with elevation changes of topographic surfaces or the velocity of a glacier (information, knowledge). We propose to employ the computer vision paradigm to extract information and knowledge as it pertains to a wide range of geoscience applications. After introducing the paradigm we describe the major steps to be undertaken for extracting information and knowledge from sensory input data. Features play an important role in this process. Thus we focus on extracting features and their perceptual organization to higher order constructs. We demonstrate these concepts with imaging data and laser point clouds. The second part of the presentation addresses the problem of combining data obtained by different sensors. An absolute prerequisite for successful fusion is to establish a common reference frame. We elaborate on the concept of sensor invariant features that allow the registration of such disparate data sets as aerial/satellite imagery, 3D laser point clouds, and multi/hyperspectral imagery. Fusion takes place on the data level (sensor registration) and on the information level. We show how fusion increases the degree of automation for reconstructing topographic surfaces. Moreover, fused information gained from the three sensors results in a more abstract surface representation with a rich set of explicit surface information that can be readily used by an analyst for applications such as change detection.

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

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.

Statistical image quantification toward optimal scan fusion and change quantification

NASA Astrophysics Data System (ADS)

Potesil, Vaclav; Zhou, Xiang Sean

2007-03-01

Recent advance of imaging technology has brought new challenges and opportunities for automatic and quantitative analysis of medical images. With broader accessibility of more imaging modalities for more patients, fusion of modalities/scans from one time point and longitudinal analysis of changes across time points have become the two most critical differentiators to support more informed, more reliable and more reproducible diagnosis and therapy decisions. Unfortunately, scan fusion and longitudinal analysis are both inherently plagued with increased levels of statistical errors. A lack of comprehensive analysis by imaging scientists and a lack of full awareness by physicians pose potential risks in clinical practice. In this paper, we discuss several key error factors affecting imaging quantification, studying their interactions, and introducing a simulation strategy to establish general error bounds for change quantification across time. We quantitatively show that image resolution, voxel anisotropy, lesion size, eccentricity, and orientation are all contributing factors to quantification error; and there is an intricate relationship between voxel anisotropy and lesion shape in affecting quantification error. Specifically, when two or more scans are to be fused at feature level, optimal linear fusion analysis reveals that scans with voxel anisotropy aligned with lesion elongation should receive a higher weight than other scans. As a result of such optimal linear fusion, we will achieve a lower variance than naïve averaging. Simulated experiments are used to validate theoretical predictions. Future work based on the proposed simulation methods may lead to general guidelines and error lower bounds for quantitative image analysis and change detection.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

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.

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.

Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

PubMed

Correa, Nicolle M; Li, Yi-Ou; Adalı, Tülay; Calhoun, Vince D

2008-12-01

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. We demonstrate the versatility of the method with application to two datasets, an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls. CCA results for fMRI and EEG data collected for an auditory oddball task reveal associations of the temporal and motor areas with the N2 and P3 peaks. For the application to fMRI and sMRI data collected for an auditory sensorimotor task, CCA results show an interesting joint relationship between fMRI and gray matter, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. Additionally, we compare our scheme with an independent component analysis based fusion method, joint-ICA that has proven useful for such a study and note that the two methods provide complementary perspectives on data fusion.

Application of proton boron fusion reaction to radiation therapy: A Monte Carlo simulation study

NASA Astrophysics Data System (ADS)

Yoon, Do-Kun; Jung, Joo-Young; Suh, Tae Suk

2014-12-01

Three alpha particles are emitted from the point of reaction between a proton and boron. The alpha particles are effective in inducing the death of a tumor cell. After boron is accumulated in the tumor region, the emitted from outside the body proton can react with the boron in the tumor region. An increase of the proton's maximum dose level is caused by the boron and only the tumor cell is damaged more critically. In addition, a prompt gamma ray is emitted from the proton boron reaction point. Here, we show that the effectiveness of the proton boron fusion therapy was verified using Monte Carlo simulations. We found that a dramatic increase by more than half of the proton's maximum dose level was induced by the boron in the tumor region. This increase occurred only when the proton's maximum dose point was located within the boron uptake region. In addition, the 719 keV prompt gamma ray peak produced by the proton boron fusion reaction was positively detected. This therapy method features the advantages such as the application of Bragg-peak to the therapy, the accurate targeting of tumor, improved therapy effects, and the monitoring of the therapy region during treatment.

Duane's retraction syndrome: its sensory features.

PubMed

Tomaç, Suhan; Mutlu, Fatih Mehmet; Altinsoy, Halil Ibrahim

2007-11-01

To investigate binocularity in Duane's retraction syndrome (DRS) and to evaluate whether or not there is a relationship between the sensory and clinical features of the syndrome. Clinical and sensory findings of 29 patients with DRS were recorded. Binocularity was tested with the Bagolini glasses (BG), Worth four-dot (W4D), TNO and the stereo-fly plate of the Titmus test. Twenty-four (83%) patients showed fusion with the BG at near and 23 (79%) had fusion at distance. With the W4D, 23 (79%) patients had fusion at near and 19 (65%) had fusion at distance. Seven (24%) patients demonstrated normal stereoacuity, 15 (52%) had reduced stereoacuity and the remaining seven (24%) patients had no measurable stereoacuity. In patients without stereoacuity, amblyopia (p < 0.001), type 2 and 3 DRS (p = 0.031) and exotropia (p = 0.003) in primary position were more common than in those with reduced or with normal stereoacuity. Restriction of ocular ductions was also more severe in patients without stereoacuity than in those with reduced or normal stereoacuity (p = 0.019, p = 0.016). Patients with type 2 and 3 DRS were significantly more likely to have amblyopia (p = 0.037), large-angle heterotropia (p = 0.005) in primary position, upshoot or downshoot (p = 0.010) than those with type 1 DRS. Although approximately 75% of DRS patients had fusion and measurable stereoacuity, only 25% demonstrated normal binocularity. This report provides new data on the relationship of sensory features to most of the clinical findings of this syndrome. Sensory features, as well as most clinical features of the syndrome, are better in patients with type 1 DRS.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

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.

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.

Fourier domain image fusion for differential X-ray phase-contrast breast imaging.

PubMed

Coello, Eduardo; Sperl, Jonathan I; Bequé, Dirk; Benz, Tobias; Scherer, Kai; Herzen, Julia; Sztrókay-Gaul, Anikó; Hellerhoff, Karin; Pfeiffer, Franz; Cozzini, Cristina; Grandl, Susanne

2017-04-01

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng.

PubMed

Li, Yun; Zhang, Jin-Yu; Wang, Yuan-Zhong

2018-01-01

Three data fusion strategies (low-llevel, mid-llevel, and high-llevel) combined with a multivariate classification algorithm (random forest, RF) were applied to authenticate the geographical origins of Panax notoginseng collected from five regions of Yunnan province in China. In low-level fusion, the original data from two spectra (Fourier transform mid-IR spectrum and near-IR spectrum) were directly concatenated into a new matrix, which then was applied for the classification. Mid-level fusion was the strategy that inputted variables extracted from the spectral data into an RF classification model. The extracted variables were processed by iterate variable selection of the RF model and principal component analysis. The use of high-level fusion combined the decision making of each spectroscopic technique and resulted in an ensemble decision. The results showed that the mid-level and high-level data fusion take advantage of the information synergy from two spectroscopic techniques and had better classification performance than that of independent decision making. High-level data fusion is the most effective strategy since the classification results are better than those of the other fusion strategies: accuracy rates ranged between 93% and 96% for the low-level data fusion, between 95% and 98% for the mid-level data fusion, and between 98% and 100% for the high-level data fusion. In conclusion, the high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng. Graphical abstract The analytical steps of Fourier transform mid-IR and near-IR spectral data fusion for the geographical traceability of Panax notoginseng.

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

PubMed Central

Cai, Suxian; Yang, Shanshan; Zheng, Fang; Lu, Meng; Wu, Yunfeng; Krishnan, Sridhar

2013-01-01

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis. PMID:23573175

Automatic building extraction from LiDAR data fusion of point and grid-based features

NASA Astrophysics Data System (ADS)

Du, Shouji; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shenghua; He, Xue; Chen, Siyang

2017-08-01

This paper proposes a method for extracting buildings from LiDAR point cloud data by combining point-based and grid-based features. To accurately discriminate buildings from vegetation, a point feature based on the variance of normal vectors is proposed. For a robust building extraction, a graph cuts algorithm is employed to combine the used features and consider the neighbor contexture information. As grid feature computing and a graph cuts algorithm are performed on a grid structure, a feature-retained DSM interpolation method is proposed in this paper. The proposed method is validated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction and compared to the state-art-of-the methods. The evaluation shows that the proposed method can obtain a promising result both at area-level and at object-level. The method is further applied to the entire ISPRS dataset and to a real dataset of the Wuhan City. The results show a completeness of 94.9% and a correctness of 92.2% at the per-area level for the former dataset and a completeness of 94.4% and a correctness of 95.8% for the latter one. The proposed method has a good potential for large-size LiDAR data.

Multiview fusion for activity recognition using deep neural networks

NASA Astrophysics Data System (ADS)

Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

2016-07-01

Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

Speaker-independent phoneme recognition with a binaural auditory image model

NASA Astrophysics Data System (ADS)

Francis, Keith Ivan

1997-09-01

This dissertation presents phoneme recognition techniques based on a binaural fusion of outputs of the auditory image model and subsequent azimuth-selective phoneme recognition in a noisy environment. Background information concerning speech variations, phoneme recognition, current binaural fusion techniques and auditory modeling issues is explained. The research is constrained to sources in the frontal azimuthal plane of a simulated listener. A new method based on coincidence detection of neural activity patterns from the auditory image model of Patterson is used for azimuth-selective phoneme recognition. The method is tested in various levels of noise and the results are reported in contrast to binaural fusion methods based on various forms of correlation to demonstrate the potential of coincidence- based binaural phoneme recognition. This method overcomes smearing of fine speech detail typical of correlation based methods. Nevertheless, coincidence is able to measure similarity of left and right inputs and fuse them into useful feature vectors for phoneme recognition in noise.

ALLFlight: detection of moving objects in IR and ladar images

NASA Astrophysics Data System (ADS)

Doehler, H.-U.; Peinecke, Niklas; Lueken, Thomas; Schmerwitz, Sven

2013-05-01

Supporting a helicopter pilot during landing and takeoff in degraded visual environment (DVE) is one of the challenges within DLR's project ALLFlight (Assisted Low Level Flight and Landing on Unprepared Landing Sites). Different types of sensors (TV, Infrared, mmW radar and laser radar) are mounted onto DLR's research helicopter FHS (flying helicopter simulator) for gathering different sensor data of the surrounding world. A high performance computer cluster architecture acquires and fuses all the information to get one single comprehensive description of the outside situation. While both TV and IR cameras deliver images with frame rates of 25 Hz or 30 Hz, Ladar and mmW radar provide georeferenced sensor data with only 2 Hz or even less. Therefore, it takes several seconds to detect or even track potential moving obstacle candidates in mmW or Ladar sequences. Especially if the helicopter is flying with higher speed, it is very important to minimize the detection time of obstacles in order to initiate a re-planning of the helicopter's mission timely. Applying feature extraction algorithms on IR images in combination with data fusion algorithms of extracted features and Ladar data can decrease the detection time appreciably. Based on real data from flight tests, the paper describes applied feature extraction methods for moving object detection, as well as data fusion techniques for combining features from TV/IR and Ladar data.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

Gene Fusion Markup Language: a prototype for exchanging gene fusion data.

PubMed

Kalyana-Sundaram, Shanker; Shanmugam, Achiraman; Chinnaiyan, Arul M

2012-10-16

An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future. Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at http://code.google.com/p/gfml-prototype/. The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses.

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.

Hybrid testing of lumbar CHARITE discs versus fusions.

PubMed

Panjabi, Manohar; Malcolmson, George; Teng, Edward; Tominaga, Yasuhiro; Henderson, Gweneth; Serhan, Hassan

2007-04-20

An in vitro human cadaveric biomechanical study. To quantify effects on operated and other levels, including adjacent levels, due to CHARITE disc implantations versus simulated fusions, using follower load and the new hybrid test method in flexion-extension and bilateral torsion. Spinal fusion has been associated with long-term accelerated degeneration at adjacent levels. As opposed to the fusion, artificial discs are designed to preserve motion and diminish the adjacent-level effects. Five fresh human cadaveric lumbar specimens (T12-S1) underwent multidirectional testing in flexion-extension and bilateral torsion with 400 N follower load. Intact specimen total ranges of motion were determined with +/-10 Nm unconstrained pure moments. The intact range of motion was used as input for the hybrid tests of 5 constructs: 1) CHARITE disc at L5-S1; 2) fusion at L5-S1; 3) CHARITE discs at L4-L5 and L5-S1; 4) CHARITE disc at L4-L5 and fusion at L5-S1; and 5) 2-level fusion at L4-L5-S1. Using repeated-measures single factor analysis of variance and Bonferroni statistical tests (P < 0.05), intervertebral motion redistribution of each construct was compared with the intact. In flexion-extension, 1-level CHARITE disc preserved motion at the operated and other levels, while 2-level CHARITE showed some amount of other-level effects. In contrast, 1- and 2-level fusions increased other-level motions (average, 21.0% and 61.9%, respectively). In torsion, both 1- and 2-level discs preserved motions at all levels. The 2-level simulated fusion increased motions at proximal levels (22.9%), while the 1-level fusion produced no significant changes. In general, CHARITE discs preserved operated- and other-level motions. Fusion simulations affected motion redistribution at other levels, including adjacent levels.

Histology, Fusion Status, and Outcome in Alveolar Rhabdomyosarcoma With Low-Risk Clinical Features: A Report From the Children's Oncology Group.

PubMed

Arnold, Michael A; Anderson, James R; Gastier-Foster, Julie M; Barr, Frederic G; Skapek, Stephen X; Hawkins, Douglas S; Raney, R Beverly; Parham, David M; Teot, Lisa A; Rudzinski, Erin R; Walterhouse, David O

2016-04-01

Distinguishing alveolar rhabdomyosarcoma (ARMS) from embryonal rhabdomyosarcoma (ERMS) is of prognostic and therapeutic importance. Criteria for classifying these entities evolved significantly from 1995 to 2013. ARMS is associated with inferior outcome; therefore, patients with alveolar histology have generally been excluded from low-risk therapy. However, patients with ARMS and low-risk stage and group (Stage 1, Group I/II/orbit III; or Stage 2/3, Group I/II) were eligible for the Children's Oncology Group (COG) low-risk rhabdomyosarcoma (RMS) study D9602 from 1997 to 1999. The characteristics and outcomes of these patients have not been previously reported, and the histology of these cases has not been reviewed using current criteria. We re-reviewed cases that were classified as ARMS on D9602 using current histologic criteria, determined PAX3/PAX7-FOXO1 fusion status, and compared these data with outcome for this unique group of patients. Thirty-eight patients with ARMS were enrolled onto D9602. Only one-third of cases with slides available for re-review (11/33) remained classified as ARMS by current histologic criteria. Most cases were reclassified as ERMS (17/33, 51.5%). Cases that remained classified as ARMS were typically fusion-positive (8/11, 73%), therefore current classification results in a similar rate of fusion-positive ARMS for all clinical risk groups. In conjunction with data from COG intermediate-risk treatment protocol D9803, our data demonstrate excellent outcomes for fusion-negative ARMS with otherwise low-risk clinical features. Patients with fusion-positive RMS with low-risk clinical features should be classified and treated as intermediate risk, while patients with fusion-negative ARMS could be appropriately treated with reduced intensity therapy. © 2016 Wiley Periodicals, Inc.

Range and Panoramic Image Fusion Into a Textured Range Image for Culture Heritage Documentation

NASA Astrophysics Data System (ADS)

Bila, Z.; Reznicek, J.; Pavelka, K.

2013-07-01

This paper deals with a fusion of range and panoramic images, where the range image is acquired by a 3D laser scanner and the panoramic image is acquired with a digital still camera mounted on a panoramic head and tripod. The fused resulting dataset, called "textured range image", provides more reliable information about the investigated object for conservators and historians, than using both datasets separately. A simple example of fusion of a range and panoramic images, both obtained in St. Francis Xavier Church in town Opařany, is given here. Firstly, we describe the process of data acquisition, then the processing of both datasets into a proper format for following fusion and the process of fusion. The process of fusion can be divided into a two main parts: transformation and remapping. In the first, transformation, part, both images are related by matching similar features detected on both images with a proper detector, which results in transformation matrix enabling transformation of the range image onto a panoramic image. Then, the range data are remapped from the range image space into a panoramic image space and stored as an additional "range" channel. The process of image fusion is validated by comparing similar features extracted on both datasets.

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.

Fusion Rate and Clinical Outcomes in Two-Level Posterior Lumbar Interbody Fusion.

PubMed

Aono, Hiroyuki; Takenaka, Shota; Nagamoto, Yukitaka; Tobimatsu, Hidekazu; Yamashita, Tomoya; Furuya, Masayuki; Iwasaki, Motoki

2018-04-01

Posterior lumbar interbody fusion (PLIF) has become a general surgical method for degenerative lumbar diseases. Although many reports have focused on single-level PLIF, few have focused on 2-level PLIF, and no report has covered the fusion status of 2-level PLIF. The purpose of this study is to investigate clinical outcomes and fusion for 2-level PLIF by using a combination of dynamic radiographs and multiplanar-reconstruction computed tomography scans. This study consisted of 48 consecutive patients who underwent 2-level PLIF for degenerative lumbar diseases. We assessed surgery duration, estimated blood loss, complications, clinical outcomes as measured by the Japanese Orthopaedic Association score, lumbar sagittal alignment as measured on standing lateral radiographs, and fusion status as measured by dynamic radiographs and multiplanar-reconstruction computed tomography. Patients were examined at a follow-up point of 4.8 ± 2.2 years after surgery. Thirty-eight patients who did not undergo lumbosacral fusion comprised the lumbolumbar group, and 10 patients who underwent lumbosacral fusion comprised the lumbosacral group. The mean Japanese Orthopaedic Association score improved from 12.1 to 22.4 points by the final follow-up examination. Sagittal alignment also was improved. All patients had fusion in the cranial level. Seven patients had nonunion in the caudal level, and the lumbosacral group (40%) had a significantly poorer fusion rate than the lumbolumbar group (97%) did. Surgical outcomes of 2-level PLIF were satisfactory. The fusion rate at both levels was 85%. All nonunion was observed at the caudal level and concentrated at L5-S level in L4-5-S PLIF. Copyright © 2018 Elsevier Inc. All rights reserved.

High Level Information Fusion (HLIF) with nested fusion loops

NASA Astrophysics Data System (ADS)

Woodley, Robert; Gosnell, Michael; Fischer, Amber

2013-05-01

Situation modeling and threat prediction require higher levels of data fusion in order to provide actionable information. Beyond the sensor data and sources the analyst has access to, the use of out-sourced and re-sourced data is becoming common. Through the years, some common frameworks have emerged for dealing with information fusion—perhaps the most ubiquitous being the JDL Data Fusion Group and their initial 4-level data fusion model. Since these initial developments, numerous models of information fusion have emerged, hoping to better capture the human-centric process of data analyses within a machine-centric framework. 21st Century Systems, Inc. has developed Fusion with Uncertainty Reasoning using Nested Assessment Characterizer Elements (FURNACE) to address challenges of high level information fusion and handle bias, ambiguity, and uncertainty (BAU) for Situation Modeling, Threat Modeling, and Threat Prediction. It combines JDL fusion levels with nested fusion loops and state-of-the-art data reasoning. Initial research has shown that FURNACE is able to reduce BAU and improve the fusion process by allowing high level information fusion (HLIF) to affect lower levels without the double counting of information or other biasing issues. The initial FURNACE project was focused on the underlying algorithms to produce a fusion system able to handle BAU and repurposed data in a cohesive manner. FURNACE supports analyst's efforts to develop situation models, threat models, and threat predictions to increase situational awareness of the battlespace. FURNACE will not only revolutionize the military intelligence realm, but also benefit the larger homeland defense, law enforcement, and business intelligence markets.

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.

Kinase fusions are frequent in Spitz tumors and spitzoid melanomas

PubMed Central

Esteve-Puig, Rosaura; Botton, Thomas; Yeh, Iwei; Lipson, Doron; Otto, Geoff; Brennan, Kristina; Murali, Rajmohan; Garrido, Maria; Miller, Vincent A.; Ross, Jeffrey S; Berger, Michael F.; Sparatta, Alyssa; Palmedo, Gabriele; Cerroni, Lorenzo; Busam, Klaus J.; Kutzner, Heinz; Cronin, Maureen T; Stephens, Philip J; Bastian, Boris C.

2014-01-01

Spitzoid neoplasms are a group of melanocytic tumors with distinctive histopathologic features. They include benign tumors (Spitz nevi), malignant tumors (spitzoid melanomas), and tumors with borderline histopathologic features and uncertain clinical outcome (atypical Spitz tumors). Their genetic underpinnings are poorly understood, and alterations in common melanoma-associated oncogenes are typically absent. Here we show that spitzoid neoplasms harbor kinase fusions of ROS1 (17%), NTRK1 (16%), ALK (10%), BRAF (5%), and RET (3%) in a mutually exclusive pattern. The chimeric proteins are constitutively active, stimulate oncogenic signaling pathways, are tumorigenic, and are found in the entire biologic spectrum of spitzoid neoplasms, including 55% of Spitz nevi, 56% of atypical Spitz tumors, and 39% of spitzoid melanomas. Kinase inhibitors suppress the oncogenic signaling of the fusion proteins in vitro. In summary, kinase fusions account for the majority of oncogenic aberrations in spitzoid neoplasms, and may serve as therapeutic targets for metastatic spitzoid melanomas. PMID:24445538

Kinase fusions are frequent in Spitz tumours and spitzoid melanomas

NASA Astrophysics Data System (ADS)

Wiesner, Thomas; He, Jie; Yelensky, Roman; Esteve-Puig, Rosaura; Botton, Thomas; Yeh, Iwei; Lipson, Doron; Otto, Geoff; Brennan, Kristina; Murali, Rajmohan; Garrido, Maria; Miller, Vincent A.; Ross, Jeffrey S.; Berger, Michael F.; Sparatta, Alyssa; Palmedo, Gabriele; Cerroni, Lorenzo; Busam, Klaus J.; Kutzner, Heinz; Cronin, Maureen T.; Stephens, Philip J.; Bastian, Boris C.

2014-01-01

Spitzoid neoplasms are a group of melanocytic tumours with distinctive histopathological features. They include benign tumours (Spitz naevi), malignant tumours (spitzoid melanomas) and tumours with borderline histopathological features and uncertain clinical outcome (atypical Spitz tumours). Their genetic underpinnings are poorly understood, and alterations in common melanoma-associated oncogenes are typically absent. Here we show that spitzoid neoplasms harbour kinase fusions of ROS1 (17%), NTRK1 (16%), ALK (10%), BRAF (5%) and RET (3%) in a mutually exclusive pattern. The chimeric proteins are constitutively active, stimulate oncogenic signalling pathways, are tumourigenic and are found in the entire biologic spectrum of spitzoid neoplasms, including 55% of Spitz naevi, 56% of atypical Spitz tumours and 39% of spitzoid melanomas. Kinase inhibitors suppress the oncogenic signalling of the fusion proteins in vitro. In summary, kinase fusions account for the majority of oncogenic aberrations in spitzoid neoplasms and may serve as therapeutic targets for metastatic spitzoid melanomas.

Hybrid Biosynthetic Autograft Extender for Use in Posterior Lumbar Interbody Fusion: Safety and Clinical Effectiveness.

PubMed

Chedid, Mokbel K; Tundo, Kelly M; Block, Jon E; Muir, Jeffrey M

2015-01-01

Autologous iliac crest bone graft is the preferred option for spinal fusion, but the morbidity associated with bone harvest and the need for graft augmentation in more demanding cases necessitates combining local bone with bone substitutes. The purpose of this study was to document the clinical effectiveness and safety of a novel hybrid biosynthetic scaffold material consisting of poly(D,L-lactide-co-glycolide) (PLGA, 75:25) combined by lyophilization with unmodified high molecular weight hyaluronic acid (10-12% wt:wt) as an extender for a broad range of spinal fusion procedures. We retrospectively evaluated all patients undergoing single- and multi-level posterior lumbar interbody fusion at an academic medical center over a 3-year period. A total of 108 patients underwent 109 procedures (245 individual vertebral levels). Patient-related outcomes included pain measured on a Visual Analog Scale. Radiographic outcomes were assessed at 6 weeks, 3-6 months, and 1 year postoperatively. Radiographic fusion or progression of fusion was documented in 221 of 236 index levels (93.6%) at a mean (±SD) time to fusion of 10.2+4.1 months. Single and multi-level fusions were not associated with significantly different success rates. Mean pain scores (+SD) for all patients improved from 6.8+2.5 at baseline to 3.6+2.9 at approximately 12 months. Improvements in VAS were greatest in patients undergoing one- or two-level fusion, with patients undergoing multi-level fusion demonstrating lesser but still statistically significant improvements. Overall, stable fusion was observed in 64.8% of vertebral levels; partial fusion was demonstrated in 28.8% of vertebral levels. Only 15 of 236 levels (6.4%) were non-fused at final follow-up.

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.

TFE3-Fusion Variant Analysis Defines Specific Clinicopathologic Associations Among Xp11 Translocation Cancers

PubMed Central

Argani, Pedram; Zhong, Minghao; Reuter, Victor E.; Fallon, John T.; Epstein, Jonathan I.; Netto, George J.; Antonescu, Cristina R.

2016-01-01

Xp11 translocation cancers include Xp11 translocation renal cell carcinoma (RCC), Xp11 translocation perivascular epithelioid cell tumor (PEComa), and melanotic Xp11 translocation renal cancer. In Xp11 translocation cancers, oncogenic activation of TFE3 is driven by the fusion of TFE3 with a number of different gene partners, however, the impact of individual fusion variant on specific clinicopathologic features of Xp11 translocation cancers has not been well defined. In this study, we analyze 60 Xp11 translocation cancers by fluorescence in situ hybridization (FISH) using custom BAC probes to establish their TFE3 fusion gene partner. In 5 cases RNA sequencing (RNA-seq) was also used to further characterize the fusion transcripts. The 60 Xp11 translocation cancers included 47 Xp11 translocation RCC, 8 Xp11 translocation PEComas, and 5 melanotic Xp11 translocation renal cancers. A fusion partner was identified in 53/60 (88%) cases, including 18 SFPQ (PSF), 16 PRCC, 12 ASPSCR1 (ASPL), 6 NONO, and 1 DVL2. We provide the first morphologic description of the NONO-TFE3 RCC, which frequently demonstrates sub-nuclear vacuoles leading to distinctive suprabasal nuclear palisading. Similar sub-nuclear vacuolization was also characteristic of SFPQ-TFE3 RCC, creating overlapping features with clear cell papillary RCC. We also describe the first RCC with a DVL2-TFE3 gene fusion, in addition to an extrarenal pigmented PEComa with a NONO-TFE3 gene fusion. Furthermore, among neoplasms with the SFPQ-TFE3, NONO-TFE3, DVL2-TFE3 and ASPL-TFE3 gene fusions, the RCC are almost always PAX8-positive, cathepsin K-negative by immunohistochemistry, whereas the mesenchymal counterparts (Xp11 translocation PEComas, melanotic Xp11 translocation renal cancers, and alveolar soft part sarcoma) are PAX8-negative, cathepsin K-positive. These findings support the concept that despite an identical gene fusion, the RCCs are distinct from the corresponding mesenchymal neoplasms, perhaps due to the cellular context in which the translocation occurs. We corroborate prior data showing that the PRCC-TFE3 RCC are the only known Xp11 translocation RCC molecular subtype which is consistently cathepsin K positive. In summary, our data expand further the clinicopathologic features of cancers with specific TFE3 gene fusions, and should allow for more meaningful clinicopathologic associations to be drawn. PMID:26975036

Oncogenous osteomalacia and myopericytoma of the thoracic spine: a case report.

PubMed

Brunschweiler, Benoit; Guedj, Nathalie; Lenoir, Thibault; Faillot, Thierry; Rillardon, Ludovic; Guigui, Pierre

2009-11-01

A case report. To illustrate a rare case of oncogenous osteomalacia caused by a spinal thoracic myopericytoma. Osteomalacia related to a tumor is well known. The cause of the disorder is usually a highly vascularized, benign tumor of mesenchymal origin. Location of the tumor in the spine is very rare. Removal of the tumor is followed by resolution of osteomalacia. Diagnosis of oseomalacia was established on the presence of cardinal clinical, biologic, and radiologic features of osteomalacia. Localization of the tumor at T5 and T6 levels was obtained by magnetic resonance imaging. Surgical treatment consisted in a circumferential correction-fusion with hemivertebrectomy of T5 and T6 and tumor removal. Tumor removal was rapidly followed by disappearance of the clinical symptoms of osteomalacia, and by correction of hypophosphatemia. At 2-years follow-up, no recurrence of the tumor was detectable on imaging studies-the correction fusion remained stable. Histologically, the tumor was classified as a myopericytoma. There was no relapse of the clinical features of osteomalacia. However, secondary recurrence of the biologic markers due to an incomplete tumor removal was disclosed. Removal of the tumor was followed by healing of the clinical features of osteomalacia, demonstrating the causal connection between the myopericytoma and the osteopathy.

Use of the Nanofitin Alternative Scaffold as a GFP-Ready Fusion Tag

PubMed Central

Huet, Simon; Gorre, Harmony; Perrocheau, Anaëlle; Picot, Justine; Cinier, Mathieu

2015-01-01

With the continuous diversification of recombinant DNA technologies, the possibilities for new tailor-made protein engineering have extended on an on-going basis. Among these strategies, the use of the green fluorescent protein (GFP) as a fusion domain has been widely adopted for cellular imaging and protein localization. Following the lead of the direct head-to-tail fusion of GFP, we proposed to provide additional features to recombinant proteins by genetic fusion of artificially derived binders. Thus, we reported a GFP-ready fusion tag consisting of a small and robust fusion-friendly anti-GFP Nanofitin binding domain as a proof-of-concept. While limiting steric effects on the carrier, the GFP-ready tag allows the capture of GFP or its blue (BFP), cyan (CFP) and yellow (YFP) alternatives. Here, we described the generation of the GFP-ready tag from the selection of a Nanofitin variant binding to the GFP and its spectral variants with a nanomolar affinity, while displaying a remarkable folding stability, as demonstrated by its full resistance upon thermal sterilization process or the full chemical synthesis of Nanofitins. To illustrate the potential of the Nanofitin-based tag as a fusion partner, we compared the expression level in Escherichia coli and activity profile of recombinant human tumor necrosis factor alpha (TNFα) constructs, fused to a SUMO or GFP-ready tag. Very similar expression levels were found with the two fusion technologies. Both domains of the GFP-ready tagged TNFα were proved fully active in ELISA and interferometry binding assays, allowing the simultaneous capture by an anti-TNFα antibody and binding to the GFP, and its spectral mutants. The GFP-ready tag was also shown inert in a L929 cell based assay, demonstrating the potent TNFα mediated apoptosis induction by the GFP-ready tagged TNFα. Eventually, we proposed the GFP-ready tag as a versatile capture and labeling system in addition to expected applications of anti-GFP Nanofitins (as illustrated with previously described state-of-the-art anti-GFP binders applied to living cells and in vitro applications). Through a single fusion domain, the GFP-ready tagged proteins benefit from subsequent customization within a wide range of fluorescence spectra upon indirect binding of a chosen GFP variant. PMID:26539718

Use of the Nanofitin Alternative Scaffold as a GFP-Ready Fusion Tag.

PubMed

Huet, Simon; Gorre, Harmony; Perrocheau, Anaëlle; Picot, Justine; Cinier, Mathieu

2015-01-01

With the continuous diversification of recombinant DNA technologies, the possibilities for new tailor-made protein engineering have extended on an on-going basis. Among these strategies, the use of the green fluorescent protein (GFP) as a fusion domain has been widely adopted for cellular imaging and protein localization. Following the lead of the direct head-to-tail fusion of GFP, we proposed to provide additional features to recombinant proteins by genetic fusion of artificially derived binders. Thus, we reported a GFP-ready fusion tag consisting of a small and robust fusion-friendly anti-GFP Nanofitin binding domain as a proof-of-concept. While limiting steric effects on the carrier, the GFP-ready tag allows the capture of GFP or its blue (BFP), cyan (CFP) and yellow (YFP) alternatives. Here, we described the generation of the GFP-ready tag from the selection of a Nanofitin variant binding to the GFP and its spectral variants with a nanomolar affinity, while displaying a remarkable folding stability, as demonstrated by its full resistance upon thermal sterilization process or the full chemical synthesis of Nanofitins. To illustrate the potential of the Nanofitin-based tag as a fusion partner, we compared the expression level in Escherichia coli and activity profile of recombinant human tumor necrosis factor alpha (TNFα) constructs, fused to a SUMO or GFP-ready tag. Very similar expression levels were found with the two fusion technologies. Both domains of the GFP-ready tagged TNFα were proved fully active in ELISA and interferometry binding assays, allowing the simultaneous capture by an anti-TNFα antibody and binding to the GFP, and its spectral mutants. The GFP-ready tag was also shown inert in a L929 cell based assay, demonstrating the potent TNFα mediated apoptosis induction by the GFP-ready tagged TNFα. Eventually, we proposed the GFP-ready tag as a versatile capture and labeling system in addition to expected applications of anti-GFP Nanofitins (as illustrated with previously described state-of-the-art anti-GFP binders applied to living cells and in vitro applications). Through a single fusion domain, the GFP-ready tagged proteins benefit from subsequent customization within a wide range of fluorescence spectra upon indirect binding of a chosen GFP variant.

Disc replacement adjacent to cervical fusion: a biomechanical comparison of hybrid construct versus two-level fusion.

PubMed

Lee, Michael J; Dumonski, Mark; Phillips, Frank M; Voronov, Leonard I; Renner, Susan M; Carandang, Gerard; Havey, Robert M; Patwardhan, Avinash G

2011-11-01

A cadaveric biomechanical study. To investigate the biomechanical behavior of the cervical spine after cervical total disc replacement (TDR) adjacent to a fusion as compared to a two-level fusion. There are concerns regarding the biomechanical effects of cervical fusion on the mobile motion segments. Although previous biomechanical studies have demonstrated that cervical disc replacement normalizes adjacent segment motion, there is a little information regarding the function of a cervical disc replacement adjacent to an anterior cervical decompression and fusion, a potentially common clinical application. Nine cadaveric cervical spines (C3-T1, age: 60.2 ± 3.5 years) were tested under load- and displacement-control testing. After intact testing, a simulated fusion was performed at C4-C5, followed by C6-C7. The simulated fusion was then reversed, and the response of TDR at C5-C6 was measured. A hybrid construct was then tested with the TDR either below or above a single-level fusion and contrasted with a simulated two-level fusion (C4-C6 and C5-C7). The external fixator device used to simulate fusion significantly reduced range of motion (ROM) at C4-C5 and C6-C7 by 74.7 ± 8.1% and 78.1 ± 11.5%, respectively (P < 0.05). Removal of the fusion construct restored the motion response of the spinal segments to their intact state. Arthroplasty performed at C5-C6 using the porous-coated motion disc prosthesis maintained the total flexion-extension ROM to the level of the intact controls when used as a stand-alone procedure or when implanted adjacent to a single-level fusion (P > 0.05). The location of the single-level fusion, whether above or below the arthroplasty, did not significantly affect the motion response of the arthroplasty in the hybrid construct. Performing a two-level fusion significantly increased the motion demands on the nonoperated segments as compared to a hybrid TDR-plus fusion construct when the spine was required to reach the same motion end points. The spine with a hybrid construct required significantly less extension moment than the spine with a two-level fusion to reach the same extension end point. The porous-coated motion cervical prosthesis restored the ROM of the treated level to the intact state. When the porous-coated motion prosthesis was used in a hybrid construct, the TDR response was not adversely affected. A hybrid construct seems to offer significant biomechanical advantages over two-level fusion in terms of reducing compensatory adjacent-level hypermobility and also loads required to achieve a predetermined ROM.

Morphometric MRI Imaging Study of the Corridor for the Oblique Lumbar Interbody Fusion Technique at L1-L5.

PubMed

Julian Li, Jia Xi; Mobbs, Ralph J; Phan, Kevin

2018-03-01

Anterior lumbar interbody fusion and lateral lumbar interbody fusion are associated with approach-related disadvantages. Oblique lumbar interbody fusion (OLIF) is the proposed solution, especially for upper lumbar levels. We analyzed the size and regional anatomy of the corridor used in the OLIF technique between levels L1 and L5. This is a morphometric study of 200 randomly selected magnetic resonance imaging (MRI) studies with features of lumbar degenerative disease. On MRI, the oblique corridor was defined as the smallest distance between the psoas major muscle and aorta or inferior vena cava (or common iliac artery) and measured at the L1/L2, L2/L3, L3/L4, and L4/L5 disc levels on both the left and right on the axial images at the mid-disc level. Mean distances of the oblique corridor on the left side were L1/L2 = 18.90 mm, L2/L3 = 15.50 mm; L3/L4 = 12.75 mm, and L4/L5 = 8.92 mm; on the right side, they were L1/L2 = 14.80 mm, L2/L3 = 5.50 mm, L3/L4 = 3.00 mm, and L4/L5 = 1.46 mm. For both sides, the corridor size was not significantly affected by sex, and it increased with age and decreased at the inferior lumbar disc levels. The L1/L2 and L2/L3 levels may be obstructed by the ipsilateral kidney and renal vasculature on both sides and the liver on the right side. A left-sided OLIF approach is viable for both sexes. Oblique access to the L1/L2 and L2/L3 disc levels is feasible regardless of age, whereas the L3/L4 and L4/L5 levels may be more suitable in older patients, especially for male patients. The right-sided approach is less likely to be performed effectively. Copyright © 2018 Elsevier Inc. All rights reserved.

Study on Hybrid Image Search Technology Based on Texts and Contents

NASA Astrophysics Data System (ADS)

Wang, H. T.; Ma, F. L.; Yan, C.; Pan, H.

2018-05-01

Image search was studied first here based on texts and contents, respectively. The text-based image feature extraction was put forward by integrating the statistical and topic features in view of the limitation of extraction of keywords only by means of statistical features of words. On the other hand, a search-by-image method was put forward based on multi-feature fusion in view of the imprecision of the content-based image search by means of a single feature. The layered-searching method depended on primarily the text-based image search method and additionally the content-based image search was then put forward in view of differences between the text-based and content-based methods and their difficult direct fusion. The feasibility and effectiveness of the hybrid search algorithm were experimentally verified.

Non-small cell lung cancer with EML4-ALK translocation in Chinese male never-smokers is characterized with early-onset.

PubMed

Guo, Yongjun; Ma, Jie; Lyu, Xiaodong; Liu, Hai; Wei, Bing; Zhao, Jiuzhou; Fu, Shuang; Ding, Lu; Zhang, Jihong

2014-11-18

The translocations of the anaplastic lymphoma kinase (ALK) gene with the echinoderm microtubule-associated protein-like 4 (EML4) gene on chromosome 2p have been identified in non-small-cell lung cancers (NSCLCs) as oncogenic driver mutations. It has been suggested that EML4-ALK fusion is associated with the resistance in NSCLCs to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs), such as gefitinib and erlotinib. In contrast, ALK tyrosine kinase inhibitor (ALK TKI) crizotinib has shown superior effects in combating NSCLCs with EML4-ALK. Thus, characterization of EML4-ALK fusion genes and clinical features of resulting carcinomas would be a great benefit to disease diagnosis and designing customized treatment plans. Studies have suggested that EML4-ALK translocation occurs more frequently in never-smokers with NSCLC, especially in female patients. However, it is not clear whether this is the case in male patients, too. In this study, we have determined the frequency of EML4-ALK translocation in male never-smokers with NSCLC in a cohort of Chinese patients. The clinical features associated with EML4-ALK translocation were also investigated. A cohort of 95 Chinese male never-smokers with NSCLC was enrolled in this study. EML4-ALK fusion genes were detected using one-step real time RT-PCR and DNA sequencing. We further determined the expression levels of ALK mRNA by RT-PCR and ALK protein by immunohistochemistry in these specimens. The clinical features of EML4-ALK-positive carcinomas were also determined. We have identified EML4-ALK fusion genes in 8 out of 95 carcinoma cases, accounting for 8.42% in Chinese male never-smokers with NSCLC. It is significantly higher than that in all Chinese male patients (3.44%) regardless smoking habit. It is also significantly higher than that in all Chinese smokers (8/356 or 2.25%) or in smokers worldwide (2.9%) by comparing to published data. Interestingly, EML4-ALK fusion genes are more frequently found in younger patients and associated with less-differentiated carcinomas. The frequency of EML4-ALK translocation is strongly associated with smoking habits in Chinese male patients with higher frequency in male never-smokers. EML4-ALK translocation is associated with early-onset and less-differentiated carcinomas.

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.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

PubMed Central

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques. PMID:29694429

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

PubMed

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

PubMed

Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh

2017-11-01

Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Gene Fusion Markup Language: a prototype for exchanging gene fusion data

PubMed Central

2012-01-01

Background An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future. Results Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at http://code.google.com/p/gfml-prototype/. Conclusion The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses. PMID:23072312

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.

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

Biomechanical Analysis of Cervical Disc Replacement and Fusion Using Single Level, Two Level, and Hybrid Constructs.

PubMed

Gandhi, Anup A; Kode, Swathi; DeVries, Nicole A; Grosland, Nicole M; Smucker, Joseph D; Fredericks, Douglas C

2015-10-15

A biomechanical study comparing arthroplasty with fusion using human cadaveric C2-T1 spines. To compare the kinematics of the cervical spine after arthroplasty and fusion using single level, 2 level and hybrid constructs. Previous studies have shown that spinal levels adjacent to a fusion experience increased motion and higher stress which may lead to adjacent segment disc degeneration. Cervical arthroplasty achieves similar decompression but preserves the motion at the operated level, potentially decreasing the occurrence of adjacent segment disc degeneration. 11 specimens (C2-T1) were divided into 2 groups (BRYAN and PRESTIGE LP). The specimens were tested in the following order; intact, single level total disc replacement (TDR) at C5-C6, 2-level TDR at C5-C6-C7, fusion at C5-C6 and TDR at C6-C7 (Hybrid construct), and lastly a 2-level fusion. The intact specimens were tested up to a moment of 2.0 Nm. After each surgical intervention, the specimens were loaded until the primary motion (C2-T1) matched the motion of the respective intact state (hybrid control). An arthroplasty preserved motion at the implanted level and maintained normal motion at the nonoperative levels. Arthrodesis resulted in a significant decrease in motion at the fused level and an increase in motion at the unfused levels. In the hybrid construct, the TDR adjacent to fusion preserved motion at the arthroplasty level, thereby reducing the demand on the other levels. Cervical disc arthroplasty with both the BRYAN and PRESTIGE LP discs not only preserved the motion at the operated level, but also maintained the normal motion at the adjacent levels. Under simulated physiologic loading, the motion patterns of the spine with the BRYAN or PRESTIGE LP disc were very similar and were closer than fusion to the intact motion pattern. An adjacent segment disc replacement is biomechanically favorable to a fusion in the presence of a pre-existing fusion.

The importance of proximal fusion level selection for outcomes of multi-level lumbar posterolateral fusion.

PubMed

Nam, Woo Dong; Cho, Jae Hwan

2015-03-01

There are few studies about risk factors for poor outcomes from multi-level lumbar posterolateral fusion limited to three or four level lumbar posterolateral fusions. The purpose of this study was to analyze the outcomes of multi-level lumbar posterolateral fusion and to search for possible risk factors for poor surgical outcomes. We retrospectively analyzed 37 consecutive patients who underwent multi-level lumbar or lumbosacral posterolateral fusion with posterior instrumentation. The outcomes were deemed either 'good' or 'bad' based on clinical and radiological results. Many demographic and radiological factors were analyzed to examine potential risk factors for poor outcomes. Student t-test, Fisher exact test, and the chi-square test were used based on the nature of the variables. Multiple logistic regression analysis was used to exclude confounding factors. Twenty cases showed a good outcome (group A, 54.1%) and 17 cases showed a bad outcome (group B, 45.9%). The overall fusion rate was 70.3%. The revision procedures (group A: 1/20, 5.0%; group B: 4/17, 23.5%), proximal fusion to L2 (group A: 5/20, 25.0%; group B: 10/17, 58.8%), and severity of stenosis (group A: 12/19, 63.3%; group B: 3/11, 27.3%) were adopted as possible related factors to the outcome in univariate analysis. Multiple logistic regression analysis revealed that only the proximal fusion level (superior instrumented vertebra, SIV) was a significant risk factor. The cases in which SIV was L2 showed inferior outcomes than those in which SIV was L3. The odds ratio was 6.562 (95% confidence interval, 1.259 to 34.203). The overall outcome of multi-level lumbar or lumbosacral posterolateral fusion was not as high as we had hoped it would be. Whether the SIV was L2 or L3 was the only significant risk factor identified for poor outcomes in multi-level lumbar or lumbosacral posterolateral fusion in the current study. Thus, the authors recommend that proximal fusion levels be carefully determined when multi-level lumbar fusions are considered.

The Importance of Proximal Fusion Level Selection for Outcomes of Multi-Level Lumbar Posterolateral Fusion

PubMed Central

Nam, Woo Dong

2015-01-01

Background There are few studies about risk factors for poor outcomes from multi-level lumbar posterolateral fusion limited to three or four level lumbar posterolateral fusions. The purpose of this study was to analyze the outcomes of multi-level lumbar posterolateral fusion and to search for possible risk factors for poor surgical outcomes. Methods We retrospectively analyzed 37 consecutive patients who underwent multi-level lumbar or lumbosacral posterolateral fusion with posterior instrumentation. The outcomes were deemed either 'good' or 'bad' based on clinical and radiological results. Many demographic and radiological factors were analyzed to examine potential risk factors for poor outcomes. Student t-test, Fisher exact test, and the chi-square test were used based on the nature of the variables. Multiple logistic regression analysis was used to exclude confounding factors. Results Twenty cases showed a good outcome (group A, 54.1%) and 17 cases showed a bad outcome (group B, 45.9%). The overall fusion rate was 70.3%. The revision procedures (group A: 1/20, 5.0%; group B: 4/17, 23.5%), proximal fusion to L2 (group A: 5/20, 25.0%; group B: 10/17, 58.8%), and severity of stenosis (group A: 12/19, 63.3%; group B: 3/11, 27.3%) were adopted as possible related factors to the outcome in univariate analysis. Multiple logistic regression analysis revealed that only the proximal fusion level (superior instrumented vertebra, SIV) was a significant risk factor. The cases in which SIV was L2 showed inferior outcomes than those in which SIV was L3. The odds ratio was 6.562 (95% confidence interval, 1.259 to 34.203). Conclusions The overall outcome of multi-level lumbar or lumbosacral posterolateral fusion was not as high as we had hoped it would be. Whether the SIV was L2 or L3 was the only significant risk factor identified for poor outcomes in multi-level lumbar or lumbosacral posterolateral fusion in the current study. Thus, the authors recommend that proximal fusion levels be carefully determined when multi-level lumbar fusions are considered. PMID:25729522

Application of proton boron fusion reaction to radiation therapy: A Monte Carlo simulation study

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

Yoon, Do-Kun; Jung, Joo-Young; Suh, Tae Suk, E-mail: suhsanta@catholic.ac.kr

2014-12-01

Three alpha particles are emitted from the point of reaction between a proton and boron. The alpha particles are effective in inducing the death of a tumor cell. After boron is accumulated in the tumor region, the emitted from outside the body proton can react with the boron in the tumor region. An increase of the proton's maximum dose level is caused by the boron and only the tumor cell is damaged more critically. In addition, a prompt gamma ray is emitted from the proton boron reaction point. Here, we show that the effectiveness of the proton boron fusion therapymore » was verified using Monte Carlo simulations. We found that a dramatic increase by more than half of the proton's maximum dose level was induced by the boron in the tumor region. This increase occurred only when the proton's maximum dose point was located within the boron uptake region. In addition, the 719 keV prompt gamma ray peak produced by the proton boron fusion reaction was positively detected. This therapy method features the advantages such as the application of Bragg-peak to the therapy, the accurate targeting of tumor, improved therapy effects, and the monitoring of the therapy region during treatment.« less

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.

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

Dahlburg, Jill; Corones, James; Batchelor, Donald

Fusion is potentially an inexhaustible energy source whose exploitation requires a basic understanding of high-temperature plasmas. The development of a science-based predictive capability for fusion-relevant plasmas is a challenge central to fusion energy science, in which numerical modeling has played a vital role for more than four decades. A combination of the very wide range in temporal and spatial scales, extreme anisotropy, the importance of geometric detail, and the requirement of causality which makes it impossible to parallelize over time, makes this problem one of the most challenging in computational physics. Sophisticated computational models are under development for many individualmore » features of magnetically confined plasmas and increases in the scope and reliability of feasible simulations have been enabled by increased scientific understanding and improvements in computer technology. However, full predictive modeling of fusion plasmas will require qualitative improvements and innovations to enable cross coupling of a wider variety of physical processes and to allow solution over a larger range of space and time scales. The exponential growth of computer speed, coupled with the high cost of large-scale experimental facilities, makes an integrated fusion simulation initiative a timely and cost-effective opportunity. Worldwide progress in laboratory fusion experiments provides the basis for a recent FESAC recommendation to proceed with a burning plasma experiment (see FESAC Review of Burning Plasma Physics Report, September 2001). Such an experiment, at the frontier of the physics of complex systems, would be a huge step in establishing the potential of magnetic fusion energy to contribute to the world’s energy security. An integrated simulation capability would dramatically enhance the utilization of such a facility and lead to optimization of toroidal fusion plasmas in general. This science-based predictive capability, which was cited in the FESAC integrated planning document (IPPA, 2000), represents a significant opportunity for the DOE Office of Science to further the understanding of fusion plasmas to a level unparalleled worldwide.« less

NASA-NIAC 2001 Phase I Research Grant on Aneutronic Fusion Spacecraft Architecture

NASA Technical Reports Server (NTRS)

Tarditi, Alfonso G. (Principal Investigator); Scott, John H.; Miley, George H.

2012-01-01

This study was developed because the recognized need of defining of a new spacecraft architecture suitable for aneutronic fusion and featuring game-changing space travel capabilities. The core of this architecture is the definition of a new kind of fusion-based space propulsion system. This research is not about exploring a new fusion energy concept, it actually assumes the availability of an aneutronic fusion energy reactor. The focus is on providing the best (most efficient) utilization of fusion energy for propulsion purposes. The rationale is that without a proper architecture design even the utilization of a fusion reactor as a prime energy source for spacecraft propulsion is not going to provide the required performances for achieving a substantial change of current space travel capabilities.

Analysis of clinicopathological features of the echinoderm microtubule-associated protein-like-4-anaplastic lymphoma kinase fusion gene in Chinese patients with advanced non-small-cell lung cancer

PubMed Central

Liu, Yu-Tao; Shi, Yuan-Kai; Hao, Xue-Zhi; Wang, Lin; Li, Jun-Ling; Han, Xiao-Hong; Li, Dan; Zhou, Yu-Jie; Tang, Le

2014-01-01

Background The echinoderm microtubule-associated protein-like-4-anaplastic lymphoma kinase (EML4-ALK) fusion gene defines a novel molecular subset of non-small-cell lung cancer (NSCLC). However, the clinicopathological features of patients with the EML4-ALK fusion gene have not been defined completely. Methods Clinicopathological data of 200 Chinese patients with advanced NSCLC were analyzed retrospectively to explore their possible correlations with EML4-ALK fusions. Results The EML4-ALK fusion gene was detected in 56 (28.0%) of the 200 NSCLC patients, and undetected in 22 (11.0%) patients because of an insufficient amount of pathological tissue. The median age of the patients with positive and negative EML4-ALK was 48 and 55 years, respectively. Patients with the EML4-ALK fusion gene were significantly younger (P< 0.001). The detection rate of the EML4-ALK fusion gene in patients who received primary tumor or metastatic lymph node resection was significantly higher than in patients who received fine-needle biopsy (P= 0.003). The detection rate of the EML4-ALK fusion gene in patients with a time lag from obtainment of the pathological tissue to EML4-ALK fusion gene detection ≤48 months was significantly higher than in patients >48 months (P= 0.020). The occurrence of the EML4-ALK fusion gene in patients with wild-type epidermal growth factor receptor (EGFR) was significantly higher than in patients with mutant-type EGFR (42.5% [37/87] vs. 6.3% [1/16], P= 0.005). Conclusions Younger age and wild-type EGFR were identified as clinicopathological characteristics of patients with advanced NSCLC who harbored the EML4-ALK fusion gene. The optimal time lag from the obtainment of the pathological tissue to the time of EML4-ALK fusion gene detection is ≤48 months. PMID:26767009

Ontological Issues in Higher Levels of Information Fusion: User Refinement of the Fusion Process

DTIC Science & Technology

2003-01-01

fusion question, the thing that is separates the Greek We explore the higher-level purpose offusion systems by philosophical questions and modem day...the The Greeks focused on both data fusion and the Fusion02 conference there are common fusion questions philosophical questions of an ontology - the...data World of Visible Things Belief (pistis) fusion - user refinement. The rest of the paper is as Appearances follows: Section 2 details the Greek

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.

Multisensor Fusion for Change Detection

NASA Astrophysics Data System (ADS)

Schenk, T.; Csatho, B.

2005-12-01

Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach with detecting surface elevation changes on the Byrd Glacier, Antarctica, with aerial imagery from 1980s and ICESat laser altimetry data from 2003-05. Change detection from such disparate data sets is an intricate fusion problem, beginning with sensor alignment, and on to reasoning with spatial information as to where changes occurred and to what extent.

Helium-3 blankets for tritium breeding in fusion reactors

NASA Technical Reports Server (NTRS)

Steiner, Don; Embrechts, Mark; Varsamis, Georgios; Vesey, Roger; Gierszewski, Paul

1988-01-01

It is concluded that He-3 blankets offers considerable promise for tritium breeding in fusion reactors: good breeding potential, low operational risk, and attractive safety features. The availability of He-3 resources is the key issue for this concept. There is sufficient He-3 from decay of military stockpiles to meet the International Thermonuclear Experimental Reactor needs. Extraterrestrial sources of He-3 would be required for a fusion power economy.

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

NASA Astrophysics Data System (ADS)

S. Al-Kaltakchi, Musab T.; Woo, Wai L.; Dlay, Satnam; Chambers, Jonathon A.

2017-12-01

In this study, a speaker identification system is considered consisting of a feature extraction stage which utilizes both power normalized cepstral coefficients (PNCCs) and Mel frequency cepstral coefficients (MFCC). Normalization is applied by employing cepstral mean and variance normalization (CMVN) and feature warping (FW), together with acoustic modeling using a Gaussian mixture model-universal background model (GMM-UBM). The main contributions are comprehensive evaluations of the effect of both additive white Gaussian noise (AWGN) and non-stationary noise (NSN) (with and without a G.712 type handset) upon identification performance. In particular, three NSN types with varying signal to noise ratios (SNRs) were tested corresponding to street traffic, a bus interior, and a crowded talking environment. The performance evaluation also considered the effect of late fusion techniques based on score fusion, namely, mean, maximum, and linear weighted sum fusion. The databases employed were TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3600 speech utterances. As recommendations from the study, mean fusion is found to yield overall best performance in terms of speaker identification accuracy (SIA) with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings.

Reaching to a featured formula to deduce the energy of the heaviest particles producing from the controlled thermonuclear fusion reactions

NASA Astrophysics Data System (ADS)

Majeed, Raad H.; Oudah, Osamah N.

2018-05-01

Thermonuclear fusion reaction plays an important role in developing and construction any power plant system. Studying the physical behavior for the possible mechanism governed energies released by the fusion products to precise understanding the related kinematics. In this work a theoretical formula controlled the general applied thermonuclear fusion reactions is achieved to calculating the fusion products energy depending upon the reactants physical properties and therefore, one can calculate other parameters governed a given reaction. By using this formula, the energy spectrum of 4He produced from T-3He fusion reaction has been sketched with respect to reaction angle and incident energy ranged from (0.08-0.6) MeV.

CRISPR/Cas9 Engineering of Adult Mouse Liver Demonstrates That the Dnajb1-Prkaca Gene Fusion Is Sufficient to Induce Tumors Resembling Fibrolamellar Hepatocellular Carcinoma.

PubMed

Engelholm, Lars H; Riaz, Anjum; Serra, Denise; Dagnæs-Hansen, Frederik; Johansen, Jens V; Santoni-Rugiu, Eric; Hansen, Steen H; Niola, Francesco; Frödin, Morten

2017-12-01

Fibrolamellar hepatocellular carcinoma (FL-HCC) is a primary liver cancer that predominantly affects children and young adults with no underlying liver disease. A somatic, 400 Kb deletion on chromosome 19 that fuses part of the DnaJ heat shock protein family (Hsp40) member B1 gene (DNAJB1) to the protein kinase cAMP-activated catalytic subunit alpha gene (PRKACA) has been repeatedly identified in patients with FL-HCC. However, the DNAJB1-PRKACA gene fusion has not been shown to induce liver tumorigenesis. We used the CRISPR/Cas9 technique to delete in mice the syntenic region on chromosome 8 to create a Dnajb1-Prkaca fusion and monitored the mice for liver tumor development. We delivered CRISPR/Cas9 vectors designed to juxtapose exon 1 of Dnajb1 with exon 2 of Prkaca to create the Dnajb1-Prkaca gene fusion associated with FL-HCC, or control Cas9 vector, via hydrodynamic tail vein injection to livers of 8-week-old female FVB/N mice. These mice did not have any other engineered genetic alterations and were not exposed to liver toxins or carcinogens. Liver tissues were collected 14 months after delivery; genomic DNA was analyzed by PCR to detect the Dnajb1-Prkaca fusion, and tissues were characterized by histology, immunohistochemistry, RNA sequencing, and whole-exome sequencing. Livers from 12 of the 15 mice given the vectors to induce the Dnajb1-Prkaca gene fusion, but none of the 11 mice given the control vector, developed neoplasms. The tumors contained the Dnajb1-Prkaca gene fusion and had histologic and cytologic features of human FL-HCCs: large polygonal cells with granular, eosinophilic, and mitochondria-rich cytoplasm, prominent nucleoli, and markers of hepatocytes and cholangiocytes. In comparing expression levels of genes between the mouse tumor and non-tumor liver cells, we identified changes similar to those detected in human FL-HCC, which included genes that affect cell cycle and mitosis regulation. Genomic analysis of mouse neoplasms induced by the Dnajb1-Prkaca fusion revealed a lack of mutations in genes commonly associated with liver cancers, as observed in human FL-HCC. Using CRISPR/Cas9 technology, we found generation of the Dnajb1-Prkaca fusion gene in wild-type mice to be sufficient to initiate formation of tumors that have many features of human FL-HCC. Strategies to block DNAJB1-PRKACA might be developed as therapeutics for this form of liver cancer. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Semantic Shot Classification in Sports Video

NASA Astrophysics Data System (ADS)

Duan, Ling-Yu; Xu, Min; Tian, Qi

2003-01-01

In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of a specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervised learning and classification of the given sports video with low-level and middle-level features extracted from the sports video. It is observed that for each sport we can predefine a small number of semantic shot classes, about 5~10, which covers 90~95% of sports broadcasting video. With the supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot classes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 4 types of sports videos: tennis, basketball, volleyball and soccer. Good classification accuracy of 85~95% has been achieved. With correctly classified sports video shots, further structural and temporal analysis, such as event detection, video skimming, table of content, etc, will be greatly facilitated.

Epileptic seizure onset detection based on EEG and ECG data fusion.

PubMed

Qaraqe, Marwa; Ismail, Muhammad; Serpedin, Erchin; Zulfi, Haneef

2016-05-01

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case. Copyright © 2016 Elsevier Inc. All rights reserved.

Design and Preparation of Two ReBCO-CORC® Cable-In-Conduit Conductors for Fusion and Detector Magnets

NASA Astrophysics Data System (ADS)

Mulder, T.; van der Laan, D.; Weiss, J. D.; Dudarev, A.; Dhallé, M.; ten Kate, H. H. J.

2017-12-01

Two new ReBCO-CORC® based cable-in-conduit conductors (CICC) are developed by CERN in collaboration with ACT-Boulder. Both conductors feature a critical current of about 80 kA at 4.5 K and 12 T. One conductor is designed for operation in large detector magnets, while the other is aimed for application in fusion type magnets. The conductors use a six-around-one cable geometry with six flexible ReBCO CORC® strands twisted around a central tube. The fusion CICC is designed to be cooled by the internal forced flow of either helium gas or supercritical helium to cope with high heat loads in superconducting magnets in large fusion experimental reactors. In addition, the cable is enclosed by a stainless steel jacket to accommodate with the high level of Lorentz forces present in such magnets. Detector type magnets require stable, high-current conductors. Therefore, the detector CORC® CICC comprises an OFHC copper jacket with external conduction cooling, which is advantageous due to its simplicity. A 2.8 m long sample of each conductor is manufactured and prepared for testing in the Sultan facility at PSI Villigen. In the paper, the conductor design and assembly steps for both CORC® CICCs are highlighted.

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.

SFPQ/PSF-TFE3 renal cell carcinoma: a clinicopathologic study emphasizing extended morphology and reviewing the differences between SFPQ-TFE3 RCC and the corresponding mesenchymal neoplasm despite an identical gene fusion.

PubMed

Wang, Xiao-Tong; Xia, Qiu-Yuan; Ni, Hao; Ye, Sheng-Bing; Li, Rui; Wang, Xuan; Shi, Shan-Shan; Zhou, Xiao-Jun; Rao, Qiu

2017-05-01

Xp11 translocation renal cell carcinoma (RCC) with SFPQ/PSF-TFE3 gene fusion is a rare epithelial tumor. Of note, the appearance of the gene fusion does not necessarily mean that it is renal cell carcinoma. The corresponding mesenchymal neoplasms, including Xp11 neoplasm with melanocytic differentiation, TFE3 rearrangement-associated perivascular epithelioid cell tumor (PEComa) and melanotic Xp11 translocation renal cancer, can also harbor the identical gene fusion. However, the differences between Xp11 translocation RCC and the corresponding mesenchymal neoplasm have only recently been described. Herein, we examined 5 additional cases of SFPQ-TFE3 RCCs using clinicopathologic, immunohistochemical, and molecular analyses. One tumor had the typical morphologic features of SFPQ-TFE3 RCC, whereas other 3 cases demonstrated the unusual morphologic features associated with pseudorosettes formation or clusters of smaller cells, mimicking TFEB RCC. The remaining one showed branching tubules and papillary structure composed of clear and eosinophilic tumor cells. Immunohistochemically, all 5 cases demonstrated moderate (2+) or strong (3+) positive staining for TFE3, PAX-8 and CD10, whereas no cases demonstrated TFEB, Cathepsin K, CA-IX, CK7, Melan-A, or HMB-45 expression. Genetically, the fusion transcripts were identified in 3 cases by reverse-transcription polymerase chain reaction (RT-PCR). On the basis of fluorescence in situ hybridization (FISH) analysis, all the cases were detected with SFPQ-TFE3 gene fusion. Clinical follow-up data were available for all the patients, and no one developed tumor recurrence, progression, or metastasis. We also review the differences between SFPQ-TFE3 RCC and the corresponding mesenchymal neoplasm despite the identical gene fusion. The presence of pseudorosettes also expands the known histological features of SFPQ-TFE3 RCC. Copyright © 2017 Elsevier Inc. All rights reserved.

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 protein coevolution method uncovers critical features of the Hepatitis C Virus fusion mechanism

PubMed Central

Douam, Florian; Mancip, Jimmy; Mailly, Laurent; Montserret, Roland; Ding, Qiang; Verhoeyen, Els; Baumert, Thomas F.; Ploss, Alexander; Carbone, Alessandra

2018-01-01

Amino-acid coevolution can be referred to mutational compensatory patterns preserving the function of a protein. Viral envelope glycoproteins, which mediate entry of enveloped viruses into their host cells, are shaped by coevolution signals that confer to viruses the plasticity to evade neutralizing antibodies without altering viral entry mechanisms. The functions and structures of the two envelope glycoproteins of the Hepatitis C Virus (HCV), E1 and E2, are poorly described. Especially, how these two proteins mediate the HCV fusion process between the viral and the cell membrane remains elusive. Here, as a proof of concept, we aimed to take advantage of an original coevolution method recently developed to shed light on the HCV fusion mechanism. When first applied to the well-characterized Dengue Virus (DENV) envelope glycoproteins, coevolution analysis was able to predict important structural features and rearrangements of these viral protein complexes. When applied to HCV E1E2, computational coevolution analysis predicted that E1 and E2 refold interdependently during fusion through rearrangements of the E2 Back Layer (BL). Consistently, a soluble BL-derived polypeptide inhibited HCV infection of hepatoma cell lines, primary human hepatocytes and humanized liver mice. We showed that this polypeptide specifically inhibited HCV fusogenic rearrangements, hence supporting the critical role of this domain during HCV fusion. By combining coevolution analysis and in vitro assays, we also uncovered functionally-significant coevolving signals between E1 and E2 BL/Stem regions that govern HCV fusion, demonstrating the accuracy of our coevolution predictions. Altogether, our work shed light on important structural features of the HCV fusion mechanism and contributes to advance our functional understanding of this process. This study also provides an important proof of concept that coevolution can be employed to explore viral protein mediated-processes, and can guide the development of innovative translational strategies against challenging human-tropic viruses. PMID:29505618

The Relationship between Serum Vitamin D Levels and Spinal Fusion Success: A Quantitative Analysis

PubMed Central

Metzger, Melodie F.; Kanim, Linda E.; Zhao, Li; Robinson, Samuel T.; Delamarter, Rick B.

2015-01-01

Study Design An in vivo dosing study of vitamin D in a rat posterolateral spinal fusion model with autogenous bone grafting. Rats randomized to four levels of Vitamin D adjusted rat chow, longitudinal serum validation, surgeons/observers blinded to dietary conditions, and rats followed prospectively for fusion endpoint. Objective To assess the impact of dietary and serum levels of Vitamin D on fusion success, consolidation of fusion mass, and biomechanical stiffness after posterolateral spinal fusion procedure. Summary of Background Data Metabolic risk factors, including vitamin D insufficiency, are often overlooked by spine surgeons. Currently there are no published data on the causal effect of insufficient or deficient vitamin D levels on the success of establishing solid bony union after a spinal fusion procedure. Methods 50 rats were randomized to four experimentally controlled rat chow diets: normal control, vitamin D-deficient, vitamin-D insufficient, and a non-toxic high dose of vitamin D, four weeks prior to surgery and maintained post-surgery until sacrifice. Serum levels of 25(OH)D were determined at surgery and sacrifice using radioimmunoassay. Posterolateral fusion surgery with tail autograft was performed. Rats were sacrificed 12 weeks post-operatively and fusion was evaluated via manual palpation, high resolution radiographs, μCT, and biomechanical testing. Results Serum 25(OH)D and calcium levels were significantly correlated with vitamin-D adjusted chow (p<0.001). There was a dose dependent relationship between vitamin D adjusted chow and manual palpation fusion with greatest differences found in measures of radiographic density between high and deficient vitamin D (p<0.05). Adequate levels of vitamin D (high and normal control) yielded stiffer fusion than inadequate levels (insufficient and deficient) (p<0.05). Conclusions Manual palpation fusion rates increased with supplementation of dietary vitamin D. Biomechanical stiffness, bone volume and density were also positively-related to vitamin D, and calcium. PMID:25627287

Fc-fusion Proteins in Therapy: An Updated View.

PubMed

Jafari, Reza; Zolbanin, Naime M; Rafatpanah, Houshang; Majidi, Jafar; Kazemi, Tohid

2017-01-01

Fc-fusion proteins are composed of Fc region of IgG antibody (Hinge-CH2-CH3) and a desired linked protein. Fc region of Fc-fusion proteins can bind to neonatal Fc receptor (FcRn) thereby rescuing it from degradation. The first therapeutic Fc-fusion protein was introduced for the treatment of AIDS. The molecular designing is the first stage in production of Fc-fusion proteins. The amino acid residues in the Fc region and linked protein are very important in the bioactivity and affinity of the fusion proteins. Although, therapeutic monoclonal antibodies are the top selling biologics but the application of therapeutic Fc-fusion proteins in clinic is in progress and among these medications Etanercept is the most effective in therapy. At present, eleven Fc-fusion proteins have been approved by FDA. There are novel Fc-fusion proteins which are in pre-clinical and clinical development. In this article, we review the molecular and biological characteristics of Fc-fusion proteins and then further discuss the features of novel therapeutic Fc-fusion proteins. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas.

PubMed

Liu, Yanpeng; Li, Yibin; Ma, Xin; Song, Rui

2017-03-29

In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming to gain better results without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size; therefore, it can extracts more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features' dimensions are reduced by Principal Component Analysis (PCA) and we apply several classifiers to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames compared with neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. As a result, the salient areas found from different subjects are the same size. In addition, the gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

A trainable decisions-in decision-out (DEI-DEO) fusion system

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1998-03-01

Most of the decision fusion systems proposed hitherto in the literature for multiple data source (sensor) environments operate on the basis of pre-defined fusion logic, be they crisp (deterministic), probabilistic, or fuzzy in nature, with no specific learning phase. The fusion systems that are trainable, i.e., ones that have a learning phase, mostly operate in the features-in-decision-out mode, which essentially reduces the fusion process functionally to a pattern classification task in the joint feature space. In this study, a trainable decisions-in-decision-out fusion system is described which estimates a fuzzy membership distribution spread across the different decision choices based on the performance of the different decision processors (sensors) corresponding to each training sample (object) which is associated with a specific ground truth (true decision). Based on a multi-decision space histogram analysis of the performance of the different processors over the entire training data set, a look-up table associating each cell of the histogram with a specific true decision is generated which forms the basis for the operational phase. In the operational phase, for each set of decision inputs, a pointer to the look-up table learnt previously is generated from which a fused decision is derived. This methodology, although primarily designed for fusing crisp decisions from the multiple decision sources, can be adapted for fusion of fuzzy decisions as well if such are the inputs from these sources. Examples, which illustrate the benefits and limitations of the crisp and fuzzy versions of the trainable fusion systems, are also included.

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Two-level noncontiguous versus three-level anterior cervical discectomy and fusion: a biomechanical comparison.

PubMed

Finn, Michael A; Samuelson, Mical M; Bishop, Frank; Bachus, Kent N; Brodke, Darrel S

2011-03-15

Biomechanical study. To determine biomechanical forces exerted on intermediate and adjacent segments after two- or three-level fusion for treatment of noncontiguous levels. Increased motion adjacent to fused spinal segments is postulated to be a driving force in adjacent segment degeneration. Occasionally, a patient requires treatment of noncontiguous levels on either side of a normal level. The biomechanical forces exerted on the intermediate and adjacent levels are unknown. Seven intact human cadaveric cervical spines (C3-T1) were mounted in a custom seven-axis spine simulator equipped with a follower load apparatus and OptoTRAK three-dimensional tracking system. Each intact specimen underwent five cycles each of flexion/extension, lateral bending, and axial rotation under a ± 1.5 Nm moment and a 100-Nm axial follower load. Applied torque and motion data in each axis of motion and level were recorded. Testing was repeated under the same parameters after C4-C5 and C6-C7 diskectomies were performed and fused with rigid cervical plates and interbody spacers and again after a three-level fusion from C4 to C7. Range of motion was modestly increased (35%) in the intermediate and adjacent levels in the skip fusion construct. A significant or nearly significant difference was reached in seven of nine moments. With the three-level fusion construct, motion at the infra- and supra-adjacent levels was significantly or nearly significantly increased in all applied moments over the intact and the two-level noncontiguous construct. The magnitude of this change was substantial (72%). Infra- and supra-adjacent levels experienced a marked increase in strain in all moments with a three-level fusion, whereas the intermediate, supra-, and infra-adjacent segments of a two-level fusion experienced modest strain moments relative to intact. It would be appropriate to consider noncontiguous fusions instead of a three-level fusion when confronted with nonadjacent disease.

Data fusion in cyber security: first order entity extraction from common cyber data

NASA Astrophysics Data System (ADS)

Giacobe, Nicklaus A.

2012-06-01

The Joint Directors of Labs Data Fusion Process Model (JDL Model) provides a framework for how to handle sensor data to develop higher levels of inference in a complex environment. Beginning from a call to leverage data fusion techniques in intrusion detection, there have been a number of advances in the use of data fusion algorithms in this subdomain of cyber security. While it is tempting to jump directly to situation-level or threat-level refinement (levels 2 and 3) for more exciting inferences, a proper fusion process starts with lower levels of fusion in order to provide a basis for the higher fusion levels. The process begins with first order entity extraction, or the identification of important entities represented in the sensor data stream. Current cyber security operational tools and their associated data are explored for potential exploitation, identifying the first order entities that exist in the data and the properties of these entities that are described by the data. Cyber events that are represented in the data stream are added to the first order entities as their properties. This work explores typical cyber security data and the inferences that can be made at the lower fusion levels (0 and 1) with simple metrics. Depending on the types of events that are expected by the analyst, these relatively simple metrics can provide insight on their own, or could be used in fusion algorithms as a basis for higher levels of inference.

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.

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Impurity seeding for suppression of the near scrape-off layer heat flux feature in tokamak limited plasmas

NASA Astrophysics Data System (ADS)

Nespoli, F.; Labit, B.; Furno, I.; Theiler, C.; Sheikh, U. A.; Tsui, C. K.; Boedo, J. A.; TCV Team

2018-05-01

In inboard-limited plasmas, foreseen to be used in future fusion reactor start-up and ramp down phases, the Scrape-Off Layer (SOL) exhibits two regions: the "near" and "far" SOL. The steep radial gradient of the parallel heat flux associated with the near SOL can result in excessive thermal loads onto the solid surfaces, damaging them and/or limiting the operational space of a fusion reactor. In this article, leveraging the results presented in the study by F. Nespoli et al. [Nucl. Fusion 57, 126029 (2017)], we propose a technique for the mitigation and suppression of the near SOL heat flux feature by impurity seeding. The first successful experimental results from the TCV tokamak are presented and discussed.

Integrating image quality in 2nu-SVM biometric match score fusion.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2007-10-01

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.

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.

Nuclear fusion of advanced fuels using converging focused ion beams

NASA Astrophysics Data System (ADS)

Egle, Brian James

The Six Ion Gun Fusion Experiment (SIGFE) was designed and built to investigate a possible avenue to increase the reaction rate efficiency of the D-D and D-3He nuclear fusion reactions in Inertial Electrostatic Confinement (IEC) devices to the levels required for several non-electric applications of nuclear fusion. The SIGFE is based on the seminal IEC experiment published by Hirsch in 1967, and is the first experiment to recreate the results and unique features of the Hirsch device. The SIGFE used six identical ion beams to focus and converge deuterium and helium-3 ions into a sphere of less than 2 mm at nearly mono-energetic ion energies up to 150 keV. With improved ion optics and diagnostics, the SIGFE concluded that within the investigated parameter space, the region where the ion beams converged accounted for less than 0.2% of the total D-D fusion reactions. The maximum D-D fusion rates were observed when the ion beams were intentionally defocused to strike the inside surface of the cathode lenses. In this defocused state, the total D-D fusion rate increased when the chamber pressure was decreased. The maximum D-D fusion rate was 4.3 x 107 neutrons per second at a cathode voltage of -130 kV, a total cathode current of 10 mA, and a chamber pressure of 27 mPa. The D and 3He ion beams were produced in six self-contained ion gun modules. The modules were each capable of at least 4 mA of ion current while maintaining a main chamber pressure as low as 13 mPa. The theoretically calculated extractable ion current agreed with the experiment within a factor of 2. A concept was also developed and evaluated for the production of radioisotopes from the 14.7 MeV D-3He fusion protons produced in an IEC device. Monte Carlo simulations of this concept determined that a D-3He fusion rate on the order of 1011 s-1 would be required for an IEC device to produce 1 mCi of the 11C radioisotope.

TFG-MET fusion in an infantile spindle cell sarcoma with neural features.

PubMed

Flucke, Uta; van Noesel, Max M; Wijnen, Marc; Zhang, Lei; Chen, Chun-Liang; Sung, Yun-Shao; Antonescu, Cristina R

2017-09-01

An increasing number of congenital and infantile sarcomas displaying a primitive, monomorphic spindle cell phenotype have been characterized to harbor recurrent gene fusions, including infantile fibrosarcoma and congenital spindle cell rhabdomyosarcoma. Here, we report an unusual spindle cell sarcoma presenting as a large and infiltrative pelvic soft tissue mass in a 4-month-old girl, which revealed a novel TFG-MET gene fusion by whole transcriptome RNA sequencing. The tumor resembled the morphology of an infantile fibrosarcoma with both fascicular and patternless growth, however, it expressed strong S100 protein immunoreactivity, while lacking SOX10 staining and retaining H3K27me3 expression. Although this immunoprofile suggested partial neural/neuroectodermal differentiation, overall features were unusual and did not fit into any known tumor types (cellular schwannoma, MPNST), raising the possibility of a novel pathologic entity. The TFG-MET gene fusion expands the genetic spectrum implicated in the pathogenesis of congenital spindle cell sarcomas, with yet another example of kinase oncogenic activation through chromosomal translocation. The discovery of this new fusion is significant since the resulting MET activation can potentially be inhibited by targeted therapy, as MET inhibitors are presently available in clinical trials. © 2017 Wiley Periodicals, Inc.

Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Hu, Gang; Hu, Kai

2018-01-01

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

Multidirectional testing of one- and two-level ProDisc-L versus simulated fusions.

PubMed

Panjabi, Manohar; Henderson, Gweneth; Abjornson, Celeste; Yue, James

2007-05-20

An in vitro human cadaveric biomechanical study. To evaluate intervertebral rotation changes due to lumbar ProDisc-L compared with simulated fusion, using follower load and multidirectional testing. Artificial discs, as opposed to the fusions, are thought to decrease the long-term accelerated degeneration at adjacent levels. A biomechanical assessment can be helpful, as the long-term clinical evaluation is impractical. Six fresh human cadaveric lumbar specimens (T12-S1) underwent multidirectional testing in flexion-extension, bilateral lateral bending, and bilateral torsion using the Hybrid test method. First, intact specimen total range of rotation (T12-S1) was determined. Second, using pure moments again, this range of rotation was achieved in each of the 5 constructs: A) ProDisc-L at L5-S1; B) fusion at L5-S1; C) ProDisc-L at L4-L5 and fusion at L5-S1; D) ProDisc-L at L4-L5 and L5-S1; and E) 2-level fusion at L4-L5 to L5-S1. Significant changes in the intervertebral rotations due to each construct were determined at the operated and nonoperated levels using repeated measures single factor ANOVA and Bonferroni statistical tests (P < 0.05). Adjacent-level effects (ALEs) were defined as the percentage changes in intervertebral rotations at the nonoperated levels due to the constructs. One- and 2-level ProDisc-L constructs showed only small ALE in any of the 3 rotations. In contrast, 1- and 2-level fusions showed increased ALE in all 3 directions (average, 7.8% and 35.3%, respectively, for 1 and 2 levels). In the disc plus fusion combination (construct C), the ALEs were similar to the 1-level fusion alone. In general, ProDisc-L preserved physiologic motions at all spinal levels, while the fusion simulations resulted in significant ALE.

Angiogram, fundus, and oxygen saturation optic nerve head image fusion

NASA Astrophysics Data System (ADS)

Cao, Hua; Khoobehi, Bahram

2009-02-01

A novel multi-modality optic nerve head image fusion approach has been successfully designed. The new approach has been applied on three ophthalmologic modalities: angiogram, fundus, and oxygen saturation retinal optic nerve head images. It has achieved an excellent result by giving the visualization of fundus or oxygen saturation images with a complete angiogram overlay. During this study, two contributions have been made in terms of novelty, efficiency, and accuracy. The first contribution is the automated control point detection algorithm for multi-sensor images. The new method employs retina vasculature and bifurcation features by identifying the initial good-guess of control points using the Adaptive Exploratory Algorithm. The second contribution is the heuristic optimization fusion algorithm. In order to maximize the objective function (Mutual-Pixel-Count), the iteration algorithm adjusts the initial guess of the control points at the sub-pixel level. A refinement of the parameter set is obtained at the end of each loop, and finally an optimal fused image is generated at the end of the iteration. It is the first time that Mutual-Pixel-Count concept has been introduced into biomedical image fusion area. By locking the images in one place, the fused image allows ophthalmologists to match the same eye over time and get a sense of disease progress and pinpoint surgical tools. The new algorithm can be easily expanded to human or animals' 3D eye, brain, or body image registration and fusion.

An Investigation for Ground State Features of Some Structural Fusion Materials

NASA Astrophysics Data System (ADS)

Aytekin, H.; Tel, E.; Baldik, R.; Aydin, A.

2011-02-01

Environmental concerns associated with fossil fuels are creating increased interest in alternative non-fossil energy sources. Nuclear fusion can be one of the most attractive sources of energy from the viewpoint of safety and minimal environmental impact. When considered in all energy systems, the requirements for performance of structural materials in a fusion reactor first wall, blanket or diverter, are arguably more demanding or difficult than for other energy system. The development of fusion materials for the safety of fusion power systems and understanding nuclear properties is important. In this paper, ground state properties for some structural fusion materials as 27Al, 51V, 52Cr, 55Mn, and 56Fe are investigated using Skyrme-Hartree-Fock method. The obtained results have been discussed and compared with the available experimental data.

Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

NASA Astrophysics Data System (ADS)

Sirmacek, B.; Lindenbergh, R. C.; Menenti, M.

2013-10-01

Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

Fusion of Enveloped Viruses in Endosomes

PubMed Central

White, Judith M.; Whittaker, Gary R.

2016-01-01

Ari Helenius launched the field of enveloped virus fusion in endosomes with a seminal paper in the Journal of Cell Biology in 1980. In the intervening years a great deal has been learned about the structures and mechanisms of viral membrane fusion proteins as well as about the endosomes in which different enveloped viruses fuse and the endosomal cues that trigger fusion. We now recognize three classes of viral membrane fusion proteins based on structural criteria and four mechanisms of fusion triggering. After reviewing general features of viral membrane fusion proteins and viral fusion in endosomes, we delve into three characterized mechanisms for viral fusion triggering in endosomes: by low pH, by receptor binding plus low pH, and by receptor binding plus the action of a protease. We end with a discussion of viruses that may employ novel endosomal fusion triggering mechanisms. A key take home message is that enveloped viruses that enter cells by fusing in endosomes traverse the endocytic pathway until they reach an endosome that has all of the environmental conditions (pH, proteases, ions, intracellular receptors, and lipid composition) to (if needed) prime and (in all cases) trigger the fusion protein and to support membrane fusion. PMID:26935856

chimeraviz: a tool for visualizing chimeric RNA.

PubMed

Lågstad, Stian; Zhao, Sen; Hoff, Andreas M; Johannessen, Bjarne; Lingjærde, Ole Christian; Skotheim, Rolf I

2017-09-15

Advances in high-throughput RNA sequencing have enabled more efficient detection of fusion transcripts, but the technology and associated software used for fusion detection from sequencing data often yield a high false discovery rate. Good prioritization of the results is important, and this can be helped by a visualization framework that automatically integrates RNA data with known genomic features. Here we present chimeraviz , a Bioconductor package that automates the creation of chimeric RNA visualizations. The package supports input from nine different fusion-finder tools: deFuse, EricScript, InFusion, JAFFA, FusionCatcher, FusionMap, PRADA, SOAPfuse and STAR-FUSION. chimeraviz is an R package available via Bioconductor ( https://bioconductor.org/packages/release/bioc/html/chimeraviz.html ) under Artistic-2.0. Source code and support is available at GitHub ( https://github.com/stianlagstad/chimeraviz ). rolf.i.skotheim@rr-research.no. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Classification of Sporting Activities Using Smartphone Accelerometers

PubMed Central

Mitchell, Edmond; Monaghan, David; O'Connor, Noel E.

2013-01-01

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach. PMID:23604031

Choice of surgical approach for ossification of the posterior longitudinal ligament in combination with cervical disc hernia.

PubMed

Yang, Hai-song; Chen, De-yu; Lu, Xu-hua; Yang, Li-li; Yan, Wang-jun; Yuan, Wen; Chen, Yu

2010-03-01

Ossification of the posterior longitudinal ligament (OPLL) is a common spinal disorder that presents with or without cervical myelopathy. Furthermore, there is evidence suggesting that OPLL often coexists with cervical disc hernia (CDH), and that the latter is the more important compression factor. To raise the awareness of CDH in OPLL for spinal surgeons, we performed a retrospective study on 142 patients with radiologically proven OPLL who had received surgery between January 2004 and January 2008 in our hospital. Plain radiograph, three-dimensional computed tomography construction (3D CT), and magnetic resonance imaging (MRI) of the cervical spine were all performed. Twenty-six patients with obvious CDH (15 of segmental-type, nine of mixed-type, two of continuous-type) were selected via clinical and radiographic features, and intraoperative findings. By MRI, the most commonly involved level was C5/6, followed by C3/4, C4/5, and C6/7. The areas of greatest spinal cord compression were at the disc levels because of herniated cervical discs. Eight patients were decompressed via anterior cervical discectomy and fusion (ACDF), 13 patients via anterior cervical corpectomy and fusion (ACCF), and five patients via ACDF combined with posterior laminectomy and fusion. The outcomes were all favorable. In conclusion, surgeons should consider the potential for CDH when performing spinal cord decompression and deciding the surgical approach in patients presenting with OPLL.

Fusion Studies in Japan

NASA Astrophysics Data System (ADS)

Ogawa, Yuichi

2016-05-01

A new strategic energy plan decided by the Japanese Cabinet in 2014 strongly supports the steady promotion of nuclear fusion development activities, including the ITER project and the Broader Approach activities from the long-term viewpoint. Atomic Energy Commission (AEC) in Japan formulated the Third Phase Basic Program so as to promote an experimental fusion reactor project. In 2005 AEC has reviewed this Program, and discussed on selection and concentration among many projects of fusion reactor development. In addition to the promotion of ITER project, advanced tokamak research by JT-60SA, helical plasma experiment by LHD, FIREX project in laser fusion research and fusion engineering by IFMIF were highly prioritized. Although the basic concept is quite different between tokamak, helical and laser fusion researches, there exist a lot of common features such as plasma physics on 3-D magnetic geometry, high power heat load on plasma facing component and so on. Therefore, a synergetic scenario on fusion reactor development among various plasma confinement concepts would be important.

[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.

Sacroiliac joint tuberculosis: surgical management by posterior open-window focal debridement and joint fusion.

PubMed

Zhu, Guo; Jiang, Li-Yuan; Yi, Zhang; Ping, Li; Duan, Chun-Yue; Yong, Cao; Liu, Jin-Yang; Hu, Jian-Zhong

2017-11-29

Sacroiliac joint tuberculosis(SJT) is relatively uncommon, but it may cause severe sacroiliac joint destruction and functional disorder. Few studies in the literature have been presented on SJT, reports of surgical treatment for SJT are even fewer. In this study, we retrospectively reviewed surgical management of patients with severe SJT of 3 different types and proposed to reveal the clinical manifestations and features and aim to determine the efficiency and security of such surgical treatment. We reviewed 17 patients with severe SJT of 3 different types who underwent posterior open-window focal debridement and bone graft for joint fusion. Among them,five patients with anterior sacral abscess had anterior abscess curettage before debridement. Two patients with lumbar vertebral tuberculosis received one-stage posterior tuberculous debridement, interbody fusion and instrumentation. Follow-up was performed 36 months (26 to 45 months) using the following parameters: erythrocyte sedimentation rate(ESR), status of joint bony fusion on CT scan, visual analogue scale (VAS) and the Oswestry Disability Index (ODI). Buttock pain and low back pain were progressively relieved with time. 6 months later, pain was not obvious, and ESR resumed to normal levels within 3 months. Solid fusion of the sacroiliac joint occurred within 12 months in all cases. No complications or recurrence occurred. At final follow-up, all patients had no pain or only minimal discomfort over the affected joint and almost complete functional recovery. Posterior open-window focal debridement and joint fusion is an efficient and secure surgical method to treat severe SJT. If there is an abscess in the front of the sacroiliac joint, anterior abscess curettage should be performed as a supplement.

Characterization of the Bas-Congo virus glycoprotein and its function in pseudotyped viruses.

PubMed

Steffen, Imke; Liss, Nathan M; Schneider, Bradley S; Fair, Joseph N; Chiu, Charles Y; Simmons, Graham

2013-09-01

Bas-Congo virus (BASV) is a novel rhabdovirus recently identified from a patient with acute hemorrhagic fever in the Bas-Congo province of the Democratic Republic of Congo (DRC). Here we show that the BASV glycoprotein (BASV-G) can be successfully used to pseudotype glycoprotein-deficient vesicular stomatitis virus (VSV), allowing studies of BASV-G-driven membrane fusion and viral entry into target cells without replication-competent virus. BASV-G displayed broad tissue and species tropism in vitro, and BASV-G-mediated membrane fusion was pH dependent. The conformational changes induced in BASV-G by acidification were fully reversible and did not lead to inactivation of the viral fusion protein. Our data combined with comparative sequence similarity analyses suggest that BASV-G shares structural and functional features with other rhabdovirus glycoproteins and falls into the group of class III viral fusion proteins. However, activation of BASV-G-driven fusion required a lower pH and higher temperatures than did VSV-G-mediated fusion. Moreover, in contrast to VSV-G, mature BASV-G in VSV pseudotypes consists of a mixture of high-mannose and complex glycans that enables it to bind to certain C-type lectins, thereby enhancing its attachment to target cells. Taken together, the results presented in this study will facilitate future investigations of BASV-G-mediated cell entry and its inhibition in the absence of an infectious cell culture assay for BASV and at lower biosafety levels. Moreover, serology testing based on BASV-G pseudotype neutralization can be used to uncover the prevalence and importance of BASV as a potential novel human pathogen in the DRC and throughout Central Africa.

Paramyxovirus F1 protein has two fusion peptides: implications for the mechanism of membrane fusion.

PubMed

Peisajovich, S G; Samuel, O; Shai, Y

2000-03-10

Viral fusion proteins contain a highly hydrophobic segment, named the fusion peptide, which is thought to be responsible for the merging of the cellular and viral membranes. Paramyxoviruses are believed to contain a single fusion peptide at the N terminus of the F1 protein. However, here we identified an additional internal segment in the Sendai virus F1 protein (amino acids 214-226) highly homologous to the fusion peptides of HIV-1 and RSV. A synthetic peptide, which includes this region, was found to induce membrane fusion of large unilamellar vesicles, at concentrations where the known N-terminal fusion peptide is not effective. A scrambled peptide as well as several peptides from other regions of the F1 protein, which strongly bind to membranes, are not fusogenic. The functional and structural characterization of this active segment suggest that the F1 protein has an additional internal fusion peptide that could participate in the actual fusion event. The presence of homologous regions in other members of the same family suggests that the concerted action of two fusion peptides, one N-terminal and the other internal, is a general feature of paramyxoviruses. Copyright 2000 Academic Press.

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

Krishnan, Kalpagam; Liu, Jeff; Kohli, Kirpal

Purpose: Fusion of electrical impedance tomography (EIT) with computed tomography (CT) can be useful as a clinical tool for providing additional physiological information about tissues, but requires suitable fusion algorithms and validation procedures. This work explores the feasibility of fusing EIT and CT images using an algorithm for coregistration. The imaging performance is validated through feature space assessment on phantom contrast targets. Methods: EIT data were acquired by scanning a phantom using a circuit, configured for injecting current through 16 electrodes, placed around the phantom. A conductivity image of the phantom was obtained from the data using electrical impedance andmore » diffuse optical tomography reconstruction software (EIDORS). A CT image of the phantom was also acquired. The EIT and CT images were fused using a region of interest (ROI) coregistration fusion algorithm. Phantom imaging experiments were carried out on objects of different contrasts, sizes, and positions. The conductive medium of the phantoms was made of a tissue-mimicking bolus material that is routinely used in clinical radiation therapy settings. To validate the imaging performance in detecting different contrasts, the ROI of the phantom was filled with distilled water and normal saline. Spatially separated cylindrical objects of different sizes were used for validating the imaging performance in multiple target detection. Analyses of the CT, EIT and the EIT/CT phantom images were carried out based on the variations of contrast, correlation, energy, and homogeneity, using a gray level co-occurrence matrix (GLCM). A reference image of the phantom was simulated using EIDORS, and the performances of the CT and EIT imaging systems were evaluated and compared against the performance of the EIT/CT system using various feature metrics, detectability, and structural similarity index measures. Results: In detecting distilled and normal saline water in bolus medium, EIT as a stand-alone imaging system showed contrast discrimination of 47%, while the CT imaging system showed a discrimination of only 1.5%. The structural similarity index measure showed a drop of 24% with EIT imaging compared to CT imaging. The average detectability measure for CT imaging was found to be 2.375 ± 0.19 before fusion. After complementing with EIT information, the detectability measure increased to 11.06 ± 2.04. Based on the feature metrics, the functional imaging quality of CT and EIT were found to be 2.29% and 86%, respectively, before fusion. Structural imaging quality was found to be 66% for CT and 16% for EIT. After fusion, functional imaging quality improved in CT imaging from 2.29% to 42% and the structural imaging quality of EIT imaging changed from 16% to 66%. The improvement in image quality was also observed in detecting objects of different sizes. Conclusions: The authors found a significant improvement in the contrast detectability performance of CT imaging when complemented with functional imaging information from EIT. Along with the feature assessment metrics, the concept of complementing CT with EIT imaging can lead to an EIT/CT imaging modality which might fully utilize the functional imaging abilities of EIT imaging, thereby enhancing the quality of care in the areas of cancer diagnosis and radiotherapy treatment planning.« less

Lumbar lordosis restoration following single-level instrumented fusion comparing 4 commonly used techniques.

PubMed

Dimar, John R; Glassman, Steven D; Vemuri, Venu M; Esterberg, Justin L; Howard, Jennifer M; Carreon, Leah Y

2011-11-09

A major sequelae of lumbar fusion is acceleration of adjacent-level degeneration due to decreased lumbar lordosis. We evaluated the effectiveness of 4 common fusion techniques in restoring lordosis: instrumented posterolateral fusion, translumbar interbody fusion, anteroposterior fusion with posterior instrumentation, and anterior interbody fusion with lordotic threaded (LT) cages (Medtronic Sofamor Danek, Memphis, Tennessee). Radiographs were measured preoperatively, immediately postoperatively, and a minimum of 6 months postoperatively. Parameters measured included anterior and posterior disk space height, lumbar lordosis from L3 to S1, and surgical level lordosis.No significant difference in demographics existed among the 4 groups. All preoperative parameters were similar among the 4 groups. Lumbar lordosis at final follow-up showed no difference between the anteroposterior fusion with posterior instrumentation, translumbar interbody fusion, and LT cage groups, although the posterolateral fusion group showed a significant loss of lordosis (-10°) (P<.001). Immediately postoperatively and at follow-up, the LT cage group had a significantly greater amount of lordosis and showed maintenance of anterior and posterior disk space height postoperatively compared with the other groups. Instrumented posterolateral fusion produces a greater loss of lordosis compared with anteroposterior fusion with posterior instrumentation, translumbar interbody fusion, and LT cages. Maintenance of lordosis and anterior and posterior disk space height is significantly better with anterior interbody fusion with LT cages. Copyright 2011, SLACK Incorporated.

Infrared and visible image fusion with the target marked based on multi-resolution visual attention mechanisms

NASA Astrophysics Data System (ADS)

Huang, Yadong; Gao, Kun; Gong, Chen; Han, Lu; Guo, Yue

2016-03-01

During traditional multi-resolution infrared and visible image fusion processing, the low contrast ratio target may be weakened and become inconspicuous because of the opposite DN values in the source images. So a novel target pseudo-color enhanced image fusion algorithm based on the modified attention model and fast discrete curvelet transformation is proposed. The interesting target regions are extracted from source images by introducing the motion features gained from the modified attention model, and source images are performed the gray fusion via the rules based on physical characteristics of sensors in curvelet domain. The final fusion image is obtained by mapping extracted targets into the gray result with the proper pseudo-color instead. The experiments show that the algorithm can highlight dim targets effectively and improve SNR of fusion image.

Reprogramming of Somatic Cells Towards Pluripotency by Cell Fusion.

PubMed

Malinowski, Andrzej R; Fisher, Amanda G

2016-01-01

Pluripotent reprogramming can be dominantly induced in a somatic nucleus upon fusion with a pluripotent cell such as embryonic stem (ES) cell. Cell fusion between ES cells and somatic cells results in the formation of heterokaryons, in which the somatic nuclei begin to acquire features of the pluripotent partner. The generation of interspecies heterokaryons between mouse ES- and human somatic cells allows an experimenter to distinguish the nuclear events occurring specifically within the reprogrammed nucleus. Therefore, cell fusion provides a simple and rapid approach to look at the early nuclear events underlying pluripotent reprogramming. Here, we describe a polyethylene glycol (PEG)-mediated cell fusion protocol to generate interspecies heterokaryons and intraspecies hybrids between ES cells and B lymphocytes or fibroblasts.

Fusion peptide of influenza hemagglutinin requires a fixed angle boomerang structure for activity.

PubMed

Lai, Alex L; Park, Heather; White, Judith M; Tamm, Lukas K

2006-03-03

The fusion peptide of influenza hemagglutinin is crucial for cell entry of this virus. Previous studies showed that this peptide adopts a boomerang-shaped structure in lipid model membranes at the pH of membrane fusion. To examine the role of the boomerang in fusion, we changed several residues proposed to stabilize the kink in this structure and measured fusion. Among these, mutants E11A and W14A expressed hemagglutinins with hemifusion and no fusion activities, and F9A and N12A had no effect on fusion, respectively. Binding enthalpies and free energies of mutant peptides to model membranes and their ability to perturb lipid bilayer structures correlated well with the fusion activities of the parent full-length molecules. The structure of W14A determined by NMR and site-directed spin labeling features a flexible kink that points out of the membrane, in sharp contrast to the more ordered boomerang of the wild-type, which points into the membrane. A specific fixed angle boomerang structure is thus required to support membrane fusion.

Distinct features of multivesicular body-lysosome fusion revealed by a new cell-free content-mixing assay.

PubMed

Karim, Mahmoud Abdul; Samyn, Dieter Ronny; Mattie, Sevan; Brett, Christopher Leonard

2018-02-01

When marked for degradation, surface receptor and transporter proteins are internalized and delivered to endosomes where they are packaged into intralumenal vesicles (ILVs). Many rounds of ILV formation create multivesicular bodies (MVBs) that fuse with lysosomes exposing ILVs to hydrolases for catabolism. Despite being critical for protein degradation, the molecular underpinnings of MVB-lysosome fusion remain unclear, although machinery underlying other lysosome fusion events is implicated. But how then is specificity conferred? And how is MVB maturation and fusion coordinated for efficient protein degradation? To address these questions, we developed a cell-free MVB-lysosome fusion assay using Saccharomyces cerevisiae as a model. After confirming that the Rab7 ortholog Ypt7 and the multisubunit tethering complex HOPS (homotypic fusion and vacuole protein sorting complex) are required, we found that the Qa-SNARE Pep12 distinguishes this event from homotypic lysosome fusion. Mutations that impair MVB maturation block fusion by preventing Ypt7 activation, confirming that a Rab-cascade mechanism harmonizes MVB maturation with lysosome fusion. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Log-Gabor Weber descriptor for face recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Sang, Nong; Gao, Changxin

2015-09-01

The Log-Gabor transform, which is suitable for analyzing gradually changing data such as in iris and face images, has been widely used in image processing, pattern recognition, and computer vision. In most cases, only the magnitude or phase information of the Log-Gabor transform is considered. However, the complementary effect taken by combining magnitude and phase information simultaneously for an image-feature extraction problem has not been systematically explored in the existing works. We propose a local image descriptor for face recognition, called Log-Gabor Weber descriptor (LGWD). The novelty of our LGWD is twofold: (1) to fully utilize the information from the magnitude or phase feature of multiscale and orientation Log-Gabor transform, we apply the Weber local binary pattern operator to each transform response. (2) The encoded Log-Gabor magnitude and phase information are fused at the feature level by utilizing kernel canonical correlation analysis strategy, considering that feature level information fusion is effective when the modalities are correlated. Experimental results on the AR, Extended Yale B, and UMIST face databases, compared with those available from recent experiments reported in the literature, show that our descriptor yields a better performance than state-of-the art methods.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Feature-based characterisation of signature topography in laser powder bed fusion of metals

NASA Astrophysics Data System (ADS)

Senin, Nicola; Thompson, Adam; Leach, Richard

2018-04-01

The use of state-of-the-art areal topography measurement instrumentation allows for a high level of detail in the acquisition of topographic information at micrometric scales. The 3D geometric models of surface topography obtained from measured data create new opportunities for the investigation of manufacturing processes through characterisation of the surfaces of manufactured parts. Conventional methods for quantitative assessment of topography usually only involve the computation of texture parameters, summary indicators of topography-related characteristics that are computed over the investigated area. However, further useful information may be obtained through characterisation of signature topographic formations, as more direct indicators of manufacturing process behaviour and performance. In this work, laser powder bed fusion of metals is considered. An original algorithmic method is proposed to isolate relevant topographic formations and to quantify their dimensional and geometric properties, using areal topography data acquired by state-of-the-art areal topography measurement instrumentation.

Inversion-mediated gene fusions involving NAB2-STAT6 in an unusual malignant meningioma.

PubMed

Gao, F; Ling, C; Shi, L; Commins, D; Zada, G; Mack, W J; Wang, K

2013-08-20

Meningiomas are the most common primary intracranial tumours, with ∼3% meeting current histopathologic criteria for malignancy. In this study, we explored the transcriptome of meningiomas using RNA-Seq. Inversion-mediated fusions between two adjacent genes, NAB2 and STAT6, were detected in one malignant tumour, creating two novel in-frame transcripts that were validated by RT-PCR and Sanger sequencing. Gene fusions of NAB2-STAT6 were recently implicated in the pathogenesis of solitary fibrous tumours; our study suggested that similar fusions may also have a role in a malignant meningioma with unusual histopathologic features.

Structural basis of viral invasion: lessons from paramyxovirus F

PubMed Central

Lamb, Robert A.; Jardetzky, Theodore S.

2007-01-01

Summary The structures of glycoproteins that mediate enveloped virus entry into cells have revealed dramatic structural changes that accompany membrane fusion and provided mechanistic insights into this process. The group of class I viral fusion proteins includes the influenza hemagglutinin, paramyxovirus F, HIV env and other mechanistically related fusogens, but these proteins are unrelated in sequence and exhibit clearly distinct structural features. Recently determined crystal structures of the paramyxovirus F protein in two conformations, representing prefusion and postfusion states, reveal a novel protein architecture that undergoes large-scale, irreversible refolding during membrane fusion, extending our understanding of this diverse group of membrane fusion machines. PMID:17870467

Recognizing human activities using appearance metric feature and kinematics feature

NASA Astrophysics Data System (ADS)

Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye

2017-05-01

The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

Airborne Infrared and Visible Image Fusion Combined with Region Segmentation

PubMed Central

Zuo, Yujia; Liu, Jinghong; Bai, Guanbing; Wang, Xuan; Sun, Mingchao

2017-01-01

This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method involves segmenting the region in an IR image by significance, and identifying the target region and the background region; then, fusing the low-frequency components in the DTCWT region according to the region segmentation result. For high-frequency components, the region weights need to be assigned by the information richness of region details to conduct fusion based on both weights and adaptive phases, and then introducing a shrinkage function to suppress noise; Finally, the fused low-frequency and high-frequency components are reconstructed to obtain the fusion image. The experimental results show that the proposed method can fully extract complementary information from the source images to obtain a fusion image with good target indication and rich information on scene details. They also give a fusion result superior to existing popular fusion methods, based on eithers subjective or objective evaluation. With good stability and high fusion accuracy, this method can meet the fusion requirements of IR-visible image fusion systems. PMID:28505137

Airborne Infrared and Visible Image Fusion Combined with Region Segmentation.

PubMed

Zuo, Yujia; Liu, Jinghong; Bai, Guanbing; Wang, Xuan; Sun, Mingchao

2017-05-15

This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method involves segmenting the region in an IR image by significance, and identifying the target region and the background region; then, fusing the low-frequency components in the DTCWT region according to the region segmentation result. For high-frequency components, the region weights need to be assigned by the information richness of region details to conduct fusion based on both weights and adaptive phases, and then introducing a shrinkage function to suppress noise; Finally, the fused low-frequency and high-frequency components are reconstructed to obtain the fusion image. The experimental results show that the proposed method can fully extract complementary information from the source images to obtain a fusion image with good target indication and rich information on scene details. They also give a fusion result superior to existing popular fusion methods, based on eithers subjective or objective evaluation. With good stability and high fusion accuracy, this method can meet the fusion requirements of IR-visible image fusion systems.

Nonlinear three-dimensional verification of the SPECYL and PIXIE3D magnetohydrodynamics codes for fusion plasmas

NASA Astrophysics Data System (ADS)

Bonfiglio, D.; Chacón, L.; Cappello, S.

2010-08-01

With the increasing impact of scientific discovery via advanced computation, there is presently a strong emphasis on ensuring the mathematical correctness of computational simulation tools. Such endeavor, termed verification, is now at the center of most serious code development efforts. In this study, we address a cross-benchmark nonlinear verification study between two three-dimensional magnetohydrodynamics (3D MHD) codes for fluid modeling of fusion plasmas, SPECYL [S. Cappello and D. Biskamp, Nucl. Fusion 36, 571 (1996)] and PIXIE3D [L. Chacón, Phys. Plasmas 15, 056103 (2008)], in their common limit of application: the simple viscoresistive cylindrical approximation. SPECYL is a serial code in cylindrical geometry that features a spectral formulation in space and a semi-implicit temporal advance, and has been used extensively to date for reversed-field pinch studies. PIXIE3D is a massively parallel code in arbitrary curvilinear geometry that features a conservative, solenoidal finite-volume discretization in space, and a fully implicit temporal advance. The present study is, in our view, a first mandatory step in assessing the potential of any numerical 3D MHD code for fluid modeling of fusion plasmas. Excellent agreement is demonstrated over a wide range of parameters for several fusion-relevant cases in both two- and three-dimensional geometries.

Nonlinear three-dimensional verification of the SPECYL and PIXIE3D magnetohydrodynamics codes for fusion plasmas

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

Bonfiglio, Daniele; Chacon, Luis; Cappello, Susanna

2010-01-01

With the increasing impact of scientific discovery via advanced computation, there is presently a strong emphasis on ensuring the mathematical correctness of computational simulation tools. Such endeavor, termed verification, is now at the center of most serious code development efforts. In this study, we address a cross-benchmark nonlinear verification study between two three-dimensional magnetohydrodynamics (3D MHD) codes for fluid modeling of fusion plasmas, SPECYL [S. Cappello and D. Biskamp, Nucl. Fusion 36, 571 (1996)] and PIXIE3D [L. Chacon, Phys. Plasmas 15, 056103 (2008)], in their common limit of application: the simple viscoresistive cylindrical approximation. SPECYL is a serial code inmore » cylindrical geometry that features a spectral formulation in space and a semi-implicit temporal advance, and has been used extensively to date for reversed-field pinch studies. PIXIE3D is a massively parallel code in arbitrary curvilinear geometry that features a conservative, solenoidal finite-volume discretization in space, and a fully implicit temporal advance. The present study is, in our view, a first mandatory step in assessing the potential of any numerical 3D MHD code for fluid modeling of fusion plasmas. Excellent agreement is demonstrated over a wide range of parameters for several fusion-relevant cases in both two- and three-dimensional geometries.« less

Nodular fasciitis: a novel model of transient neoplasia induced by MYH9-USP6 gene fusion.

PubMed

Erickson-Johnson, Michele R; Chou, Margaret M; Evers, Barbara R; Roth, Christopher W; Seys, Amber R; Jin, Long; Ye, Ying; Lau, Alan W; Wang, Xiaoke; Oliveira, Andre M

2011-10-01

Nodular fasciitis (NF) is a relatively common mass-forming and self-limited subcutaneous pseudosarcomatous myofibroblastic proliferation of unknown pathogenesis. Due to its rapid growth and high mitotic activity, NF is often misdiagnosed as a sarcoma. While studying the USP6 biology in aneurysmal bone cyst and other mesenchymal tumors, we identified high expression levels of USP6 mRNA in two examples of NF. This finding led us to further examine the mechanisms underlying USP6 overexpression in these lesions. Upon subsequent investigation, genomic rearrangements of the USP6 locus were found in 92% (44 of 48) of NF. Rapid amplification of 5'-cDNA ends identified MYH9 as the translocation partner. RT-PCR and direct sequencing revealed the fusion of the MYH9 promoter region to the entire coding region of USP6. Control tumors and tissues were negative for this fusion. Xenografts of cells overexpressing USP6 in nude mice exhibited clinical and histological features similar to human NF. The identification of a sensitive and specific abnormality in NF holds the potential to be used diagnostically. Considering the self-limited nature of the lesion, NF may represent a model of 'transient neoplasia', as it is, to our knowledge, the first example of a self-limited human disease characterized by a recurrent somatic gene fusion event. © 2011 USCAP, Inc All rights reserved

A testbed for architecture and fidelity trade studies in the Bayesian decision-level fusion of ATR products

NASA Astrophysics Data System (ADS)

Erickson, Kyle J.; Ross, Timothy D.

2007-04-01

Decision-level fusion is an appealing extension to automatic/assisted target recognition (ATR) as it is a low-bandwidth technique bolstered by a strong theoretical foundation that requires no modification of the source algorithms. Despite the relative simplicity of decision-level fusion, there are many options for fusion application and fusion algorithm specifications. This paper describes a tool that allows trade studies and optimizations across these many options, by feeding an actual fusion algorithm via models of the system environment. Models and fusion algorithms can be specified and then exercised many times, with accumulated results used to compute performance metrics such as probability of correct identification. Performance differences between the best of the contributing sources and the fused result constitute examples of "gain." The tool, constructed as part of the Fusion for Identifying Targets Experiment (FITE) within the Air Force Research Laboratory (AFRL) Sensors Directorate ATR Thrust, finds its main use in examining the relationships among conditions affecting the target, prior information, fusion algorithm complexity, and fusion gain. ATR as an unsolved problem provides the main challenges to fusion in its high cost and relative scarcity of training data, its variability in application, the inability to produce truly random samples, and its sensitivity to context. This paper summarizes the mathematics underlying decision-level fusion in the ATR domain and describes a MATLAB-based architecture for exploring the trade space thus defined. Specific dimensions within this trade space are delineated, providing the raw material necessary to define experiments suitable for multi-look and multi-sensor ATR systems.

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.

Local electrostatic interactions determine the diameter of fusion pores

PubMed Central

Guček, Alenka; Jorgačevski, Jernej; Górska, Urszula; Rituper, Boštjan; Kreft, Marko; Zorec, Robert

2015-01-01

In regulated exocytosis vesicular and plasma membranes merge to form a fusion pore in response to stimulation. The nonselective cation HCN channels are involved in the regulation of unitary exocytotic events by at least 2 mechanisms. They can affect SNARE-dependent exocytotic activity indirectly, via the modulation of free intracellular calcium; and/or directly, by altering local cation concentration, which affects fusion pore geometry likely via electrostatic interactions. By monitoring membrane capacitance, we investigated how extracellular cation concentration affects fusion pore diameter in pituitary cells and astrocytes. At low extracellular divalent cation levels predominantly transient fusion events with widely open fusion pores were detected. However, fusion events with predominately narrow fusion pores were present at elevated levels of extracellular trivalent cations. These results show that electrostatic interactions likely help determine the stability of discrete fusion pore states by affecting fusion pore membrane composition. PMID:25835258

Biomechanical effects of fusion levels on the risk of proximal junctional failure and kyphosis in lumbar spinal fusion surgery.

PubMed

Park, Won Man; Choi, Dae Kyung; Kim, Kyungsoo; Kim, Yongjung J; Kim, Yoon Hyuk

2015-12-01

Spinal fusion surgery is a widely used surgical procedure for sagittal realignment. Clinical studies have reported that spinal fusion may cause proximal junctional kyphosis and failure with disc failure, vertebral fracture, and/or failure at the implant-bone interface. However, the biomechanical injury mechanisms of proximal junctional kyphosis and failure remain unclear. A finite element model of the thoracolumbar spine was used. Nine fusion models with pedicle screw systems implanted at the L2-L3, L3-L4, L4-L5, L5-S1, L2-L4, L3-L5, L4-S1, L2-L5, and L3-S1 levels were developed based on the respective surgical protocols. The developed models simulated flexion-extension using hybrid testing protocol. When spinal fusion was performed at more distal levels, particularly at the L5-S1 level, the following biomechanical properties increased during flexion-extension: range of motion, stress on the annulus fibrosus fibers and vertebra at the adjacent motion segment, and the magnitude of axial forces on the pedicle screw at the uppermost instrumented vertebra. The results of this study demonstrate that more distal fusion levels, particularly in spinal fusion including the L5-S1 level, lead to greater increases in the risk of proximal junctional kyphosis and failure, as evidenced by larger ranges of motion, higher stresses on fibers of the annulus fibrosus and vertebra at the adjacent segment, and higher axial forces on the screw at the uppermost instrumented vertebra in flexion-extension. Therefore, fusion levels should be carefully selected to avoid proximal junctional kyphosis and failure. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

NASA Astrophysics Data System (ADS)

Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

2017-10-01

So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

A Fusion Architecture for Tracking a Group of People Using a Distributed Sensor Network

DTIC Science & Technology

2013-07-01

Determining the composition of the group is done using several classifiers. The fusion is done at the UGS level to fuse information from all the modalities to...to classification and counting of the targets. Section III also presents the algorithms for fusion of distributed sensor data at the UGS level and...ultrasonic sensors. Determining the composition of the group is done using several classifiers. The fusion is done at the UGS level to fuse

Field-Reversed Configuration Power Plant Critical-Issue Scoping Study

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

Santarius, J. F.; Mogahed, E. A.; Emmert, G. A.

A team from the Universities of Wisconsin, Washington, and Illinois performed an engineering scoping study of critical issues for field-reversed configuration (FRC) power plants. The key tasks for this research were (1) systems analysis for deuterium-tritium (D-T) FRC fusion power plants, and (2) conceptual design of the blanket and shield module for an FRC fusion core. For the engineering conceptual design of the fusion core, the project team focused on intermediate-term technology. For example, one decision was to use steele structure. The FRC systems analysis led to a fusion power plant with attractive features including modest size, cylindrical symmetry, goodmore » thermal efficiency (52%), relatively easy maintenance, and a high ratio of electric power to fusion core mass, indicating that it would have favorable economics.« less

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

Multi-dimensional genomic analysis of myoepithelial carcinoma identifies prevalent oncogenic gene fusions.

PubMed

Dalin, Martin G; Katabi, Nora; Persson, Marta; Lee, Ken-Wing; Makarov, Vladimir; Desrichard, Alexis; Walsh, Logan A; West, Lyndsay; Nadeem, Zaineb; Ramaswami, Deepa; Havel, Jonathan J; Kuo, Fengshen; Chadalavada, Kalyani; Nanjangud, Gouri J; Ganly, Ian; Riaz, Nadeem; Ho, Alan L; Antonescu, Cristina R; Ghossein, Ronald; Stenman, Göran; Chan, Timothy A; Morris, Luc G T

2017-10-30

Myoepithelial carcinoma (MECA) is an aggressive salivary gland cancer with largely unknown genetic features. Here we comprehensively analyze molecular alterations in 40 MECAs using integrated genomic analyses. We identify a low mutational load, and high prevalence (70%) of oncogenic gene fusions. Most fusions involve the PLAG1 oncogene, which is associated with PLAG1 overexpression. We find FGFR1-PLAG1 in seven (18%) cases, and the novel TGFBR3-PLAG1 fusion in six (15%) cases. TGFBR3-PLAG1 promotes a tumorigenic phenotype in vitro, and is absent in 723 other salivary gland tumors. Other novel PLAG1 fusions include ND4-PLAG1; a fusion between mitochondrial and nuclear DNA. We also identify higher number of copy number alterations as a risk factor for recurrence, independent of tumor stage at diagnosis. Our findings indicate that MECA is a fusion-driven disease, nominate TGFBR3-PLAG1 as a hallmark of MECA, and provide a framework for future diagnostic and therapeutic research in this lethal cancer.

Multispectral image fusion based on fractal features

NASA Astrophysics Data System (ADS)

Tian, Jie; Chen, Jie; Zhang, Chunhua

2004-01-01

Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the composition of source pyramid images. So this fusion scheme is a multi-resolution analysis. The wavelet decomposition of image can be actually considered as special pyramid decomposition. According to wavelet decomposition theories, the approximation of image (formula available in paper) at resolution 2j+1 equal to its orthogonal projection in space , that is, where Ajf is the low-frequency approximation of image f(x, y) at resolution 2j and , , represent the vertical, horizontal and diagonal wavelet coefficients respectively at resolution 2j. These coefficients describe the high-frequency information of image at direction of vertical, horizontal and diagonal respectively. Ajf, , and are independent and can be considered as images. In this paper J is set to be 1, so the source image is decomposed to produce the son-images Af, D1f, D2f and D3f. To solve the problem of detecting artifacts, the concepts of vertical fractal dimension FD1, horizontal fractal dimension FD2 and diagonal fractal dimension FD3 are proposed in this paper. The vertical fractal dimension FD1 corresponds to the vertical wavelet coefficients image after the wavelet decomposition of source image, the horizontal fractal dimension FD2 corresponds to the horizontal wavelet coefficients and the diagonal fractal dimension FD3 the diagonal one. These definitions enrich the illustration of source images. Therefore they are helpful to classify the targets. Then the detection of artifacts in the decomposed images is a problem of pattern recognition in 4-D space. The combination of FD0, FD1, FD2 and FD3 make a vector of (FD0, FD1, FD2, FD3), which can be considered as a united feature vector of the studied image. All the parts of the images are classified in the 4-D pattern space created by the vector of (FD0, FD1, FD2, FD3) so that the area that contains man-made objects could be detected. This detection can be considered as a coarse recognition, and then the significant areas in each son-images are signed so that they can be dealt with special rules. There has been various fusion rules developed with each one aiming at a special problem. These rules have different performance, so it is very important to select an appropriate rule during the design of an image fusion system. Recent research denotes that the rule should be adjustable so that it is always suitable to extrude the features of targets and to preserve the pixels of useful information. In this paper, owing to the consideration that fractal dimension is one of the main features to distinguish man-made targets from natural objects, the fusion rule was defined that if the studied region of image contains man-made target, the pixels of the source image whose fractal dimension is minimal are saved to be the pixels of the fused image, otherwise, a weighted average operator is adopted to avoid loss of information. The main idea of this rule is to store the pixels with low fractal dimensions, so it can be named Minimal Fractal dimensions (MFD) fusion rule. This fractal-based algorithm is compared with a common weighted average fusion algorithm. An objective assessment is taken to the two fusion results. The criteria of Entropy, Cross-Entropy, Peak Signal-to-Noise Ratio (PSNR) and Standard Gray Scale Difference are defined in this paper. Reversely to the idea of constructing an ideal image as the assessing reference, the source images are selected to be the reference in this paper. It can be deemed that this assessment is to calculate how much the image quality has been enhanced and the quantity of information has been increased when the fused image is compared with the source images. The experimental results imply that the fractal-based multi-spectral fusion algorithm can effectively preserve the information of man-made objects with a high contrast. It is proved that this algorithm could well preserve features of military targets because that battlefield targets are most man-made objects and in common their images differ from fractal models obviously. Furthermore, the fractal features are not sensitive to the imaging conditions and the movement of targets, so this fractal-based algorithm may be very practical.

Comparative charge analysis of one- and two-level lumbar total disc arthroplasty versus circumferential lumbar fusion.

PubMed

Levin, David A; Bendo, John A; Quirno, Martin; Errico, Thomas; Goldstein, Jeffrey; Spivak, Jeffrey

2007-12-01

This is a retrospective, independent study comparing 2 groups of patients treated surgically for discogenic low back pain associated with degenerative disc disease (DDD) in the lumbosacral spine. To compare the surgical and hospitalization charges associated with 1- and 2-level lumbar total disc replacement and circumferential lumbar fusion. Reported series of lumbar total disc replacement have been favorable. However, economic aspects of lumbar total disc replacement (TDR) have not been published or studied. This information is important considering the recent widespread utilization of new technologies. Recent studies have demonstrated comparable short-term clinical results between TDR and lumbar fusion recipients. Relative charges may be another important indicator of the most appropriate procedure. We report a hospital charge-analysis comparing ProDisc lumbar disc replacement with circumferential fusion for discogenic low back pain. In a cohort of 53 prospectively selected patients with severe, disabling back pain and lumbar disc degeneration, 36 received Synthes ProDisc TDR and 17 underwent circumferential fusion for 1- and 2-level degenerative disc disease between L3 and S1. Randomization was performed using a 2-to-1 ratio of ProDisc recipients to control spinal fusion recipients. Charge comparisons, including operating room charges, inpatient hospital charges, and implant charges, were made from hospital records using inflation-corrected 2006 U.S. dollars. Operating room times, estimated blood loss, and length of stay were obtained from hospital records as well. Surgeon and anesthesiologist fees were, for the purposes of comparison, based on Medicare reimbursement rates. Statistical analysis was performed using a 2-tailed Student t test. For patients with 1-level disease, significant differences were noted between the TDR and fusion control group. The mean total charge for the TDR group was $35,592 versus $46,280 for the fusion group (P = 0.0018). Operating room charges were $12,000 and $18,950, respectively, for the TDR and fusion groups (P < 0.05). Implant charges averaged $13,990 for the fusion group, which is slightly higher than the $13,800 for the ProDisc (P = 0.9). Estimated blood loss averaged 794 mL in the fusion group versus 412 mL in the TDR group (P = 0.0058). Mean OR minutes averaged 344 minutes for the fusion group and 185 minutes for the TDR (P < 0.05) Mean length of stay was 4.78 days for fusion versus 4.32 days for TDR (P = 0.394). For patients with 2-level disease, charges were similar between the TDR and fusion groups. The mean total charge for the 2-level TDR group was $55,524 versus $56,823 for the fusion group (P = 0.55). Operating room charges were $15,340 and $20,560, respectively, for the TDR and fusion groups (P = 0.0003). Surgeon fees and anesthesiologist charges based on Medicare reimbursement rates were $5857 and $525 for the fusion group, respectively, versus $2826 and $331 for the TDR group (P < 0.05 for each). Implant charges were significantly lower for the fusion group (mean, $18,460) than those for 2-level Synthes ProDisc ($27,600) (P < 0.05). Operative time averaged 387 minutes for fusion versus 242 minutes for TDR (P < 0.0001). EBL and length of stay were similar. Patients undergoing 1- and 2-level ProDisc total disc replacement spent significantly less time in the OR and had less EBL than controls. Charges were significantly lower for TDR compared with circumferential fusions in the 1-level patient group, while charges were similar in the 2-level group.

Introducing two Random Forest based methods for cloud detection in remote sensing images

NASA Astrophysics Data System (ADS)

Ghasemian, Nafiseh; Akhoondzadeh, Mehdi

2018-07-01

Cloud detection is a necessary phase in satellite images processing to retrieve the atmospheric and lithospheric parameters. Currently, some cloud detection methods based on Random Forest (RF) model have been proposed but they do not consider both spectral and textural characteristics of the image. Furthermore, they have not been tested in the presence of snow/ice. In this paper, we introduce two RF based algorithms, Feature Level Fusion Random Forest (FLFRF) and Decision Level Fusion Random Forest (DLFRF) to incorporate visible, infrared (IR) and thermal spectral and textural features (FLFRF) including Gray Level Co-occurrence Matrix (GLCM) and Robust Extended Local Binary Pattern (RELBP_CI) or visible, IR and thermal classifiers (DLFRF) for highly accurate cloud detection on remote sensing images. FLFRF first fuses visible, IR and thermal features. Thereafter, it uses the RF model to classify pixels to cloud, snow/ice and background or thick cloud, thin cloud and background. DLFRF considers visible, IR and thermal features (both spectral and textural) separately and inserts each set of features to RF model. Then, it holds vote matrix of each run of the model. Finally, it fuses the classifiers using the majority vote method. To demonstrate the effectiveness of the proposed algorithms, 10 Terra MODIS and 15 Landsat 8 OLI/TIRS images with different spatial resolutions are used in this paper. Quantitative analyses are based on manually selected ground truth data. Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves. Also, the average cloud kappa values of FLFRF and DLFRF on MODIS images (1 and 0.99) are higher than other machine learning methods, Linear Discriminate Analysis (LDA), Classification And Regression Tree (CART), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) (0.96). The average snow/ice kappa values of FLFRF and DLFRF on MODIS images (1 and 0.85) are higher than other traditional methods. The quantitative values on Landsat 8 images show similar trend. Consequently, while SVM and K-nearest neighbor show overestimation in predicting cloud and snow/ice pixels, our Random Forest (RF) based models can achieve higher cloud, snow/ice kappa values on MODIS and thin cloud, thick cloud and snow/ice kappa values on Landsat 8 images. Our algorithms predict both thin and thick cloud on Landsat 8 images while the existing cloud detection algorithm, Fmask cannot discriminate them. Compared to the state-of-the-art methods, our algorithms have acquired higher average cloud and snow/ice kappa values for different spatial resolutions.

Molecular and cellular aspects of rhabdovirus entry.

PubMed

Albertini, Aurélie A V; Baquero, Eduard; Ferlin, Anna; Gaudin, Yves

2012-01-01

Rhabdoviruses enter the cell via the endocytic pathway and subsequently fuse with a cellular membrane within the acidic environment of the endosome. Both receptor recognition and membrane fusion are mediated by a single transmembrane viral glycoprotein (G). Fusion is triggered via a low-pH induced structural rearrangement. G is an atypical fusion protein as there is a pH-dependent equilibrium between its pre- and post-fusion conformations. The elucidation of the atomic structures of these two conformations for the vesicular stomatitis virus (VSV) G has revealed that it is different from the previously characterized class I and class II fusion proteins. In this review, the pre- and post-fusion VSV G structures are presented in detail demonstrating that G combines the features of the class I and class II fusion proteins. In addition to these similarities, these G structures also reveal some particularities that expand our understanding of the working of fusion machineries. Combined with data from recent studies that revealed the cellular aspects of the initial stages of rhabdovirus infection, all these data give an integrated view of the entry pathway of rhabdoviruses into their host cell.

Molecular and Cellular Aspects of Rhabdovirus Entry

PubMed Central

Albertini, Aurélie A. V.; Baquero, Eduard; Ferlin, Anna; Gaudin, Yves

2012-01-01

Rhabdoviruses enter the cell via the endocytic pathway and subsequently fuse with a cellular membrane within the acidic environment of the endosome. Both receptor recognition and membrane fusion are mediated by a single transmembrane viral glycoprotein (G). Fusion is triggered via a low-pH induced structural rearrangement. G is an atypical fusion protein as there is a pH-dependent equilibrium between its pre- and post-fusion conformations. The elucidation of the atomic structures of these two conformations for the vesicular stomatitis virus (VSV) G has revealed that it is different from the previously characterized class I and class II fusion proteins. In this review, the pre- and post-fusion VSV G structures are presented in detail demonstrating that G combines the features of the class I and class II fusion proteins. In addition to these similarities, these G structures also reveal some particularities that expand our understanding of the working of fusion machineries. Combined with data from recent studies that revealed the cellular aspects of the initial stages of rhabdovirus infection, all these data give an integrated view of the entry pathway of rhabdoviruses into their host cell. PMID:22355455

Long-range dismount activity classification: LODAC

NASA Astrophysics Data System (ADS)

Garagic, Denis; Peskoe, Jacob; Liu, Fang; Cuevas, Manuel; Freeman, Andrew M.; Rhodes, Bradley J.

2014-06-01

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non-parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self-tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti-access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross-modal signal generation) and discovers the critical cross-modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non-parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

Biomechanics of Artificial Disc Replacements Adjacent to a 2-Level Fusion in 4-Level Hybrid Constructs: An In Vitro Investigation

PubMed Central

Liao, Zhenhua; Fogel, Guy R.; Wei, Na; Gu, Hongsheng; Liu, Weiqiang

2015-01-01

Background The ideal procedure for multilevel cervical degenerative disc diseases remains controversial. Recent studies on hybrid surgery combining anterior cervical discectomy and fusion (ACDF) and artificial cervical disc replacement (ACDR) for 2-level and 3-level constructs have been reported in the literature. The purpose of this study was to estimate the biomechanics of 3 kinds of 4-level hybrid constructs, which are more likely to be used clinically compared to 4-level arthrodesis. Material/Methods Eighteen human cadaveric spines (C2–T1) were evaluated in different testing conditions: intact, with 3 kinds of 4-level hybrid constructs (hybrid C3–4 ACDR+C4–6 ACDF+C6–7ACDR; hybrid C3–5ACDF+C5–6ACDR+C6–7ACDR; hybrid C3–4ACDR+C4–5ACDR+C5–7ACDF); and 4-level fusion. Results Four-level fusion resulted in significant decrease in the C3–C7 ROM compared with the intact spine. The 3 different 4-level hybrid treatment groups caused only slight change at the instrumented levels compared to intact except for flexion. At the adjacent levels, 4-level fusion resulted in significant increase of contribution of both upper and lower adjacent levels. However, for the 3 hybrid constructs, significant changes of motion increase far lower than 4P at adjacent levels were only noted in partial loading conditions. No destabilizing effect or hypermobility were observed in any 4-level hybrid construct. Conclusions Four-level fusion significantly eliminated motion within the construct and increased motion at the adjacent segments. For all 3 different 4-level hybrid constructs, ACDR normalized motion of the index segment and adjacent segments with no significant hypermobility. Compared with the 4-level ACDF condition, the artificial discs in 4-level hybrid constructs had biomechanical advantages compared to fusion in normalizing adjacent level motion. PMID:26694835

Biomechanics of Artificial Disc Replacements Adjacent to a 2-Level Fusion in 4-Level Hybrid Constructs: An In Vitro Investigation.

PubMed

Liao, Zhenhua; Fogel, Guy R; Wei, Na; Gu, Hongsheng; Liu, Weiqiang

2015-12-23

BACKGROUND The ideal procedure for multilevel cervical degenerative disc diseases remains controversial. Recent studies on hybrid surgery combining anterior cervical discectomy and fusion (ACDF) and artificial cervical disc replacement (ACDR) for 2-level and 3-level constructs have been reported in the literature. The purpose of this study was to estimate the biomechanics of 3 kinds of 4-level hybrid constructs, which are more likely to be used clinically compared to 4-level arthrodesis. MATERIAL AND METHODS Eighteen human cadaveric spines (C2-T1) were evaluated in different testing conditions: intact, with 3 kinds of 4-level hybrid constructs (hybrid C3-4 ACDR+C4-6 ACDF+C6-7ACDR; hybrid C3-5ACDF+C5-6ACDR+C6-7ACDR; hybrid C3-4ACDR+C4-5ACDR+C5-7ACDF); and 4-level fusion. RESULTS Four-level fusion resulted in significant decrease in the C3-C7 ROM compared with the intact spine. The 3 different 4-level hybrid treatment groups caused only slight change at the instrumented levels compared to intact except for flexion. At the adjacent levels, 4-level fusion resulted in significant increase of contribution of both upper and lower adjacent levels. However, for the 3 hybrid constructs, significant changes of motion increase far lower than 4P at adjacent levels were only noted in partial loading conditions. No destabilizing effect or hypermobility were observed in any 4-level hybrid construct. CONCLUSIONS Four-level fusion significantly eliminated motion within the construct and increased motion at the adjacent segments. For all 3 different 4-level hybrid constructs, ACDR normalized motion of the index segment and adjacent segments with no significant hypermobility. Compared with the 4-level ACDF condition, the artificial discs in 4-level hybrid constructs had biomechanical advantages compared to fusion in normalizing adjacent level motion.

Hypoglycemia alarm enhancement using data fusion.

PubMed

Skladnev, Victor N; Tarnavskii, Stanislav; McGregor, Thomas; Ghevondian, Nejhdeh; Gourlay, Steve; Jones, Timothy W

2010-01-01

The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features. Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions. The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels. Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms. 2010 Diabetes Technology Society.

Feature Selection and Pedestrian Detection Based on Sparse Representation.

PubMed

Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

2015-01-01

Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony.

Ensemble Classifier Strategy Based on Transient Feature Fusion in Electronic Nose

NASA Astrophysics Data System (ADS)

Bagheri, Mohammad Ali; Montazer, Gholam Ali

2011-09-01

In this paper, we test the performance of several ensembles of classifiers and each base learner has been trained on different types of extracted features. Experimental results show the potential benefits introduced by the usage of simple ensemble classification systems for the integration of different types of transient features.

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

PubMed Central

Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references. PMID:29581722

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

PubMed

Yu, Yunlong; Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

Deep features for efficient multi-biometric recognition with face and ear images

NASA Astrophysics Data System (ADS)

Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

2017-07-01

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

Ureter Injury as a Complication of Oblique Lumbar Interbody Fusion.

PubMed

Lee, Hyeong-Jin; Kim, Jin-Sung; Ryu, Kyeong-Sik; Park, Choon Keun

2017-06-01

Oblique lumbar interbody fusion is a commonly used surgical method of achieving lumbar interbody fusion. There have been some reports about complications of oblique lumbar interbody fusion at the L2-L3 level. However, to our knowledge, there have been no reports about ureter injury during oblique lumbar interbody fusion. We report a case of ureter injury during oblique lumbar interbody fusion to share our experience. A 78-year-old male patient presented with a history of lower back pain and neurogenic intermittent claudication. He was diagnosed with spinal stenosis at L2-L3, L4-L5 level and spondylolisthesis at L4-L5 level. Symptoms were not improved after several months of medical treatments. Then, oblique lumbar interbody fusion was performed at L2-L3, L4-L5 level. During the surgery, anesthesiologist noticed hematuria. A retrourethrogram was performed immediately by urologist, and ureter injury was found. Ureteroureterostomy and double-J catheter insertion were performed. The patient was discharged 2 weeks after surgery without urologic or neurologic complications. At 2 months after surgery, an intravenous pyelogram was performed, which showed an intact ureter. Our study shows that a low threshold of suspicion of ureter injury and careful manipulation of retroperitoneal fat can be helpful to prevent ureter injury during oblique lumbar interbody fusion at the upper level. Copyright © 2017 Elsevier Inc. All rights reserved.

Fast multi-scale feature fusion for ECG heartbeat classification

NASA Astrophysics Data System (ADS)

Ai, Danni; Yang, Jian; Wang, Zeyu; Fan, Jingfan; Ai, Changbin; Wang, Yongtian

2015-12-01

Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

Binaural fusion and the representation of virtual pitch in the human auditory cortex.

PubMed

Pantev, C; Elbert, T; Ross, B; Eulitz, C; Terhardt, E

1996-10-01

The auditory system derives the pitch of complex tones from the tone's harmonics. Research in psychoacoustics predicted that binaural fusion was an important feature of pitch processing. Based on neuromagnetic human data, the first neurophysiological confirmation of binaural fusion in hearing is presented. The centre of activation within the cortical tonotopic map corresponds to the location of the perceived pitch and not to the locations that are activated when the single frequency constituents are presented. This is also true when the different harmonics of a complex tone are presented dichotically. We conclude that the pitch processor includes binaural fusion to determine the particular pitch location which is activated in the auditory cortex.

Comparing fusion techniques for the ImageCLEF 2013 medical case retrieval task.

PubMed

G Seco de Herrera, Alba; Schaer, Roger; Markonis, Dimitrios; Müller, Henning

2015-01-01

Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case-based retrieval approaches. This paper focuses on the case-based task and adds results of the compound figure separation and modality classification tasks. Several fusion approaches are compared to identify the approaches best adapted to the heterogeneous data of the task. Fusion of visual and textual features is analyzed, demonstrating that the selection of the fusion strategy can improve the best performance on the case-based retrieval task. Copyright © 2014 Elsevier Ltd. All rights reserved.

Data fusion: principles and applications in air defense

NASA Astrophysics Data System (ADS)

Maltese, Dominique; Lucas, Andre

1998-07-01

Within a Surveillance and Reconnaissance System, the Fusion Process is an essential part of the software package since the different sensors measurements are combined by this process; each sensor sends its data to a fusion center whose task is to elaborate the best tactical situation. In this paper, a practical algorithm of data fusion applied to a military application context is presented; the case studied here is a medium-range surveillance situation featuring a dual-sensor platform which combines a surveillance Radar and an IRST; both sensors are collocated. The presented performances were obtained on validation scenarios via simulations performed by SAGEM with the ESSOR ('Environnement de Simulation de Senseurs Optroniques et Radar') multisensor simulation test bench.

Cytogenetic analyses of four solid tumours in dogs.

PubMed

Mayr, B; Reifinger, M; Weissenböck, H; Schleger, W; Eisenmenger, E

1994-07-01

Four solid tumours (one haemangiopericytoma, one haemangioendothelioma, one spindle-cell sarcoma and one mammary carcinoma) in dogs were analysed cytogenetically. In the haemangiopericytoma, an additional small chromosomal segment was present. Very complex changes including centric fusions and symmetric meta-centrics 1, 6, 10 and 12 were conspicuous in the highly unbalanced karyotype of the haemangioendothelioma. Complex changes, particularly many centric fusions and a tandem translocation 4/14, were features of the spindle-cell sarcoma. One centric fusion and a symmetric metacentric 13 were present in the mammary carcinoma.

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.

Choice of surgical approach for ossification of the posterior longitudinal ligament in combination with cervical disc hernia

PubMed Central

Yang, Hai-song; Lu, Xu-hua; Yang, Li–li; Yan, Wang-jun; Yuan, Wen; Chen, Yu

2009-01-01

Ossification of the posterior longitudinal ligament (OPLL) is a common spinal disorder that presents with or without cervical myelopathy. Furthermore, there is evidence suggesting that OPLL often coexists with cervical disc hernia (CDH), and that the latter is the more important compression factor. To raise the awareness of CDH in OPLL for spinal surgeons, we performed a retrospective study on 142 patients with radiologically proven OPLL who had received surgery between January 2004 and January 2008 in our hospital. Plain radiograph, three-dimensional computed tomography construction (3D CT), and magnetic resonance imaging (MRI) of the cervical spine were all performed. Twenty-six patients with obvious CDH (15 of segmental-type, nine of mixed-type, two of continuous-type) were selected via clinical and radiographic features, and intraoperative findings. By MRI, the most commonly involved level was C5/6, followed by C3/4, C4/5, and C6/7. The areas of greatest spinal cord compression were at the disc levels because of herniated cervical discs. Eight patients were decompressed via anterior cervical discectomy and fusion (ACDF), 13 patients via anterior cervical corpectomy and fusion (ACCF), and five patients via ACDF combined with posterior laminectomy and fusion. The outcomes were all favorable. In conclusion, surgeons should consider the potential for CDH when performing spinal cord decompression and deciding the surgical approach in patients presenting with OPLL. PMID:20012451

Matched Comparison of Fusion Rates between Hydroxyapatite Demineralized Bone Matrix and Autograft in Lumbar Interbody Fusion.

PubMed

Kim, Dae Hwan; Lee, Nam; Shin, Dong Ah; Yi, Seong; Kim, Keung Nyun; Ha, Yoon

2016-07-01

To compare the fusion rate of a hydroxyapatite demineralized bone matrix (DBM) with post-laminectomy acquired autograft in lumbar interbody fusion surgery and to evaluate the correlation between fusion rate and clinical outcome. From January 2013 to April 2014, 98 patients underwent lumbar interbody fusion surgery with hydroxyapatite DBM (HA-DBM group) in our institute. Of those patients, 65 received complete CT scans for 12 months postoperatively in order to evaluate fusion status. For comparison with autograft, we selected another 65 patients who underwent lumbar interbody fusion surgery with post-laminectomy acquired autograft (Autograft group) during the same period. Both fusion material groups were matched in terms of age, sex, body mass index (BMI), and bone mineral density (BMD). To evaluate the clinical outcomes, we analyzed the results of visual analogue scale (VAS), Oswestry Disability Index (ODI), and Short Form Health Survey (SF-36). We reviewed the CT scans of 149 fusion levels in 130 patients (HA-DBM group, 75 levels/65 patients; Autograft group, 74 levels/65 patients). Age, sex, BMI, and BMD were not significantly different between the groups (p=0.528, p=0.848, p=0.527, and p=0.610, respectively). The HA-DBM group showed 39 of 75 fused levels (52%), and the Autograft group showed 46 of 74 fused levels (62.2%). This difference was not statistically significant (p=0.21). In the HA-DBM group, older age and low BMD were significantly associated with non-fusion (61.24 vs. 66.68, p=0.027; -1.63 vs. -2.29, p=0.015, respectively). VAS and ODI showed significant improvement after surgery when fusion was successfully achieved in both groups (p=0.004, p=0.002, HA-DBM group; p=0.012, p=0.03, Autograft group). The fusion rates of the hydroxyapatite DBM and Autograft groups were not significantly different. In addition, clinical outcomes were similar between the groups. However, older age and low BMD are risk factors that might induce non-union after surgery with hydroxyapatite DBM.

Matched Comparison of Fusion Rates between Hydroxyapatite Demineralized Bone Matrix and Autograft in Lumbar Interbody Fusion

PubMed Central

Kim, Dae Hwan; Lee, Nam; Shin, Dong Ah; Yi, Seong; Kim, Keung Nyun

2016-01-01

Objective To compare the fusion rate of a hydroxyapatite demineralized bone matrix (DBM) with post-laminectomy acquired autograft in lumbar interbody fusion surgery and to evaluate the correlation between fusion rate and clinical outcome. Methods From January 2013 to April 2014, 98 patients underwent lumbar interbody fusion surgery with hydroxyapatite DBM (HA-DBM group) in our institute. Of those patients, 65 received complete CT scans for 12 months postoperatively in order to evaluate fusion status. For comparison with autograft, we selected another 65 patients who underwent lumbar interbody fusion surgery with post-laminectomy acquired autograft (Autograft group) during the same period. Both fusion material groups were matched in terms of age, sex, body mass index (BMI), and bone mineral density (BMD). To evaluate the clinical outcomes, we analyzed the results of visual analogue scale (VAS), Oswestry Disability Index (ODI), and Short Form Health Survey (SF-36). Results We reviewed the CT scans of 149 fusion levels in 130 patients (HA-DBM group, 75 levels/65 patients; Autograft group, 74 levels/65 patients). Age, sex, BMI, and BMD were not significantly different between the groups (p=0.528, p=0.848, p=0.527, and p=0.610, respectively). The HA-DBM group showed 39 of 75 fused levels (52%), and the Autograft group showed 46 of 74 fused levels (62.2%). This difference was not statistically significant (p=0.21). In the HA-DBM group, older age and low BMD were significantly associated with non-fusion (61.24 vs. 66.68, p=0.027; -1.63 vs. -2.29, p=0.015, respectively). VAS and ODI showed significant improvement after surgery when fusion was successfully achieved in both groups (p=0.004, p=0.002, HA-DBM group; p=0.012, p=0.03, Autograft group). Conclusion The fusion rates of the hydroxyapatite DBM and Autograft groups were not significantly different. In addition, clinical outcomes were similar between the groups. However, older age and low BMD are risk factors that might induce non-union after surgery with hydroxyapatite DBM. PMID:27446517

Optimal Time-Resource Allocation for Energy-Efficient Physical Activity Detection

PubMed Central

Thatte, Gautam; Li, Ming; Lee, Sangwon; Emken, B. Adar; Annavaram, Murali; Narayanan, Shrikanth; Spruijt-Metz, Donna; Mitra, Urbashi

2011-01-01

The optimal allocation of samples for physical activity detection in a wireless body area network for health-monitoring is considered. The number of biometric samples collected at the mobile device fusion center, from both device-internal and external Bluetooth heterogeneous sensors, is optimized to minimize the transmission power for a fixed number of samples, and to meet a performance requirement defined using the probability of misclassification between multiple hypotheses. A filter-based feature selection method determines an optimal feature set for classification, and a correlated Gaussian model is considered. Using experimental data from overweight adolescent subjects, it is found that allocating a greater proportion of samples to sensors which better discriminate between certain activity levels can result in either a lower probability of error or energy-savings ranging from 18% to 22%, in comparison to equal allocation of samples. The current activity of the subjects and the performance requirements do not significantly affect the optimal allocation, but employing personalized models results in improved energy-efficiency. As the number of samples is an integer, an exhaustive search to determine the optimal allocation is typical, but computationally expensive. To this end, an alternate, continuous-valued vector optimization is derived which yields approximately optimal allocations and can be implemented on the mobile fusion center due to its significantly lower complexity. PMID:21796237

Efficient sensor network vehicle classification using peak harmonics of acoustic emissions

NASA Astrophysics Data System (ADS)

William, Peter E.; Hoffman, Michael W.

2008-04-01

An application is proposed for detection and classification of battlefield ground vehicles using the emitted acoustic signal captured at individual sensor nodes of an ad hoc Wireless Sensor Network (WSN). We make use of the harmonic characteristics of the acoustic emissions of battlefield vehicles, in reducing both the computations carried on the sensor node and the transmitted data to the fusion center for reliable and effcient classification of targets. Previous approaches focus on the lower frequency band of the acoustic emissions up to 500Hz; however, we show in the proposed application how effcient discrimination between battlefield vehicles is performed using features extracted from higher frequency bands (50 - 1500Hz). The application shows that selective time domain acoustic features surpass equivalent spectral features. Collaborative signal processing is utilized, such that estimation of certain signal model parameters is carried by the sensor node, in order to reduce the communication between the sensor node and the fusion center, while the remaining model parameters are estimated at the fusion center. The transmitted data from the sensor node to the fusion center ranges from 1 ~ 5% of the sampled acoustic signal at the node. A variety of classification schemes were examined, such as maximum likelihood, vector quantization and artificial neural networks. Evaluation of the proposed application, through processing of an acoustic data set with comparison to previous results, shows that the improvement is not only in the number of computations but also in the detection and false alarm rate as well.

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

Probabilistic combination of static and dynamic gait features for verification

NASA Astrophysics Data System (ADS)

Bazin, Alex I.; Nixon, Mark S.

2005-03-01

This paper describes a novel probabilistic framework for biometric identification and data fusion. Based on intra and inter-class variation extracted from training data, posterior probabilities describing the similarity between two feature vectors may be directly calculated from the data using the logistic function and Bayes rule. Using a large publicly available database we show the two imbalanced gait modalities may be fused using this framework. All fusion methods tested provide an improvement over the best modality, with the weighted sum rule giving the best performance, hence showing that highly imbalanced classifiers may be fused in a probabilistic setting; improving not only the performance, but also generalized application capability.

Registration and Fusion of Multiple Source Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline

2004-01-01

Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

Visual saliency in MPEG-4 AVC video stream

NASA Astrophysics Data System (ADS)

Ammar, M.; Mitrea, M.; Hasnaoui, M.; Le Callet, P.

2015-03-01

Visual saliency maps already proved their efficiency in a large variety of image/video communication application fields, covering from selective compression and channel coding to watermarking. Such saliency maps are generally based on different visual characteristics (like color, intensity, orientation, motion,…) computed from the pixel representation of the visual content. This paper resumes and extends our previous work devoted to the definition of a saliency map solely extracted from the MPEG-4 AVC stream syntax elements. The MPEG-4 AVC saliency map thus defined is a fusion of static and dynamic map. The static saliency map is in its turn a combination of intensity, color and orientation features maps. Despite the particular way in which all these elementary maps are computed, the fusion techniques allowing their combination plays a critical role in the final result and makes the object of the proposed study. A total of 48 fusion formulas (6 for combining static features and, for each of them, 8 to combine static to dynamic features) are investigated. The performances of the obtained maps are evaluated on a public database organized at IRCCyN, by computing two objective metrics: the Kullback-Leibler divergence and the area under curve.

A case of PSF-TFE3 gene fusion in Xp11.2 renal cell carcinoma with melanotic features.

PubMed

Zhan, He-Qin; Chen, Hong; Wang, Chao-Fu; Zhu, Xiong-Zeng

2015-03-01

Xp11.2 translocation renal cell carcinoma (Xp11.2 RCC) with PSF-TFE3 gene fusion is a rare neoplasm. Only 22 cases of Xp11.2 RCCs with PSF-TFE3 have been reported to date. We describe an additional case of Xp11.2 RCC with PSF-TFE3 showing melanotic features. Microscopically, the histologic features mimic clear cell renal cell carcinoma. However, the dark-brown pigments were identified and could be demonstrated as melanins. Immunohistochemically, the tumor cells were widely positive for CD10, human melanoma black 45, and TFE3 but negative for cytokeratins, vimentin, Melan-A, microphthalmia-associated transcription factor, smooth muscle actin, and S-100 protein. Genetically, we demonstrated PSF-TFE3 fusion between exon 9 of PSF and exon 5 of TFE3. The patient was free of disease with 50 months of follow-up. The prognosis of this type of tumor requires more cases because of limited number of cases and follow-up period. Xp11.2 RCC with PSF-TFE3 inevitably requires differentiation from other kidney neoplasms. Immunohistochemical and molecular genetic analyses are essential for accurate diagnosis. Copyright © 2015 Elsevier Inc. All rights reserved.

Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

PubMed

Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

2018-06-15

Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

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.

Line-Tension Controlled Mechanism for Influenza Fusion

PubMed Central

Risselada, Herre Jelger; Smirnova, Yuliya G.; Grubmüller, Helmut; Marrink, Siewert Jan; Müller, Marcus

2012-01-01

Our molecular simulations reveal that wild-type influenza fusion peptides are able to stabilize a highly fusogenic pre-fusion structure, i.e. a peptide bundle formed by four or more trans-membrane arranged fusion peptides. We rationalize that the lipid rim around such bundle has a non-vanishing rim energy (line-tension), which is essential to (i) stabilize the initial contact point between the fusing bilayers, i.e. the stalk, and (ii) drive its subsequent evolution. Such line-tension controlled fusion event does not proceed along the hypothesized standard stalk-hemifusion pathway. In modeled influenza fusion, single point mutations in the influenza fusion peptide either completely inhibit fusion (mutants G1V and W14A) or, intriguingly, specifically arrest fusion at a hemifusion state (mutant G1S). Our simulations demonstrate that, within a line-tension controlled fusion mechanism, these known point mutations either completely inhibit fusion by impairing the peptide’s ability to stabilize the required peptide bundle (G1V and W14A) or stabilize a persistent bundle that leads to a kinetically trapped hemifusion state (G1S). In addition, our results further suggest that the recently discovered leaky fusion mutant G13A, which is known to facilitate a pronounced leakage of the target membrane prior to lipid mixing, reduces the membrane integrity by forming a ‘super’ bundle. Our simulations offer a new interpretation for a number of experimentally observed features of the fusion reaction mediated by the prototypical fusion protein, influenza hemagglutinin, and might bring new insights into mechanisms of other viral fusion reactions. PMID:22761674

ALIF and total disc replacement versus 2-level circumferential fusion with TLIF: a prospective, randomized, clinical and radiological trial.

PubMed

Hoff, Eike K; Strube, Patrick; Pumberger, Matthias; Zahn, Robert K; Putzier, Michael

2016-05-01

Prospective, randomized trial. The treatment of degenerative disc disease (DDD) with two-level fusion has been associated with a reasonable rate of complications. The aim of the present study was to compare (Hybrid) stand-alone anterior lumbar interbody fusion (ALIF) at L5/S1 with total disc replacement at L4/5 (TDR) as an alternative surgical strategy to (Fusion) 2-level circumferential fusion employing transforaminal lumbar interbody fusion (TLIF) with transpedicular stabilization at L4-S1. A total of 62 patients with symptomatic DDD of segments L5/S1 (Modic ≥2°) and L4/5 (Modic ≤2°; positive discography) were enrolled; 31 were treated with Hybrid and 31 with Fusion. Preoperatively, at 0, 12, and a mean follow-up of 37 months, clinical (ODI, VAS) and radiological evaluations (plain/extension-flexion radiographs evaluated for implant failure, fusion, global and segmental lordosis, and ROM) were performed. In 26 of 31 Hybrid and 24 of 31 Fusion patients available at the final follow-up, we found a significant clinical improvement compared to preoperatively. Hybrid patients had significantly lower VAS scores immediately postoperatively and at follow-up compared to Fusion patients. The complication rates were low and similar between the groups. Lumbar lordosis increased in both groups. The increase was mainly located at L4-S1 in the Hybrid group and at L1-L4 in the Fusion group. Hybrid patients presented with increased ROM at L4/5 and L3/4, and Fusion patients presented with increased ROM at L3/4, with significantly greater ROM at L3/4 compared to Hybrid patients at follow-up. Hybrid surgery is a viable surgical alternative for the presented indication. Approach-related inferior trauma and the balanced restoration of lumbar lordosis resulted in superior clinical outcomes compared to two-level circumferential fusion with TLIF.

BRAF Fusion Analysis in Pilocytic Astrocytomas: KIAA1549-BRAF 15-9 Fusions Are More Frequent in the Midline Than Within the Cerebellum

PubMed Central

Faulkner, Claire; Ellis, Hayley Patricia; Shaw, Abigail; Penman, Catherine; Palmer, Abigail; Wragg, Christopher; Greenslade, Mark; Haynes, Harry Russell; Williams, Hannah; Lowis, Stephen; White, Paul; Williams, Maggie; Capper, David; Kurian, Kathreena Mary

2015-01-01

Abstract Pilocytic astrocytomas (PAs) are increasingly tested for KIAA1549-BRAF fusions. We used reverse transcription polymerase chain reaction for the 3 most common KIAA1549-BRAF fusions, together with BRAF V600E and histone H3.3 K27M analyses to identify relationships of these molecular characteristics with clinical features in a cohort of 32 PA patients. In this group, the overall BRAF fusion detection rate was 24 (75%). Ten (42%) of the 24 had the 16-9 fusion, 8 (33%) had only the 15-9 fusion, and 1 (4%) of the patients had only the 16-11 fusion. In the PAs with only the 15-9 fusion, 1 PA was in the cerebellum and 7 were centered in the midline outside of the cerebellum, that is, in the hypothalamus (n = 4), optic pathways (n = 2), and brainstem (n = 1). Tumors within the cerebellum were negatively associated with fusion 15-9. Seven (22%) of the 32 patients had tumor-related deaths and 25 of the patients (78%) were alive between 2 and 14 years after initial biopsy. Age, sex, tumor location, 16-9 fusion, and 15-9 fusion were not associated with overall survival. Thus, in this small cohort, 15-9 KIAA1549-BRAF fusion was associated with midline PAs located outside of the cerebellum; these tumors, which are generally difficult to resect, are prone to recurrence. PMID:26222501

Radiographic and Clinical Outcome of Silicate-substituted Calcium Phosphate (Si-CaP) Ceramic Bone Graft in Spinal Fusion Procedures.

PubMed

Alimi, Marjan; Navarro-Ramirez, Rodrigo; Parikh, Karishma; Njoku, Innocent; Hofstetter, Christoph P; Tsiouris, Apostolos J; Härtl, Roger

2017-07-01

Retrospective cohort study. To evaluate the radiographic and clinical outcome of silicate-substituted calcium phosphate (Si-CaP), utilized as a graft substance in spinal fusion procedures. Specific properties of Si-CaP provide the graft with negative surface charge that can result in a positive effect on the osteoblast activity and neovascularization of the bone. This study included those patients who underwent spinal fusion procedures between 2007 and 2011 in which Si-CaP was used as the only bone graft substance. Fusion was evaluated on follow-up CT scans. Clinical outcome was assessed using Oswestry Disability Index, Neck Disability Index, and the visual analogue scale (VAS) for back, leg, neck, and arm pain. A total of 234 patients (516 spinal fusion levels) were studied. Surgical procedures consisted of 57 transforaminal lumbar interbody fusion, 49 anterior cervical discectomy and fusion, 44 extreme lateral interbody fusion, 30 posterior cervical fusions, 19 thoracic fusion surgeries, 17 axial lumbar interbody fusions, 16 combined anterior and posterior cervical fusions, and 2 anterior lumbar interbody fusion. At a mean radiographic follow-up of 14.2±4.3 months, fusion was found to be present in 82.9% of patients and 86.8% of levels. The highest fusion rate was observed in the cervical region. At the latest clinical follow-up of 21.7±14.2 months, all clinical outcome parameters showed significant improvement. The Oswestry Disability Index improved from 45.6 to 13.3 points, Neck Disability Index from 40.6 to 29.3, VAS back from 6.1 to 3.5, VAS leg from 5.6 to 2.4, VAS neck from 4.7 to 2.7, and VAS arm from 4.1 to 1.7. Of 7 cases with secondary surgical procedure at the index level, the indication for surgery was nonunion in 3 patients. Si-CaP is an effective bone graft substitute. At the latest follow-up, favorable radiographic and clinical outcome was observed in the majority of patients. Level-III.

Development of a Dehalogenase-Based Protein Fusion Tag Capable of Rapid, Selective and Covalent Attachment to Customizable Ligands

PubMed Central

Encell, Lance P; Friedman Ohana, Rachel; Zimmerman, Kris; Otto, Paul; Vidugiris, Gediminas; Wood, Monika G; Los, Georgyi V; McDougall, Mark G; Zimprich, Chad; Karassina, Natasha; Learish, Randall D; Hurst, Robin; Hartnett, James; Wheeler, Sarah; Stecha, Pete; English, Jami; Zhao, Kate; Mendez, Jacqui; Benink, Hélène A; Murphy, Nancy; Daniels, Danette L; Slater, Michael R; Urh, Marjeta; Darzins, Aldis; Klaubert, Dieter H; Bulleit, Robert F; Wood, Keith V

2012-01-01

Our fundamental understanding of proteins and their biological significance has been enhanced by genetic fusion tags, as they provide a convenient method for introducing unique properties to proteins so that they can be examinedin isolation. Commonly used tags satisfy many of the requirements for applications relating to the detection and isolation of proteins from complex samples. However, their utility at low concentration becomes compromised if the binding affinity for a detection or capture reagent is not adequate to produce a stable interaction. Here, we describe HaloTag® (HT7), a genetic fusion tag based on a modified haloalkane dehalogenase designed and engineered to overcome the limitation of affinity tags by forming a high affinity, covalent attachment to a binding ligand. HT7 and its ligand have additional desirable features. The tag is relatively small, monomeric, and structurally compatible with fusion partners, while the ligand is specific, chemically simple, and amenable to modular synthetic design. Taken together, the design features and molecular evolution of HT7 have resulted in a superior alternative to common tags for the overexpression, detection, and isolation of target proteins. PMID:23248739

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

PubMed

Guo, Lu; Wang, Ping; Sun, Ranran; Yang, Chengwen; Zhang, Ning; Guo, Yu; Feng, Yuanming

2018-02-19

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

Fusion genes with ALK as recurrent partner in ependymoma-like gliomas: a new brain tumor entity?

PubMed Central

Olsen, Thale Kristin; Panagopoulos, Ioannis; Meling, Torstein R.; Micci, Francesca; Gorunova, Ludmila; Thorsen, Jim; Due-Tønnessen, Bernt; Scheie, David; Lund-Iversen, Marius; Krossnes, Bård; Saxhaug, Cathrine; Heim, Sverre; Brandal, Petter

2015-01-01

Background We have previously characterized 19 ependymal tumors using Giemsa banding and high-resolution comparative genomic hybridization. The aim of this study was to analyze these tumors searching for fusion genes. Methods RNA sequencing was performed in 12 samples. Potential fusion transcripts were assessed by seed count and structural chromosomal aberrations. Transcripts of interest were validated using fluorescence in situ hybridization and PCR followed by direct sequencing. Results RNA sequencing identified rearrangements of the anaplastic lymphoma kinase gene (ALK) in 2 samples. Both tumors harbored structural aberrations involving the ALK locus 2p23. Tumor 1 had an unbalanced t(2;14)(p23;q22) translocation which led to the fusion gene KTN1-ALK. Tumor 2 had an interstitial del(2)(p16p23) deletion causing the fusion of CCDC88A and ALK. In both samples, the breakpoint of ALK was located between exons 19 and 20. Both patients were infants and both tumors were supratentorial. The tumors were well demarcated from surrounding tissue and had both ependymal and astrocytic features but were diagnosed and treated as ependymomas. Conclusions By combining karyotyping and RNA sequencing, we identified the 2 first ever reported ALK rearrangements in CNS tumors. Such rearrangements may represent the hallmark of a new entity of pediatric glioma characterized by both ependymal and astrocytic features. Our findings are of particular importance because crizotinib, a selective ALK inhibitor, has demonstrated effect in patients with lung cancer harboring ALK rearrangements. Thus, ALK emerges as an interesting therapeutic target in patients with ependymal tumors carrying ALK fusions. PMID:25795305

A mechanism of protein-mediated fusion: coupling between refolding of the influenza hemagglutinin and lipid rearrangements.

PubMed Central

Kozlov, M M; Chernomordik, L V

1998-01-01

Although membrane fusion mediated by influenza virus hemagglutinin (HA) is the best characterized example of ubiquitous protein-mediated fusion, it is still not known how the low-pH-induced refolding of HA trimers causes fusion. This refolding involves 1) repositioning of the hydrophobic N-terminal sequence of the HA2 subunit of HA ("fusion peptide"), and 2) the recruitment of additional residues to the alpha-helical coiled coil of a rigid central rod of the trimer. We propose here a mechanism by which these conformational changes can cause local bending of the viral membrane, priming it for fusion. In this model fusion is triggered by incorporation of fusion peptides into viral membrane. Refolding of a central rod exerts forces that pull the fusion peptides, tending to bend the membrane around HA trimer into a saddle-like shape. Elastic energy drives self-assembly of these HA-containing membrane elements in the plane of the membrane into a ring-like cluster. Bulging of the viral membrane within such cluster yields a dimple growing toward the bound target membrane. Bending stresses in the lipidic top of the dimple facilitate membrane fusion. We analyze the energetics of this proposed sequence of membrane rearrangements, and demonstrate that this simple mechanism may explain some of the known phenomenological features of fusion. PMID:9726939

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

Stewart Zweben; Samuel Cohen; Hantao Ji

Small ''concept exploration'' experiments have for many years been an important part of the fusion research program at the Princeton Plasma Physics Laboratory (PPPL). this paper describes some of the present and planned fusion concept exploration experiments at PPPL. These experiments are a University-scale research level, in contrast with the larger fusion devices at PPPL such as the National Spherical Torus Experiment (NSTX) and the Tokamak Fusion Test Reactor (TFTR), which are at ''proof-of-principle'' and ''proof-of-performance'' levels, respectively.

Mathematical Fundamentals of Probabilistic Semantics for High-Level Fusion

DTIC Science & Technology

2013-12-02

understanding of the fundamental aspects of uncertainty representation and reasoning that a theory of hard and soft high-level fusion must encompass...representation and reasoning that a theory of hard and soft high-level fusion must encompass. Successful completion requires an unbiased, in-depth...and soft information is the lack of a fundamental HLIF theory , backed by a consistent mathematical framework and supporting algorithms. Although there

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.

A robust color image fusion for low light level and infrared images

NASA Astrophysics Data System (ADS)

Liu, Chao; Zhang, Xiao-hui; Hu, Qing-ping; Chen, Yong-kang

2016-09-01

The low light level and infrared color fusion technology has achieved great success in the field of night vision, the technology is designed to make the hot target of fused image pop out with intenser colors, represent the background details with a nearest color appearance to nature, and improve the ability in target discovery, detection and identification. The low light level images have great noise under low illumination, and that the existing color fusion methods are easily to be influenced by low light level channel noise. To be explicit, when the low light level image noise is very large, the quality of the fused image decreases significantly, and even targets in infrared image would be submerged by the noise. This paper proposes an adaptive color night vision technology, the noise evaluation parameters of low light level image is introduced into fusion process, which improve the robustness of the color fusion. The color fuse results are still very good in low-light situations, which shows that this method can effectively improve the quality of low light level and infrared fused image under low illumination conditions.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

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.

Clinical Efficacy of Anterior Partial Corpectomy and Titanium Mesh Fusion and Internal Fixation for Treatment of Old Fracture Dislocation of the Lower Cervical Spine

PubMed Central

Miao, De-chao; Zhang, Bao-yang; Lei, Tao; Shen, Yong

2017-01-01

Background The aim of this study was to analyze the clinical features and to evaluate the efficacy of anterior partial corpectomy and titanium mesh fusion and internal fixation of old fracture dislocation of the lower cervical spine. Material/Methods We retrospectively analyzed the clinical data of 52 patients with old lower cervical fracture and dislocation treated with anterior partial corpectomy and titanium mesh fusion fixation between January 2008 and December 2013, with a mean follow-up period of 4.1 years. There were 35 males and 17 females. Patient radiological data and clinical parameters were recorded and compared before and after the operations. Results The average follow-up was 4.1 years. Intervertebral height and physiological curvature were well-reconstructed for all cases. No loosening or rupturing of titanium plate or screw occurred. The neurological function of the patients with incomplete spinal cord injury was significantly improved, and the function of the nerve roots at the injury level was also improved in patients with complete spinal cord injury. Bone fusion was completed within 6 months to 1 year after surgery. Conclusions Completed decompression, sequence and physiological curvature of the cervical vertebra, immediate and long-term anterior cervical column support, and nerve function restoration can be achieved by using anterior partial corpectomy and titanium mesh fusion and internal fixation to treat old fracture dislocation of the lower cervical spine. For cases with locked facet joints or posterior structures invading the vertebral canal, the combined anterior and posterior approaches should be performed, when necessary, to achieve better results. PMID:29184051

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

NASA Astrophysics Data System (ADS)

Yang, G.; Lin, Y.; Bhattacharya, P.

2007-12-01

To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Surface apposition and multiple cell contacts promote myoblast fusion in Drosophila flight muscles

PubMed Central

Dhanyasi, Nagaraju; Segal, Dagan; Shimoni, Eyal; Shinder, Vera

2015-01-01

Fusion of individual myoblasts to form multinucleated myofibers constitutes a widely conserved program for growth of the somatic musculature. We have used electron microscopy methods to study this key form of cell–cell fusion during development of the indirect flight muscles (IFMs) of Drosophila melanogaster. We find that IFM myoblast–myotube fusion proceeds in a stepwise fashion and is governed by apparent cross talk between transmembrane and cytoskeletal elements. Our analysis suggests that cell adhesion is necessary for bringing myoblasts to within a minimal distance from the myotubes. The branched actin polymerization machinery acts subsequently to promote tight apposition between the surfaces of the two cell types and formation of multiple sites of cell–cell contact, giving rise to nascent fusion pores whose expansion establishes full cytoplasmic continuity. Given the conserved features of IFM myogenesis, this sequence of cell interactions and membrane events and the mechanistic significance of cell adhesion elements and the actin-based cytoskeleton are likely to represent general principles of the myoblast fusion process. PMID:26459604

Investigation of complete and incomplete fusion in the 7Li+124Sn reaction near Coulomb barrier energies

NASA Astrophysics Data System (ADS)

Parkar, V. V.; Sharma, Sushil K.; Palit, R.; Upadhyaya, S.; Shrivastava, A.; Pandit, S. K.; Mahata, K.; Jha, V.; Santra, S.; Ramachandran, K.; Nag, T. N.; Rath, P. K.; Kanagalekar, Bhushan; Trivedi, T.

2018-01-01

The complete and incomplete fusion cross sections for the 7Li+124Sn reaction were measured using online and offline characteristic γ -ray detection techniques. The complete fusion (CF) cross sections at energies above the Coulomb barrier were found to be suppressed by ˜26 % compared to the coupled channel calculations. This suppression observed in complete fusion cross sections is found to be commensurate with the measured total incomplete fusion (ICF) cross sections. There is a distinct feature observed in the ICF cross sections, i.e., t capture is found to be dominant compared to α capture at all the measured energies. A simultaneous explanation of complete, incomplete, and total fusion (TF) data was also obtained from the calculations based on the continuum discretized coupled channel method with short range imaginary potentials. The cross section ratios of CF/TF and ICF/TF obtained from the data as well as the calculations showed the dominance of ICF at below-barrier energies and CF at above-barrier energies.

The transition zone above a lumbosacral fusion.

PubMed

Hambly, M F; Wiltse, L L; Raghavan, N; Schneiderman, G; Koenig, C

1998-08-15

The clinical and radiographic effect of a lumbar or lumbosacral fusion was studied in 42 patients who had undergone a posterolateral fusion with an average follow-up of 22.6 years. To examine the long-term effects of posterolateral lumbar or lumbosacral fusion on the cephalad two motion segments (transition zone). It is commonly held that accelerated degeneration occurs in the motion segments adjacent to a fusion. Most studies are of short-term, anecdotal, uncontrolled reports that pay particular attention only to the first motion segment immediately cephalad to the fusion. Forty-two patients who had previously undergone a posterolateral lumbar or lumbosacral fusion underwent radiographic and clinical evaluation. Rate of fusion, range of motion, osteophytes, degenerative spondylolisthesis, retrolisthesis, facet arthrosis, disc ossification, dynamic instability, and disc space height were all studied and statistically compared with an age- and gender-matched control group. The patient's self-reported clinical outcome was also recorded. Degenerative changes occurred at the second level above the fused levels with a frequency equal to those occurring in the first level. There was no statistical difference between the study group and the cohort group in the presence of radiographic changes within the transition zone. In those patients undergoing fusion for degenerative processes, 75% reported a good to excellent outcome, whereas 84% of those undergoing fusion for spondylolysis or spondylolisthesis reported a good to excellent outcome. Radiographic changes occur within the transition zone cephalad to a lumbar or lumbosacral fusion. However, these changes are also seen in control subjects who have had no surgery.

Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms

PubMed Central

Kangaspeska, Sara; Hultsch, Susanne; Edgren, Henrik; Nicorici, Daniel; Murumägi, Astrid; Kallioniemi, Olli

2012-01-01

RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts. PMID:23119097

Reanalysis of RNA-sequencing data reveals several additional fusion genes with multiple isoforms.

PubMed

Kangaspeska, Sara; Hultsch, Susanne; Edgren, Henrik; Nicorici, Daniel; Murumägi, Astrid; Kallioniemi, Olli

2012-01-01

RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.

[Rumination and cognitive fusion in dementia family caregivers].

PubMed

Romero-Moreno, Rosa; Márquez-González, María; Losada, Andrés; Fernández-Fernández, Virginia; Nogales-González, Celia

2015-01-01

Rumination has been described as a dysfunctional coping strategy related to emotional distress. Recently, it has been highlighted from the Acceptance and Commitment Therapy therapeutic approach, the negative role that cognitive fusion (the extent to which we are psychologically tangled with and dominated by the form or content of our thoughts) has on the explanation of distress. The aim of this study is to simultaneously analyze the role of rumination and cognitive fusion in the caregiving stress process. The sample of 176 dementia caregivers was divided in four groups, taking into account their levels of rumination and cognitive fusion: HRHF=high rumination+high cognitive fusion; HRLF=high rumination+low cognitive fusion; LRHF= low rumination+high cognitive fusion; and LRLC=low rumination and low cognitive fusion. Caregiver stress factors, frequency of pleasant events, experiential avoidance, coherence and satisfaction with personal values, depression, anxiety and satisfaction with life, were measured. The HRHF group showed higher levels of depression, anxiety, experiential avoidance and lower levels of satisfaction with life, frequency of pleasant events, coherence and satisfaction with personal values, than the other three groups. Considering simultaneously rumination and cognitive fusion may contribute to a better understanding of caregiver coping and distress. Copyright © 2014 SEGG. Published by Elsevier Espana. All rights reserved.

A Fusion-Inhibiting Peptide against Rift Valley Fever Virus Inhibits Multiple, Diverse Viruses

PubMed Central

Koehler, Jeffrey W.; Smith, Jeffrey M.; Ripoll, Daniel R.; Spik, Kristin W.; Taylor, Shannon L.; Badger, Catherine V.; Grant, Rebecca J.; Ogg, Monica M.; Wallqvist, Anders; Guttieri, Mary C.; Garry, Robert F.; Schmaljohn, Connie S.

2013-01-01

For enveloped viruses, fusion of the viral envelope with a cellular membrane is critical for a productive infection to occur. This fusion process is mediated by at least three classes of fusion proteins (Class I, II, and III) based on the protein sequence and structure. For Rift Valley fever virus (RVFV), the glycoprotein Gc (Class II fusion protein) mediates this fusion event following entry into the endocytic pathway, allowing the viral genome access to the cell cytoplasm. Here, we show that peptides analogous to the RVFV Gc stem region inhibited RVFV infectivity in cell culture by inhibiting the fusion process. Further, we show that infectivity can be inhibited for diverse, unrelated RNA viruses that have Class I (Ebola virus), Class II (Andes virus), or Class III (vesicular stomatitis virus) fusion proteins using this single peptide. Our findings are consistent with an inhibition mechanism similar to that proposed for stem peptide fusion inhibitors of dengue virus in which the RVFV inhibitory peptide first binds to both the virion and cell membranes, allowing it to traffic with the virus into the endocytic pathway. Upon acidification and rearrangement of Gc, the peptide is then able to specifically bind to Gc and prevent fusion of the viral and endocytic membranes, thus inhibiting viral infection. These results could provide novel insights into conserved features among the three classes of viral fusion proteins and offer direction for the future development of broadly active fusion inhibitors. PMID:24069485

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

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

PubMed Central

Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

NASA Astrophysics Data System (ADS)

Mohammed Anzar, Sharafudeen Thaha; Sathidevi, Puthumangalathu Savithri

2014-12-01

In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics. An efficient matching score preprocessing technique based on multi-normalization is employed for improving the performance of the multimodal system, under various noise conditions. Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation. Reliability (dispersion) and the separability (inter-/intra-class distance and d-prime statistics) measures under various noise conditions are estimated from the individual modalities, during the training/validation stage. The `best integration weights' are then computed by algebraically combining these measures using the weighted sum rule. The computed integration weights are then optimized against the recognition accuracy using techniques such as grid search, genetic algorithm and particle swarm optimization. The experimental results show that, the proposed biometric solution leads to considerable improvement in the recognition performance even under low signal-to-noise ratio (SNR) conditions and reduces the false acceptance rate (FAR) and false rejection rate (FRR), making the system useful for security as well as forensic applications.

Machinery Diagnostic Feature Extraction and Fusion Techniques Using Diverse Sources

DTIC Science & Technology

2001-04-05

laboratories AMTEC Corporation P.O. Box 077777 500 Wynn Drive, Suite 314 Huntsville, AL 35807-7777 Huntsville, AL 35816-3429 chongchanlee(~hnt.wvlelabs.com...for helicopters. Other distinguished features of JAHUMS system developed by AMTEC Corporation and Wyle Laboratories Incorporated are their data

Aperture tolerances for neutron-imaging systems in inertial confinement fusion.

PubMed

Ghilea, M C; Sangster, T C; Meyerhofer, D D; Lerche, R A; Disdier, L

2008-02-01

Neutron-imaging systems are being considered as an ignition diagnostic for the National Ignition Facility (NIF) [Hogan et al., Nucl. Fusion 41, 567 (2001)]. Given the importance of these systems, a neutron-imaging design tool is being used to quantify the effects of aperture fabrication and alignment tolerances on reconstructed neutron images for inertial confinement fusion. The simulations indicate that alignment tolerances of more than 1 mrad would introduce measurable features in a reconstructed image for both pinholes and penumbral aperture systems. These simulations further show that penumbral apertures are several times less sensitive to fabrication errors than pinhole apertures.

Tyrosine kinase gene rearrangements in epithelial malignancies

PubMed Central

Shaw, Alice T.; Hsu, Peggy P.; Awad, Mark M.; Engelman, Jeffrey A.

2014-01-01

Chromosomal rearrangements that lead to oncogenic kinase activation are observed in many epithelial cancers. These cancers express activated fusion kinases that drive the initiation and progression of malignancy, and often have a considerable response to small-molecule kinase inhibitors, which validates these fusion kinases as ‘druggable’ targets. In this Review, we examine the aetiologic, pathogenic and clinical features that are associated with cancers harbouring oncogenic fusion kinases, including anaplastic lymphoma kinase (ALK), ROS1 and RET. We discuss the clinical outcomes with targeted therapies and explore strategies to discover additional kinases that are activated by chromosomal rearrangements in solid tumours. PMID:24132104

Method for vacuum fusion bonding

DOEpatents

Ackler, Harold D.; Swierkowski, Stefan P.; Tarte, Lisa A.; Hicks, Randall K.

2001-01-01

An improved vacuum fusion bonding structure and process for aligned bonding of large area glass plates, patterned with microchannels and access holes and slots, for elevated glass fusion temperatures. Vacuum pumpout of all components is through the bottom platform which yields an untouched, defect free top surface which greatly improves optical access through this smooth surface. Also, a completely non-adherent interlayer, such as graphite, with alignment and location features is located between the main steel platform and the glass plate pair, which makes large improvements in quality, yield, and ease of use, and enables aligned bonding of very large glass structures.

Fusion bonding and alignment fixture

DOEpatents

Ackler, Harold D.; Swierkowski, Stefan P.; Tarte, Lisa A.; Hicks, Randall K.

2000-01-01

An improved vacuum fusion bonding structure and process for aligned bonding of large area glass plates, patterned with microchannels and access holes and slots, for elevated glass fusion temperatures. Vacuum pumpout of all the components is through the bottom platform which yields an untouched, defect free top surface which greatly improves optical access through this smooth surface. Also, a completely non-adherent interlayer, such as graphite, with alignment and location features is located between the main steel platform and the glass plate pair, which makes large improvements in quality, yield, and ease of use, and enables aligned bonding of very large glass structures.

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

Wieser, Patti; Hopkins, David

The DOE Princeton Plasma Physics Laboratory (PPPL) collaborates to develop fusion as a safe, clean and abundant energy source for the future. This video discusses PPPL's research and development on plasma, the fourth state of matter. In this simulation of plasma turbulence inside PPPL's National Spherical Torus Experiment, the colorful strings represent higher and lower electron density in turbulent plasma as it circles around a donut-shaped fusion reactor; red and orange are higher density. This image is among those featured in the slide show, "Plasmas are Hot and Fusion is Cool," a production of PPPL and the Princeton University Broadcastmore » Center.« 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

Cell fusion and nuclear fusion in plants.

PubMed

Maruyama, Daisuke; Ohtsu, Mina; Higashiyama, Tetsuya

2016-12-01

Eukaryotic cells are surrounded by a plasma membrane and have a large nucleus containing the genomic DNA, which is enclosed by a nuclear envelope consisting of the outer and inner nuclear membranes. Although these membranes maintain the identity of cells, they sometimes fuse to each other, such as to produce a zygote during sexual reproduction or to give rise to other characteristically polyploid tissues. Recent studies have demonstrated that the mechanisms of plasma membrane or nuclear membrane fusion in plants are shared to some extent with those of yeasts and animals, despite the unique features of plant cells including thick cell walls and intercellular connections. Here, we summarize the key factors in the fusion of these membranes during plant reproduction, and also focus on "non-gametic cell fusion," which was thought to be rare in plant tissue, in which each cell is separated by a cell wall. Copyright © 2016 Elsevier Ltd. All rights reserved.

Weber-aware weighted mutual information evaluation for infrared-visible image fusion

NASA Astrophysics Data System (ADS)

Luo, Xiaoyan; Wang, Shining; Yuan, Ding

2016-10-01

A performance metric for infrared and visible image fusion is proposed based on Weber's law. To indicate the stimulus of source images, two Weber components are provided. One is differential excitation to reflect the spectral signal of visible and infrared images, and the other is orientation to capture the scene structure feature. By comparing the corresponding Weber component in infrared and visible images, the source pixels can be marked with different dominant properties in intensity or structure. If the pixels have the same dominant property label, the pixels are grouped to calculate the mutual information (MI) on the corresponding Weber components between dominant source and fused images. Then, the final fusion metric is obtained via weighting the group-wise MI values according to the number of pixels in different groups. Experimental results demonstrate that the proposed metric performs well on popular image fusion cases and outperforms other image fusion metrics.

Structure of the uncleaved ectodomain of the paramyxovirus (hPIV3) fusion protein

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

Yin, Hsien-Sheng; Paterson, Reay G.; Wen, Xiaolin

2010-03-08

Class I viral fusion proteins share common mechanistic and structural features but little sequence similarity. Structural insights into the protein conformational changes associated with membrane fusion are based largely on studies of the influenza virus hemagglutinin in pre- and postfusion conformations. Here, we present the crystal structure of the secreted, uncleaved ectodomain of the paramyxovirus, human parainfluenza virus 3 fusion (F) protein, a member of the class I viral fusion protein group. The secreted human parainfluenza virus 3 F forms a trimer with distinct head, neck, and stalk regions. Unexpectedly, the structure reveals a six-helix bundle associated with the postfusionmore » form of F, suggesting that the anchor-minus ectodomain adopts a conformation largely similar to the postfusion state. The transmembrane anchor domains of F may therefore profoundly influence the folding energetics that establish and maintain a metastable, prefusion state.« less

Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

PubMed

Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

2018-02-05

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of feature generation methods for PTZ camera object tracking

NASA Astrophysics Data System (ADS)

Doyle, Daniel D.; Black, Jonathan T.

2012-06-01

Object detection and tracking using computer vision (CV) techniques have been widely applied to sensor fusion applications. Many papers continue to be written that speed up performance and increase learning of artificially intelligent systems through improved algorithms, workload distribution, and information fusion. Military application of real-time tracking systems is becoming more and more complex with an ever increasing need of fusion and CV techniques to actively track and control dynamic systems. Examples include the use of metrology systems for tracking and measuring micro air vehicles (MAVs) and autonomous navigation systems for controlling MAVs. This paper seeks to contribute to the determination of select tracking algorithms that best track a moving object using a pan/tilt/zoom (PTZ) camera applicable to both of the examples presented. The select feature generation algorithms compared in this paper are the trained Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the Mixture of Gaussians (MoG) background subtraction method, the Lucas- Kanade optical flow method (2000) and the Farneback optical flow method (2003). The matching algorithm used in this paper for the trained feature generation algorithms is the Fast Library for Approximate Nearest Neighbors (FLANN). The BSD licensed OpenCV library is used extensively to demonstrate the viability of each algorithm and its performance. Initial testing is performed on a sequence of images using a stationary camera. Further testing is performed on a sequence of images such that the PTZ camera is moving in order to capture the moving object. Comparisons are made based upon accuracy, speed and memory.

Fusion-nonfusion hybrid construct versus anterior cervical hybrid decompression and fusion: a comparative study for 3-level cervical degenerative disc diseases.

PubMed

Ding, Fan; Jia, Zhiwei; Wu, Yaohong; Li, Chao; He, Qing; Ruan, Dike

2014-11-01

A retrospective analysis. This study aimed to compare the safety and efficacy between the fusion-nonfusion hybrid construct (HC: anterior cervical corpectomy and fusion plus artificial disc replacement, ACCF plus cADR) and anterior cervical hybrid decompression and fusion (ACHDF: anterior cervical corpectomy and fusion plus discectomy and fusion, ACCF plus ACDF) for 3-level cervical degenerative disc diseases (cDDD). The optimal anterior technique for 3-level cDDD remains uncertain. Long-segment fusion substantially induced biomechanical changes at adjacent levels, which may lead to symptomatic adjacent segment degeneration. Hybrid surgery consisting of ACDF and cADR has been reported with good results for 2-level cDDD. In this context, ACCF combining with cADR may be an alternative to ACHDF for 3-level cDDD. Between 2009 and 2012, 28 patients with 3-level cDDD who underwent HC (n=13) and ACHDF (15) were retrospectively reviewed. Clinical assessments were based on Neck Disability Index, Japanese Orthopedic Association disability scale, visual analogue scale, Japanese Orthopedic Association recovery rate, and Odom criteria. Radiological analysis included range of motion of C2-C7 and adjacent segments and cervical lordosis. Perioperative parameters, radiological adjacent-level changes, and the complications were also assessed. HC showed better Neck Disability Index improvement at 12 and 24 months, as well as Japanese Orthopedic Association and visual analogue scale improvement at 24 months postoperatively (P<0.05). HC had better outcome according to Odom criteria but not significantly (P>0.05). The range of motion of C2-C7 and adjacent segments was less compromised in HC (P<0.05). Both 2 groups showed significant lordosis recovery postoperatively (P<0.05), but no difference was found between groups (P>0.05). The incidence of adjacent-level degenerative changes and complications was higher in ACHDF but not significantly (P>0.05). HC may be an alternative to ACHDF for 3-level cDDD due to the equivalent or superior early clinical outcomes, less compromised C2-C7 range of motion, and less impact at adjacent levels. 3.

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

Development of a DC Glow Discharge Exhibit for the Demonstration of Plasma Behavior in a Magnetic Field

NASA Astrophysics Data System (ADS)

Bruder, Daniel

2010-11-01

The DC Glow Discharge Exhibit is intended to demonstrate the effects a magnetic field produces on a plasma in a vacuum chamber. The display, which will be featured as a part of The Liberty Science Center's ``Energy Quest Exhibition,'' consists of a DC glow discharge tube and information panels to educate the general public on plasma and its relation to fusion energy. Wall posters and an information booklet will offer brief descriptions of fusion-based science and technology, and will portray plasma's role in the development of fusion as a viable source of energy. The display features a horse-shoe magnet on a movable track, allowing viewers to witness the effects of a magnetic field upon a plasma. The plasma is created from air within a vacuum averaging between 100-200 mTorr. Signage within the casing describes the hardware components. The display is pending delivery to The Liberty Science Center, and will replace a similar, older exhibit presently at the museum.

Sensor fusion II: Human and machine strategies; Proceedings of the Meeting, Philadelphia, PA, Nov. 6-9, 1989

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1990-01-01

Various papers on human and machine strategies in sensor fusion are presented. The general topics addressed include: active vision, measurement and analysis of visual motion, decision models for sensor fusion, implementation of sensor fusion algorithms, applying sensor fusion to image analysis, perceptual modules and their fusion, perceptual organization and object recognition, planning and the integration of high-level knowledge with perception, using prior knowledge and context in sensor fusion.

The role of physical therapy and rehabilitation after lumbar fusion surgery for degenerative disease: a systematic review.

PubMed

Madera, Marcella; Brady, Jeremy; Deily, Sylvia; McGinty, Trent; Moroz, Lee; Singh, Devender; Tipton, George; Truumees, Eeric

2017-06-01

OBJECTIVE The purpose of this study was to provide a systematic and comprehensive review of the existing literature regarding postfusion rehabilitation. METHODS Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the authors conducted an exhaustive review of multiple electronic databases. Potential articles were screened using inclusion/exclusion criteria. Two authors independently analyzed these studies using predefined data fields, including study quality indicators such as level of evidence and availability of accepted patient-reported outcomes measures. These findings were synthesized in a narrative format. A third author resolved disagreements regarding the inclusion of a study. RESULTS Twenty-one articles with I or II levels of evidence were included in the review. The authors divided the findings of the literature review into several groups: rehabilitation terminology, timing and duration of postfusion rehabilitation, the need for rehabilitation relative to surgery-related morbidity, rehabilitation's relationship to outcomes, and cognitive and psychosocial aspects of postsurgical rehabilitation. Current evidence generally supports formal rehabilitation after lumbar fusion surgery. Starting physical therapy at the 12-week postoperative mark results in better outcomes at lower cost than an earlier, 6-week start. Where available, psychosocial support improves outcomes. However, a number of the questions could not be answered with high-grade evidence. In these cases, the authors used "best evidence available" to make recommendations. There are many cases in which different types of caregivers use clinical terminology differently. The data supporting an optimal protocol for postfusion rehabilitation remains elusive but, using the data available, the authors have crafted recommendations and a model protocol, which is currently undergoing prospective study. CONCLUSIONS Rehabilitation has long been a common feature in the postoperative management of patients undergoing spinal fusion. Although caregivers from multiple disciplines agree that the majority of their patients will benefit from this effort, the supporting data remain sparse. In creating a model protocol for postlumbar fusion rehabilitation, the authors hope to share a starting point for future postoperative lumbar fusion rehabilitation research.

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

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

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

[MRI/TRUS fusion-guided prostate biopsy : Value in the context of focal therapy].

PubMed

Franz, T; von Hardenberg, J; Blana, A; Cash, H; Baumunk, D; Salomon, G; Hadaschik, B; Henkel, T; Herrmann, J; Kahmann, F; Köhrmann, K-U; Köllermann, J; Kruck, S; Liehr, U-B; Machtens, S; Peters, I; Radtke, J P; Roosen, A; Schlemmer, H-P; Sentker, L; Wendler, J J; Witzsch, U; Stolzenburg, J-U; Schostak, M; Ganzer, R

2017-02-01

Several systems for MRI/TRUS fusion-guided biopsy of the prostate are commercially available. Many studies have shown superiority of fusion systems for tumor detection and diagnostic quality compared to random biopsy. The benefit of fusion systems in focal therapy of prostate cancer (PC) is less clear. Critical considerations of fusion systems for planning and monitoring of focal therapy of PC were investigated. A systematic literature review of available fusion systems for the period 2013-5/2016 was performed. A checklist of technical details, suitability for special anatomic situations and suitability for focal therapy was established by the German working group for focal therapy (Arbeitskreis fokale und Mikrotherapie). Eight fusion systems were considered (Artemis™, BioJet, BiopSee®, iSR´obot™ Mona Lisa, Hitachi HI-RVS, UroNav and Urostation®). Differences were found for biopsy mode (transrectal, perineal, both), fusion mode (elastic or rigid), navigation (image-based, electromagnetic sensor-based or mechanical sensor-based) and space requirements. Several consensus groups recommend fusion systems for focal therapy. Useful features are "needle tracking" and compatibility between fusion system and treatment device (available for Artemis™, BiopSee® and Urostation® with Focal One®; BiopSee®, Hitachi HI-RVS with NanoKnife®; BioJet, BiopSee® with cryoablation, brachytherapy). There are a few studies for treatment planning. However, studies on treatment monitoring after focal therapy are missing.

High-Energy Space Propulsion Based on Magnetized Target Fusion

NASA Technical Reports Server (NTRS)

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

1999-01-01

A conceptual study is made to explore the feasibility of applying magnetized target fusion (MTF) to space propulsion for omniplanetary travel. Plasma-jet driven MTF not only is highly amenable to space propulsion, but also has a number of very attractive features for this application: 1) The pulsed fusion scheme provides in situ a very dense hydrogenous liner capable of moderating the neutrons, converting more than 97% of the neutron energy into charged particle energy of the fusion plasma available for propulsion. 2) The fusion yield per pulse can be maintained at an attractively low level (< 1 GJ) despite a respectable gain in excess of 70. A compact, low-weight engine is the result. An engine with a jet power of 25 GW, a thrust of 66 kN, and a specific impulse of 77,000 s, can be achieved with an overall engine mass of about 41 metric tons, with a specific power density of 605 kW/kg, and a specific thrust density of 1.6 N/kg. The engine is rep-rated at 40 Hz to provide this power and thrust level. At a practical rep-rate limit of 200 Hz, the engine can deliver 128 GW jet power and 340 kN of thrust, at specific power and thrust density of 1,141 kW/kg and 3 N/kg respectively. 3) It is possible to operate the magnetic nozzle as a magnetic flux compression generator in this scheme, while attaining a high nozzle efficiency of 80% in converting the spherically radial momentum of the fusion plasma to an axial impulse. 4) A small fraction of the electrical energy generated from the flux compression is used directly to recharge the capacitor bank and other energy storage equipment, without the use of a highvoltage DC power supply. A separate electrical generator is not necessary. 5) Due to the simplicity of the electrical circuit and the components, involving mainly inductors, capacitors, and plasma guns, which are connected directly to each other without any intermediate equipment, a high rep-rate (with a maximum of 200 Hz) appears practicable. 6) All fusion related components are within the current state of the art for pulsed power technology. Experimental facilities with the required pulsed power capabilities already exist. 7) The scheme does not require prefabricated fuel target and liner hardware in any esoteric form or state. All necessary fuel and liner material are introduced into the engine in the form of ordinary matter in gaseous state at room temperature, greatly simplifying their handling on board. They are delivered into the fusion reaction chamber in a completely standoff manner.

Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations.

PubMed

Cao, Houwei; Savran, Arman; Verma, Ragini; Nenkova, Ani

2015-01-01

In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as corpus-dependent representations. We find that corpus-dependent bag of words approach with mutual information between word and emotion dimensions is by far the best representation. For the analysis of acoustics, we zero in on the question of granularity. We confirm on our corpus that utterance-level features are more predictive than word-level features. Further, we study more detailed representations in which the utterance is divided into regions of interest (ROI), each with separate representation. We introduce two ROI representations, which significantly outperform less informed approaches. In addition we show that acoustic models of emotion can be improved considerably by taking into account annotator agreement and training the model on smaller but reliable dataset. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities. Simple fusion methods do not lead to consistent improvements over lexical classifiers alone but improve over acoustic models.

Design of the DEMO Fusion Reactor Following ITER.

PubMed

Garabedian, Paul R; McFadden, Geoffrey B

2009-01-01

Runs of the NSTAB nonlinear stability code show there are many three-dimensional (3D) solutions of the advanced tokamak problem subject to axially symmetric boundary conditions. These numerical simulations based on mathematical equations in conservation form predict that the ITER international tokamak project will encounter persistent disruptions and edge localized mode (ELMS) crashes. Test particle runs of the TRAN transport code suggest that for quasineutrality to prevail in tokamaks a certain minimum level of 3D asymmetry of the magnetic spectrum is required which is comparable to that found in quasiaxially symmetric (QAS) stellarators. The computational theory suggests that a QAS stellarator with two field periods and proportions like those of ITER is a good candidate for a fusion reactor. For a demonstration reactor (DEMO) we seek an experiment that combines the best features of ITER, with a system of QAS coils providing external rotational transform, which is a measure of the poloidal field. We have discovered a configuration with unusually good quasisymmetry that is ideal for this task.

Design of the DEMO Fusion Reactor Following ITER

PubMed Central

Garabedian, Paul R.; McFadden, Geoffrey B.

2009-01-01

Runs of the NSTAB nonlinear stability code show there are many three-dimensional (3D) solutions of the advanced tokamak problem subject to axially symmetric boundary conditions. These numerical simulations based on mathematical equations in conservation form predict that the ITER international tokamak project will encounter persistent disruptions and edge localized mode (ELMS) crashes. Test particle runs of the TRAN transport code suggest that for quasineutrality to prevail in tokamaks a certain minimum level of 3D asymmetry of the magnetic spectrum is required which is comparable to that found in quasiaxially symmetric (QAS) stellarators. The computational theory suggests that a QAS stellarator with two field periods and proportions like those of ITER is a good candidate for a fusion reactor. For a demonstration reactor (DEMO) we seek an experiment that combines the best features of ITER, with a system of QAS coils providing external rotational transform, which is a measure of the poloidal field. We have discovered a configuration with unusually good quasisymmetry that is ideal for this task. PMID:27504224

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

Atomic-Level Quality Assessment of Enzymes Encapsulated in Bioinspired Silica.

PubMed

Martelli, Tommaso; Ravera, Enrico; Louka, Alexandra; Cerofolini, Linda; Hafner, Manuel; Fragai, Marco; Becker, Christian F W; Luchinat, Claudio

2016-01-04

Among protein immobilization strategies, encapsulation in bioinspired silica is increasingly popular. Encapsulation offers high yields and the solid support is created through a protein-catalyzed polycondensation reaction that occurs under mild conditions. An integrated strategy is reported for the characterization of both the protein and bioinspired silica scaffold generated by the encapsulation of enzymes with an external silica-forming promoter or with the promoter expressed as a fusion to the enzyme. This strategy is applied to the catalytic domain of matrix metalloproteinase 12. Analysis reveals that the structure of the protein encapsulated by either method is not significantly altered with respect to the native form. The structural features of silica obtained by either strategy are also similar, but differ from those obtained by other approaches. In case of the covalently linked R5-enzyme construct, immobilization yields are higher. Encapsulation through a fusion protein, therefore, appears to be the method of choice. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system

NASA Astrophysics Data System (ADS)

Poinsot, Audrey; Yang, Fan; Brost, Vincent

2011-02-01

Including multiple sources of information in personal identity recognition and verification gives the opportunity to greatly improve performance. We propose a contactless biometric system that combines two modalities: palmprint and face. Hardware implementations are proposed on the Texas Instrument Digital Signal Processor and Xilinx Field-Programmable Gate Array (FPGA) platforms. The algorithmic chain consists of a preprocessing (which includes palm extraction from hand images), Gabor feature extraction, comparison by Hamming distance, and score fusion. Fusion possibilities are discussed and tested first using a bimodal database of 130 subjects that we designed (uB database), and then two common public biometric databases (AR for face and PolyU for palmprint). High performance has been obtained for recognition and verification purpose: a recognition rate of 97.49% with AR-PolyU database and an equal error rate of 1.10% on the uB database using only two training samples per subject have been obtained. Hardware results demonstrate that preprocessing can easily be performed during the acquisition phase, and multimodal biometric recognition can be treated almost instantly (0.4 ms on FPGA). We show the feasibility of a robust and efficient multimodal hardware biometric system that offers several advantages, such as user-friendliness and flexibility.

Membrane-on-a-chip: microstructured silicon/silicon-dioxide chips for high-throughput screening of membrane transport and viral membrane fusion.

PubMed

Kusters, Ilja; van Oijen, Antoine M; Driessen, Arnold J M

2014-04-22

Screening of transport processes across biological membranes is hindered by the challenge to establish fragile supported lipid bilayers and the difficulty to determine at which side of the membrane reactants reside. Here, we present a method for the generation of suspended lipid bilayers with physiological relevant lipid compositions on microstructured Si/SiO2 chips that allow for high-throughput screening of both membrane transport and viral membrane fusion. Simultaneous observation of hundreds of single-membrane channels yields statistical information revealing population heterogeneities of the pore assembly and conductance of the bacterial toxin α-hemolysin (αHL). The influence of lipid composition and ionic strength on αHL pore formation was investigated at the single-channel level, resolving features of the pore-assembly pathway. Pore formation is inhibited by a specific antibody, demonstrating the applicability of the platform for drug screening of bacterial toxins and cell-penetrating agents. Furthermore, fusion of H3N2 influenza viruses with suspended lipid bilayers can be observed directly using a specialized chip architecture. The presented micropore arrays are compatible with fluorescence readout from below using an air objective, thus allowing high-throughput screening of membrane transport in multiwell formats in analogy to plate readers.

Neutronics Evaluation of Lithium-Based Ternary Alloys in IFE Blankets

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

Jolodosky, A.; Fratoni, M.

2014-11-20

Pre-conceptual fusion blanket designs require research and development to reflect important proposed changes in the design of essential systems, and the new challenges they impose on related fuel cycle systems. One attractive feature of using liquid lithium as the breeder and coolant is that it has very high tritium solubility and results in very low levels of tritium permeation throughout the facility infrastructure. However, lithium metal vigorously reacts with air and water and presents plant safety concerns. If the chemical reactivity of lithium could be overcome, the result would have a profound impact on fusion energy and associated safety basis.more » The overriding goal of this project is to develop a lithium-based alloy that maintains beneficial properties of lithium (e.g. high tritium breeding and solubility) while reducing overall flammability concerns. To minimize the number of alloy combinations that must be explored, only those alloys that meet certain nuclear performance metrics will be considered for subsequent thermodynamic study. The specific scope of this study is to evaluate the neutronics performance of lithium-based alloys in the blanket of an inertial confinement fusion (ICF) engine. The results of this study will inform the development of lithium alloys that would guarantee acceptable neutronics performance while mitigating the chemical reactivity issues of pure lithium.« less

Data fusion concept in multispectral system for perimeter protection of stationary and moving objects

NASA Astrophysics Data System (ADS)

Ciurapiński, Wieslaw; Dulski, Rafal; Kastek, Mariusz; Szustakowski, Mieczyslaw; Bieszczad, Grzegorz; Życzkowski, Marek; Trzaskawka, Piotr; Piszczek, Marek

2009-09-01

The paper presents the concept of multispectral protection system for perimeter protection for stationary and moving objects. The system consists of active ground radar, thermal and visible cameras. The radar allows the system to locate potential intruders and to control an observation area for system cameras. The multisensor construction of the system ensures significant improvement of detection probability of intruder and reduction of false alarms. A final decision from system is worked out using image data. The method of data fusion used in the system has been presented. The system is working under control of FLIR Nexus system. The Nexus offers complete technology and components to create network-based, high-end integrated systems for security and surveillance applications. Based on unique "plug and play" architecture, system provides unmatched flexibility and simplistic integration of sensors and devices in TCP/IP networks. Using a graphical user interface it is possible to control sensors and monitor streaming video and other data over the network, visualize the results of data fusion process and obtain detailed information about detected intruders over a digital map. System provides high-level applications and operator workload reduction with features such as sensor to sensor cueing from detection devices, automatic e-mail notification and alarm triggering.

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.

Loose fusion based on SLAM and IMU for indoor environment

NASA Astrophysics Data System (ADS)

Zhu, Haijiang; Wang, Zhicheng; Zhou, Jinglin; Wang, Xuejing

2018-04-01

The simultaneous localization and mapping (SLAM) method based on the RGB-D sensor is widely researched in recent years. However, the accuracy of the RGB-D SLAM relies heavily on correspondence feature points, and the position would be lost in case of scenes with sparse textures. Therefore, plenty of fusion methods using the RGB-D information and inertial measurement unit (IMU) data have investigated to improve the accuracy of SLAM system. However, these fusion methods usually do not take into account the size of matched feature points. The pose estimation calculated by RGB-D information may not be accurate while the number of correct matches is too few. Thus, considering the impact of matches in SLAM system and the problem of missing position in scenes with few textures, a loose fusion method combining RGB-D with IMU is proposed in this paper. In the proposed method, we design a loose fusion strategy based on the RGB-D camera information and IMU data, which is to utilize the IMU data for position estimation when the corresponding point matches are quite few. While there are a lot of matches, the RGB-D information is still used to estimate position. The final pose would be optimized by General Graph Optimization (g2o) framework to reduce error. The experimental results show that the proposed method is better than the RGB-D camera's method. And this method can continue working stably for indoor environment with sparse textures in the SLAM system.

Return to Work and Multilevel Versus Single-Level Cervical Fusion for Radiculopathy in a Workers' Compensation Setting.

PubMed

Faour, Mhamad; Anderson, Joshua T; Haas, Arnold R; Percy, Rick; Woods, Stephen T; Ahn, Uri M; Ahn, Nicholas U

2017-01-15

Retrospective comparative cohort study. Examine the impact of multilevel fusion on return to work (RTW) status and compare RTW status after multi- versus single-level cervical fusion for patients with work-related injury. Patients with work-related injuries in the workers' compensation systems have less favorable surgical outcomes. Cervical fusion provides a greater than 90% likelihood of relieving radiculopathy and stabilizing or improving myelopathy. However, more levels fused at index surgery are reportedly associated with poorer surgical outcomes than single-level fusion. Data was collected from the Ohio Bureau of Workers' Compensation (BWC) between 1993 and 2011. The study population included patients who underwent cervical fusion for radiculopathy. Two groups were constructed (multilevel fusion [MLF] vs. single-level fusion [SLF]). Outcomes measures evaluated were: RTW criteria, RTW <1year, reoperation, surgical complication, disability, and legal litigation after surgery. After accounting for a number of independent variables in the regression model, multilevel fusion was a negative predictor of successful RTW status within 3-year follow-up after surgery (OR = 0.82, 95% CI: 0.70-0.95, P <0.05).RTW criteria were met 62.9% of SLF group compared with 54.8% of MLF group. The odds of having a stable RTW for MLF patients were 0.71% compared with the SLF patients (95% CI: 0.61-0.83; P: 0.0001).At 1 year after surgery, RTW rate was 53.1% for the SLF group compared with 43.7% for the MLF group. The odds of RTW within 1 year after surgery for the MLF group were 0.69% compared with SLF patients (95% CI: 0.59-0.80; P: 0.0001).Higher rate of disability after surgery was observed in the MLF group compared with the SLF group (P: 0.0001) CONCLUSION.: Multilevel cervical fusion for radiculopathy was associated with poor return to work profile after surgery. Multilevel cervical fusion was associated with lower RTW rates, less likelihood of achieving stable return to work, and higher rate of disability after surgery. 3.

Personal recognition using hand shape and texture.

PubMed

Kumar, Ajay; Zhang, David

2006-08-01

This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.

DUX4 Immunohistochemistry Is a Highly Sensitive and Specific Marker for CIC-DUX4 Fusion-positive Round Cell Tumor.

PubMed

Siegele, Bradford; Roberts, Jon; Black, Jennifer O; Rudzinski, Erin; Vargas, Sara O; Galambos, Csaba

2017-03-01

The histologic differential diagnosis of pediatric and adult round cell tumors is vast and includes the recently recognized entity CIC-DUX4 fusion-positive round cell tumor. The diagnosis of CIC-DUX4 tumor can be suggested by light microscopic and immunohistochemical features, but currently, definitive diagnosis requires ancillary genetic testing such as conventional karyotyping, fluorescence in situ hybridization, or molecular methods. We sought to determine whether DUX4 expression would serve as a fusion-specific immunohistochemical marker distinguishing CIC-DUX4 tumor from potential histologic mimics. A cohort of CIC-DUX4 fusion-positive round cell tumors harboring t(4;19)(q35;q13) and t(10;19)(q26;q13) translocations was designed, with additional inclusion of a case with a translocation confirmed to involve the CIC gene without delineation of the partner. Round cell tumors with potentially overlapping histologic features were also collected. Staining with a monoclonal antibody raised against the C-terminus of the DUX4 protein was applied to all cases. DUX4 immunohistochemistry exhibited diffuse, crisp, strong nuclear staining in all CIC-DUX4 fusion-positive round cell tumors (5/5, 100% sensitivity), and exhibited negative staining in nuclei of all of the other tested round cell tumors, including 20 Ewing sarcomas, 1 Ewing-like sarcoma, 11 alveolar rhabdomyosarcomas, 9 embryonal rhabdomyosarcomas, 12 synovial sarcomas, 7 desmoplastic small round cell tumors, 3 malignant rhabdoid tumors, 9 neuroblastomas, and 4 clear cell sarcomas (0/76, 100% specificity). Thus, in our experience, DUX4 immunostaining distinguishes CIC-DUX4 tumors from other round cell mimics. We recommend its use when CIC-DUX4 fusion-positive round cell tumor enters the histologic differential diagnosis.

Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images.

PubMed

Yang Li; Wei Liang; Yinlong Zhang; Haibo An; Jindong Tan

2016-08-01

Automatic and accurate lumbar vertebrae detection is an essential step of image-guided minimally invasive spine surgery (IG-MISS). However, traditional methods still require human intervention due to the similarity of vertebrae, abnormal pathological conditions and uncertain imaging angle. In this paper, we present a novel convolutional neural network (CNN) model to automatically detect lumbar vertebrae for C-arm X-ray images. Training data is augmented by DRR and automatic segmentation of ROI is able to reduce the computational complexity. Furthermore, a feature fusion deep learning (FFDL) model is introduced to combine two types of features of lumbar vertebrae X-ray images, which uses sobel kernel and Gabor kernel to obtain the contour and texture of lumbar vertebrae, respectively. Comprehensive qualitative and quantitative experiments demonstrate that our proposed model performs more accurate in abnormal cases with pathologies and surgical implants in multi-angle views.

Minimally invasive versus open fusion for Grade I degenerative lumbar spondylolisthesis: analysis of the Quality Outcomes Database.

PubMed

Mummaneni, Praveen V; Bisson, Erica F; Kerezoudis, Panagiotis; Glassman, Steven; Foley, Kevin; Slotkin, Jonathan R; Potts, Eric; Shaffrey, Mark; Shaffrey, Christopher I; Coric, Domagoj; Knightly, John; Park, Paul; Fu, Kai-Ming; Devin, Clinton J; Chotai, Silky; Chan, Andrew K; Virk, Michael; Asher, Anthony L; Bydon, Mohamad

2017-08-01

OBJECTIVE Lumbar spondylolisthesis is a degenerative condition that can be surgically treated with either open or minimally invasive decompression and instrumented fusion. Minimally invasive surgery (MIS) approaches may shorten recovery, reduce blood loss, and minimize soft-tissue damage with resultant reduced postoperative pain and disability. METHODS The authors queried the national, multicenter Quality Outcomes Database (QOD) registry for patients undergoing posterior lumbar fusion between July 2014 and December 2015 for Grade I degenerative spondylolisthesis. The authors recorded baseline and 12-month patient-reported outcomes (PROs), including Oswestry Disability Index (ODI), EQ-5D, numeric rating scale (NRS)-back pain (NRS-BP), NRS-leg pain (NRS-LP), and satisfaction (North American Spine Society satisfaction questionnaire). Multivariable regression models were fitted for hospital length of stay (LOS), 12-month PROs, and 90-day return to work, after adjusting for an array of preoperative and surgical variables. RESULTS A total of 345 patients (open surgery, n = 254; MIS, n = 91) from 11 participating sites were identified in the QOD. The follow-up rate at 12 months was 84% (83.5% [open surgery]; 85% [MIS]). Overall, baseline patient demographics, comorbidities, and clinical characteristics were similarly distributed between the cohorts. Two hundred fifty seven patients underwent 1-level fusion (open surgery, n = 181; MIS, n = 76), and 88 patients underwent 2-level fusion (open surgery, n = 73; MIS, n = 15). Patients in both groups reported significant improvement in all primary outcomes (all p < 0.001). MIS was associated with a significantly lower mean intraoperative estimated blood loss and slightly longer operative times in both 1- and 2-level fusion subgroups. Although the LOS was shorter for MIS 1-level cases, this was not significantly different. No difference was detected with regard to the 12-month PROs between the 1-level MIS versus the 1-level open surgical groups. However, change in functional outcome scores for patients undergoing 2-level fusion was notably larger in the MIS cohort for ODI (-27 vs -16, p = 0.1), EQ-5D (0.27 vs 0.15, p = 0.08), and NRS-BP (-3.5 vs -2.7, p = 0.41); statistical significance was shown only for changes in NRS-LP scores (-4.9 vs -2.8, p = 0.02). On risk-adjusted analysis for 1-level fusion, open versus minimally invasive approach was not significant for 12-month PROs, LOS, and 90-day return to work. CONCLUSIONS Significant improvement was found in terms of all functional outcomes in patients undergoing open or MIS fusion for lumbar spondylolisthesis. No difference was detected between the 2 techniques for 1-level fusion in terms of patient-reported outcomes, LOS, and 90-day return to work. However, patients undergoing 2-level MIS fusion reported significantly better improvement in NRS-LP at 12 months than patients undergoing 2-level open surgery. Longer follow-up is needed to provide further insight into the comparative effectiveness of the 2 procedures.

Emerging S-shaped curves in congenital scoliosis after hemivertebra resection and short segmental fusion.

PubMed

Yang, Xi; Song, Yueming; Liu, Limin; Zhou, Chunguang; Zhou, Zhongjie; Wang, Lei; Wang, Liang

2016-10-01

Posterior hemivertebra resection with short fusion has gradually become the mainstream treatment for the congenital scoliosis due to single fully segmented hemivertebra. A kind of unexpected emerging S-shaped scoliosis was found secondary to this surgery, and that has not been reported yet. The aim of the present study was to analyze the possible pathogenesis, clinical feature, and treatment of the emerging S-shaped scoliosis after posterior hemivertebra resection and short fusion. This study is a retrospective case series. A total of 128 patients participated. Preoperative and postoperative whole spine radiographs were used to measure the Cobb angle of main curve, compensatory curve, and emerging curves. And the hemivertebra location, the fused segment, the apical and ending vertebrae of postoperative-emerging curve (and preoperative compensatory curves) were assessed. Both the demographics and radiographic data were reviewed. Postoperative-emerging scoliosis was defined as the curve with an increasing angle of 20° and an apical vertebra locating at least two levels away from fusion region. Of the 128 patients, 9 (7%) showed postoperative-emerging S-shaped scoliosis. The mean age was 11.4 years old. The mean main curve was 36.1±14.4° preoperatively and been significantly corrected to 6.9±6.1° (p<.001). No significant difference was found in the main curve, kyphosis, coronal balance, or sagittal balance during follow-up. The emerging scoliosis occurred at 3 months (in four patients) or 6 months (in five patients) after initial surgery with an average angle of 42.6±12.9° at last follow-up. All patients underwent bracing or observation when the S-shaped scoliosis was arising, and four patients underwent a revision surgery because of deformity developing. The emerging S-shaped scoliosis was an extraordinary complication that may be developing from the preoperative compensatory scoliosis and usually occurred at 3-6 months after hemivertebra resection. The feature of these curves was similar to the adolescent idiopathic scoliosis (AIS) and brace or revision surgeries were suitable for therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

Processing LiDAR Data to Predict Natural Hazards

NASA Technical Reports Server (NTRS)

Fairweather, Ian; Crabtree, Robert; Hager, Stacey

2008-01-01

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method.

PubMed

Haddadpour, Mozhdeh; Daneshvar, Sabalan; Seyedarabi, Hadi

2017-08-01

The process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. We used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (D k ) as an assessing spectral features and Average Gradient (AG k ) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. In this paper we used three common evaluation metrics like Average Gradient (AG k ) and the lowest Discrepancy (D k ) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. Since that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AG k ), Discrepancy (D k ) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images. Copyright © 2017 Chang Gung University. Published by Elsevier B.V. All rights reserved.

Feature-Based Methods for Landmine Detection with Ground Penetrating Radar

DTIC Science & Technology

2012-09-27

of abstraction without having to resort to assumptions about the events. DS fusion was applied to handwriting recognition [67], decision making [68...has been applied to landmine detection [80], and (in a different way) to handwriting recognition [46], and fusion of social choices (voting...applications to handwriting recognition, IEEE Transactions on Systems, Man and Cybernetics 22 (3) (1992) 418–435. [68] M. Beynon, D. Cosker, A.D. Marshall

A new method for fusion, denoising and enhancement of x-ray images retrieved from Talbot-Lau grating interferometry.

PubMed

Scholkmann, Felix; Revol, Vincent; Kaufmann, Rolf; Baronowski, Heidrun; Kottler, Christian

2014-03-21

This paper introduces a new image denoising, fusion and enhancement framework for combining and optimal visualization of x-ray attenuation contrast (AC), differential phase contrast (DPC) and dark-field contrast (DFC) images retrieved from x-ray Talbot-Lau grating interferometry. The new image fusion framework comprises three steps: (i) denoising each input image (AC, DPC and DFC) through adaptive Wiener filtering, (ii) performing a two-step image fusion process based on the shift-invariant wavelet transform, i.e. first fusing the AC with the DPC image and then fusing the resulting image with the DFC image, and finally (iii) enhancing the fused image to obtain a final image using adaptive histogram equalization, adaptive sharpening and contrast optimization. Application examples are presented for two biological objects (a human tooth and a cherry) and the proposed method is compared to two recently published AC/DPC/DFC image processing techniques. In conclusion, the new framework for the processing of AC, DPC and DFC allows the most relevant features of all three images to be combined in one image while reducing the noise and enhancing adaptively the relevant image features. The newly developed framework may be used in technical and medical applications.

Ignition threshold for non-Maxwellian plasmas

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

Hay, Michael J., E-mail: hay@princeton.edu; Fisch, Nathaniel J.; Princeton Plasma Physics Laboratory, Princeton, New Jersey 08543

2015-11-15

An optically thin p-{sup 11}B plasma loses more energy to bremsstrahlung than it gains from fusion reactions, unless the ion temperature can be elevated above the electron temperature. In thermal plasmas, the temperature differences required are possible in small Coulomb logarithm regimes, characterized by high density and low temperature. Ignition could be reached more easily if the fusion reactivity can be improved with nonthermal ion distributions. To establish an upper bound for the potential utility of a nonthermal distribution, we consider a monoenergetic beam with particle energy selected to maximize the beam-thermal reactivity. Comparing deuterium-tritium (DT) and p-{sup 11}B, themore » minimum Lawson criteria and minimum ρR required for inertial confinement fusion (ICF) volume ignition are calculated with and without the nonthermal feature. It turns out that channeling fusion alpha energy to maintain such a beam facilitates ignition at lower densities and ρR, improves reactivity at constant pressure, and could be used to remove helium ash. On the other hand, the reactivity gains that could be realized in DT plasmas are significant, the excess electron density in p-{sup 11}B plasmas increases the recirculated power cost to maintain a nonthermal feature and thereby constrains its utility to ash removal.« less

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

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.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

NASA Astrophysics Data System (ADS)

Blasch, Erik

2015-06-01

Assessment of multi-intelligence fusion techniques includes credibility of algorithm performance, quality of results against mission needs, and usability in a work-domain context. Situation awareness (SAW) brings together low-level information fusion (tracking and identification), high-level information fusion (threat and scenario-based assessment), and information fusion level 5 user refinement (physical, cognitive, and information tasks). To measure SAW, we discuss the SAGAT (Situational Awareness Global Assessment Technique) technique for a multi-intelligence fusion (MIF) system assessment that focuses on the advantages of MIF against single intelligence sources. Building on the NASA TLX (Task Load Index), SAGAT probes, SART (Situational Awareness Rating Technique) questionnaires, and CDM (Critical Decision Method) decision points; we highlight these tools for use in a Multi-Intelligence Critical Rating Assessment of Fusion Techniques (MiCRAFT). The focus is to measure user refinement of a situation over the information fusion quality of service (QoS) metrics: timeliness, accuracy, confidence, workload (cost), and attention (throughput). A key component of any user analysis includes correlation, association, and summarization of data; so we also seek measures of product quality and QuEST of information. Building a notion of product quality from multi-intelligence tools is typically subjective which needs to be aligned with objective machine metrics.

Real-time acquisition and tracking system with multiple Kalman filters

NASA Astrophysics Data System (ADS)

Beard, Gary C.; McCarter, Timothy G.; Spodeck, Walter; Fletcher, James E.

1994-07-01

The design of a real-time, ground-based, infrared tracking system with proven field success in tracking boost vehicles through burnout is presented with emphasis on the software design. The system was originally developed to deliver relative angular positions during boost, and thrust termination time to a sensor fusion station in real-time. Autonomous target acquisition and angle-only tracking features were developed to ensure success under stressing conditions. A unique feature of the system is the incorporation of multiple copies of a Kalman filter tracking algorithm running in parallel in order to minimize run-time. The system is capable of updating the state vector for an object at measurement rates approaching 90 Hz. This paper will address the top-level software design, details of the algorithms employed, system performance history in the field, and possible future upgrades.

Enhanced bactericidal potency of nanoliposomes by modification of the fusion activity between liposomes and bacterium.

PubMed

Ma, Yufan; Wang, Zhao; Zhao, Wen; Lu, Tingli; Wang, Rutao; Mei, Qibing; Chen, Tao

2013-01-01

Pseudomonas aeruginosa represents a good model of antibiotic resistance. These organisms have an outer membrane with a low level of permeability to drugs that is often combined with multidrug efflux pumps, enzymatic inactivation of the drug, or alteration of its molecular target. The acute and growing problem of antibiotic resistance of Pseudomonas to conventional antibiotics made it imperative to develop new liposome formulations to overcome these mechanisms, and investigate the fusion between liposome and bacterium. The rigidity, stability and charge properties of phospholipid vesicles were modified by varying the cholesterol, 1,2-dioleoyl-sn-glycero-3-phosphatidylethanolamine (DOPE), and negatively charged lipids 1,2-dimyristoyl-sn-glycero-3-phosphoglycerol sodium salt (DMPG), 1,2-dimyristoyl-sn-glycero-3-phopho-L-serine sodium salt (DMPS), 1,2-dimyristoyl-sn-glycero-3-phosphate monosodium salt (DMPA), nature phosphatidylserine sodium salt from brain and nature phosphatidylinositol sodium salt from soybean concentrations in liposomes. Liposomal fusion with intact bacteria was monitored using a lipid-mixing assay. It was discovered that the fluid liposomes-bacterium fusion is not dependent on liposomal size and lamellarity. A similar degree of fusion was observed for liposomes with a particle size from 100 to 800 nm. The fluidity of liposomes is an essential pre-request for liposomes fusion with bacteria. Fusion was almost completely inhibited by incorporation of cholesterol into fluid liposomes. The increase in the amount of negative charges in fluid liposomes reduces fluid liposomes-bacteria fusion when tested without calcium cations due to electric repulsion, but addition of calcium cations brings the fusion level of fluid liposomes to similar or higher levels. Among the negative phospholipids examined, DMPA gave the highest degree of fusion, DMPS and DMPG had intermediate fusion levels, and PI resulted in the lowest degree of fusion. Furthermore, the fluid liposomal encapsulated tobramycin was prepared, and the bactericidal effect occurred more quickly when bacteria were cultured with liposomal encapsulated tobramycin. The bactericidal potency of fluid liposomes is dramatically enhanced with respect to fusion ability when the fusogenic lipid, DOPE, is included. Regardless of changes in liposome composition, fluid liposomes-bacterium fusion is universally enhanced by calcium ions. The information obtained in this study will increase our understanding of fluid liposomal action mechanisms, and help in optimizing the new generation of fluid liposomal formulations for the treatment of pulmonary bacterial infections.

Enhanced bactericidal potency of nanoliposomes by modification of the fusion activity between liposomes and bacterium

PubMed Central

Ma, Yufan; Wang, Zhao; Zhao, Wen; Lu, Tingli; Wang, Rutao; Mei, Qibing; Chen, Tao

2013-01-01

Background Pseudomonas aeruginosa represents a good model of antibiotic resistance. These organisms have an outer membrane with a low level of permeability to drugs that is often combined with multidrug efflux pumps, enzymatic inactivation of the drug, or alteration of its molecular target. The acute and growing problem of antibiotic resistance of Pseudomonas to conventional antibiotics made it imperative to develop new liposome formulations to overcome these mechanisms, and investigate the fusion between liposome and bacterium. Methods The rigidity, stability and charge properties of phospholipid vesicles were modified by varying the cholesterol, 1,2-dioleoyl-sn-glycero-3-phosphatidylethanolamine (DOPE), and negatively charged lipids 1,2-dimyristoyl-sn-glycero-3-phosphoglycerol sodium salt (DMPG), 1,2-dimyristoyl-sn-glycero-3-phopho-L-serine sodium salt (DMPS), 1,2-dimyristoyl-sn-glycero-3-phosphate monosodium salt (DMPA), nature phosphatidylserine sodium salt from brain and nature phosphatidylinositol sodium salt from soybean concentrations in liposomes. Liposomal fusion with intact bacteria was monitored using a lipid-mixing assay. Results It was discovered that the fluid liposomes-bacterium fusion is not dependent on liposomal size and lamellarity. A similar degree of fusion was observed for liposomes with a particle size from 100 to 800 nm. The fluidity of liposomes is an essential pre-request for liposomes fusion with bacteria. Fusion was almost completely inhibited by incorporation of cholesterol into fluid liposomes. The increase in the amount of negative charges in fluid liposomes reduces fluid liposomes-bacteria fusion when tested without calcium cations due to electric repulsion, but addition of calcium cations brings the fusion level of fluid liposomes to similar or higher levels. Among the negative phospholipids examined, DMPA gave the highest degree of fusion, DMPS and DMPG had intermediate fusion levels, and PI resulted in the lowest degree of fusion. Furthermore, the fluid liposomal encapsulated tobramycin was prepared, and the bactericidal effect occurred more quickly when bacteria were cultured with liposomal encapsulated tobramycin. Conclusion The bactericidal potency of fluid liposomes is dramatically enhanced with respect to fusion ability when the fusogenic lipid, DOPE, is included. Regardless of changes in liposome composition, fluid liposomes-bacterium fusion is universally enhanced by calcium ions. The information obtained in this study will increase our understanding of fluid liposomal action mechanisms, and help in optimizing the new generation of fluid liposomal formulations for the treatment of pulmonary bacterial infections. PMID:23847417

Pairwise diversity ranking of polychotomous features for ensemble physiological signal classifiers.

PubMed

Gupta, Lalit; Kota, Srinivas; Molfese, Dennis L; Vaidyanathan, Ravi

2013-06-01

It is well known that fusion classifiers for physiological signal classification with diverse components (classifiers or data sets) outperform those with less diverse components. Determining component diversity, therefore, is of the utmost importance in the design of fusion classifiers that are often employed in clinical diagnostic and numerous other pattern recognition problems. In this article, a new pairwise diversity-based ranking strategy is introduced to select a subset of ensemble components, which when combined will be more diverse than any other component subset of the same size. The strategy is unified in the sense that the components can be classifiers or data sets. Moreover, the classifiers and data sets can be polychotomous. Classifier-fusion and data-fusion systems are formulated based on the diversity-based selection strategy, and the application of the two fusion strategies are demonstrated through the classification of multichannel event-related potentials. It is observed that for both classifier and data fusion, the classification accuracy tends to increase/decrease when the diversity of the component ensemble increases/decreases. For the four sets of 14-channel event-related potentials considered, it is shown that data fusion outperforms classifier fusion. Furthermore, it is demonstrated that the combination of data components that yield the best performance, in a relative sense, can be determined through the diversity-based selection strategy.

Effect of particle pinch on the fusion performance and profile features of an international thermonuclear experimental reactor-like fusion reactor

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

Wang, Shijia, E-mail: wangsg@mail.ustc.edu.cn; Wang, Shaojie

2015-04-15

The evolution of the plasma temperature and density in an international thermonuclear experimental reactor (ITER)-like fusion device has been studied by numerically solving the energy transport equation coupled with the particle transport equation. The effect of particle pinch, which depends on the magnetic curvature and the safety factor, has been taken into account. The plasma is primarily heated by the alpha particles which are produced by the deuterium-tritium fusion reactions. A semi-empirical method, which adopts the ITERH-98P(y,2) scaling law, has been used to evaluate the transport coefficients. The fusion performances (the fusion energy gain factor, Q) similar to the ITERmore » inductive scenario and non-inductive scenario (with reversed magnetic shear) are obtained. It is shown that the particle pinch has significant effects on the fusion performance and profiles of a fusion reactor. When the volume-averaged density is fixed, particle pinch can lower the pedestal density by ∼30%, with the Q value and the central pressure almost unchanged. When the particle source or the pedestal density is fixed, the particle pinch can significantly enhance the Q value by  60%, with the central pressure also significantly raised.« less

Physiological and molecular triggers for SARS-CoV membrane fusion and entry into host cells.

PubMed

Millet, Jean Kaoru; Whittaker, Gary R

2018-04-01

During viral entry, enveloped viruses require the fusion of their lipid envelope with host cell membranes. For coronaviruses, this critical step is governed by the virally-encoded spike (S) protein, a class I viral fusion protein that has several unique features. Coronavirus entry is unusual in that it is often biphasic in nature, and can occur at or near the cell surface or in late endosomes. Recent advances in structural, biochemical and molecular biology of the coronavirus S protein has shed light on the intricacies of coronavirus entry, in particular the molecular triggers of coronavirus S-mediated membrane fusion. Furthermore, characterization of the coronavirus fusion peptide (FP), the segment of the fusion protein that inserts to a target lipid bilayer during membrane fusion, has revealed its particular attributes which imparts some of the unusual properties of the S protein, such as Ca 2+ -dependency. These unusual characteristics can explain at least in part the biphasic nature of coronavirus entry. In this review, using severe acute respiratory syndrome coronavirus (SARS-CoV) as model virus, we give an overview of advances in research on the coronavirus fusion peptide with an emphasis on its role and properties within the biological context of host cell entry. Copyright © 2017 Elsevier Inc. All rights reserved.

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

Comprehensive characterization of RSPO fusions in colorectal traditional serrated adenomas.

PubMed

Sekine, Shigeki; Ogawa, Reiko; Hashimoto, Taiki; Motohiro, Kojima; Yoshida, Hiroshi; Taniguchi, Hirokazu; Saito, Yutaka; Yasuhiro, Ohno; Ochiai, Atsushi; Hiraoka, Nobuyoshi

2017-10-01

Traditional serrated adenoma (TSA) is a rare but distinct type of colorectal polyp. Our previous study showed that PTPRK-RSPO3 fusions are frequent and characteristic genetic alterations in TSAs. This study aimed to characterize comprehensively the prevalence and variability of RSPO fusions in colorectal TSAs. We examined RSPO expression and explored novel RSPO fusions in 129 TSAs, including 66 lesions analysed previously for WNT pathway gene mutations. Quantitative polymerase chain reaction (qPCR) analyses identified three and 43 TSAs overexpressing RSPO2 and RSPO3, respectively, whereas the expression of RSPO1 and RSPO4 was marginal or undetectable in all cases. RSPO overexpression was always mutually exclusive with other WNT pathway gene mutations. Known PTPRK-RSPO3 fusions were detected in 37 TSAs, all but one of which overexpressed RSPO3. In addition, rapid amplification of cDNA ends revealed three novel RSPO fusion transcripts, an NRIP1-RSPO2 fusion and two PTPRK-RSPO3 fusion isoforms, in six TSAs. Overall, 43 TSAs had RSPO fusions (33%), whereas four TSAs (3%) overexpressed RSPO in the absence of RSPO fusions. TSAs with RSPO fusions showed several clinicopathological features, including distal localization (P = 0.0063), larger size (P = 0.0055), prominent ectopic crypt foci (P = 8.4 × 10 -4 ), association of a high-grade component (P = 1.1 × 10 -4 ), and the presence of KRAS mutations (P = 4.5 × 10 -5 ). The present study identified RSPO fusion transcripts, including three novel transcripts, in one-third of colorectal TSAs and showed that PTPRK-RSPO3 fusions were the predominant cause of RSPO overexpression in colorectal TSA. © 2017 John Wiley & Sons Ltd.

The cytoplasmic domain of the gamete membrane fusion protein HAP2 targets the protein to the fusion site in Chlamydomonas and regulates the fusion reaction.

PubMed

Liu, Yanjie; Pei, Jimin; Grishin, Nick; Snell, William J

2015-03-01

Cell-cell fusion between gametes is a defining step during development of eukaryotes, yet we know little about the cellular and molecular mechanisms of the gamete membrane fusion reaction. HAP2 is the sole gamete-specific protein in any system that is broadly conserved and shown by gene disruption to be essential for gamete fusion. The wide evolutionary distribution of HAP2 (also known as GCS1) indicates it was present in the last eukaryotic common ancestor and, therefore, dissecting its molecular properties should provide new insights into fundamental features of fertilization. HAP2 acts at a step after membrane adhesion, presumably directly in the merger of the lipid bilayers. Here, we use the unicellular alga Chlamydomonas to characterize contributions of key regions of HAP2 to protein location and function. We report that mutation of three strongly conserved residues in the ectodomain has no effect on targeting or fusion, although short deletions that include those residues block surface expression and fusion. Furthermore, HAP2 lacking a 237-residue segment of the cytoplasmic region is expressed at the cell surface, but fails to localize at the apical membrane patch specialized for fusion and fails to rescue fusion. Finally, we provide evidence that the ancient HAP2 contained a juxta-membrane, multi-cysteine motif in its cytoplasmic region, and that mutation of a cysteine dyad in this motif preserves protein localization, but substantially impairs HAP2 fusion activity. Thus, the ectodomain of HAP2 is essential for its surface expression, and the cytoplasmic region targets HAP2 to the site of fusion and regulates the fusion reaction. © 2015. Published by The Company of Biologists Ltd.

A dual small-molecule rheostat for precise control of protein concentration in Mammalian cells.

PubMed

Lin, Yu Hsuan; Pratt, Matthew R

2014-04-14

One of the most successful strategies for controlling protein concentrations in living cells relies on protein destabilization domains (DD). Under normal conditions, a DD will be rapidly degraded by the proteasome. However, the same DD can be stabilized or "shielded" in a stoichiometric complex with a small molecule, enabling dose-dependent control of its concentration. This process has been exploited by several labs to post-translationally control the expression levels of proteins in vitro as well as in vivo, although the previous technologies resulted in permanent fusion of the protein of interest to the DD, which can affect biological activity and complicate results. We previously reported a complementary strategy, termed traceless shielding (TShld), in which the protein of interest is released in its native form. Here, we describe an optimized protein concentration control system, TTShld, which retains the traceless features of TShld but utilizes two tiers of small molecule control to set protein concentrations in living cells. These experiments provide the first protein concentration control system that results in both a wide range of protein concentrations and proteins free from engineered fusion constructs. The TTShld system has a greatly improved dynamic range compared to our previously reported system, and the traceless feature is attractive for elucidation of the consequences of protein concentration in cell biology. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prediction of crime occurrence from multi-modal data using deep learning

PubMed Central

Kang, Hyeon-Woo

2017-01-01

In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models. PMID:28437486

Prediction of crime occurrence from multi-modal data using deep learning.

PubMed

Kang, Hyeon-Woo; Kang, Hang-Bong

2017-01-01

In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models.

URREF Reliability Versus Credibility in Information Fusion

DTIC Science & Technology

2013-07-01

Fusion, Vol. 3, No. 2, December, 2008. [31] E. Blasch, J. Dezert, and P. Valin , “DSMT Applied to Seismic and Acoustic Sensor Fusion,” Proc. IEEE Nat...44] E. Blasch, P. Valin , E. Bossé, “Measures of Effectiveness for High- Level Fusion,” Int. Conference on Information Fusion, 2010. [45] X. Mei, H...and P. Valin , “Information Fusion Measures of Effectiveness (MOE) for Decision Support,” Proc. SPIE 8050, 2011. [49] Y. Zheng, W. Dong, and E

Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems

NASA Astrophysics Data System (ADS)

Zein-Sabatto, Saleh; Mikhail, Maged; Bodruzzaman, Mohammad; DeSimio, Martin; Derriso, Mark; Behbahani, Alireza

2012-06-01

It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers; however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties; randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes, implementation results of the developed fusion algorithms on structural health monitoring data collected from experimental tests are reported in this paper.

Is the behavior of disc replacement adjacent to fusion affected by the location of the fused level in hybrid surgery?

PubMed

Wu, Ting-Kui; Meng, Yang; Wang, Bei-Yu; Hong, Ying; Rong, Xin; Ding, Chen; Chen, Hua; Liu, Hao

2018-04-27

Hybrid surgery (HS), consisting of cervical disc arthroplasty (CDA) at the mobile level, along with anterior cervical discectomy and fusion at the spondylotic level, could be a promising treatment for patients with multilevel cervical degenerative disc disease (DDD). An advantage of this technique is that it uses an optimal procedure according to the status of each level. However, information is lacking regarding the influence of the relative location of the replacement and the fusion segment in vivo. We conducted the present study to investigate whether the location of the fusion affected the behavior of the disc replacement and adjacent segments in HS in vivo. This is an observational study. The numbers of patients in the arthroplasty-fusion (AF) and fusion-arthroplasty (FA) groups were 51 and 24, respectively. The Japanese Orthopedic Association (JOA), Neck Disability Index (NDI), and Visual Analog Scale (VAS) scores were evaluated. Global and segmental lordosis, the range of motion (ROM) of C2-C7, and the operated and adjacent segments were measured. Fusion rate and radiological changes at adjacent levels were observed. Between January 2010 and July 2016, 75 patients with cervical DDD at two contiguous levels undergoing a two-level HS were retrospectively reviewed. The patients were divided into AF and FA groups according to the locations of the disc replacement. Clinical outcomes were evaluated according to the JOA, NDI, and VAS scores. Radiological parameters, including global and segmental lordosis, the ROM of C2-C7, the operated and adjacent segments, and complications, were also evaluated. Although the JOA, NDI, and VAS scores were improved in both the AF and the FA groups, no significant differences were found between the two groups at any follow-up point. Both groups maintained cervical lordosis, but no difference was found between the groups. Segmental lordosis at the fusion segment was significantly improved postoperatively (p<.001), whereas it was maintained at the arthroplasty segment. The ROM of C2-C7 was significantly decreased in both groups postoperatively (AF p=.001, FA p=.014), but no difference was found between the groups. The FA group exhibited a non-significant improvement in ROM at the arthroplasty segment. The ROM adjacent to the arthroplasty segment was increased, although not significantly, whereas the ROM adjacent to the fusion segment was significantly improved after surgery in both groups (p<.001). Fusion was achieved in all patients. No significant difference in complications was found between the groups. In HS, cephalic or caudal fusion segments to the arthroplasty segment did not affect the clinical outcomes and the behavior of CDA. However, the ROM of adjacent segments was affected by the location of the fusion segment; segments adjacent to fusion segments had greater ROMs than segments adjacent to arthroplasty segments. Copyright © 2018 Elsevier Inc. All rights reserved.

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

Induction of Cell-Cell Fusion by Ebola Virus Glycoprotein: Low pH Is Not a Trigger.

PubMed

Markosyan, Ruben M; Miao, Chunhui; Zheng, Yi-Min; Melikyan, Gregory B; Liu, Shan-Lu; Cohen, Fredric S

2016-01-01

Ebola virus (EBOV) is a highly pathogenic filovirus that causes hemorrhagic fever in humans and animals. Currently, how EBOV fuses its envelope membrane within an endosomal membrane to cause infection is poorly understood. We successfully measure cell-cell fusion mediated by the EBOV fusion protein, GP, assayed by the transfer of both cytoplasmic and membrane dyes. A small molecule fusion inhibitor, a neutralizing antibody, as well as mutations in EBOV GP known to reduce viral infection, all greatly reduce fusion. By monitoring redistribution of small aqueous dyes between cells and by electrical capacitance measurements, we discovered that EBOV GP-mediated fusion pores do not readily enlarge-a marked difference from the behavior of other viral fusion proteins. EBOV GP must be cleaved by late endosome-resident cathepsins B or L in order to become fusion-competent. Cleavage of cell surface-expressed GP appears to occur in endosomes, as evidenced by the fusion block imposed by cathepsin inhibitors, agents that raise endosomal pH, or an inhibitor of anterograde trafficking. Treating effector cells with a recombinant soluble cathepsin B or thermolysin, which cleaves GP into an active form, increases the extent of fusion, suggesting that a fraction of surface-expressed GP is not cleaved. Whereas the rate of fusion is increased by a brief exposure to acidic pH, fusion does occur at neutral pH. Importantly, the extent of fusion is independent of external pH in experiments in which cathepsin activity is blocked and EBOV GP is cleaved by thermolysin. These results imply that low pH promotes fusion through the well-known pH-dependent activity of cathepsins; fusion induced by cleaved EBOV GP is a process that is fundamentally independent of pH. The cell-cell fusion system has revealed some previously unappreciated features of EBOV entry, which could not be readily elucidated in the context of endosomal entry.

Induction of Cell-Cell Fusion by Ebola Virus Glycoprotein: Low pH Is Not a Trigger

PubMed Central

Zheng, Yi-Min; Melikyan, Gregory B.; Liu, Shan-Lu; Cohen, Fredric S.

2016-01-01

Ebola virus (EBOV) is a highly pathogenic filovirus that causes hemorrhagic fever in humans and animals. Currently, how EBOV fuses its envelope membrane within an endosomal membrane to cause infection is poorly understood. We successfully measure cell-cell fusion mediated by the EBOV fusion protein, GP, assayed by the transfer of both cytoplasmic and membrane dyes. A small molecule fusion inhibitor, a neutralizing antibody, as well as mutations in EBOV GP known to reduce viral infection, all greatly reduce fusion. By monitoring redistribution of small aqueous dyes between cells and by electrical capacitance measurements, we discovered that EBOV GP-mediated fusion pores do not readily enlarge—a marked difference from the behavior of other viral fusion proteins. EBOV GP must be cleaved by late endosome-resident cathepsins B or L in order to become fusion-competent. Cleavage of cell surface-expressed GP appears to occur in endosomes, as evidenced by the fusion block imposed by cathepsin inhibitors, agents that raise endosomal pH, or an inhibitor of anterograde trafficking. Treating effector cells with a recombinant soluble cathepsin B or thermolysin, which cleaves GP into an active form, increases the extent of fusion, suggesting that a fraction of surface-expressed GP is not cleaved. Whereas the rate of fusion is increased by a brief exposure to acidic pH, fusion does occur at neutral pH. Importantly, the extent of fusion is independent of external pH in experiments in which cathepsin activity is blocked and EBOV GP is cleaved by thermolysin. These results imply that low pH promotes fusion through the well-known pH-dependent activity of cathepsins; fusion induced by cleaved EBOV GP is a process that is fundamentally independent of pH. The cell-cell fusion system has revealed some previously unappreciated features of EBOV entry, which could not be readily elucidated in the context of endosomal entry. PMID:26730950

Mitochondrial fusion increases the mitochondrial DNA copy number in budding yeast.

PubMed

Hori, Akiko; Yoshida, Minoru; Ling, Feng

2011-05-01

Mitochondrial fusion plays an important role in mitochondrial DNA (mtDNA) maintenance, although the underlying mechanisms are unclear. In budding yeast, certain levels of reactive oxygen species (ROS) can promote recombination-mediated mtDNA replication, and mtDNA maintenance depends on the homologous DNA pairing protein Mhr1. Here, we show that the fusion of isolated yeast mitochondria, which can be monitored by the bimolecular fluorescence complementation-derived green fluorescent protein (GFP) fluorescence, increases the mtDNA copy number in a manner dependent on Mhr1. The fusion event, accompanied by the degradation of dissociated electron transport chain complex IV and transient reductions in the complex IV subunits by the inner membrane AAA proteases such as Yme1, increases ROS levels. Analysis of the initial stage of mitochondrial fusion in early log-phase cells produced similar results. Moreover, higher ROS levels in mitochondrial fusion-deficient mutant cells increased the amount of newly synthesized mtDNA, resulting in increases in the mtDNA copy number. In contrast, reducing ROS levels in yme1 null mutant cells significantly decreased the mtDNA copy number, leading to an increase in cells lacking mtDNA. Our results indicate that mitochondrial fusion induces mtDNA synthesis by facilitating ROS-triggered, recombination-mediated replication and thereby prevents the generation of mitochondria lacking DNA. © 2011 The Authors. Journal compilation © 2011 by the Molecular Biology Society of Japan/Blackwell Publishing Ltd.

Reoperation After Cervical Disc Arthroplasty Versus Anterior Cervical Discectomy and Fusion: A Meta-analysis.

PubMed

Zhong, Zhao-Ming; Zhu, Shi-Yuan; Zhuang, Jing-Shen; Wu, Qian; Chen, Jian-Ting

2016-05-01

Anterior cervical discectomy and fusion is a standard surgical treatment for cervical radiculopathy and myelopathy, but reoperations sometimes are performed to treat complications of fusion such as pseudarthrosis and adjacent-segment degeneration. A cervical disc arthroplasty is designed to preserve motion and avoid the shortcomings of fusion. Available evidence suggests that a cervical disc arthroplasty can provide pain relief and functional improvements similar or superior to an anterior cervical discectomy and fusion. However, there is controversy regarding whether a cervical disc arthroplasty can reduce the frequency of reoperations. We performed a meta-analysis of randomized controlled trials (RCTs) to compare cervical disc arthroplasty with anterior cervical discectomy and fusion regarding (1) the overall frequency of reoperation at the index and adjacent levels; (2) the frequency of reoperation at the index level; and (3) the frequency of reoperation at the adjacent levels. PubMed, EMBASE, and the Cochrane Register of Controlled Trials databases were searched to identify RCTs comparing cervical disc arthroplasty with anterior cervical discectomy and fusion and reporting the frequency of reoperation. We also manually searched the reference lists of articles and reviews for possible relevant studies. Twelve RCTs with a total of 3234 randomized patients were included. Eight types of disc prostheses were used in the included studies. In the anterior cervical discectomy and fusion group, autograft was used in one study and allograft in 11 studies. Nine of 12 studies were industry sponsored. Pooled risk ratio (RR) and associated 95% CI were calculated for the frequency of reoperation using random-effects or fixed-effects models depending on the heterogeneity of the included studies. A funnel plot suggested the possible presence of publication bias in the available pool of studies; that is, the shape of the plot suggests that smaller negative or no-difference studies may have been performed but have not been published, and so were not identified and included in this meta-analysis. The overall frequency of reoperation at the index and adjacent levels was lower in the cervical disc arthroplasty group (6%; 108/1762) than in the anterior cervical discectomy and fusion group (12%; 171/1472) (RR, 0.54; 95% CI, 0.36-0.80; p = 0.002). Subgroup analyses were performed according to secondary surgical level. Compared with anterior cervical discectomy and fusion, cervical disc arthroplasty was associated with fewer reoperations at the index level (RR, 0.50; 95% CI, 0.37-0.68; p < 0.001) and adjacent levels (RR, 0.52; 95% CI, 0.37-0.74; p < 0.001). Cervical disc arthroplasty is associated with fewer reoperations than anterior cervical discectomy and fusion, indicating that it is a safe and effective alternative to fusion for cervical radiculopathy and myelopathy. However, because of some limitations, these findings should be interpreted with caution. Additional studies are needed. Level I, therapeutic study.

Halofuginone inhibits Smad3 phosphorylation via the PI3K/Akt and MAPK/ERK pathways in muscle cells: Effect on myotube fusion

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

Roffe, Suzy; Hagai, Yosey; Institute of Animal Sciences, Volcani Center, Bet Dagan 50250

2010-04-01

Halofuginone, a novel inhibitor of Smad3 phosphorylation, has been shown to inhibit muscle fibrosis and to improve cardiac and skeletal muscle functions in the mdx mouse model of Duchenne muscular dystrophy. Here, we demonstrate that halofuginone promotes the phosphorylation of Akt and mitogen-activated protein kinase (MAPK) family members in a C2 muscle cell line and in primary myoblasts derived from wild-type and mdx mice diaphragms. Halofuginone enhanced the association of phosphorylated Akt and MAPK/extracellular signal-regulated protein kinase (ERK) with the non-phosphorylated form of Smad3, accompanied by a reduction in Smad3 phosphorylation levels. This reduction was reversed by inhibitors of themore » phosphoinositide 3'-kinase/Akt (PI3K/Akt) and MAPK/ERK pathways, suggesting their specific role in mediating halofuginone's inhibitory effect on Smad3 phosphorylation. Halofuginone enhanced Akt, MAPK/ERK and p38 MAPK phosphorylation and inhibited Smad3 phosphorylation in myotubes, all of which are crucial for myotube fusion. In addition, halofuginone increased the association Akt and MAPK/ERK with Smad3. As a consequence, halofuginone promoted myotube fusion, as reflected by an increased percentage of C2 and mdx myotubes containing high numbers of nuclei, and this was reversed by specific inhibitors of the PI3K and MAPK/ERK pathways. Together, the data suggest a role, either direct or via inhibition of Smad3 phosphorylation, for Akt or MAPK/ERK in halofuginone-enhanced myotube fusion, a feature which is crucial to improving muscle function in muscular dystrophies.« less

Mammalian target of rapamycin (mTOR) signaling is required for a late-stage fusion process during skeletal myotube maturation.

PubMed

Park, In-Hyun; Chen, Jie

2005-09-09

Skeletal myogenesis is a well orchestrated cascade of events regulated by multiple signaling pathways, one of which is recently characterized by its sensitivity to the bacterial macrolide rapamycin. Previously we reported that the mammalian target of rapamycin (mTOR) regulates the initiation of the differentiation program in mouse C2C12 myoblasts by controlling the expression of insulin-like growth factor-II in a kinase-independent manner. Here we provide experimental evidence suggesting that a different mode of mTOR signaling regulates skeletal myogenesis at a later stage. In the absence of endogenous mTOR function in C2C12 cells treated with rapamycin, a kinase-inactive mTOR fully supports myogenin expression, but causes a delay in contractile protein expression. Myoblasts fuse to form nascent myotubes in the absence of kinase-active mTOR, whereas the formation of mature myotubes by further fusion requires the catalytic activity of mTOR. Therefore, the two stages of myocyte fusion are molecularly separable at the level of mTOR signaling. In addition, our data suggest that a factor secreted into the culture medium is responsible for mediating the function of mTOR in regulating the late-stage fusion leading to mature myotubes. Furthermore, taking advantage of the unique features of cells stably expressing a mutant mTOR, we have performed cDNA microarray analysis to compare global gene expression profiles between mature and nascent myotubes, the results of which have implicated classes of genes and revealed candidate regulators in myotube maturation or functions of mature myotubes.

European DEMO design strategy and consequences for materials

NASA Astrophysics Data System (ADS)

Federici, G.; Biel, W.; Gilbert, M. R.; Kemp, R.; Taylor, N.; Wenninger, R.

2017-09-01

Demonstrating the production of net electricity and operating with a closed fuel-cycle remain unarguably the crucial steps towards the exploitation of fusion power. These are the aims of a demonstration fusion reactor (DEMO) proposed to be built after ITER. This paper briefly describes the DEMO design options that are being considered in Europe for the current conceptual design studies as part of the Roadmap to Fusion Electricity Horizon 2020. These are not intended to represent fixed and exclusive design choices but rather ‘proxies’ of possible plant design options to be used to identify generic design/material issues that need to be resolved in future fusion reactor systems. The materials nuclear design requirements and the effects of radiation damage are briefly analysed with emphasis on a pulsed ‘low extrapolation’ system, which is being used for the initial design integration studies, based as far as possible on mature technologies and reliable regimes of operation (to be extrapolated from the ITER experience), and on the use of materials suitable for the expected level of neutron fluence. The main technical issues arising from the plasma and nuclear loads and the effects of radiation damage particularly on the structural and heat sink materials of the vessel and in-vessel components are critically discussed. The need to establish realistic target performance and a development schedule for near-term electricity production tends to favour more conservative technology choices. The readiness of the technical (physics and technology) assumptions that are being made is expected to be an important factor for the selection of the technical features of the device.

Semantic Context Detection Using Audio Event Fusion

NASA Astrophysics Data System (ADS)

Chu, Wei-Ta; Cheng, Wen-Huang; Wu, Ja-Ling

2006-12-01

Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model) and discriminative (support vector machine (SVM)) approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.

Affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness

NASA Astrophysics Data System (ADS)

Bowman, Christopher; Haith, Gary; Steinberg, Alan; Morefield, Charles; Morefield, Michael

2013-05-01

This paper describes methods to affordably improve the robustness of distributed fusion systems by opportunistically leveraging non-traditional data sources. Adaptive methods help find relevant data, create models, and characterize the model quality. These methods also can measure the conformity of this non-traditional data with fusion system products including situation modeling and mission impact prediction. Non-traditional data can improve the quantity, quality, availability, timeliness, and diversity of the baseline fusion system sources and therefore can improve prediction and estimation accuracy and robustness at all levels of fusion. Techniques are described that automatically learn to characterize and search non-traditional contextual data to enable operators integrate the data with the high-level fusion systems and ontologies. These techniques apply the extension of the Data Fusion & Resource Management Dual Node Network (DNN) technical architecture at Level 4. The DNN architecture supports effectively assessment and management of the expanded portfolio of data sources, entities of interest, models, and algorithms including data pattern discovery and context conformity. Affordable model-driven and data-driven data mining methods to discover unknown models from non-traditional and `big data' sources are used to automatically learn entity behaviors and correlations with fusion products, [14 and 15]. This paper describes our context assessment software development, and the demonstration of context assessment of non-traditional data to compare to an intelligence surveillance and reconnaissance fusion product based upon an IED POIs workflow.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

Kinematics of a selectively constrained radiolucent anterior lumbar disc: comparisons to hybrid and circumferential fusion.

PubMed

Daftari, Tapan K; Chinthakunta, Suresh R; Ingalhalikar, Aditya; Gudipally, Manasa; Hussain, Mir; Khalil, Saif

2012-10-01

Despite encouraging clinical outcomes of one-level total disc replacements reported in literature, there is no compelling evidence regarding the stability following two-level disc replacement and hybrid constructs. The current study is aimed at evaluating the multidirectional kinematics of a two-level disc arthroplasty and hybrid construct with disc replacement adjacent to rigid circumferential fusion, compared to two-level fusion using a novel selectively constrained radiolucent anterior lumbar disc. Nine osteoligamentous lumbosacral spines (L1-S1) were tested in the following sequence: 1) Intact; 2) One-level disc replacement; 3) Hybrid; 4) Two-level disc replacement; and 5) Two-level fusion. Range of motion (at both implanted and adjacent level), and center of rotation in sagittal plane were recorded and calculated. At the level of implantation, motion was restored when one-level disc replacement was used but tended to decrease with two-level disc arthroplasty. The findings also revealed that both one-level and two-level disc replacement and hybrid constructs did not significantly change adjacent level kinematics compared to the intact condition, whereas the two-level fusion construct demonstrated a significant increase in flexibility at the adjacent level. The location of center of rotation in the sagittal plane at L4-L5 for the one-level disc replacement construct was similar to that of the intact condition. The one-level disc arthroplasty tended to mimic a motion profile similar to the intact spine. However, the two-level disc replacement construct tended to reduce motion and clinical stability of a two-level disc arthroplasty requires additional investigation. Hybrid constructs may be used as a surgical alternative for treating two-level lumbar degenerative disc disease. Published by Elsevier Ltd.

Comparison between free-hand and O-arm-based navigated posterior lumbar interbody fusion in elderly cohorts with three-level lumbar degenerative disease.

PubMed

Wang, Yucheng; Chen, Kangwu; Chen, Hao; Zhang, Kai; Lu, Jian; Mao, Haiqing; Yang, Huilin

2018-06-06

This retrospective cohort study aims to evaluate the effects of introducing the O-arm-based navigation technique into the traditional posterior lumbar interbody fusion (PLIF) procedure treating elderly patients with three-level lumbar degenerative diseases. Forty-one consecutive elderly patients were enrolled according to the criteria. There were 21 patients in the free-hand group and 20 patients in the O-arm group. Both two groups underwent the PLIF with or without the O-arm-based navigation technique. The demographic features, clinical data and outcomes, and radiological information were collected for further analysis. The average follow-up time was 18.3 (range, 12-28) months in the free-hand group and 16.7 (range, 12-24) months in the O-arm group. Comparison between two groups revealed no significant difference regarding demographic features. The operation time took in the navigation group was significantly less than that in the free-hand group (222.55 ± 38.00 mins versus 255.19 ± 40.26 mins, P < 0.05). Both VAS and ODI were improved post-operatively in two groups while comparison between groups showed no difference. The accuracy rate of pedicle screw positioning was 88.7% in the free-hand group to 96.9% in the O-arm group (P < 0.05). The O-arm-based navigation is an efficacious auxiliary technique which could significantly improve the accuracy of pedicle screw insertion, especially in cases of patients with complex anatomic degenerative diseases, without sacrificing the feasibility and reliable outcome of traditional PLIF.

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

PubMed

Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K

2018-06-01

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Outcomes of Posterolateral Fusion with and without Instrumentation and of Interbody Fusion for Isthmic Spondylolisthesis: A Prospective Study.

PubMed

Endler, Peter; Ekman, Per; Möller, Hans; Gerdhem, Paul

2017-05-03

Various methods for the treatment of isthmic spondylolisthesis are available. The aim of this study was to compare outcomes after posterolateral fusion without instrumentation, posterolateral fusion with instrumentation, and interbody fusion. The Swedish Spine Register was used to identify 765 patients who had been operated on for isthmic spondylolisthesis and had at least preoperative and 2-year outcome data; 586 of them had longer follow-up (a mean of 6.9 years). The outcome measures were a global assessment of leg and back pain, the Oswestry Disability Index (ODI), the EuroQol-5 Dimensions (EQ-5D) Questionnaire, the Short Form-36 (SF-36), a visual analog scale (VAS) for back and leg pain, and satisfaction with treatment. Data on additional lumbar spine surgery was searched for in the register, with the mean duration of follow-up for this variable being 10.6 years after the index procedure. Statistical analyses were performed with analysis of covariance or competing-risks proportional hazards regression, adjusted for baseline differences in the studied variables, smoking, employment status, and level of fusion. Posterolateral fusion without instrumentation was performed in 102 patients; posterolateral fusion with instrumentation, in 452; and interbody fusion, in 211. At 1 year, improvement was reported in the global assessment for back pain by 54% of the patients who had posterolateral fusion without instrumentation, 68% of those treated with posterolateral fusion with instrumentation, and 70% of those treated with interbody fusion (p = 0.009). The VAS for back pain and reported satisfaction with treatment showed similar patterns (p = 0.003 and p = 0.017, respectively), whereas other outcomes did not differ among the treatment groups at 1 year. At 2 years, the global assessment for back pain indicated improvement in 57% of the patients who had undergone posterolateral fusion without instrumentation, 70% of those who had posterolateral fusion with instrumentation, and 71% of those treated with interbody fusion (p = 0.022). There were no significant outcome differences at the mean 6.9-year follow-up interval. There was an increased hazard ratio for additional lumbar spine surgery after interbody fusion (4.34; 95% confidence interval [CI] = 1.71 to 11.03) and posterolateral fusion with instrumentation (2.56; 95% CI = 1.02 to 6.42) compared with after posterolateral fusion without instrumentation (1.00; reference). Fusion with instrumentation, with or without interbody fusion, was associated with more improvement in back pain scores and higher satisfaction with treatment compared with fusion without instrumentation at 1 year, but the difference was attenuated with longer follow-up. Fusion with instrumentation was associated with a significantly higher risk of additional spine surgery. Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

NASA Astrophysics Data System (ADS)

He, Tongdi; Che, Zongxi

2018-01-01

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

Method of constructing a microwave antenna

NASA Technical Reports Server (NTRS)

Ngo, Phong (Inventor); Arndt, G. Dickey (Inventor); Carl, James (Inventor)

2003-01-01

A method, simulation, and apparatus are provided that are highly suitable for treatment of benign prostatic hyperplasia (BPH). A catheter is disclosed that includes a small diameter disk loaded monopole antenna surrounded by fusion material having a high heat of fusion and a melting point preferably at or near body temperature. Microwaves from the antenna heat prostatic tissue to promote necrosing of the prostatic tissue that relieves the pressure of the prostatic tissue against the urethra as the body reabsorbs the necrosed or dead tissue. The fusion material keeps the urethra cool by means of the heat of fusion of the fusion material. This prevents damage to the urethra while the prostatic tissue is necrosed. A computer simulation is provided that can be used to predict the resulting temperature profile produced in the prostatic tissue. By changing the various control features of the catheter and method of applying microwave energy a temperature profile can be predicted and produced that is similar to the temperature profile desired for the particular patient.

Method of Constructing a Microwave Antenna

NASA Technical Reports Server (NTRS)

Arndt, G. Dickey (Inventor); Carl, James (Inventor); Ngo, Phong (Inventor)

2003-01-01

A method, simulation, and apparatus are provided that are highly suitable for treatment of benign prostatic hyperplasia (BPH). A catheter is disclosed that includes a small diameter disk loaded monopole antenna surrounded by fusion material having a high heat of fusion and a melting point preferably at or near body temperature. Microwaves from the antenna heat prostatic tissue to promote necrosing of the prostatic tissue that relieves the pressure of the prostatic tissue against the urethra as the body reabsorbs the necrosed or dead tissue. The fusion material keeps the urethra cool by means of the heat of fusion of the fusion material. This prevents damage to the urethra while the prostatic tissue is necrosed. A computer simulation is provided that can be used to predict the resulting temperature profile produced in the prostatic tissue. By changing the various control features of the catheter and method of applying microwave energy a temperature profile can be predicted and produced that is similar to the temperature profile desired for the particular patient.

Method for selective thermal ablation

NASA Technical Reports Server (NTRS)

Ngo, Phong (Inventor); Arndt, G. Dickey (Inventor); Raffoul, George W. (Inventor); Carl, James (Inventor)

2003-01-01

A method, simulation, and apparatus are provided that are highly suitable for treatment of benign prostatic hyperplasia (BPH). A catheter is disclosed that includes a small diameter disk loaded monopole antenna surrounded by fusion material having a high heat of fusion and a melting point preferably at or near body temperature. Microwaves from the antenna heat prostatic tissue to promote necrosing of the prostatic tissue that relieves the pressure of the prostatic tissue against the urethra as the body reabsorbs the necrosed or dead tissue. The fusion material keeps the urethra cool by means of the heat of fusion of the fusion material. This prevents damage to the urethra while the prostatic tissue is necrosed. A computer simulation is provided that can be used to predict the resulting temperature profile produced in the prostatic tissue. By changing the various control features of the catheter and method of applying microwave energy a temperature profile can be predicted and produced that is similar to the temperature profile desired for the particular patient.

Method for Selective Thermal Ablation

NASA Technical Reports Server (NTRS)

Arndt, G. Dickey (Inventor); Carl, James (Inventor); Ngo, Phong (Inventor); Raffoul, George W. (Inventor)

2003-01-01

A method, simulation, and apparatus are provided that are highly suitable for treatment of benign prostatic hyperplasia (BPH). A catheter is disclosed that includes a small diameter disk loaded monopole antenna surrounded by fusion material having a high heat of fusion and a melting point preferably at or near body temperature. Microwaves from the antenna heat prostatic tissue to promote necrosing of the prostatic tissue that relieves the pressure of the prostatic tissue against the urethra as the body reabsorbs the necrosed or dead tissue. The fusion material keeps the urethra cool by means of the heat of fusion of the fusion material. This prevents damage to the urethra while the prostatic tissue is necrosed. A computer simulation is provided that can be used to predict the resulting temperature profile produced in the prostatic tissue. By changing the various control features of the catheter and method of applying microwave energy a temperature profile can be predicted and produced that is similar to the temperature profile desired for the particular patient.

Effective Multifocus Image Fusion Based on HVS and BP Neural Network

PubMed Central

Yang, Yong

2014-01-01

The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations. PMID:24683327

The next large helical devices

NASA Astrophysics Data System (ADS)

Iiyoshi, Atsuo; Yamazaki, Kozo

1995-06-01

Helical systems have the strong advantage of inherent steady-state operation for fusion reactors. Two large helical devices with fully superconducting coil systems are presently under design and construction. One is the LHD (Large Helical Device) [Fusion Technol. 17, 169 (1990)] with major radius=3.9 m and magnetic field=3-4 T, that is under construction during 1990-1997 at NIFS (National Institute for Fusion Science), Nagoya/Toki, Japan; it features continuous helical coils and a clean helical divertor focusing on edge configuration optimization. The other one in the W7-X (Wendelstein 7-X) [in Plasma Physics and Controlled Fusion Nuclear Research, 1990, (International Atomic Energy Agency, Vienna, 1991), Vol. 3, p. 525] with major radius=5.5 m and magnetic field=3 T, that is under review at IPP (Max-Planck Institute for Plasma Physics), Garching, Germany; it has adopted a modular coil system after elaborate optimization studies. These two programs are complementary in promoting world helical fusion research and in extending the understanding of toroidal plasmas through comparisons with large tokamaks.

Transcatheter Microwave Antenna

NASA Technical Reports Server (NTRS)

Arndt, Dickey G. (Inventor); Carl, James R. (Inventor); Ngo, Phong (Inventor); Raffoul, George W. (Inventor)

2001-01-01

A method, simulation, and apparatus are provided that are highly suitable for treatment of benign prostatic hyperplasia (BPH). A catheter is disclosed that includes a small diameter disk loaded monopole antenna surrounded by fusion material having a high heat of fusion and a melting point preferably at or near body temperature. Microwaves from the antenna heat prostatic tissue to promote necrosing of the prostatic tissue that relieves the pressure of the prostatic tissue against the urethra as the body reabsorbs the necrosed or dead tissue. The fusion material keeps the urethra cool by means of the heat of fusion of the fusion material. This prevents damage to the urethra while the prostatic tissue is necrosed. A computer simulation is provided that can be used to predict the resulting temperature profile produced in the prostatic tissue. By changing the various control features of the catheter and method of applying microwave energy a temperature profile can be predicted and produced that is similar to the temperature profile desired for the particular patient.

The biomechanical stability of a novel spacer with integrated plate in contiguous two-level and three-level ACDF models: an in vitro cadaveric study.

PubMed

Clavenna, Andrew L; Beutler, William J; Gudipally, Manasa; Moldavsky, Mark; Khalil, Saif

2012-02-01

Anterior cervical plating increases stability and hence improves fusion rates to treat cervical spine pathologies, which are often symptomatic at multiple levels. However, plating is not without complications, such as dysphagia, injury to neural elements, and plate breakage. The biomechanics of a spacer with integrated plate system combined with posterior instrumentation (PI), in two-level and three-level surgical models, has not yet been investigated. The purpose of the study was to biomechanically evaluate the multidirectional rigidity of spacer with integrated plate (SIP) at multiple levels as comparable to traditional spacers and plating. An in vitro cervical cadaveric model. Eight fresh human cervical (C2-C7) cadaver spines were tested under pure moments of ±1.5 Nm on spine simulator test frame. Each spine was tested in intact condition, with only anterior fixation and with both anterior and PI. Range of motion (ROM) was measured using Optotrak Certus (NDI, Inc., Waterloo, Ontario, Canada) motion analysis system in flexion-extension (FE), lateral bending (LB), and axial rotation (AR) at the instrumented levels (C3-C6). Repeated-measures analysis of variance was used for statistical analysis. All the surgical constructs showed significant reduction in motion compared with intact condition. In two-level fusion, SIP (C4-C6) construct significantly reduced ROM by 66.5%, 65.4%, and 60.3% when compared with intact in FE, LB, and AR, respectively. In three-level fusion, SIP (C3-C6) construct significantly reduced ROM by 65.8%, 66%, and 49.6% when compared with intact in FE, LB, and AR, respectively. Posterior instrumentation showed significant stability only in three-level fusion when compared with their respective anterior constructs. In both two-level and three-level fusion, SIP showed comparable stability to traditional spacer and plate constructs in all loading modes. The anatomically profiled spacer with integrated plate allows treatment of cervical disorders with fewer steps and less impact to cervical structures. In this biomechanical study, spacer with integrated plate construct showed comparable stability to traditional spacer and plate for two-level and three-level fusion. Posterior instrumentation showed significant effect only in three-level fusion. Clinical data are required for further validation of using spacer with integrated plate at multiple levels. Copyright © 2012 Elsevier Inc. All rights reserved.

Results from levels 2/3 fusion implementations: issues, challenges, retrospectives, and perspectives for the future an annotated perspective

NASA Astrophysics Data System (ADS)

Kadar, Ivan; Bosse, Eloi; Salerno, John; Lambert, Dale A.; Das, Subrata; Ruspini, Enrique H.; Rhodes, Bradley J.; Biermann, Joachim

2008-04-01

Even though the definition of the Joint Director of Laboratories (JDL) "fusion levels" were established in 1987, published 1991, revised in 1999 and 2004, the meaning, effects, control and optimization of interactions among the fusion levels have not as yet been fully explored and understood. Specifically, this is apparent from the abstract JDL definitions of "Levels 2/3 Fusion" - situation and threat assessment (SA/TA), which involve deriving relations among entities, e.g., the aggregation of object states (i.e., classification and location) in SA, while TA uses SA products to estimate/predict the impact of actions/interactions effects on situations taken by the participant entities involved. Given all the existing knowledge in the information fusion and human factors literature, (both prior to and after the introduction of "fusion levels" in 1987) there are still open questions remaining in regard to implementation of knowledge representation and reasoning methods under uncertainty to afford SA/TA. Therefore, to promote exchange of ideas and to illuminate the historical, current and future issues associated with Levels 2/3 implementations, leading experts were invited to present their respective views on various facets of this complex problem. This paper is a retrospective annotated view of the invited panel discussion organized by Ivan Kadar (first author), supported by John Salerno, in order to provide both a historical perspective of the evolution of the state-of-the-art (SOA) in higher-level "Levels 2/3" information fusion implementations by looking back over the past ten or more years (before JDL), and based upon the lessons learned to forecast where focus should be placed to further enhance and advance the SOA by addressing key issues and challenges. In order to convey the panel discussion to audiences not present at the panel, annotated position papers summarizing the panel presentation are included.

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

PubMed

Liu, Da; Li, Jianxun

2016-12-16

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

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

PubMed

Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

2011-04-01

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

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.

Real-time sensor validation and fusion for distributed autonomous sensors

NASA Astrophysics Data System (ADS)

Yuan, Xiaojing; Li, Xiangshang; Buckles, Bill P.

2004-04-01

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Predicting the Valence of a Scene from Observers’ Eye Movements

PubMed Central

R.-Tavakoli, Hamed; Atyabi, Adham; Rantanen, Antti; Laukka, Seppo J.; Nefti-Meziani, Samia; Heikkilä, Janne

2015-01-01

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images. PMID:26407322

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.

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.

Paediatric and adult soft tissue sarcomas with NTRK1 gene fusions: a subset of spindle cell sarcomas unified by a prominent myopericytic/haemangiopericytic pattern.

PubMed

Haller, Florian; Knopf, Jasmin; Ackermann, Anne; Bieg, Matthias; Kleinheinz, Kortine; Schlesner, Matthias; Moskalev, Evgeny A; Will, Rainer; Satir, Ali Abdel; Abdelmagid, Ibtihalat E; Giedl, Johannes; Carbon, Roman; Rompel, Oliver; Hartmann, Arndt; Wiemann, Stefan; Metzler, Markus; Agaimy, Abbas

2016-04-01

Neoplasms with a myopericytomatous pattern represent a morphological spectrum of lesions encompassing myopericytoma of the skin and soft tissue, angioleiomyoma, myofibromatosis/infantile haemangiopericytoma and putative neoplasms reported as malignant myopericytoma. Lack of reproducible phenotypic and genetic features of malignant myopericytic neoplasms have prevented the establishment of myopericytic sarcoma as an acceptable diagnostic category. Following detection of a LMNA-NTRK1 gene fusion in an index case of paediatric haemangiopericytoma-like sarcoma by combined whole-genome and RNA sequencing, we identified three additional sarcomas harbouring NTRK1 gene fusions, termed 'spindle cell sarcoma, NOS with myo/haemangiopericytic growth pattern'. The patients were two children aged 11 months and 2 years and two adults aged 51 and 80 years. While the tumours of the adults were strikingly myopericytoma-like, but with clear-cut atypical features, the paediatric cases were more akin to infantile myofibromatosis/haemangiopericytoma. All cases contained numerous thick-walled dysplastic-like vessels with segmental or diffuse nodular myxohyaline myo-intimal proliferations of smooth muscle actin-positive cells, occasionally associated with thrombosis. Immunohistochemistry showed variable expression of smooth muscle actin and CD34, but other mesenchymal markers, including STAT6, were negative. This study showed a novel variant of myo/haemangiopericytic sarcoma with recurrent NTRK1 gene fusions. Given the recent introduction of a novel therapeutic approach targeting NTRK fusion-positive neoplasms, recognition of this rare but likely under-reported sarcoma variant is strongly encouraged. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Surface Density of the Hendra G Protein Modulates Hendra F Protein-Promoted Membrane Fusion: Role for Hendra G Protein Trafficking and Degradation

PubMed Central

Whitman, Shannon D.; Dutch, Rebecca Ellis

2007-01-01

Hendra virus, like most paramyxoviruses, requires both a fusion (F) and attachment (G) protein for promotion of cell-cell fusion. Recent studies determined that Hendra F is proteolytically processed by the cellular protease cathepsin L after endocytosis. This unique cathepsin L processing results in a small percentage of Hendra F on the cell surface. To determine how the surface densities of the two Hendra glycoproteins affect fusion promotion, we performed experiments that varied the levels of glycoproteins expressed in transfected cells. Using two different fusion assays, we found a marked increase in fusion when expression of the Hendra G protein was increased, with a 1:1 molar ratio of Hendra F:G on the cell surface resulting in optimal membrane fusion. Our results also showed that Hendra G protein levels are modulated by both more rapid protein turnover and slower protein trafficking than is seen for Hendra F. PMID:17328935

Multiplier, moderator, and reflector materials for lithium-vanadium fusion blankets.

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

Gohar, Y.; Smith, D. L.

1999-10-07

The self-cooled lithium-vanadium fusion blanket concept has several attractive operational and environmental features. In this concept, liquid lithium works as the tritium breeder and coolant to alleviate issues of coolant breeder compatibility and reactivity. Vanadium alloy (V-4Cr-4Ti) is used as the structural material because of its superior performance relative to other alloys for this application. However, this concept has poor attenuation characteristics and energy multiplication for the DT neutrons. An advanced self-cooled lithium-vanadium fusion blanket concept has been developed to eliminate these drawbacks while maintaining all the attractive features of the conventional concept. An electrical insulator coating for the coolantmore » channels, spectral shifter (multiplier, and moderator) and reflector were utilized in the blanket design to enhance the blanket performance. In addition, the blanket was designed to have the capability to operate at high loading conditions of 2 MW/m{sup 2} surface heat flux and 10 MW/m{sup 2} neutron wall loading. This paper assesses the spectral shifter and the reflector materials and it defines the technological requirements of this advanced blanket concept.« less

Multiplier, moderator, and reflector materials for advanced lithium?vanadium fusion blankets

NASA Astrophysics Data System (ADS)

Gohar, Y.; Smith, D. L.

2000-12-01

The self-cooled lithium-vanadium fusion blanket concept has several attractive operational and environmental features. In this concept, liquid lithium works as the tritium breeder and coolant to alleviate issues of coolant breeder compatibility and reactivity. Vanadium alloy (V-4Cr-4Ti) is used as the structural material because of its superior performance relative to other alloys for this application. However, this concept has poor attenuation characteristics and energy multiplication for the DT neutrons. An advanced self-cooled lithium-vanadium fusion blanket concept has been developed to eliminate these drawbacks while maintaining all the attractive features of the conventional concept. An electrical insulator coating for the coolant channels, spectral shifter (multiplier, and moderator) and reflector were utilized in the blanket design to enhance the blanket performance. In addition, the blanket was designed to have the capability to operate at average loading conditions of 2 MW/m 2 surface heat flux and 10 MW/m 2 neutron wall loading. This paper assesses the spectral shifter and the reflector materials and it defines the technological requirements of this advanced blanket concept.

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 posterolateral lumbar fusion rates of Grafton Putty and OP-1 Putty in an athymic rat model.

PubMed

Bomback, David A; Grauer, Jonathan N; Lugo, Roberto; Troiano, Nancy; Patel, Tushar Ch; Friedlaender, Gary E

2004-08-01

Posterolateral lumbar spine fusions in athymic rats. To compare spine fusion rates of two different osteoinductive products. Many osteoinductive bone graft alternatives are available. Grafton (a demineralized bone matrix [DBM]) and Osteogenic Protein-1 (OP-1, an individual recombinant bone morphogenetic protein) are two such alternatives. The relative efficacy of products from these two classes has not been previously studied. The athymic rat spine fusion model has been validated and demonstrated useful to minimize inflammatory responses to xenogeneic or differentially expressed proteins such as those presented by DBMs of human etiology. Single-level intertransverse process fusions were performed in 60 athymic nude rats with 2 cc/kg of Grafton or OP-1 Putty. Half of each study group was killed at 3 weeks and half at 6 weeks. Fusion masses were assessed by radiography, manual palpation, and histology. At 3 weeks, manual palpation revealed a 13% fusion rate with Grafton and a 100% fusion rate with OP-1 (P = 0.0001). At 6 weeks, manual palpation revealed a 39% fusion rate of with Grafton and a 100% fusion rate with OP-1 (P = 0.0007). Similar fusion rates were found by histology at 3 and 6 weeks. Of note, one or two adjacent levels were fused in all of the OP-1 animals and none of the Grafton animals. Significant differences between the ability of Grafton and OP-1 to induce bone formation in an athymic rat posterolateral lumbar spine fusion model were found.

Effect of Interbody Fusion on the Remaining Discs of the Lumbar Spine in Subjects with Disc Degeneration.

PubMed

Ryu, Robert; Techy, Fernando; Varadarajan, Ravikumar; Amirouche, Farid

2016-02-01

To study effects (stress loads) of lumbar fusion on the remaining segments (adjacent or not) of the lumbar spine in the setting of degenerated adjacent discs. A lumbar spine finite element model was built and validated. The full model of the lumbar spine was a parametric finite element model of segments L 1-5 . Numerous hypothetical combinations of one-level lumbar spine fusion and one-level disc degeneration were created. These models were subjected to 10 Nm flexion and extension moments and the stresses on the endplates and consequently on the intervertebral lumbar discs measured. These values were compared to the stresses on healthy lumbar spine discs under the same load and fusion scenarios. Increased stress at endplates was observed only in the settings of L4-5 fusion and L3-4 disc degeneration (8% stress elevation at L2,3 in flexion or extension, and 25% elevation at L3,4 in flexion only). All other combinations showed less endplate stress than did the control model. For fusion at L3-4 and degeneration at L4-5 , the stresses in the endplates at the adjacent level inferior to the fused disc decreased for both loading disc height reductions. Stresses in flexion decreased after fusion by 29.5% and 25.8% for degeneration I and II, respectively. Results for extension were similar. For fusion at L2-3 and degeneration at L4-5 , stresses in the endplates decreased more markedly at the degenerated (30%), than at the fused level (14%) in the presence of 25% disc height reduction and 10 Nm flexion, whereas in extension stresses decreased more at the fused (24.3%) than the degenerated level (5.86%). For fusion at L3-4 and degeneration at L2-3 , there were no increases in endplate stress in any scenario. For fusion at L4-5 and degeneration at L3-4 , progression of degeneration from I to II had a significant effect only in flexion. A dramatic increase in stress was noted in the endplates of the degenerated disc (L3-4 ) in flexion for degeneration II. Stresses are greater in flexion at the endplates of L3-4 and in flexion and extension at L2-3 in the presence of L3-4 disc disease and L4-5 fusion than in the control group. In all other combinations of fusion and disc disease, endplate stress was less for all levels tested than in the control model. © 2016 Chinese Orthopaedic Association and John Wiley & Sons Australia, Ltd.

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

PubMed Central

Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

2016-01-01

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

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.

FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud.

PubMed

Márquez, Cristina; López, M Isabel; Ruisánchez, Itziar; Callao, M Pilar

2016-12-01

Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96-100% and 88-100% for the mid- and high-level data fusion strategies, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Local bone graft harvesting and volumes in posterolateral lumbar fusion: a technical report.

PubMed

Carragee, Eugene J; Comer, Garet C; Smith, Micah W

2011-06-01

In lumbar surgery, local bone graft is often harvested and used in posterolateral fusion procedures. The volume of local bone graft available for posterolateral fusion has not been determined in North American patients. Some authors have described this as minimal, but others have suggested the volume was sufficient to be reliably used as a stand-alone bone graft substitute for single-level fusion. To describe the technique used and determine the volume of local bone graft available in a cohort of patients undergoing single-level primary posterolateral fusion by the authors harvesting technique. Technical description and cohort report. Consecutive patients undergoing lumbar posterolateral fusion with or without instrumentation for degenerative processes. Local bone graft volume. Consecutive patients undergoing lumbar posterolateral fusion with or without instrumentation for degenerative processes of were studied. Local bone graft was harvested by a standard method in each patient and the volume measured by a standard procedure. Twenty-five patients were studied, and of these 11 (44%) had a previous decompression. The mean volume of local bone graft harvested was measured to be 25 cc (range, 12-36 cc). Local bone graft was augmented by iliac crest bone in six of 25 patients (24%) if the posterolateral fusion bed was not well packed with local bone alone. There was a trend to greater local bone graft volumes in men and in patients without previous decompression. Large volumes of local bone can be harvested during posterolateral lumbar fusion surgery. Even in patients with previous decompression the volume harvested is similar to that reported harvested from the posterior iliac crest for single-level fusion. Copyright © 2011 Elsevier Inc. All rights reserved.

Welcome Back: Responses of Female Bonobos (Pan paniscus) to Fusions.

PubMed

Moscovice, Liza R; Deschner, Tobias; Hohmann, Gottfried

2015-01-01

In species with a high degree of fission-fusion social dynamics, fusions may trigger social conflict and thus provide an opportunity to identify sources of social tension and mechanisms related to its alleviation. We characterized behavioral and endocrine responses of captive female bonobos (Pan paniscus) to fusions within a zoo facility designed to simulate naturalistic fission-fusion social dynamics. We compared urinary cortisol levels and frequencies of aggression, grooming and socio-sexual interactions between female bonobos while in stable sub-groups and when one "joiner" was reunited with the "residents" of another sub-group. We hypothesized that fusions would trigger increases in aggression and cortisol levels among reunited joiners and resident females. We further predicted that females who face more uncertainty in their social interactions following fusions may use grooming and/or socio-sexual behavior to reduce social tension and aggression. The only aggression on reunion days occurred between reunited females, but frequencies of aggression remained low across non-reunion and reunion days, and there was no effect of fusions on cortisol levels. Fusions did not influence patterns of grooming, but there were increases in socio-sexual solicitations and socio-sexual interactions between joiners and resident females. Joiners who had been separated from residents for longer received the most solicitations, but were also more selective in their acceptance of solicitations and preferred to have socio-sexual interactions with higher-ranking residents. Our results suggest that socio-sexual interactions play a role in reintegrating female bonobos into social groups following fusions. In addition, females who receive a high number of solicitations are able to gain more control over their socio-sexual interactions and may use socio-sexual interactions for other purposes, such as to enhance their social standing.

Welcome Back: Responses of Female Bonobos (Pan paniscus) to Fusions

PubMed Central

Moscovice, Liza R.; Deschner, Tobias; Hohmann, Gottfried

2015-01-01

In species with a high degree of fission-fusion social dynamics, fusions may trigger social conflict and thus provide an opportunity to identify sources of social tension and mechanisms related to its alleviation. We characterized behavioral and endocrine responses of captive female bonobos (Pan paniscus) to fusions within a zoo facility designed to simulate naturalistic fission-fusion social dynamics. We compared urinary cortisol levels and frequencies of aggression, grooming and socio-sexual interactions between female bonobos while in stable sub-groups and when one “joiner” was reunited with the “residents” of another sub-group. We hypothesized that fusions would trigger increases in aggression and cortisol levels among reunited joiners and resident females. We further predicted that females who face more uncertainty in their social interactions following fusions may use grooming and/or socio-sexual behavior to reduce social tension and aggression. The only aggression on reunion days occurred between reunited females, but frequencies of aggression remained low across non-reunion and reunion days, and there was no effect of fusions on cortisol levels. Fusions did not influence patterns of grooming, but there were increases in socio-sexual solicitations and socio-sexual interactions between joiners and resident females. Joiners who had been separated from residents for longer received the most solicitations, but were also more selective in their acceptance of solicitations and preferred to have socio-sexual interactions with higher-ranking residents. Our results suggest that socio-sexual interactions play a role in reintegrating female bonobos into social groups following fusions. In addition, females who receive a high number of solicitations are able to gain more control over their socio-sexual interactions and may use socio-sexual interactions for other purposes, such as to enhance their social standing. PMID:25996476

Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Shengpei; Li, Zuoyong; Zhang, Jishui; Peng, Yun; He, Huiguang

2017-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

Membranes linked by trans-SNARE complexes require lipids prone to non-bilayer structure for progression to fusion.

PubMed

Zick, Michael; Stroupe, Christopher; Orr, Amy; Douville, Deborah; Wickner, William T

2014-01-01

Like other intracellular fusion events, the homotypic fusion of yeast vacuoles requires a Rab GTPase, a large Rab effector complex, SNARE proteins which can form a 4-helical bundle, and the SNARE disassembly chaperones Sec17p and Sec18p. In addition to these proteins, specific vacuole lipids are required for efficient fusion in vivo and with the purified organelle. Reconstitution of vacuole fusion with all purified components reveals that high SNARE levels can mask the requirement for a complex mixture of vacuole lipids. At lower, more physiological SNARE levels, neutral lipids with small headgroups that tend to form non-bilayer structures (phosphatidylethanolamine, diacylglycerol, and ergosterol) are essential. Membranes without these three lipids can dock and complete trans-SNARE pairing but cannot rearrange their lipids for fusion. DOI: http://dx.doi.org/10.7554/eLife.01879.001.

Biomechanical comparison of a two-level Maverick disc replacement with a hybrid one-level disc replacement and one-level anterior lumbar interbody fusion.

PubMed

Erkan, Serkan; Rivera, Yamil; Wu, Chunhui; Mehbod, Amir A; Transfeldt, Ensor E

2009-10-01

Multilevel lumbar disc disease (MLDD) is a common finding in many patients. Surgical solutions for MLDD include fusion or disc replacement. The hybrid model, combining fusion and disc replacement, is a potential alternative for patients who require surgical intervention at both L5-S1 and L4-L5. The indications for this hybrid model could be posterior element insufficiency, severe facet pathology, calcified ligamentum flavum, and subarticular disease confirming spinal stenosis at L5-S1 level, or previous fusion surgery at L5-S1 and new symptomatic pathology at L4-L5. Biomechanical data of the hybrid model with the Maverick disc and anterior fusion are not available in the literature. To compare the biomechanical properties of a two-level Maverick disc replacement at L4-L5, L5-S1, and a hybrid model consisting of an L4-L5 Maverick disc replacement with an L5-S1 anterior lumbar interbody fusion using multidirectional flexibility test. An in vitro human cadaveric biomechanical study. Six fresh human cadaveric lumbar specimens (L4-S1) were subjected to unconstrained load in axial torsion (AT), lateral bending (LB), flexion (F), extension (E), and flexion-extension (FE) using multidirectional flexibility test. Four surgical treatments-intact, one-level Maverick at L5-S1, two-level Maverick between L4 and S1, and the hybrid model (anterior fusion at L5-S1 and Maverick at L4-L5) were tested in sequential order. The range of motion of each treatment was calculated. The Maverick disc replacement slightly reduced intact motion in AT and LB at both levels. The total FE motion was similar to the intact motion. However, the E motion is significantly increased (approximately 50% higher) and F motion is significantly decreased (30%-50% lower). The anterior fusion using a cage and anterior plate significantly reduced spinal motion compared with the condition (p<.05). No significant differences were found between two-level Maverick disc prosthesis and the hybrid model in terms of all motion types at L4-L5 level (p>.05). The Maverick disc preserved total motion but altered the motion pattern of the intact condition. This result is similar to unconstrained devices such as Charité. The motion at L4-L5 of the hybrid model is similar to that of two-level Maverick disc replacement. The fusion procedure using an anterior plate significantly reduced intact motion. Clinical studies are recommended to validate the efficacy of the hybrid model.

Selection of Fusion Levels Using the Fulcrum Bending Radiograph for the Management of Adolescent Idiopathic Scoliosis Patients with Alternate Level Pedicle Screw Strategy: Clinical Decision-making and Outcomes.

PubMed

Samartzis, Dino; Leung, Yee; Shigematsu, Hideki; Natarajan, Deepa; Stokes, Oliver; Mak, Kin-Cheung; Yao, Guanfeng; Luk, Keith D K; Cheung, Kenneth M C

2015-01-01

Selecting fusion levels based on the Luk et al criteria for operative management of thoracic adolescent idiopathic scoliosis (AIS) with hook and hybrid systems yields acceptable curve correction and balance parameters; however, it is unknown whether utilizing a purely pedicle screw strategy is effective. Utilizing the fulcrum bending radiographic (FBR) to assess curve flexibility to select fusion levels, the following study assessed the efficacy of pedicle screw fixation with alternate level screw strategy (ALSS) for thoracic AIS. A retrospective study with prospective radiographic data collection/analyses (preoperative, postoperative 1-week and minimum 2-year follow-up) of 28 operative thoracic AIS patients undergoing ALSS was performed. Standing coronal/sagittal and FBR Cobb angles, FBR flexibility, fulcrum bending correction index (FBCI), trunkal shift, radiographic shoulder height (RSH), and list were assessed on x-rays. Fusion level selection was based on the Luk et al criteria and compared to conventional techniques. In the primary curve, the mean preoperative and postoperative 1 week and last follow-up standing coronal Cobb angles were 59.9, 17.2 and 20.0 degrees, respectively. Eighteen patients (64.3%) had distal levels saved (mean: 1.6 levels) in comparison to conventional techniques. Mean immediate and last follow-up FBCIs were 122.6% and 115.0%, respectively. Sagittal alignment did not statistically differ between any assessment intervals (p>0.05). A decrease in trunkal shift was noted from preoperative to last follow-up (p = 0.003). No statistically significant difference from preoperative to last follow-up was noted in RSH and list (p>0.05). No "add-on" of other vertebra or decompensation was noted and all patients achieved fusion. This is the first report to note that using the FBR for decision-making in selecting fusion levels in thoracic AIS patients undergoing management with pedicle screw constructs (e.g. ALSS) is a cost-effective strategy that can achieve clinically-relevant deformity correction that is maintained and without compromising fusion levels.

Influence of Alendronate and Endplate Degeneration to Single Level Posterior Lumbar Spinal Interbody Fusion

PubMed Central

Rhee, Wootack; Ha, Seongil; Lim, Jae Hyeon; Jang, Il Tae

2014-01-01

Objective Using alendronate after spinal fusion is a controversial issue due to the inhibition of osteoclast mediated bone resorption. In addition, there are an increasing number of reports that the endplate degeneration influences the lumbar spinal fusion. The object of this retrospective controlled study was to evaluate how the endplate degeneration and the bisphosphonate medication influence the spinal fusion through radiographic evaluation. Methods In this study, 44 patients who underwent single-level posterior lumbar interbody fusion (PLIF) using cage were examined from April 2007 to March 2009. All patients had been diagnosed as osteoporosis and would be recommended for alendronate medication. Endplate degeneration is categorized by the Modic changes. The solid fusion is defined if there was bridging bone between the vertebral bodies, either within or external to the cage on the plain X-ray and if there is less than 5° of angular difference in dynamic X-ray. Results In alendronate group, fusion was achieved in 66.7% compared to 73.9% in control group (no medication). Alendronate did not influence the fusion rate of PLIF. However, there was the statistical difference of fusion rate between the endplate degeneration group and the group without endplate degeneration. A total of 52.4% of fusion rate was seen in the endplate degeneration group compared to 91.3% in the group without endplate degeneration. The endplate degeneration suppresses the fusion process of PLIF. Conclusion Alendronate does not influence the fusion process in osteoporotic patients. The endplate degeneration decreases the fusion rate. PMID:25620981

[New features in the 2014 WHO classification of uterine neoplasms].

PubMed

Lax, S F

2016-11-01

The 2014 World Health Organization (WHO) classification of uterine tumors revealed simplification of the classification by fusion of several entities and the introduction of novel entities. Among the multitude of alterations, the following are named: a simplified classification for precursor lesions of endometrial carcinoma now distinguishes between hyperplasia without atypia and atypical hyperplasia, the latter also known as endometrioid intraepithelial neoplasia (EIN). For endometrial carcinoma a differentiation is made between type 1 (endometrioid carcinoma with variants and mucinous carcinoma) and type 2 (serous and clear cell carcinoma). Besides a papillary architecture serous carcinomas may show solid and glandular features and TP53 immunohistochemistry with an "all or null pattern" assists in the diagnosis of serous carcinoma with ambiguous features. Neuroendocrine neoplasms are categorized in a similar way to the gastrointestinal tract into well differentiated neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas (small cell and large cell types). Leiomyosarcomas of the uterus are typically high grade and characterized by marked nuclear atypia and lively mitotic activity. Low grade stromal neoplasms frequently show gene fusions, such as JAZF1/SUZ12. High grade endometrial stromal sarcoma is newly defined by cyclin D1 overexpression and the presence of the fusion gene YWHAE/FAM22 and must be distinguished from undifferentiated uterine sarcoma. Carcinosarcomas (malignant mixed Mullerian tumors MMMT) show biological and molecular similarities to high-grade carcinomas.

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.

Dynamic Creation of Social Networks for Syndromic Surveillance Using Information Fusion

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Sudit, Moises; Stotz, Adam

To enhance the effectiveness of health care, many medical institutions have started transitioning to electronic health and medical records and sharing these records between institutions. The large amount of complex and diverse data makes it difficult to identify and track relationships and trends, such as disease outbreaks, from the data points. INFERD: Information Fusion Engine for Real-Time Decision-Making is an information fusion tool that dynamically correlates and tracks event progressions. This paper presents a methodology that utilizes the efficient and flexible structure of INFERD to create social networks representing progressions of disease outbreaks. Individual symptoms are treated as features allowing multiple hypothesis being tracked and analyzed for effective and comprehensive syndromic surveillance.

Self-similar structure and experimental signatures of suprathermal ion distribution in inertial confinement fusion implosions

DOE PAGES

Kagan, Grigory; Svyatskiy, D.; Rinderknecht, H. G.; ...

2015-09-03

The distribution function of suprathermal ions is found to be self-similar under conditions relevant to inertial confinement fusion hot spots. By utilizing this feature, interference between the hydrodynamic instabilities and kinetic effects is for the first time assessed quantitatively to find that the instabilities substantially aggravate the fusion reactivity reduction. Thus, the ion tail depletion is also shown to lower the experimentally inferred ion temperature, a novel kinetic effect that may explain the discrepancy between the exploding pusher experiments and rad-hydro simulations and contribute to the observation that temperature inferred from DD reaction products is lower than from DT atmore » the National Ignition Facility.« less

Self-Similar Structure and Experimental Signatures of Suprathermal Ion Distribution in Inertial Confinement Fusion Implosions

NASA Astrophysics Data System (ADS)

Kagan, Grigory; Svyatskiy, D.; Rinderknecht, H. G.; Rosenberg, M. J.; Zylstra, A. B.; Huang, C.-K.; McDevitt, C. J.

2015-09-01

The distribution function of suprathermal ions is found to be self-similar under conditions relevant to inertial confinement fusion hot spots. By utilizing this feature, interference between the hydrodynamic instabilities and kinetic effects is for the first time assessed quantitatively to find that the instabilities substantially aggravate the fusion reactivity reduction. The ion tail depletion is also shown to lower the experimentally inferred ion temperature, a novel kinetic effect that may explain the discrepancy between the exploding pusher experiments and rad-hydro simulations and contribute to the observation that temperature inferred from DD reaction products is lower than from DT at the National Ignition Facility.

Titanium vs. polyetheretherketone (PEEK) interbody fusion: Meta-analysis and review of the literature.

PubMed

Seaman, Scott; Kerezoudis, Panagiotis; Bydon, Mohamad; Torner, James C; Hitchon, Patrick W

2017-10-01

Spinal interbody fusion is a standard and accepted method for spinal fusion. Interbody fusion devices include titanium (Ti) and polyetheretherketone (PEEK) cages with distinct biomechanical properties. Titanium and PEEK cages have been evaluated in the cervical and lumbar spine, with conflicting results in bony fusion and subsidence. Using Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) guidelines, we reviewed the available literature evaluating Ti and PEEK cages to assess subsidence and fusion rates. Six studies were included in the analysis, 3 of which were class IV evidence, 2 were class III, and 1 was class II. A total of 410 patients (Ti-228, PEEK-182) and 587 levels (Ti-327, PEEK-260) were studied. Pooled mean age was 50.8years in the Ti group, and 53.1years in the PEEK group. Anterior cervical discectomy was performed in 4 studies (395 levels) and transforaminal interbody fusion in 2 studies (192 levels). No statistically significant difference was found between groups with fusion (OR 1.16, 95% C.I 0.59-2.89, p=0.686, I 2 =49.7%) but there was a statistically significant the rate of subsidence with titanium (OR 3.59, 95% C.I 1.28-10.07, p=0.015, I 2 =56.9%) at last follow-up. Titanium and PEEK cages are associated with a similar rate of fusion, but there is an increased rate of subsidence with titanium cage. Future prospective randomized controlled trials are needed to further evaluate these cages using surgical and patient-reported outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Face Liveness Detection Using Defocus

PubMed Central

Kim, Sooyeon; Ban, Yuseok; Lee, Sangyoun

2015-01-01

In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g., 2D pictures). To defend from these attacks and to enhance the reliability of FR systems, many anti-spoofing approaches have been recently developed. In this paper, we propose a method for face liveness detection using the effect of defocus. From two images sequentially taken at different focuses, three features, focus, power histogram and gradient location and orientation histogram (GLOH), are extracted. Afterwards, we detect forged faces through the feature-level fusion approach. For reliable performance verification, we develop two databases with a handheld digital camera and a webcam. The proposed method achieves a 3.29% half total error rate (HTER) at a given depth of field (DoF) and can be extended to camera-equipped devices, like smartphones. PMID:25594594

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering

PubMed Central

Savran, Arman; Cao, Houwei; Shah, Miraj; Nenkova, Ani; Verma, Ragini

2013-01-01

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively. PMID:25300451

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering.

PubMed

Savran, Arman; Cao, Houwei; Shah, Miraj; Nenkova, Ani; Verma, Ragini

2012-01-01

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively.

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).