A Probabilistic, Non-parametric Framework for Inter-modality Label Fusion

DEFF Research Database (Denmark)

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen

2013-01-01

Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights...

Multi-sensor image fusion and its applications

CERN Document Server

Blum, Rick S

2005-01-01

Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies.After a review of state-of-the-art image fusion techniques,

A multi-modality concept for radiotherapy planning with imaging techniques

International Nuclear Information System (INIS)

Schultze, J.

1993-01-01

The reported multi-modality concept of radiotherapy planning in the LAN can be realised in any hospital with standard equipment, although in some cases by way of auxiliary configurations. A software is currently developed as a tool for reducing the entire planning work. The heart of any radiotherapy planning is the therapy simulator, which has to be abreast with the requirements of modern radiotherapy. Integration of tomograpy, digitalisation, and electronic data processing has added important modalities to therapy planning which allow more precise target volume definition, and better biophysical planning. This is what is needed in order to achieve well differentiated radiotherapy for treatment of the manifold tumors, and the quality standards expected by the supervisory quality assurance regime and the population. At present, the CT data still are transferred indirect, on storage media, to the EDP processing system of the radiotherapy planning system. Based on the tomographic slices given by the imaging data, the contours and technical problem solutions are derived automatically, either for multi-field radiotherapy or moving field irradiation, depending on the anatomy or the targets to be protected from ionizing radiation. (orig./VHE) [de

Sensor Fusion Techniques for Phased-Array Eddy Current and Phased-Array Ultrasound Data

Energy Technology Data Exchange (ETDEWEB)

Arrowood, Lloyd F. [Y-12 National Security Complex, Oak Ridge, TN (United States)

2018-03-15

Sensor (or Data) fusion is the process of integrating multiple data sources to produce more consistent, accurate and comprehensive information than is provided by a single data source. Sensor fusion may also be used to combine multiple signals from a single modality to improve the performance of a particular inspection technique. Industrial nondestructive testing may utilize multiple sensors to acquire inspection data depending upon the object under inspection and the anticipated types of defects that can be identified. Sensor fusion can be performed at various levels of signal abstraction with each having its strengths and weaknesses. A multimodal data fusion strategy first proposed by Heideklang and Shokouhi that combines spatially scattered detection locations to improve detection performance of surface-breaking and near-surface cracks in ferromagnetic metals is shown using a surface inspection example and is then extended for volumetric inspections. Utilizing data acquired from an Olympus Omniscan MX2 from both phased array eddy current and ultrasound probes on test phantoms, single and multilevel fusion techniques are employed to integrate signals from the two modalities. Preliminary results demonstrate how confidence in defect identification and interpretation benefit from sensor fusion techniques. Lastly, techniques for integrating data into radiographic and volumetric imagery from computed tomography are described and results are presented.

A novel technique to incorporate structural prior information into multi-modal tomographic reconstruction

International Nuclear Information System (INIS)

Kazantsev, Daniil; Dobson, Katherine J; Withers, Philip J; Lee, Peter D; Ourselin, Sébastien; Arridge, Simon R; Hutton, Brian F; Kaestner, Anders P; Lionheart, William R B

2014-01-01

There has been a rapid expansion of multi-modal imaging techniques in tomography. In biomedical imaging, patients are now regularly imaged using both single photon emission computed tomography (SPECT) and x-ray computed tomography (CT), or using both positron emission tomography and magnetic resonance imaging (MRI). In non-destructive testing of materials both neutron CT (NCT) and x-ray CT are widely applied to investigate the inner structure of material or track the dynamics of physical processes. The potential benefits from combining modalities has led to increased interest in iterative reconstruction algorithms that can utilize the data from more than one imaging mode simultaneously. We present a new regularization term in iterative reconstruction that enables information from one imaging modality to be used as a structural prior to improve resolution of the second modality. The regularization term is based on a modified anisotropic tensor diffusion filter, that has shape-adapted smoothing properties. By considering the underlying orientations of normal and tangential vector fields for two co-registered images, the diffusion flux is rotated and scaled adaptively to image features. The images can have different greyscale values and different spatial resolutions. The proposed approach is particularly good at isolating oriented features in images which are important for medical and materials science applications. By enhancing the edges it enables both easy identification and volume fraction measurements aiding segmentation algorithms used for quantification. The approach is tested on a standard denoising and deblurring image recovery problem, and then applied to 2D and 3D reconstruction problems; thereby highlighting the capabilities of the algorithm. Using synthetic data from SPECT co-registered with MRI, and real NCT data co-registered with x-ray CT, we show how the method can be used across a range of imaging modalities. (paper)

Multi-Valued Modal Fixed Point Logics for Model Checking

Science.gov (United States)

Nishizawa, Koki

In this paper, I will show how multi-valued logics are used for model checking. Model checking is an automatic technique to analyze correctness of hardware and software systems. A model checker is based on a temporal logic or a modal fixed point logic. That is to say, a system to be checked is formalized as a Kripke model, a property to be satisfied by the system is formalized as a temporal formula or a modal formula, and the model checker checks that the Kripke model satisfies the formula. Although most existing model checkers are based on 2-valued logics, recently new attempts have been made to extend the underlying logics of model checkers to multi-valued logics. I will summarize these new results.

Advances in multi-sensor data fusion: algorithms and applications.

Science.gov (United States)

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

A tri-modality image fusion method for target delineation of brain tumors in radiotherapy.

Directory of Open Access Journals (Sweden)

Lu Guo

Full Text Available To develop a tri-modality image fusion method for better target delineation in image-guided radiotherapy for patients with brain tumors.A new method of tri-modality image fusion was developed, which can fuse and display all image sets in one panel and one operation. And a feasibility study in gross tumor volume (GTV delineation using data from three patients with brain tumors was conducted, which included images of simulation CT, MRI, and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET examinations before radiotherapy. Tri-modality image fusion was implemented after image registrations of CT+PET and CT+MRI, and the transparency weight of each modality could be adjusted and set by users. Three radiation oncologists delineated GTVs for all patients using dual-modality (MRI/CT and tri-modality (MRI/CT/PET image fusion respectively. Inter-observer variation was assessed by the coefficient of variation (COV, the average distance between surface and centroid (ADSC, and the local standard deviation (SDlocal. Analysis of COV was also performed to evaluate intra-observer volume variation.The inter-observer variation analysis showed that, the mean COV was 0.14(± 0.09 and 0.07(± 0.01 for dual-modality and tri-modality respectively; the standard deviation of ADSC was significantly reduced (p<0.05 with tri-modality; SDlocal averaged over median GTV surface was reduced in patient 2 (from 0.57 cm to 0.39 cm and patient 3 (from 0.42 cm to 0.36 cm with the new method. The intra-observer volume variation was also significantly reduced (p = 0.00 with the tri-modality method as compared with using the dual-modality method.With the new tri-modality image fusion method smaller inter- and intra-observer variation in GTV definition for the brain tumors can be achieved, which improves the consistency and accuracy for target delineation in individualized radiotherapy.

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

Directory of Open Access Journals (Sweden)

Naveed ur Rehman

2015-05-01

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

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

Science.gov (United States)

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.

Multi-modal Behavioural Biometric Authentication for Mobile Devices

OpenAIRE

Saevanee , Hataichanok; Clarke , Nathan ,; Furnell , Steven ,

2012-01-01

Part 12: Authentication and Delegation; International audience; The potential advantages of behavioural biometrics are that they can be utilised in a transparent (non-intrusive) and continuous authentication system. However, individual biometric techniques are not suited to all users and scenarios. One way to increase the reliability of transparent and continuous authentication systems is create a multi-modal behavioural biometric authentication system. This research investigated three behavi...

A SCHEME FOR TEMPLATE SECURITY AT FEATURE FUSION LEVEL IN MULTIMODAL BIOMETRIC SYSTEM

Directory of Open Access Journals (Sweden)

Arvind Selwal

2016-09-01

Full Text Available Biometric is the science of human recognition based upon using their biological, chemical or behavioural traits. These systems are used in many real life applications simply from biometric based attendance system to providing security at very sophisticated level. A biometric system deals with raw data captured using a sensor and feature template extracted from raw image. One of the challenges being faced by designers of these systems is to secure template data extracted from the biometric modalities of the user and protect the raw images. To minimize spoof attacks on biometric systems by unauthorised users one of the solutions is to use multi-biometric systems. Multi-modal biometric system works by using fusion technique to merge feature templates generated from different modalities of the human. In this work a new scheme is proposed to secure template during feature fusion level. Scheme is based on union operation of fuzzy relations of templates of modalities during fusion process of multimodal biometric systems. This approach serves dual purpose of feature fusion as well as transformation of templates into a single secured non invertible template. The proposed technique is cancelable and experimentally tested on a bimodal biometric system comprising of fingerprint and hand geometry. Developed scheme removes the problem of an attacker learning the original minutia position in fingerprint and various measurements of hand geometry. Given scheme provides improved performance of the system with reduction in false accept rate and improvement in genuine accept rate.

Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

Science.gov (United States)

Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

2013-01-01

Accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Directory of Open Access Journals (Sweden)

Timo Korthals

2018-03-01

Full Text Available Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar and cameras (stereo and thermal. Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles. Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.

Multi-modal locomotion: from animal to application

International Nuclear Information System (INIS)

Lock, R J; Burgess, S C; Vaidyanathan, R

2014-01-01

The majority of robotic vehicles that can be found today are bound to operations within a single media (i.e. land, air or water). This is very rarely the case when considering locomotive capabilities in natural systems. Utility for small robots often reflects the exact same problem domain as small animals, hence providing numerous avenues for biological inspiration. This paper begins to investigate the various modes of locomotion adopted by different genus groups in multiple media as an initial attempt to determine the compromise in ability adopted by the animals when achieving multi-modal locomotion. A review of current biologically inspired multi-modal robots is also presented. The primary aim of this research is to lay the foundation for a generation of vehicles capable of multi-modal locomotion, allowing ambulatory abilities in more than one media, surpassing current capabilities. By identifying and understanding when natural systems use specific locomotion mechanisms, when they opt for disparate mechanisms for each mode of locomotion rather than using a synergized singular mechanism, and how this affects their capability in each medium, similar combinations can be used as inspiration for future multi-modal biologically inspired robotic platforms. (topical review)

Development of comprehensive image processing technique for differential diagnosis of liver disease by using multi-modality images. Pixel-based cross-correlation method using a profile

International Nuclear Information System (INIS)

Inoue, Akira; Okura, Yasuhiko; Akiyama, Mitoshi; Ishida, Takayuki; Kawashita, Ikuo; Ito, Katsuyoshi; Matsunaga, Naofumi; Sanada, Taizo

2009-01-01

Imaging techniques such as high magnetic field imaging and multidetector-row CT have been markedly improved recently. The final image-reading systems easily produce more than a thousand diagnostic images per patient. Therefore, we developed a comprehensive cross-correlation processing technique using multi-modality images, in order to decrease the considerable time and effort involved in the interpretation of a radiogram (multi-formatted display and/or stack display method, etc). In this scheme, the criteria of an attending radiologist for the differential diagnosis of liver cyst, hemangioma of liver, hepatocellular carcinoma, and metastatic liver cancer on magnetic resonance images with various sequences and CT images with and without contrast enhancement employ a cross-correlation coefficient. Using a one-dimensional cross-correlation method, comprehensive image processing could be also adapted for various artifacts (some depending on modality imaging, and some on patients), which may be encountered at the clinical scene. This comprehensive image-processing technique could assist radiologists in the differential diagnosis of liver diseases. (author)

Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer’s Disease

Science.gov (United States)

Jie, Biao; Cheng, Bo

2014-01-01

Accurate diagnosis of Alzheimer’s disease (AD), as well as its pro-dromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method. PMID:24505676

Multi-modal RGB–Depth–Thermal Human Body Segmentation

DEFF Research Database (Denmark)

Palmero, Cristina; Clapés, Albert; Bahnsen, Chris

2016-01-01

This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB-Depth-Thermal dataset along with a multi-modal seg- mentation baseline. The several modalities are registered us- ing a calibration...... to other state-of-the-art meth- ods, obtaining an overlap above 75% on the novel dataset when compared to the manually annotated ground-truth of human segmentations....

Simulation for evaluation of the multi-ion-irradiation Laboratory of TechnoFusion facility and its relevance for fusion applications

International Nuclear Information System (INIS)

Jimenez-Rey, D.; Mota, F.; Vila, R.; Ibarra, A.; Ortiz, Christophe J.; Martinez-Albertos, J.L.; Roman, R.; Gonzalez, M.; Garcia-Cortes, I.; Perlado, J.M.

2011-01-01

Thermonuclear fusion requires the development of several research facilities, in addition to ITER, needed to advance the technologies for future fusion reactors. TechnoFusion will focus in some of the priority areas identified by international fusion programmes. Specifically, the TechnoFusion Area of Irradiation of Materials aims at surrogating experimentally the effects of neutron irradiation on materials using a combination of ion beams. This paper justifies this approach using computer simulations to validate the multi-ion-irradiation Laboratory. The planned irradiation facility will investigate the effects of high energetic radiations on reactor-relevant materials. In a second stage, it will also be used to analyze the performance of such materials and evaluate newly designed materials. The multi-ion-irradiation Laboratory, both triple irradiation and high-energy proton irradiation, can provide valid experimental techniques to reproduce the effect of neutron damage in fusion environment.

Embedded security system for multi-modal surveillance in a railway carriage

Science.gov (United States)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

Multi-criteria appraisal of multi-modal urban public transport systems

NARCIS (Netherlands)

Keyvan Ekbatani, M.; Cats, O.

2015-01-01

This study proposes a multi-criteria decision making (MCDM) modelling framework for the appraisal of multi-modal urban public transportation services. MCDM is commonly used to obtain choice alternatives that satisfy a range of performance indicators. The framework embraces both compensatory and

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017.

Science.gov (United States)

Jin, Xue-Bo; Sun, Shuli; Wei, Hong; Yang, Feng-Bao

2018-04-11

The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017

Directory of Open Access Journals (Sweden)

Xue-Bo Jin

2018-04-01

Full Text Available The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

Fusion-based multi-target tracking and localization for intelligent surveillance systems

Science.gov (United States)

Rababaah, Haroun; Shirkhodaie, Amir

2008-04-01

In this paper, we have presented two approaches addressing visual target tracking and localization in complex urban environment. The two techniques presented in this paper are: fusion-based multi-target visual tracking, and multi-target localization via camera calibration. For multi-target tracking, the data fusion concepts of hypothesis generation/evaluation/selection, target-to-target registration, and association are employed. An association matrix is implemented using RGB histograms for associated tracking of multi-targets of interests. Motion segmentation of targets of interest (TOI) from the background was achieved by a Gaussian Mixture Model. Foreground segmentation, on other hand, was achieved by the Connected Components Analysis (CCA) technique. The tracking of individual targets was estimated by fusing two sources of information, the centroid with the spatial gating, and the RGB histogram association matrix. The localization problem is addressed through an effective camera calibration technique using edge modeling for grid mapping (EMGM). A two-stage image pixel to world coordinates mapping technique is introduced that performs coarse and fine location estimation of moving TOIs. In coarse estimation, an approximate neighborhood of the target position is estimated based on nearest 4-neighbor method, and in fine estimation, we use Euclidean interpolation to localize the position within the estimated four neighbors. Both techniques were tested and shown reliable results for tracking and localization of Targets of interests in complex urban environment.

Visualization of graphical information fusion results

Science.gov (United States)

Blasch, Erik; Levchuk, Georgiy; Staskevich, Gennady; Burke, Dustin; Aved, Alex

2014-06-01

Graphical fusion methods are popular to describe distributed sensor applications such as target tracking and pattern recognition. Additional graphical methods include network analysis for social, communications, and sensor management. With the growing availability of various data modalities, graphical fusion methods are widely used to combine data from multiple sensors and modalities. To better understand the usefulness of graph fusion approaches, we address visualization to increase user comprehension of multi-modal data. The paper demonstrates a use case that combines graphs from text reports and target tracks to associate events and activities of interest visualization for testing Measures of Performance (MOP) and Measures of Effectiveness (MOE). The analysis includes the presentation of the separate graphs and then graph-fusion visualization for linking network graphs for tracking and classification.

ANALYSIS OF MULTIMODAL FUSION TECHNIQUES FOR AUDIO-VISUAL SPEECH RECOGNITION

Directory of Open Access Journals (Sweden)

D.V. Ivanko

2016-05-01

Full Text Available The paper deals with analytical review, covering the latest achievements in the field of audio-visual (AV fusion (integration of multimodal information. We discuss the main challenges and report on approaches to address them. One of the most important tasks of the AV integration is to understand how the modalities interact and influence each other. The paper addresses this problem in the context of AV speech processing and speech recognition. In the first part of the review we set out the basic principles of AV speech recognition and give the classification of audio and visual features of speech. Special attention is paid to the systematization of the existing techniques and the AV data fusion methods. In the second part we provide a consolidated list of tasks and applications that use the AV fusion based on carried out analysis of research area. We also indicate used methods, techniques, audio and video features. We propose classification of the AV integration, and discuss the advantages and disadvantages of different approaches. We draw conclusions and offer our assessment of the future in the field of AV fusion. In the further research we plan to implement a system of audio-visual Russian continuous speech recognition using advanced methods of multimodal fusion.

TU-C-BRD-01: Image Guided SBRT I: Multi-Modality 4D Imaging

International Nuclear Information System (INIS)

Cai, J; Mageras, G; Pan, T

2014-01-01

Motion management is one of the critical technical challenges for radiation therapy. 4D imaging has been rapidly adopted as essential tool to assess organ motion associated with respiratory breathing. A variety of 4D imaging techniques have been developed and are currently under development based on different imaging modalities such as CT, MRI, PET, and CBCT. Each modality provides specific and complementary information about organ and tumor respiratory motion. Effective use of each different technique or combined use of different techniques can introduce a comprehensive management of tumor motion. Specifically, these techniques have afforded tremendous opportunities to better define and delineate tumor volumes, more accurately perform patient positioning, and effectively apply highly conformal therapy techniques such as IMRT and SBRT. Successful implementation requires good understanding of not only each technique, including unique features, limitations, artifacts, imaging acquisition and process, but also how to systematically apply the information obtained from different imaging modalities using proper tools such as deformable image registration. Furthermore, it is important to understand the differences in the effects of breathing variation between different imaging modalities. A comprehensive motion management strategy using multi-modality 4D imaging has shown promise in improving patient care, but at the same time faces significant challenges. This session will focuses on the current status and advances in imaging respiration-induced organ motion with different imaging modalities: 4D-CT, 4D-MRI, 4D-PET, and 4D-CBCT/DTS. Learning Objectives: Understand the need and role of multimodality 4D imaging in radiation therapy. Understand the underlying physics behind each 4D imaging technique. Recognize the advantages and limitations of each 4D imaging technique

MIDA - Optimizing control room performance through multi-modal design

International Nuclear Information System (INIS)

Ronan, A. M.

2006-01-01

Multi-modal interfaces can support the integration of humans with information processing systems and computational devices to maximize the unique qualities that comprise a complex system. In a dynamic environment, such as a nuclear power plant control room, multi-modal interfaces, if designed correctly, can provide complementary interaction between the human operator and the system which can improve overall performance while reducing human error. Developing such interfaces can be difficult for a designer without explicit knowledge of Human Factors Engineering principles. The Multi-modal Interface Design Advisor (MIDA) was developed as a support tool for system designers and developers. It provides design recommendations based upon a combination of Human Factors principles, a knowledge base of historical research, and current interface technologies. MIDA's primary objective is to optimize available multi-modal technologies within a human computer interface in order to balance operator workload with efficient operator performance. The purpose of this paper is to demonstrate MIDA and illustrate its value as a design evaluation tool within the nuclear power industry. (authors)

Exogenous Molecular Probes for Targeted Imaging in Cancer: Focus on Multi-modal Imaging

International Nuclear Information System (INIS)

Joshi, Bishnu P.; Wang, Thomas D.

2010-01-01

Cancer is one of the major causes of mortality and morbidity in our healthcare system. Molecular imaging is an emerging methodology for the early detection of cancer, guidance of therapy, and monitoring of response. The development of new instruments and exogenous molecular probes that can be labeled for multi-modality imaging is critical to this process. Today, molecular imaging is at a crossroad, and new targeted imaging agents are expected to broadly expand our ability to detect and manage cancer. This integrated imaging strategy will permit clinicians to not only localize lesions within the body but also to manage their therapy by visualizing the expression and activity of specific molecules. This information is expected to have a major impact on drug development and understanding of basic cancer biology. At this time, a number of molecular probes have been developed by conjugating various labels to affinity ligands for targeting in different imaging modalities. This review will describe the current status of exogenous molecular probes for optical, scintigraphic, MRI and ultrasound imaging platforms. Furthermore, we will also shed light on how these techniques can be used synergistically in multi-modal platforms and how these techniques are being employed in current research

Semiparametric score level fusion: Gaussian copula approach

NARCIS (Netherlands)

Susyanyo, N.; Klaassen, C.A.J.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan

2015-01-01

Score level fusion is an appealing method for combining multi-algorithms, multi- representations, and multi-modality biometrics due to its simplicity. Often, scores are assumed to be independent, but even for dependent scores, accord- ing to the Neyman-Pearson lemma, the likelihood ratio is the

Multi-Modal, Multi-Touch Interaction with Maps in Disaster Management Applications

Directory of Open Access Journals (Sweden)

V. Paelke

2012-07-01

Full Text Available Multi-touch interaction has become popular in recent years and impressive advances in technology have been demonstrated, with the presentation of digital maps as a common presentation scenario. However, most existing systems are really technology demonstrators and have not been designed with real applications in mind. A critical factor in the management of disaster situations is the access to current and reliable data. New sensors and data acquisition platforms (e.g. satellites, UAVs, mobile sensor networks have improved the supply of spatial data tremendously. However, in many cases this data is not well integrated into current crisis management systems and the capabilities to analyze and use it lag behind sensor capabilities. Therefore, it is essential to develop techniques that allow the effective organization, use and management of heterogeneous data from a wide variety of data sources. Standard user interfaces are not well suited to provide this information to crisis managers. Especially in dynamic situations conventional cartographic displays and mouse based interaction techniques fail to address the need to review a situation rapidly and act on it as a team. The development of novel interaction techniques like multi-touch and tangible interaction in combination with large displays provides a promising base technology to provide crisis managers with an adequate overview of the situation and to share relevant information with other stakeholders in a collaborative setting. However, design expertise on the use of such techniques in interfaces for real-world applications is still very sparse. In this paper we report on interdisciplinary research with a user and application centric focus to establish real-world requirements, to design new multi-modal mapping interfaces, and to validate them in disaster management applications. Initial results show that tangible and pen-based interaction are well suited to provide an intuitive and visible way to

A Single Rod Multi-modality Multi-interface Level Sensor Using an AC Current Source

Directory of Open Access Journals (Sweden)

Abdulgader Hwili

2008-05-01

Full Text Available Crude oil separation is an important process in the oil industry. To make efficient use of the separators, it is important to know their internal behaviour, and to measure the levels of multi-interfaces between different materials, such as gas-foam, foam-oil, oil-emulsion, emulsion-water and water-solids. A single-rod multi-modality multi-interface level sensor is presented, which has a current source, and electromagnetic modalities. Some key issues have been addressed, including the effect of salt content and temperature i.e. conductivity on the measurement.

MINERVA - a multi-modal radiation treatment planning system

Energy Technology Data Exchange (ETDEWEB)

Wemple, C.A. E-mail: cew@enel.gov; Wessol, D.E.; Nigg, D.W.; Cogliati, J.J.; Milvich, M.L.; Frederickson, C.; Perkins, M.; Harkin, G.J

2004-11-01

Researchers at the Idaho National Engineering and Environmental Laboratory and Montana State University have undertaken development of MINERVA, a patient-centric, multi-modal, radiation treatment planning system. This system can be used for planning and analyzing several radiotherapy modalities, either singly or combined, using common modality independent image and geometry construction and dose reporting and guiding. It employs an integrated, lightweight plugin architecture to accommodate multi-modal treatment planning using standard interface components. The MINERVA design also facilitates the future integration of improved planning technologies. The code is being developed with the Java Virtual Machine for interoperability. A full computation path has been established for molecular targeted radiotherapy treatment planning, with the associated transport plugin developed by researchers at the Lawrence Livermore National Laboratory. Development of the neutron transport plugin module is proceeding rapidly, with completion expected later this year. Future development efforts will include development of deformable registration methods, improved segmentation methods for patient model definition, and three-dimensional visualization of the patient images, geometry, and dose data. Transport and source plugins will be created for additional treatment modalities, including brachytherapy, external beam proton radiotherapy, and the EGSnrc/BEAMnrc codes for external beam photon and electron radiotherapy.

Multi-Modal Intelligent Traffic Signal Systems GPS

Data.gov (United States)

Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

Fusion in computer vision understanding complex visual content

CERN Document Server

Ionescu, Bogdan; Piatrik, Tomas

2014-01-01

This book presents a thorough overview of fusion in computer vision, from an interdisciplinary and multi-application viewpoint, describing successful approaches, evaluated in the context of international benchmarks that model realistic use cases. Features: examines late fusion approaches for concept recognition in images and videos; describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods; investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, fo

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

Science.gov (United States)

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.

Modal Identification in an Automotive Multi-Component System Using HS 3D-DIC

Directory of Open Access Journals (Sweden)

Ángel Jesús Molina-Viedma

2018-02-01

Full Text Available The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials.

Modal Identification in an Automotive Multi-Component System Using HS 3D-DIC.

Science.gov (United States)

Molina-Viedma, Ángel Jesús; López-Alba, Elías; Felipe-Sesé, Luis; Díaz, Francisco A

2018-02-05

The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC) is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials.

Modal Identification in an Automotive Multi-Component System Using HS 3D-DIC

Science.gov (United States)

López-Alba, Elías; Felipe-Sesé, Luis; Díaz, Francisco A.

2018-01-01

The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC) is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials. PMID:29401725

WE-H-206-02: Recent Advances in Multi-Modality Molecular Imaging of Small Animals

Energy Technology Data Exchange (ETDEWEB)

Tsui, B. [Johns Hopkins University (United States)

2016-06-15

Lihong V. Wang: Photoacoustic tomography (PAT), combining non-ionizing optical and ultrasonic waves via the photoacoustic effect, provides in vivo multiscale functional, metabolic, and molecular imaging. Broad applications include imaging of the breast, brain, skin, esophagus, colon, vascular system, and lymphatic system in humans or animals. Light offers rich contrast but does not penetrate biological tissue in straight paths as x-rays do. Consequently, high-resolution pure optical imaging (e.g., confocal microscopy, two-photon microscopy, and optical coherence tomography) is limited to penetration within the optical diffusion limit (∼1 mm in the skin). Ultrasonic imaging, on the contrary, provides fine spatial resolution but suffers from both poor contrast in early-stage tumors and strong speckle artifacts. In PAT, pulsed laser light penetrates tissue and generates a small but rapid temperature rise, which induces emission of ultrasonic waves due to thermoelastic expansion. The ultrasonic waves, orders of magnitude less scattering than optical waves, are then detected to form high-resolution images of optical absorption at depths up to 7 cm, conquering the optical diffusion limit. PAT is the only modality capable of imaging across the length scales of organelles, cells, tissues, and organs (up to whole-body small animals) with consistent contrast. This rapidly growing technology promises to enable multiscale biological research and accelerate translation from microscopic laboratory discoveries to macroscopic clinical practice. PAT may also hold the key to label-free early detection of cancer by in vivo quantification of hypermetabolism, the quintessential hallmark of malignancy. Learning Objectives: To understand the contrast mechanism of PAT To understand the multiscale applications of PAT Benjamin M. W. Tsui: Multi-modality molecular imaging instrumentation and techniques have been major developments in small animal imaging that has contributed significantly

WE-H-206-02: Recent Advances in Multi-Modality Molecular Imaging of Small Animals

International Nuclear Information System (INIS)

Tsui, B.

2016-01-01

Lihong V. Wang: Photoacoustic tomography (PAT), combining non-ionizing optical and ultrasonic waves via the photoacoustic effect, provides in vivo multiscale functional, metabolic, and molecular imaging. Broad applications include imaging of the breast, brain, skin, esophagus, colon, vascular system, and lymphatic system in humans or animals. Light offers rich contrast but does not penetrate biological tissue in straight paths as x-rays do. Consequently, high-resolution pure optical imaging (e.g., confocal microscopy, two-photon microscopy, and optical coherence tomography) is limited to penetration within the optical diffusion limit (∼1 mm in the skin). Ultrasonic imaging, on the contrary, provides fine spatial resolution but suffers from both poor contrast in early-stage tumors and strong speckle artifacts. In PAT, pulsed laser light penetrates tissue and generates a small but rapid temperature rise, which induces emission of ultrasonic waves due to thermoelastic expansion. The ultrasonic waves, orders of magnitude less scattering than optical waves, are then detected to form high-resolution images of optical absorption at depths up to 7 cm, conquering the optical diffusion limit. PAT is the only modality capable of imaging across the length scales of organelles, cells, tissues, and organs (up to whole-body small animals) with consistent contrast. This rapidly growing technology promises to enable multiscale biological research and accelerate translation from microscopic laboratory discoveries to macroscopic clinical practice. PAT may also hold the key to label-free early detection of cancer by in vivo quantification of hypermetabolism, the quintessential hallmark of malignancy. Learning Objectives: To understand the contrast mechanism of PAT To understand the multiscale applications of PAT Benjamin M. W. Tsui: Multi-modality molecular imaging instrumentation and techniques have been major developments in small animal imaging that has contributed significantly

A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis

Directory of Open Access Journals (Sweden)

Fuyuan Xiao

2017-10-01

Full Text Available The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster’s combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis.

A Multi-Modality CMOS Sensor Array for Cell-Based Assay and Drug Screening.

Science.gov (United States)

Chi, Taiyun; Park, Jong Seok; Butts, Jessica C; Hookway, Tracy A; Su, Amy; Zhu, Chengjie; Styczynski, Mark P; McDevitt, Todd C; Wang, Hua

2015-12-01

In this paper, we present a fully integrated multi-modality CMOS cellular sensor array with four sensing modalities to characterize different cell physiological responses, including extracellular voltage recording, cellular impedance mapping, optical detection with shadow imaging and bioluminescence sensing, and thermal monitoring. The sensor array consists of nine parallel pixel groups and nine corresponding signal conditioning blocks. Each pixel group comprises one temperature sensor and 16 tri-modality sensor pixels, while each tri-modality sensor pixel can be independently configured for extracellular voltage recording, cellular impedance measurement (voltage excitation/current sensing), and optical detection. This sensor array supports multi-modality cellular sensing at the pixel level, which enables holistic cell characterization and joint-modality physiological monitoring on the same cellular sample with a pixel resolution of 80 μm × 100 μm. Comprehensive biological experiments with different living cell samples demonstrate the functionality and benefit of the proposed multi-modality sensing in cell-based assay and drug screening.

Novel hybrid Monte Carlo/deterministic technique for shutdown dose rate analyses of fusion energy systems

International Nuclear Information System (INIS)

Ibrahim, Ahmad M.; Peplow, Douglas E.; Peterson, Joshua L.; Grove, Robert E.

2014-01-01

Highlights: •Develop the novel Multi-Step CADIS (MS-CADIS) hybrid Monte Carlo/deterministic method for multi-step shielding analyses. •Accurately calculate shutdown dose rates using full-scale Monte Carlo models of fusion energy systems. •Demonstrate the dramatic efficiency improvement of the MS-CADIS method for the rigorous two step calculations of the shutdown dose rate in fusion reactors. -- Abstract: The rigorous 2-step (R2S) computational system uses three-dimensional Monte Carlo transport simulations to calculate the shutdown dose rate (SDDR) in fusion reactors. Accurate full-scale R2S calculations are impractical in fusion reactors because they require calculating space- and energy-dependent neutron fluxes everywhere inside the reactor. The use of global Monte Carlo variance reduction techniques was suggested for accelerating the R2S neutron transport calculation. However, the prohibitive computational costs of these approaches, which increase with the problem size and amount of shielding materials, inhibit their ability to accurately predict the SDDR in fusion energy systems using full-scale modeling of an entire fusion plant. This paper describes a novel hybrid Monte Carlo/deterministic methodology that uses the Consistent Adjoint Driven Importance Sampling (CADIS) method but focuses on multi-step shielding calculations. The Multi-Step CADIS (MS-CADIS) methodology speeds up the R2S neutron Monte Carlo calculation using an importance function that represents the neutron importance to the final SDDR. Using a simplified example, preliminary results showed that the use of MS-CADIS enhanced the efficiency of the neutron Monte Carlo simulation of an SDDR calculation by a factor of 550 compared to standard global variance reduction techniques, and that the efficiency enhancement compared to analog Monte Carlo is higher than a factor of 10,000

CT, MRI and PET image fusion using the ProSoma 3D simulation software

International Nuclear Information System (INIS)

Dalah, E.; Bradley, D.A.; Nisbet, A.; Reise, S.

2008-01-01

Full text: Multi-modality imaging is involved in almost all oncology applications focusing on the extent of disease and target volume delineation. Commercial image fusion software packages are becoming available but require comprehensive evaluation to ensure reliability of fusion and the underpinning registration algorithm particularly for radiotherapy. The present work seeks to assess such accuracy for a number of available registration methods provided by the commercial package ProSoma. A NEMA body phantom was used in evaluating CT, MR and PET images. In addition, discussion is provided concerning the choice and geometry of fiducial markers in phantom studies and the effect of window-level on target size, in particular in regard to the application of multi modality imaging in treatment planning. In general, the accuracy of fusion of multi-modality images was within 0.5-1.5 mm of actual feature diameters and < 2 ml volume of actual values, particularly in CT images. (author)

Evaluation of registration strategies for multi-modality images of rat brain slices

International Nuclear Information System (INIS)

Palm, Christoph; Vieten, Andrea; Salber, Dagmar; Pietrzyk, Uwe

2009-01-01

In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.

Multi-Sampling Ionization Chamber (MUSIC) for measurements of fusion reactions with radioactive beams

International Nuclear Information System (INIS)

Carnelli, P.F.F.; Almaraz-Calderon, S.; Rehm, K.E.; Albers, M.; Alcorta, M.; Bertone, P.F.; Digiovine, B.; Esbensen, H.; Fernández Niello, J.; Henderson, D.; Jiang, C.L.; Lai, J.; Marley, S.T.; Nusair, O.; Palchan-Hazan, T.; Pardo, R.C.; Paul, M.; Ugalde, C.

2015-01-01

A detection technique for high-efficiency measurements of fusion reactions with low-intensity radioactive beams was developed. The technique is based on a Multi-Sampling Ionization Chamber (MUSIC) operating as an active target and detection system, where the ionization gas acts as both target and counting gas. In this way, we can sample an excitation function in an energy range determined by the gas pressure, without changing the beam energy. The detector provides internal normalization to the incident beam and drastically reduces the measuring time. In a first experiment we tested the performance of the technique by measuring the 10,13,15 C+ 12 C fusion reactions at energies around the Coulomb barrier

Multi-Sampling Ionization Chamber (MUSIC) for measurements of fusion reactions with radioactive beams

Energy Technology Data Exchange (ETDEWEB)

Carnelli, P.F.F. [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States); Laboratorio TANDAR, Comisión Nacional de Energía Atómica, Av. Gral. Paz 1499, B1650KNA, San Martín, Buenos Aires (Argentina); Consejo Nacional de Investigaciones Científicas y Técnicas, Av. Rivadavia 1917, C1033AAJ Buenos Aires (Argentina); Almaraz-Calderon, S. [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States); Rehm, K.E., E-mail: rehm@anl.gov [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States); Albers, M.; Alcorta, M.; Bertone, P.F.; Digiovine, B.; Esbensen, H. [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States); Fernández Niello, J. [Laboratorio TANDAR, Comisión Nacional de Energía Atómica, Av. Gral. Paz 1499, B1650KNA, San Martín, Buenos Aires (Argentina); Universidad Nacional de San Martín, Campus Miguelete, B1650BWA San Martín, Buenos Aires (Argentina); Henderson, D.; Jiang, C.L. [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States); Lai, J. [Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803 (United States); Marley, S.T.; Nusair, O.; Palchan-Hazan, T.; Pardo, R.C. [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States); Paul, M. [Racah Institute of Physics, Hebrew University, Jerusalem (Israel); Ugalde, C. [Physics Division, Argonne National Laboratory, Argonne, IL 60439 (United States)

2015-11-01

A detection technique for high-efficiency measurements of fusion reactions with low-intensity radioactive beams was developed. The technique is based on a Multi-Sampling Ionization Chamber (MUSIC) operating as an active target and detection system, where the ionization gas acts as both target and counting gas. In this way, we can sample an excitation function in an energy range determined by the gas pressure, without changing the beam energy. The detector provides internal normalization to the incident beam and drastically reduces the measuring time. In a first experiment we tested the performance of the technique by measuring the {sup 10,13,15}C+{sup 12}C fusion reactions at energies around the Coulomb barrier.

[Research progress of multi-model medical image fusion and recognition].

Science.gov (United States)

Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian

2013-10-01

Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.

Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

Directory of Open Access Journals (Sweden)

Yan Sun

2015-09-01

Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

Making Faces - State-Space Models Applied to Multi-Modal Signal Processing

DEFF Research Database (Denmark)

Lehn-Schiøler, Tue

2005-01-01

The two main focus areas of this thesis are State-Space Models and multi modal signal processing. The general State-Space Model is investigated and an addition to the class of sequential sampling methods is proposed. This new algorithm is denoted as the Parzen Particle Filter. Furthermore...... optimizer can be applied to speed up convergence. The linear version of the State-Space Model, the Kalman Filter, is applied to multi modal signal processing. It is demonstrated how a State-Space Model can be used to map from speech to lip movements. Besides the State-Space Model and the multi modal...... application an information theoretic vector quantizer is also proposed. Based on interactions between particles, it is shown how a quantizing scheme based on an analytic cost function can be derived....

Multi-modal imaging, model-based tracking, and mixed reality visualisation for orthopaedic surgery

Science.gov (United States)

Fuerst, Bernhard; Tateno, Keisuke; Johnson, Alex; Fotouhi, Javad; Osgood, Greg; Tombari, Federico; Navab, Nassir

2017-01-01

Orthopaedic surgeons are still following the decades old workflow of using dozens of two-dimensional fluoroscopic images to drill through complex 3D structures, e.g. pelvis. This Letter presents a mixed reality support system, which incorporates multi-modal data fusion and model-based surgical tool tracking for creating a mixed reality environment supporting screw placement in orthopaedic surgery. A red–green–blue–depth camera is rigidly attached to a mobile C-arm and is calibrated to the cone-beam computed tomography (CBCT) imaging space via iterative closest point algorithm. This allows real-time automatic fusion of reconstructed surface and/or 3D point clouds and synthetic fluoroscopic images obtained through CBCT imaging. An adapted 3D model-based tracking algorithm with automatic tool segmentation allows for tracking of the surgical tools occluded by hand. This proposed interactive 3D mixed reality environment provides an intuitive understanding of the surgical site and supports surgeons in quickly localising the entry point and orienting the surgical tool during screw placement. The authors validate the augmentation by measuring target registration error and also evaluate the tracking accuracy in the presence of partial occlusion. PMID:29184659

The optimal algorithm for Multi-source RS image fusion.

Science.gov (United States)

Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

2016-01-01

In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

Compositional-prior-guided image reconstruction algorithm for multi-modality imaging

Science.gov (United States)

Fang, Qianqian; Moore, Richard H.; Kopans, Daniel B.; Boas, David A.

2010-01-01

The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information. We propose a composition-based image segmentation method for X-ray digital breast tomosynthesis (DBT) and a structural-prior-guided image reconstruction for a combined DBT and diffuse optical tomography (DOT) breast imaging system. Using the 3D DBT images from 31 clinically measured healthy breasts, we create an empirical relationship between the X-ray intensities for adipose and fibroglandular tissue. We use this relationship to then segment another 58 healthy breast DBT images from 29 subjects into compositional maps of different tissue types. For each breast, we build a weighted-graph in the compositional space and construct a regularization matrix to incorporate the structural priors into a finite-element-based DOT image reconstruction. Use of the compositional priors enables us to fuse tissue anatomy into optical images with less restriction than when using a binary segmentation. This allows us to recover the image contrast captured by DOT but not by DBT. We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter. By estimating the optical properties for adipose and fibroglandular tissue using the proposed algorithm, we found the results are comparable or superior to those estimated with expert-segmentations, but does not involve the time-consuming manual selection of regions-of-interest. PMID:21258460

Multi-Modal Traveler Information System - Gateway Functional Requirements

Science.gov (United States)

1997-11-17

The Multi-Modal Traveler Information System (MMTIS) project involves a large number of Intelligent Transportation System (ITS) related tasks. It involves research of all ITS initiatives in the Gary-Chicago-Milwaukee (GCM) Corridor which are currently...

Multi modal child-to-child interaction

DEFF Research Database (Denmark)

Fisker, Tine Basse

In this presentation the interaction and relation of three boys is analyzed using multi modal analysis. The analysis clearly, and surprisingly demonstrates that the boys interact via different modes and that they are able to handle several interaction partners at the same time. They co......-construct interaction in rather complex and unexpected ways using verbal as well as non-verbal modes in interaction....

Contemporary Multi-Modal Historical Representations and the Teaching of Disciplinary Understandings in History

Science.gov (United States)

Donnelly, Debra J.

2018-01-01

Traditional privileging of the printed text has been considerably eroded by rapid technological advancement and in Australia, as elsewhere, many History teaching programs feature an array of multi-modal historical representations. Research suggests that engagement with the visual and multi-modal constructs has the potential to enrich the pedagogy…

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

Science.gov (United States)

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

2018-01-01

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

Data fusion for QRS complex detection in multi-lead electrocardiogram recordings

Science.gov (United States)

Ledezma, Carlos A.; Perpiñan, Gilberto; Severeyn, Erika; Altuve, Miguel

2015-12-01

Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps.

Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.

Science.gov (United States)

Phan, John H; Hoffman, Ryan; Kothari, Sonal; Wu, Po-Yen; Wang, May D

2016-02-01

The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

Extended depth of field integral imaging using multi-focus fusion

Science.gov (United States)

Piao, Yongri; Zhang, Miao; Wang, Xiaohui; Li, Peihua

2018-03-01

In this paper, we propose a new method for depth of field extension in integral imaging by realizing the image fusion method on the multi-focus elemental images. In the proposed method, a camera is translated on a 2D grid to take multi-focus elemental images by sweeping the focus plane across the scene. Simply applying an image fusion method on the elemental images holding rich parallax information does not work effectively because registration accuracy of images is the prerequisite for image fusion. To solve this problem an elemental image generalization method is proposed. The aim of this generalization process is to geometrically align the objects in all elemental images so that the correct regions of multi-focus elemental images can be exacted. The all-in focus elemental images are then generated by fusing the generalized elemental images using the block based fusion method. The experimental results demonstrate that the depth of field of synthetic aperture integral imaging system has been extended by realizing the generation method combined with the image fusion on multi-focus elemental images in synthetic aperture integral imaging system.

Progressive multi-atlas label fusion by dictionary evolution.

Science.gov (United States)

Song, Yantao; Wu, Guorong; Bahrami, Khosro; Sun, Quansen; Shen, Dinggang

2017-02-01

Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary. Copyright © 2016 Elsevier B.V. All rights reserved.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

Science.gov (United States)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Reference resolution in multi-modal interaction: Preliminary observations

NARCIS (Netherlands)

González González, G.R.; Nijholt, Antinus

2002-01-01

In this paper we present our research on multimodal interaction in and with virtual environments. The aim of this presentation is to emphasize the necessity to spend more research on reference resolution in multimodal contexts. In multi-modal interaction the human conversational partner can apply

Reference Resolution in Multi-modal Interaction: Position paper

NARCIS (Netherlands)

Fernando, T.; Nijholt, Antinus

2002-01-01

In this position paper we present our research on multimodal interaction in and with virtual environments. The aim of this presentation is to emphasize the necessity to spend more research on reference resolution in multimodal contexts. In multi-modal interaction the human conversational partner can

A flexible data fusion architecture for persistent surveillance using ultra-low-power wireless sensor networks

Science.gov (United States)

Hanson, Jeffrey A.; McLaughlin, Keith L.; Sereno, Thomas J.

2011-06-01

We have developed a flexible, target-driven, multi-modal, physics-based fusion architecture that efficiently searches sensor detections for targets and rejects clutter while controlling the combinatoric problems that commonly arise in datadriven fusion systems. The informational constraints imposed by long lifetime requirements make systems vulnerable to false alarms. We demonstrate that our data fusion system significantly reduces false alarms while maintaining high sensitivity to threats. In addition, mission goals can vary substantially in terms of targets-of-interest, required characterization, acceptable latency, and false alarm rates. Our fusion architecture provides the flexibility to match these trade-offs with mission requirements unlike many conventional systems that require significant modifications for each new mission. We illustrate our data fusion performance with case studies that span many of the potential mission scenarios including border surveillance, base security, and infrastructure protection. In these studies, we deployed multi-modal sensor nodes - including geophones, magnetometers, accelerometers and PIR sensors - with low-power processing algorithms and low-bandwidth wireless mesh networking to create networks capable of multi-year operation. The results show our data fusion architecture maintains high sensitivities while suppressing most false alarms for a variety of environments and targets.

Non-local statistical label fusion for multi-atlas segmentation.

Science.gov (United States)

Asman, Andrew J; Landman, Bennett A

2013-02-01

Multi-atlas segmentation provides a general purpose, fully-automated approach for transferring spatial information from an existing dataset ("atlases") to a previously unseen context ("target") through image registration. The method to resolve voxelwise label conflicts between the registered atlases ("label fusion") has a substantial impact on segmentation quality. Ideally, statistical fusion algorithms (e.g., STAPLE) would result in accurate segmentations as they provide a framework to elegantly integrate models of rater performance. The accuracy of statistical fusion hinges upon accurately modeling the underlying process of how raters err. Despite success on human raters, current approaches inaccurately model multi-atlas behavior as they fail to seamlessly incorporate exogenous intensity information into the estimation process. As a result, locally weighted voting algorithms represent the de facto standard fusion approach in clinical applications. Moreover, regardless of the approach, fusion algorithms are generally dependent upon large atlas sets and highly accurate registration as they implicitly assume that the registered atlases form a collectively unbiased representation of the target. Herein, we propose a novel statistical fusion algorithm, Non-Local STAPLE (NLS). NLS reformulates the STAPLE framework from a non-local means perspective in order to learn what label an atlas would have observed, given perfect correspondence. Through this reformulation, NLS (1) seamlessly integrates intensity into the estimation process, (2) provides a theoretically consistent model of multi-atlas observation error, and (3) largely diminishes the need for large atlas sets and very high-quality registrations. We assess the sensitivity and optimality of the approach and demonstrate significant improvement in two empirical multi-atlas experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

Multi-energy soft-x-ray technique for impurity transport measurements in the fusion plasma edge

International Nuclear Information System (INIS)

Clayton, D J; Tritz, K; Stutman, D; Finkenthal, M; Kumar, D; Kaye, S M; LeBlanc, B P; Paul, S; Sabbagh, S A

2012-01-01

A new diagnostic technique was developed to produce high-resolution impurity transport measurements of the steep-gradient edge of fusion plasmas. Perturbative impurity transport measurements were performed for the first time in the NSTX plasma edge (r/a ∼ 0.6 to the SOL) with short neon gas puffs, and the resulting line and continuum emission was measured with the new edge multi-energy soft-x-ray (ME-SXR) diagnostic. Neon transport is modeled with the radial impurity transport code STRAHL and the resulting x-ray emission is computed using the ADAS atomic database. The radial transport coefficient profiles D(r) and v(r), and the particle flux from the gas puff Φ(t), are the free parameters in this model and are varied to find the best fit to experimental x-ray emissivity measurements, with bolometry used to constrain the impurity source. Initial experiments were successful and results were consistent with previous measurements of core impurity transport and neoclassical transport calculations. New diagnostic tools will be implemented on NSTX-U to further improve these transport measurements. (paper)

Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

Science.gov (United States)

Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

2016-07-01

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

Novelty detection of foreign objects in food using multi-modal X-ray imaging

DEFF Research Database (Denmark)

Einarsdottir, Hildur; Emerson, Monica Jane; Clemmensen, Line Katrine Harder

2016-01-01

In this paper we demonstrate a method for novelty detection of foreign objects in food products using grating-based multimodal X-ray imaging. With this imaging technique three modalities are available with pixel correspondence, enhancing organic materials such as wood chips, insects and soft...... plastics not detectable by conventional X-ray absorption radiography. We conduct experiments, where several food products are imaged with common foreign objects typically found in the food processing industry. To evaluate the benefit from using this multi-contrast X-ray technique over conventional X......-ray absorption imaging, a novelty detection scheme based on well known image- and statistical analysis techniques is proposed. The results show that the presented method gives superior recognition results and highlights the advantage of grating-based imaging....

Utilizing Multi-Modal Literacies in Middle Grades Science

Science.gov (United States)

Saurino, Dan; Ogletree, Tamra; Saurino, Penelope

2010-01-01

The nature of literacy is changing. Increased student use of computer-mediated, digital, and visual communication spans our understanding of adolescent multi-modal capabilities that reach beyond the traditional conventions of linear speech and written text in the science curriculum. Advancing technology opens doors to learning that involve…

Histopathology in 3D: From three-dimensional reconstruction to multi-stain and multi-modal analysis

Directory of Open Access Journals (Sweden)

Derek Magee

2015-01-01

Full Text Available Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

Multi-Modal Intelligent Traffic Signal Systems (MMITSS) Basic Safety Message

Data.gov (United States)

Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

Directory of Open Access Journals (Sweden)

Zhang Xinzheng

2017-10-01

Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

TU-AB-202-11: Tumor Segmentation by Fusion of Multi-Tracer PET Images Using Copula Based Statistical Methods

International Nuclear Information System (INIS)

Lapuyade-Lahorgue, J; Ruan, S; Li, H; Vera, P

2016-01-01

Purpose: Multi-tracer PET imaging is getting more attention in radiotherapy by providing additional tumor volume information such as glucose and oxygenation. However, automatic PET-based tumor segmentation is still a very challenging problem. We propose a statistical fusion approach to joint segment the sub-area of tumors from the two tracers FDG and FMISO PET images. Methods: Non-standardized Gamma distributions are convenient to model intensity distributions in PET. As a serious correlation exists in multi-tracer PET images, we proposed a new fusion method based on copula which is capable to represent dependency between different tracers. The Hidden Markov Field (HMF) model is used to represent spatial relationship between PET image voxels and statistical dynamics of intensities for each modality. Real PET images of five patients with FDG and FMISO are used to evaluate quantitatively and qualitatively our method. A comparison between individual and multi-tracer segmentations was conducted to show advantages of the proposed fusion method. Results: The segmentation results show that fusion with Gaussian copula can receive high Dice coefficient of 0.84 compared to that of 0.54 and 0.3 of monomodal segmentation results based on individual segmentation of FDG and FMISO PET images. In addition, high correlation coefficients (0.75 to 0.91) for the Gaussian copula for all five testing patients indicates the dependency between tumor regions in the multi-tracer PET images. Conclusion: This study shows that using multi-tracer PET imaging can efficiently improve the segmentation of tumor region where hypoxia and glucidic consumption are present at the same time. Introduction of copulas for modeling the dependency between two tracers can simultaneously take into account information from both tracers and deal with two pathological phenomena. Future work will be to consider other families of copula such as spherical and archimedian copulas, and to eliminate partial volume

Online multi-modal robust non-negative dictionary learning for visual tracking.

Science.gov (United States)

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

ADMultiImg: a novel missing modality transfer learning based CAD system for diagnosis of MCI due to AD using incomplete multi-modality imaging data

Science.gov (United States)

Liu, Xiaonan; Chen, Kewei; Wu, Teresa; Weidman, David; Lure, Fleming; Li, Jing

2018-02-01

Alzheimer's Disease (AD) is the most common cause of dementia and currently has no cure. Treatments targeting early stages of AD such as Mild Cognitive Impairment (MCI) may be most effective to deaccelerate AD, thus attracting increasing attention. However, MCI has substantial heterogeneity in that it can be caused by various underlying conditions, not only AD. To detect MCI due to AD, NIA-AA published updated consensus criteria in 2011, in which the use of multi-modality images was highlighted as one of the most promising methods. It is of great interest to develop a CAD system based on automatic, quantitative analysis of multi-modality images and machine learning algorithms to help physicians more adequately diagnose MCI due to AD. The challenge, however, is that multi-modality images are not universally available for many patients due to cost, access, safety, and lack of consent. We developed a novel Missing Modality Transfer Learning (MMTL) algorithm capable of utilizing whatever imaging modalities are available for an MCI patient to diagnose the patient's likelihood of MCI due to AD. Furthermore, we integrated MMTL with radiomics steps including image processing, feature extraction, and feature screening, and a post-processing for uncertainty quantification (UQ), and developed a CAD system called "ADMultiImg" to assist clinical diagnosis of MCI due to AD using multi-modality images together with patient demographic and genetic information. Tested on ADNI date, our system can generate a diagnosis with high accuracy even for patients with only partially available image modalities (AUC=0.94), and therefore may have broad clinical utility.

A unified framework for cross-modality multi-atlas segmentation of brain MRI

DEFF Research Database (Denmark)

Eugenio Iglesias, Juan; Rory Sabuncu, Mert; Van Leemput, Koen

2013-01-01

on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing...

Multi-modal trip planning system : Northeastern Illinois Regional Transportation Authority.

Science.gov (United States)

2013-01-01

This report evaluates the Multi-Modal Trip Planner System (MMTPS) implemented by the Northeastern Illinois Regional Transportation Authority (RTA) against the specific functional objectives enumerated by the Federal Transit Administration (FTA) in it...

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Science.gov (United States)

Liu, Manhua; Cheng, Danni; Wang, Kundong; Wang, Yaping

2018-03-23

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging

Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models

KAUST Repository

Elsheikh, Ahmed H.

2013-05-01

A novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.

Hierarchical programming language for modal multi-rate real-time stream processing applications

NARCIS (Netherlands)

Geuns, S.J.; Hausmans, J.P.H.M.; Bekooij, Marco Jan Gerrit

2014-01-01

Modal multi-rate stream processing applications with real-time constraints which are executed on multi-core embedded systems often cannot be conveniently specified using current programming languages. An important issue is that sequential programming languages do not allow for convenient programming

Multi-focus image fusion with the all convolutional neural network

Science.gov (United States)

Du, Chao-ben; Gao, She-sheng

2018-01-01

A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network (CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

Visualization of multi-INT fusion data using Java Viewer (JVIEW)

Science.gov (United States)

Blasch, Erik; Aved, Alex; Nagy, James; Scott, Stephen

2014-05-01

Visualization is important for multi-intelligence fusion and we demonstrate issues for presenting physics-derived (i.e., hard) and human-derived (i.e., soft) fusion results. Physics-derived solutions (e.g., imagery) typically involve sensor measurements that are objective, while human-derived (e.g., text) typically involve language processing. Both results can be geographically displayed for user-machine fusion. Attributes of an effective and efficient display are not well understood, so we demonstrate issues and results for filtering, correlation, and association of data for users - be they operators or analysts. Operators require near-real time solutions while analysts have the opportunities of non-real time solutions for forensic analysis. In a use case, we demonstrate examples using the JVIEW concept that has been applied to piloting, space situation awareness, and cyber analysis. Using the open-source JVIEW software, we showcase a big data solution for multi-intelligence fusion application for context-enhanced information fusion.

Multi-Modal Traveler Information System - GCM Corridor Architecture Functional Requirements

Science.gov (United States)

1997-11-17

The Multi-Modal Traveler Information System (MMTIS) project involves a large number of Intelligent Transportation System (ITS) related tasks. It involves research of all ITS initiatives in the Gary-Chicago-Milwaukee (GCM) Corridor which are currently...

Surgical techniques for lumbo-sacral fusion.

Science.gov (United States)

Tropiano, P; Giorgi, H; Faure, A; Blondel, B

2017-02-01

Lumbo-sacral (L5-S1) fusion is a widely performed procedure that has become the reference standard treatment for refractory low back pain. L5-S1 is a complex transition zone between the mobile lordotic distal lumbar spine and the fixed sacral region. The goal is to immobilise the lumbo-sacral junction in order to relieve pain originating from this site. Apart from achieving inter-vertebral fusion, the main challenge lies in the preoperative determination of the fixed L5-S1 position that will be optimal for the patient. Many lumbo-sacral fusion techniques are available. Stabilisation can be achieved using various methods. An anterior, posterior, or combined approach may be used. Recently developed minimally invasive techniques are gaining in popularity based on their good clinical outcomes and high fusion rates. The objective of this conference is to resolve the main issues faced by spinal surgeons in their everyday practice. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

An investigation of face and fingerprint feature-fusion guidelines

CSIR Research Space (South Africa)

Brown, Dane

2016-05-01

Full Text Available There are a lack of multi-modal biometric fusion guidelines at the feature-level. This paper investigates face and fingerprint features in the form of their strengths and weaknesses. This serves as a set of guidelines to authors that are planning...

Multi-Modal Intelligent Traffic Signal Systems Signal Plans for Roadside Equipment

Data.gov (United States)

Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

Multi-Modal Intelligent Traffic Signal Systems Vehicle Trajectories for Roadside Equipment

Data.gov (United States)

Department of Transportation — Data were collected during the Multi-Modal Intelligent Transportation Signal Systems (MMITSS) study. MMITSS is a next-generation traffic signal system that seeks to...

A digital 3D atlas of the marmoset brain based on multi-modal MRI.

Science.gov (United States)

Liu, Cirong; Ye, Frank Q; Yen, Cecil Chern-Chyi; Newman, John D; Glen, Daniel; Leopold, David A; Silva, Afonso C

2018-04-01

The common marmoset (Callithrix jacchus) is a New-World monkey of growing interest in neuroscience. Magnetic resonance imaging (MRI) is an essential tool to unveil the anatomical and functional organization of the marmoset brain. To facilitate identification of regions of interest, it is desirable to register MR images to an atlas of the brain. However, currently available atlases of the marmoset brain are mainly based on 2D histological data, which are difficult to apply to 3D imaging techniques. Here, we constructed a 3D digital atlas based on high-resolution ex-vivo MRI images, including magnetization transfer ratio (a T1-like contrast), T2w images, and multi-shell diffusion MRI. Based on the multi-modal MRI images, we manually delineated 54 cortical areas and 16 subcortical regions on one hemisphere of the brain (the core version). The 54 cortical areas were merged into 13 larger cortical regions according to their locations to yield a coarse version of the atlas, and also parcellated into 106 sub-regions using a connectivity-based parcellation method to produce a refined atlas. Finally, we compared the new atlas set with existing histology atlases and demonstrated its applications in connectome studies, and in resting state and stimulus-based fMRI. The atlas set has been integrated into the widely-distributed neuroimaging data analysis software AFNI and SUMA, providing a readily usable multi-modal template space with multi-level anatomical labels (including labels from the Paxinos atlas) that can facilitate various neuroimaging studies of marmosets. Published by Elsevier Inc.

Outcome of transarterial chemoembolization-based multi-modal treatment in patients with unresectable hepatocellular carcinoma.

Science.gov (United States)

Song, Do Seon; Nam, Soon Woo; Bae, Si Hyun; Kim, Jin Dong; Jang, Jeong Won; Song, Myeong Jun; Lee, Sung Won; Kim, Hee Yeon; Lee, Young Joon; Chun, Ho Jong; You, Young Kyoung; Choi, Jong Young; Yoon, Seung Kew

2015-02-28

To investigate the efficacy and safety of transarterial chemoembolization (TACE)-based multimodal treatment in patients with large hepatocellular carcinoma (HCC). A total of 146 consecutive patients were included in the analysis, and their medical records and radiological data were reviewed retrospectively. In total, 119 patients received TACE-based multi-modal treatments, and the remaining 27 received conservative management. Overall survival (P<0.001) and objective tumor response (P=0.003) were significantly better in the treatment group than in the conservative group. After subgroup analysis, survival benefits were observed not only in the multi-modal treatment group compared with the TACE-only group (P=0.002) but also in the surgical treatment group compared with the loco-regional treatment-only group (P<0.001). Multivariate analysis identified tumor stage (P<0.001) and tumor type (P=0.009) as two independent pre-treatment factors for survival. After adjusting for significant pre-treatment prognostic factors, objective response (P<0.001), surgical treatment (P=0.009), and multi-modal treatment (P=0.002) were identified as independent post-treatment prognostic factors. TACE-based multi-modal treatments were safe and more beneficial than conservative management. Salvage surgery after successful downstaging resulted in long-term survival in patients with large, unresectable HCC.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

Science.gov (United States)

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.

Multi-modal Virtual Scenario Enhances Neurofeedback Learning

Directory of Open Access Journals (Sweden)

Avihay Cohen

2016-08-01

Full Text Available In the past decade neurofeedback has become the focus of a growing body of research. With real-time fMRI enabling on-line monitoring of emotion related areas such as the amygdala, many have begun testing its therapeutic benefits. However most existing neurofeedback procedures still use monotonic uni-modal interfaces, thus possibly limiting user engagement and weakening learning efficiency. The current study tested a novel multi-sensory neurofeedback animated scenario aimed at enhancing user experience and improving learning. We examined whether relative to a simple uni-modal 2D interface, learning via an interface of complex multi-modal 3D scenario will result in improved neurofeedback learning. As a neural-probe, we used the recently developed fMRI-inspired EEG model of amygdala activity (amygdala-EEG finger print; amygdala-EFP, enabling low-cost and mobile limbic neurofeedback training. Amygdala-EFP was reflected in the animated scenario by the unrest level of a hospital waiting-room in which virtual characters become impatient, approach the admission-desk and complain loudly. Successful down-regulation was reflected as an ease in the room unrest-level. We tested whether relative to a standard uni-modal 2D graphic thermometer interface, this animated scenario could facilitate more effective learning and improve the training experience. Thirty participants underwent two separated neurofeedback sessions (one-week apart practicing down-regulation of the amygdala-EFP signal. In the first session, half trained via the animated scenario and half via a thermometer interface. Learning efficiency was tested by three parameters: (a effect-size of the change in amygdala-EFP following training, (b sustainability of the learned down-regulation in the absence of online feedback, and (c transferability to an unfamiliar context. Comparing amygdala-EFP signal amplitude between the last and the first neurofeedback trials revealed that the animated scenario

Preemptive analgesia I: physiological pathways and pharmacological modalities.

LENUS (Irish Health Repository)

Kelly, D J

2012-02-03

PURPOSE: This two-part review summarizes the current knowledge of physiological mechanisms, pharmacological modalities and controversial issues surrounding preemptive analgesia. SOURCE: Articles from 1966 to present were obtained from the MEDLINE databases. Search terms included: analgesia, preemptive; neurotransmitters; pain, postoperative; hyperalgesia; sensitization, central nervous system; pathways, nociception; anesthetic techniques; analgesics, agents. Principal findings: The physiological basis of preemptive analgesia is complex and involves modification of the pain pathways. The pharmacological modalities available may modify the physiological responses at various levels. Effective preemptive analgesic techniques require multi-modal interception of nociceptive input, increasing threshold for nociception, and blocking or decreasing nociceptor receptor activation. Although the literature is controversial regarding the effectiveness of preemptive analgesia, some general recommendations can be helpful in guiding clinical care. Regional anesthesia induced prior to surgical trauma and continued well into the postoperative period is effective in attenuating peripheral and central sensitization. Pharmacologic agents such as NSAIDs (non-steroidal anti-inflammatory drugs) opioids, and NMDA (N-methyl-D-aspartate) - and alpha-2-receptor antagonists, especially when used in combination, act synergistically to decrease postoperative pain. CONCLUSION: The variable patient characteristics and timing of preemptive analgesia in relation to surgical noxious input requires individualization of the technique(s) chosen. Multi-modal analgesic techniques appear most effective.

Multi-focus Image Fusion Using Epifluorescence Microscopy for Robust Vascular Segmentation

OpenAIRE

Pelapur, Rengarajan; Prasath, Surya; Palaniappan, Kannappan

2014-01-01

We are building a computerized image analysis system for Dura Mater vascular network from fluorescence microscopy images. We propose a system that couples a multi-focus image fusion module with a robust adaptive filtering based segmentation. The robust adaptive filtering scheme handles noise without destroying small structures, and the multi focal image fusion considerably improves the overall segmentation quality by integrating information from multiple images. Based on the segmenta...

A Selective Review of Multimodal Fusion Methods in Schizophrenia

Directory of Open Access Journals (Sweden)

Jing eSui

2012-02-01

Full Text Available Schizophrenia (SZ is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009. Though strong evidences for functional, structural and genetic abnormalities associated with this disease exist, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009, and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a. It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a. It is becoming increasingly clear that multi-modal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research.

Facile Fabrication of Animal-Specific Positioning Molds For Multi-modality Molecular Imaging

International Nuclear Information System (INIS)

Park, Jeong Chan; Oh, Ji Eun; Woo, Seung Tae

2008-01-01

Recently multi-modal imaging system has become widely adopted in molecular imaging. We tried to fabricate animal-specific positioning molds for PET/MR fusion imaging using easily available molding clay and rapid foam. The animal-specific positioning molds provide immobilization and reproducible positioning of small animal. Herein, we have compared fiber-based molding clay with rapid foam in fabricating the molds of experimental animal. The round bottomed-acrylic frame, which fitted into microPET gantry, was prepared at first. The experimental mice was anesthetized and placed on the mold for positioning. Rapid foam and fiber-based clay were used to fabricate the mold. In case of both rapid foam and the clay, the experimental animal needs to be pushed down smoothly into the mold for positioning. However, after the mouse was removed, the fabricated clay needed to be dried completely at 60 .deg. C in oven overnight for hardening. Four sealed pipe tips containing [ 18 F]FDG solution were used as fiduciary markers. After injection of [ 18 F]FDG via tail vein, microPET scanning was performed. Successively, MRI scanning was followed in the same animal. Animal-specific positioning molds were fabricated using rapid foam and fiber-based molding clay for multimodality imaging. Functional and anatomical images were obtained with microPET and MRI, respectively. The fused PET/MR images were obtained using freely available AMIDE program. Animal-specific molds were successfully prepared using easily available rapid foam, molding clay and disposable pipet tips. Thanks to animal-specific molds, fusion images of PET and MR were co-registered with negligible misalignment

Contribution to the detection of changes in multi-modal 3D MRI sequences

International Nuclear Information System (INIS)

Bosc, Marcel

2003-01-01

This research thesis reports the study of automatic techniques for the detection of changes in image sequences of brain magnetic resonance imagery (MRI), and more particularly the study of localised intensity changes occurring during pathological evolutions such as evolutions of lesions into multiple sclerosis. Thus, this work focused on the development of image processing tools allowing to decide whether changes are statistically significant or not. The author developed automatic techniques of identification and correction of the main artefacts (position, deformations, intensity variation, and so on), and proposes an original technique for cortex segmentation which introduced anatomic information for an improved automatic detection. The developed change detection system has been assessed within the frame of the study of the evolution of lesions of multiple sclerosis. Performance have been determined on a large number of multi-modal images, and the automatic system has shown better performance than a human expert [fr

Multi-modal Social Networks: A MRF Learning Approach

Science.gov (United States)

2016-06-20

Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

Cross-modal face recognition using multi-matcher face scores

Science.gov (United States)

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.

MINERVA: A multi-modality plug-in-based radiation therapy treatment planning system

International Nuclear Information System (INIS)

Wemple, C. A.; Wessol, D. E.; Nigg, D. W.; Cogliati, J. J.; Milvich, M.; Fredrickson, C. M.; Perkins, M.; Harkin, G. J.; Hartmann-Siantar, C. L.; Lehmann, J.; Flickinger, T.; Pletcher, D.; Yuan, A.; DeNardo, G. L.

2005-01-01

Researchers at the INEEL, MSU, LLNL and UCD have undertaken development of MINERVA, a patient-centric, multi-modal, radiation treatment planning system, which can be used for planning and analysing several radiotherapy modalities, either singly or combined, using common treatment planning tools. It employs an integrated, lightweight plug-in architecture to accommodate multi-modal treatment planning using standard interface components. The design also facilitates the future integration of improved planning technologies. The code is being developed with the Java programming language for inter-operability. The MINERVA design includes the image processing, model definition and data analysis modules with a central module to coordinate communication and data transfer. Dose calculation is performed by source and transport plug-in modules, which communicate either directly through the database or through MINERVA's openly published, extensible markup language (XML)-based application programmer's interface (API). All internal data are managed by a database management system and can be exported to other applications or new installations through the API data formats. A full computation path has been established for molecular-targeted radiotherapy treatment planning, with additional treatment modalities presently under development. (authors)

Multi-Modal Traveler Information System - GCM Corridor Architecture Interface Control Requirements

Science.gov (United States)

1997-10-31

The Multi-Modal Traveler Information System (MMTIS) project involves a large number of Intelligent Transportation System (ITS) related tasks. It involves research of all ITS initiatives in the Gary-Chicago-Milwaukee (GCM) Corridor which are currently...

Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning.

Science.gov (United States)

Zuluaga, Maria A; Rodionov, Roman; Nowell, Mark; Achhala, Sufyan; Zombori, Gergely; Mendelson, Alex F; Cardoso, M Jorge; Miserocchi, Anna; McEvoy, Andrew W; Duncan, John S; Ourselin, Sébastien

2015-08-01

Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying significantly associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer-assisted planning systems that can optimise the safety profile of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Twelve paired data sets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coefficient was 0.89 ± 0.04, representing a statistically significantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ± 0.03). Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity.

Strategic Mobility 21, Inland Port - Multi-Modal Terminal Operating System Design Specification

National Research Council Canada - National Science Library

Mallon, Lawrence G; Dougherty, Edmond J

2007-01-01

...) Specification identifies technical and functional requirements for procuring and integrating services required for a multi-modal node operating software system operating within a Service Oriented Architecture (SOA...

Integrative Multi-Spectral Sensor Device for Far-Infrared and Visible Light Fusion

Science.gov (United States)

Qiao, Tiezhu; Chen, Lulu; Pang, Yusong; Yan, Gaowei

2018-06-01

Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.

The Review of Visual Analysis Methods of Multi-modal Spatio-temporal Big Data

Directory of Open Access Journals (Sweden)

ZHU Qing

2017-10-01

Full Text Available The visual analysis of spatio-temporal big data is not only the state-of-art research direction of both big data analysis and data visualization, but also the core module of pan-spatial information system. This paper reviews existing visual analysis methods at three levels:descriptive visual analysis, explanatory visual analysis and exploratory visual analysis, focusing on spatio-temporal big data's characteristics of multi-source, multi-granularity, multi-modal and complex association.The technical difficulties and development tendencies of multi-modal feature selection, innovative human-computer interaction analysis and exploratory visual reasoning in the visual analysis of spatio-temporal big data were discussed. Research shows that the study of descriptive visual analysis for data visualizationis is relatively mature.The explanatory visual analysis has become the focus of the big data analysis, which is mainly based on interactive data mining in a visual environment to diagnose implicit reason of problem. And the exploratory visual analysis method needs a major break-through.

Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

DEFF Research Database (Denmark)

Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

2012-01-01

measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study...... of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi- modal detection...... system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data....

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

Science.gov (United States)

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

2015-02-01

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

Experimental modal analysis of fractal-inspired multi-frequency structures for piezoelectric energy converters

International Nuclear Information System (INIS)

Castagnetti, D

2012-01-01

An important issue in the field of energy harvesting through piezoelectric materials is the design of simple and efficient structures which are multi-frequency in the ambient vibration range. This paper deals with the experimental assessment of four fractal-inspired multi-frequency structures for piezoelectric energy harvesting. These structures, thin plates of square shape, were proposed in a previous work by the author and their modal response numerically analysed. The present work has two aims. First, to assess the modal response of these structures through an experimental investigation. Second, to evaluate, through computational simulation, the performance of a piezoelectric converter relying on one of these fractal-inspired structures. The four fractal-inspired structures are examined in the range between 0 and 100 Hz, with regard to both eigenfrequencies and eigenmodes. In the same frequency range, the modal response and power output of the piezoelectric converter are investigated. (paper)

Hybrid Image Fusion for Sharpness Enhancement of Multi-Spectral Lunar Images

Science.gov (United States)

Awumah, Anna; Mahanti, Prasun; Robinson, Mark

2016-10-01

Image fusion enhances the sharpness of a multi-spectral (MS) image by incorporating spatial details from a higher-resolution panchromatic (Pan) image [1,2]. Known applications of image fusion for planetary images are rare, although image fusion is well-known for its applications to Earth-based remote sensing. In a recent work [3], six different image fusion algorithms were implemented and their performances were verified with images from the Lunar Reconnaissance Orbiter (LRO) Camera. The image fusion procedure obtained a high-resolution multi-spectral (HRMS) product from the LRO Narrow Angle Camera (used as Pan) and LRO Wide Angle Camera (used as MS) images. The results showed that the Intensity-Hue-Saturation (IHS) algorithm results in a high-spatial quality product while the Wavelet-based image fusion algorithm best preserves spectral quality among all the algorithms. In this work we show the results of a hybrid IHS-Wavelet image fusion algorithm when applied to LROC MS images. The hybrid method provides the best HRMS product - both in terms of spatial resolution and preservation of spectral details. Results from hybrid image fusion can enable new science and increase the science return from existing LROC images.[1] Pohl, Cle, and John L. Van Genderen. "Review article multisensor image fusion in remote sensing: concepts, methods and applications." International journal of remote sensing 19.5 (1998): 823-854.[2] Zhang, Yun. "Understanding image fusion." Photogramm. Eng. Remote Sens 70.6 (2004): 657-661.[3] Mahanti, Prasun et al. "Enhancement of spatial resolution of the LROC Wide Angle Camera images." Archives, XXIII ISPRS Congress Archives (2016).

Multi-Modal Inference in Animacy Perception for Artificial Object

Directory of Open Access Journals (Sweden)

Kohske Takahashi

2011-10-01

Full Text Available Sometimes we feel animacy for artificial objects and their motion. Animals usually interact with environments through multiple sensory modalities. Here we investigated how the sensory responsiveness of artificial objects to the environment would contribute to animacy judgment for them. In a 90-s trial, observers freely viewed four objects moving in a virtual 3D space. The objects, whose position and motion were determined following Perlin-noise series, kept drifting independently in the space. Visual flashes, auditory bursts, or synchronous flashes and bursts appeared with 1–2 s intervals. The first object abruptly accelerated their motion just after visual flashes, giving an impression of responding to the flash. The second object responded to bursts. The third object responded to synchronous flashes and bursts. The forth object accelerated at a random timing independent of flashes and bursts. The observers rated how strongly they felt animacy for each object. The results showed that the object responding to the auditory bursts was rated as having weaker animacy compared to the other objects. This implies that sensory modality through which an object interacts with the environment may be a factor for animacy perception in the object and may serve as the basis of multi-modal and cross-modal inference of animacy.

Multi-modal brain imaging software for guiding invasive treatment of epilepsy

NARCIS (Netherlands)

Ossenblok, P.P.W.; Marien, S.; Meesters, S.P.L.; Florack, L.M.J.; Hofman, P.; Schijns, O.E.M.G.; Colon, A.

2017-01-01

Purpose: The surgical treatment of patients with complex epilepsies is changing more and more from open, invasive surgery towards minimally invasive, image guided treatment. Multi-modal brain imaging procedures are developed to delineate preoperatively the region of the brain which is responsible

Design and experimental study of a multi-modal piezoelectric energy harvester

Energy Technology Data Exchange (ETDEWEB)

Xiong, Xing Yu [School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing (China); Oyadiji, S. Olutunde [School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester (United States)

2017-01-15

A multi-modal piezoelectric vibration energy harvester is designed in this article. It consists of a cantilevered base beam and some upper and lower layer beams with rigid masses bonded between the beams as spacers. For a four-layer harvester subjected to random base excitations, relocating the mass positions leads to the generation of up to four close resonance frequencies over the frequency range from 10 Hz to 100 Hz with relative large power output. The harvesters are connected with a resistance decade box and the frequency response functions of the voltage and power on resistive loads are determined. The experimental results are validated with the simulation results using the finite element method. On a certain level of power output, the experimental results show that the multi-modal harvesters can generate a frequency band that is more than two times greater than the frequency band produced by a cantilevered beam harvester.

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

Science.gov (United States)

Leptoukh, Gregory; Zubko, V.; Gopalan, A.

2007-01-01

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

SU-E-I-83: Error Analysis of Multi-Modality Image-Based Volumes of Rodent Solid Tumors Using a Preclinical Multi-Modality QA Phantom

Energy Technology Data Exchange (ETDEWEB)

Lee, Y [University of Kansas Hospital, Kansas City, KS (United States); Fullerton, G; Goins, B [University of Texas Health Science Center at San Antonio, San Antonio, TX (United States)

2015-06-15

Purpose: In our previous study a preclinical multi-modality quality assurance (QA) phantom that contains five tumor-simulating test objects with 2, 4, 7, 10 and 14 mm diameters was developed for accurate tumor size measurement by researchers during cancer drug development and testing. This study analyzed the errors during tumor volume measurement from preclinical magnetic resonance (MR), micro-computed tomography (micro- CT) and ultrasound (US) images acquired in a rodent tumor model using the preclinical multi-modality QA phantom. Methods: Using preclinical 7-Tesla MR, US and micro-CT scanners, images were acquired of subcutaneous SCC4 tumor xenografts in nude rats (3–4 rats per group; 5 groups) along with the QA phantom using the same imaging protocols. After tumors were excised, in-air micro-CT imaging was performed to determine reference tumor volume. Volumes measured for the rat tumors and phantom test objects were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three imaging modalities. Then linear regression analysis was performed to compare image-based tumor volumes with the reference tumor volume and known test object volume for the rats and the phantom respectively. Results: The slopes of regression lines for in-vivo tumor volumes measured by three imaging modalities were 1.021, 1.101 and 0.862 for MRI, micro-CT and US respectively. For phantom, the slopes were 0.9485, 0.9971 and 0.9734 for MRI, micro-CT and US respectively. Conclusion: For both animal and phantom studies, random and systematic errors were observed. Random errors were observer-dependent and systematic errors were mainly due to selected imaging protocols and/or measurement method. In the animal study, there were additional systematic errors attributed to ellipsoidal assumption for tumor shape. The systematic errors measured using the QA phantom need to be taken into account to reduce measurement

Quantitative image fusion in infrared radiometry

Science.gov (United States)

Romm, Iliya; Cukurel, Beni

2018-05-01

Towards high-accuracy infrared radiance estimates, measurement practices and processing techniques aimed to achieve quantitative image fusion using a set of multi-exposure images of a static scene are reviewed. The conventional non-uniformity correction technique is extended, as the original is incompatible with quantitative fusion. Recognizing the inherent limitations of even the extended non-uniformity correction, an alternative measurement methodology, which relies on estimates of the detector bias using self-calibration, is developed. Combining data from multi-exposure images, two novel image fusion techniques that ultimately provide high tonal fidelity of a photoquantity are considered: ‘subtract-then-fuse’, which conducts image subtraction in the camera output domain and partially negates the bias frame contribution common to both the dark and scene frames; and ‘fuse-then-subtract’, which reconstructs the bias frame explicitly and conducts image fusion independently for the dark and the scene frames, followed by subtraction in the photoquantity domain. The performances of the different techniques are evaluated for various synthetic and experimental data, identifying the factors contributing to potential degradation of the image quality. The findings reflect the superiority of the ‘fuse-then-subtract’ approach, conducting image fusion via per-pixel nonlinear weighted least squares optimization.

Context-Aware Fusion of RGB and Thermal Imagery for Traffic Monitoring

DEFF Research Database (Denmark)

Alldieck, Thiemo; Bahnsen, Chris Holmberg; Moeslund, Thomas B.

2016-01-01

In order to enable a robust 24-h monitoring of traffic under changing environmental conditions, it is beneficial to observe the traffic scene using several sensors, preferably from different modalities. To fully benefit from multi-modal sensor output, however, one must fuse the data. This paper...... introduces a new approach for fusing color RGB and thermal video streams by using not only the information from the videos themselves, but also the available contextual information of a scene. The contextual information is used to judge the quality of a particular modality and guides the fusion of two...

Use of the modal superposition technique for piping system blowdown analyses

International Nuclear Information System (INIS)

Ware, A.G.; Macek, R.W.

1983-01-01

A standard method of solving for the seismic response of piping systems is the modal superposition technique. Only a limited number of structural modes are considered (typically those up to 33 Hz in the U.S.), since the effect on the calculated response due to higher modes is generally small, and the method can result in considerable computer cost savings over the direct integration method. The modal superposition technique has also been applied to piping response problems in which the forcing functions are due to fluid excitation. Application of the technique to this case is somewhat more difficult, because a well defined cutoff frequency for determining structural modes to be included has not been established. This paper outlines a method for higher mode corrections, and suggests methods to determine suitable cutoff frequencies for piping system blowdown analyses. A numerical example illustrates how uncorrected modal superposition results can produce erroneous stress results

Development of a novel fusion imaging technique in the diagnosis of hepatobiliary-pancreatic lesions

International Nuclear Information System (INIS)

Soga, Koichi; Ochiai, Jun; Miyajima, Takashi; Kassai, Kyoichi; Itani, Kenji; Yagi, Nobuaki; Naito, Yuji

2013-01-01

Multi-row detector computed tomography (MDCT) and magnetic resonance cholangiopancreatography (MRCP) play an important role in the imaging diagnosis of hepatobiliary-pancreatic lesions. Here we investigated whether unifying the MDCT and MRCP images onto the same screen using fusion imaging could overcome the limitations of each technique, while still maintaining their benefits. Moreover, because reports of fusion imaging using MDCT and MRCP are rare, we assessed the benefits and limitations of this method for its potential application in a clinical setting. The patient group included 9 men and 11 women. Among the 20 patients, the final diagnoses were as follows: 10 intraductal papillary mucinous neoplasms, 5 biliary system carcinomas, 1 pancreatic adenocarcinoma and 5 non-neoplastic lesions. After transmitting the Digital Imaging and Communication in Medicine data of the MDCT and MRCP images to a workstation, we performed a 3-D organisation of both sets of images using volume rendering for the image fusion. Fusion imaging enabled clear identification of the spatial relationship between a hepatobiliary-pancreatic lesion and the solid viscera and/or vessels. Further, this method facilitated the determination of the relationship between the anatomical position of the lesion and its surroundings more easily than either MDCT or MRCP alone. Fusion imaging is an easy technique to perform and may be a useful tool for planning treatment strategies and for examining pathological changes in hepatobiliary-pancreatic lesions. Additionally, the ease of obtaining the 3-D images suggests the possibility of using these images to plan intervention strategies.

Seizure Onset Detection based on a Uni- or Multi-modal Intelligent Seizure Acquisition (UISA/MISA) System

DEFF Research Database (Denmark)

Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

2010-01-01

An automatic Uni- or Multi-modal Inteligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic...... algorithm on motion data is extracting features as “log-sum” measures of discrete wavelet components. Classification into the two groups “seizure” versus “nonseizure” is made based on the support vector machine (SVM) algorithm. The algorithm performs with a sensitivity of 91-100%, a median latency of 1...... second and a specificity of 100% on multi-modal data from five healthy subjects simulating seizures. The uni-modal algorithm based on sEMG data from the subjects and patients performs satisfactorily in some cases. As expected, our results clearly show superiority of the multimodal approach, as compared...

A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion

Science.gov (United States)

Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

2017-09-01

Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in

Security of nuclear materials using fusion multi sensor wavelett

International Nuclear Information System (INIS)

Djoko Hari Nugroho

2010-01-01

Security of a nuclear material in an installation is determined by how far the installation is to assure that nuclear material remains at a predetermined location. This paper observed a preliminary design on nuclear material tracking system in the installation for decision making support based on multi sensor fusion that is reliable and accurate to ensure that the nuclear material remains inside the control area. Capability on decision making in the Management Information System is represented by an understanding of perception in the third level of abstraction. The second level will be achieved with the support of image analysis and organizing data. The first level of abstraction is constructed by merger between several CCD camera sensors distributed in a building in a data fusion representation. Data fusion is processed based on Wavelett approach. Simulation utilizing Matlab programming shows that Wavelett fuses multi information from sensors as well. Hope that when the nuclear material out of control regions which have been predetermined before, there will arise a warning alarm and a message in the Management Information System display. Thus the nuclear material movement time event can be obtained and tracked as well. (author)

Multimodality Image Fusion and Planning and Dose Delivery for Radiation Therapy

International Nuclear Information System (INIS)

Saw, Cheng B.; Chen Hungcheng; Beatty, Ron E.; Wagner, Henry

2008-01-01

Image-guided radiation therapy (IGRT) relies on the quality of fused images to yield accurate and reproducible patient setup prior to dose delivery. The registration of 2 image datasets can be characterized as hardware-based or software-based image fusion. Hardware-based image fusion is performed by hybrid scanners that combine 2 distinct medical imaging modalities such as positron emission tomography (PET) and computed tomography (CT) into a single device. In hybrid scanners, the patient maintains the same position during both studies making the fusion of image data sets simple. However, it cannot perform temporal image registration where image datasets are acquired at different times. On the other hand, software-based image fusion technique can merge image datasets taken at different times or with different medical imaging modalities. Software-based image fusion can be performed either manually, using landmarks, or automatically. In the automatic image fusion method, the best fit is evaluated using mutual information coefficient. Manual image fusion is typically performed at dose planning and for patient setup prior to dose delivery for IGRT. The fusion of orthogonal live radiographic images taken prior to dose delivery to digitally reconstructed radiographs will be presented. Although manual image fusion has been routinely used, the use of fiducial markers has shortened the fusion time. Automated image fusion should be possible for IGRT because the image datasets are derived basically from the same imaging modality, resulting in further shortening the fusion time. The advantages and limitations of both hardware-based and software-based image fusion methodologies are discussed

Multi-Level Sensor Fusion Algorithm Approach for BMD Interceptor Applications

National Research Council Canada - National Science Library

Allen, Doug

1998-01-01

... through fabrication and testing of advanced sensor hardware concepts and advanced sensor fusion algorithms. Advanced sensor concepts include onboard LADAR in conjunction with a multi-color passive IR sensor...

A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion

Science.gov (United States)

Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

2013-01-01

This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes. PMID:23857263

Performance evaluation of multi-sensor data-fusion systems

Indian Academy of Sciences (India)

In this paper, the utilization of multi-sensors of different types, their characteristics, and their data-fusion in launch vehicles to achieve the goal of injecting the satellite into a precise orbit is explained. Performance requirements of sensors and their redundancy management in a typical launch vehicle are also included.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

Science.gov (United States)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was

Graduate Student Perceptions of Multi-Modal Tablet Use in Academic Environments

Science.gov (United States)

Bryant, Ezzard C., Jr.

2016-01-01

The purpose of this study was to explore graduate student perceptions of use and the ease of use of multi-modal tablets to access electronic course materials, and the perceived differences based on students' gender, age, college of enrollment, and previous experience. This study used the Unified Theory of Acceptance and Use of Technology to…

The role of multi modality imaging in selecting patients and guiding lead placement for the delivery of cardiac resynchronization therapy.

Science.gov (United States)

Behar, Jonathan M; Claridge, Simon; Jackson, Tom; Sieniewicz, Ben; Porter, Bradley; Webb, Jessica; Rajani, Ronak; Kapetanakis, Stamatis; Carr-White, Gerald; Rinaldi, Christopher A

2017-02-01

Cardiac resynchronization therapy (CRT) is an effective pacemaker delivered treatment for selected patients with heart failure with the target of restoring electro-mechanical synchrony. Imaging techniques using echocardiography have as yet failed to find a metric of dyssynchrony to predict CRT response. Current guidelines are thus unchanged in recommending prolonged QRS duration, severe systolic function and refractory heart failure symptoms as criteria for CRT implantation. Evolving strain imaging techniques in 3D echocardiography, cardiac MRI and CT may however, overcome limitations of older methods and yield more powerful CRT response predictors. Areas covered: In this review, we firstly discuss the use of multi modality cardiac imaging in the selection of patients for CRT implantation and predicting the response to CRT. Secondly we examine the clinical evidence on avoiding areas of myocardial scar, targeting areas of dyssynchrony and in doing so, achieving the optimal positioning of the left ventricular lead to deliver CRT. Finally, we present the latest clinical studies which are integrating both clinical and imaging data with X-rays during the implantation in order to improve the accuracy of LV lead placement. Expert commentary: Image integration and fusion of datasets with live X-Ray angiography to guide procedures in real time is now a reality for some implanting centers. Such hybrid facilities will enable users to interact with images, allowing measurement, annotation and manipulation with instantaneous visualization on the catheter laboratory monitor. Such advances will serve as an invaluable adjunct for implanting physicians to accurately deliver pacemaker leads into the optimal position to deliver CRT.

Experimental evaluation of a quasi-modal parameter based rotor foundation identification technique

Science.gov (United States)

Yu, Minli; Liu, Jike; Feng, Ningsheng; Hahn, Eric J.

2017-12-01

Correct modelling of the foundation of rotating machinery is an invaluable asset in model-based rotor dynamic study. One attractive approach for such purpose is to identify the relevant modal parameters of an equivalent foundation using the motion measurements of rotor and foundation at the bearing supports. Previous research showed that, a complex quasi-modal parameter based system identification technique could be feasible for this purpose; however, the technique was only validated by identifying simple structures under harmonic excitation. In this paper, such identification technique is further extended and evaluated by identifying the foundation of a numerical rotor-bearing-foundation system and an experimental rotor rig respectively. In the identification of rotor foundation with multiple bearing supports, all application points of excitation forces transmitted through bearings need to be included; however the assumed vibration modes far outside the rotor operating speed cannot or not necessary to be identified. The extended identification technique allows one to identify correctly an equivalent foundation with fewer modes than the assumed number of degrees of freedom, essentially by generalising the technique to be able to handle rectangular complex modal matrices. The extended technique is robust in numerical and experimental validation and is therefore likely to be applicable in the field.

Fusion of different modalities of imaging the fist

International Nuclear Information System (INIS)

Verdenet, J.; Garbuio, P.; Runge, M.; Cardot, J.C.

1997-01-01

The standard radiographical pictures are not able always to bring out the fracture of one of the fist bones. In an early study it was shown that 40% of patients presenting a suspicion of fracture and in which the radio- image was normal, have had a fracture confirmed with quantification by MRI and scintigraphy. The last one does not allow to specify the localization and consequently we developed a code to fusion entirely automatically the radiologic image and the scintigraphic image using no external marker. The code has been installed on a PC and uses the Matlab environment. Starting from the histogram processing the contours are individualized on the interpolated radio- and scinti-images. For matching there are 3 freedom degrees: one of rotation and 2 of translation (in x and y axes). The internal axes of the forearm was chosen to effect the rotation and translation. The forehand thickness, identical for each modality, allows to match properly the images. We have obtained an anatomic image on which the contour and the hyper-fixating zones of the scintigraphy are added. On a set of 100 examinations we observed 38 fractures while the difference between a fracture of the scaphoid and of another fist bone is confirmed in 93% of cases

Automatic Identification of Physical Activity Intensity and Modality from the Fusion of Accelerometry and Heart Rate Data.

Science.gov (United States)

García-García, Fernando; Benito, Pedro J; Hernando, María E

2016-12-07

Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling. We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor. Intensity levels (low / moderate / vigorous) were recognized, as well as for vigorous exercise, its modality (sustained aerobic / resistance / mixed). In total, 178.63 h of data about PA intensity (65.55 % low / 18.96 % moderate / 15.49 % vigorous) and 17.00 h about modality were collected in two experiments: one in free-living conditions, another in a fitness center under controlled protocols. The structure used for automatic classification comprised: a) definition of 42 time-domain signal features, b) dimensionality reduction, c) data clustering, and d) temporal filtering to exploit time redundancy by means of a Hidden Markov Model (HMM). Four dimensionality reduction techniques and four clustering algorithms were studied. In order to cope with class imbalance in the dataset, a custom performance metric was defined to aggregate recognition accuracy, precision and recall. The best scheme, which comprised a projection through Linear Discriminant Analysis (LDA) and k-means clustering, was evaluated in leave-one-subject-out cross-validation; notably outperforming the standard industry procedures for PA intensity classification: score 84.65 %, versus up to 63.60 %. Errors tended to be brief and to appear around transients. The application of ML techniques for pattern identification and temporal filtering allowed to merge accelerometry and HR data in a solid manner, and achieved markedly better recognition performances than the standard methods for PA intensity estimation.

Data fusion of multi-scale representations for structural damage detection

Science.gov (United States)

Guo, Tian; Xu, Zili

2018-01-01

Despite extensive researches into structural health monitoring (SHM) in the past decades, there are few methods that can detect multiple slight damage in noisy environments. Here, we introduce a new hybrid method that utilizes multi-scale space theory and data fusion approach for multiple damage detection in beams and plates. A cascade filtering approach provides multi-scale space for noisy mode shapes and filters the fluctuations caused by measurement noise. In multi-scale space, a series of amplification and data fusion algorithms are utilized to search the damage features across all possible scales. We verify the effectiveness of the method by numerical simulation using damaged beams and plates with various types of boundary conditions. Monte Carlo simulations are conducted to illustrate the effectiveness and noise immunity of the proposed method. The applicability is further validated via laboratory cases studies focusing on different damage scenarios. Both results demonstrate that the proposed method has a superior noise tolerant ability, as well as damage sensitivity, without knowing material properties or boundary conditions.

A New Multi-Sensor Track Fusion Architecture for Multi-Sensor Information Integration

Science.gov (United States)

2004-09-01

NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION ...NAME(S) AND ADDRESS(ES) Lockheed Martin Aeronautical Systems Company,Marietta,GA,3063 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...tracking process and degrades the track accuracy. ARCHITECHTURE OF MULTI-SENSOR TRACK FUSION MODEL The Alpha

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion.

Directory of Open Access Journals (Sweden)

Da Ma

Full Text Available Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

Directory of Open Access Journals (Sweden)

Hongzhuan Zhao

2016-04-01

Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

Energy Technology Data Exchange (ETDEWEB)

Cui, Yonggang

2018-05-07

In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.

Finger multibiometric cryptosystems: fusion strategy and template security

Science.gov (United States)

Peng, Jialiang; Li, Qiong; Abd El-Latif, Ahmed A.; Niu, Xiamu

2014-03-01

We address two critical issues in the design of a finger multibiometric system, i.e., fusion strategy and template security. First, three fusion strategies (feature-level, score-level, and decision-level fusions) with the corresponding template protection technique are proposed as the finger multibiometric cryptosystems to protect multiple finger biometric templates of fingerprint, finger vein, finger knuckle print, and finger shape modalities. Second, we theoretically analyze different fusion strategies for finger multibiometric cryptosystems with respect to their impact on security and recognition accuracy. Finally, the performance of finger multibiometric cryptosystems at different fusion levels is investigated on a merged finger multimodal biometric database. The comparative results suggest that the proposed finger multibiometric cryptosystem at feature-level fusion outperforms other approaches in terms of verification performance and template security.

Multi-modality molecular imaging: pre-clinical laboratory configuration

Science.gov (United States)

Wu, Yanjun; Wellen, Jeremy W.; Sarkar, Susanta K.

2006-02-01

In recent years, the prevalence of in vivo molecular imaging applications has rapidly increased. Here we report on the construction of a multi-modality imaging facility in a pharmaceutical setting that is expected to further advance existing capabilities for in vivo imaging of drug distribution and the interaction with their target. The imaging instrumentation in our facility includes a microPET scanner, a four wavelength time-domain optical imaging scanner, a 9.4T/30cm MRI scanner and a SPECT/X-ray CT scanner. An electronics shop and a computer room dedicated to image analysis are additional features of the facility. The layout of the facility was designed with a central animal preparation room surrounded by separate laboratory rooms for each of the major imaging modalities to accommodate the work-flow of simultaneous in vivo imaging experiments. This report will focus on the design of and anticipated applications for our microPET and optical imaging laboratory spaces. Additionally, we will discuss efforts to maximize the daily throughput of animal scans through development of efficient experimental work-flows and the use of multiple animals in a single scanning session.

Hand hygiene and healthcare system change within multi-modal promotion: a narrative review.

Science.gov (United States)

Allegranzi, B; Sax, H; Pittet, D

2013-02-01

Many factors may influence the level of compliance with hand hygiene recommendations by healthcare workers. Lack of products and facilities as well as their inappropriate and non-ergonomic location represent important barriers. Targeted actions aimed at making hand hygiene practices feasible during healthcare delivery by ensuring that the necessary infrastructure is in place, defined as 'system change', are essential to improve hand hygiene in healthcare. In particular, access to alcohol-based hand rubs (AHRs) enables appropriate and timely hand hygiene performance at the point of care. The feasibility and impact of system change within multi-modal strategies have been demonstrated both at institutional level and on a large scale. The introduction of AHRs overcomes some important barriers to best hand hygiene practices and is associated with higher compliance, especially when integrated within multi-modal strategies. Several studies demonstrated the association between AHR consumption and reduction in healthcare-associated infection, in particular, meticillin-resistant Staphylococcus aureus bacteraemia. Recent reports demonstrate the feasibility and success of system change implementation on a large scale. The World Health Organization and other investigators have reported the challenges and encouraging results of implementing hand hygiene improvement strategies, including AHR introduction, in settings with limited resources. This review summarizes the available evidence demonstrating the need for system change and its importance within multi-modal hand hygiene improvement strategies. This topic is also discussed in a global perspective and highlights some controversial issues. Copyright © 2013 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Fusion of space-borne multi-baseline and multi-frequency interferometric results based on extended Kalman filter to generate high quality DEMs

Science.gov (United States)

Zhang, Xiaojie; Zeng, Qiming; Jiao, Jian; Zhang, Jingfa

2016-01-01

Repeat-pass Interferometric Synthetic Aperture Radar (InSAR) is a technique that can be used to generate DEMs. But the accuracy of InSAR is greatly limited by geometrical distortions, atmospheric effect, and decorrelations, particularly in mountainous areas, such as western China where no high quality DEM has so far been accomplished. Since each of InSAR DEMs generated using data of different frequencies and baselines has their own advantages and disadvantages, it is therefore very potential to overcome some of the limitations of InSAR by fusing Multi-baseline and Multi-frequency Interferometric Results (MMIRs). This paper proposed a fusion method based on Extended Kalman Filter (EKF), which takes the InSAR-derived DEMs as states in prediction step and the flattened interferograms as observations in control step to generate the final fused DEM. Before the fusion, detection of layover and shadow regions, low-coherence regions and regions with large height error is carried out because MMIRs in these regions are believed to be unreliable and thereafter are excluded. The whole processing flow is tested with TerraSAR-X and Envisat ASAR datasets. Finally, the fused DEM is validated with ASTER GDEM and national standard DEM of China. The results demonstrate that the proposed method is effective even in low coherence areas.

LapTrain: multi-modality training curriculum for laparoscopic cholecystectomy-results of a randomized controlled trial.

Science.gov (United States)

Kowalewski, K F; Garrow, C R; Proctor, T; Preukschas, A A; Friedrich, M; Müller, P C; Kenngott, H G; Fischer, L; Müller-Stich, B P; Nickel, F

2018-02-12

Multiple training modalities for laparoscopy have different advantages, but little research has been conducted on the benefit of a training program that includes multiple different training methods compared to one method only. This study aimed to evaluate benefits of a combined multi-modality training program for surgical residents. Laparoscopic cholecystectomy (LC) was performed on a porcine liver as the pre-test. Randomization was stratified for experience to the multi-modality Training group (12 h of training on Virtual Reality (VR) and box trainer) or Control group (no training). The post-test consisted of a VR LC and porcine LC. Performance was rated with the Global Operative Assessment of Laparoscopic Skills (GOALS) score by blinded experts. Training (n = 33) and Control (n = 31) were similar in the pre-test (GOALS: 13.7 ± 3.4 vs. 14.7 ± 2.6; p = 0.198; operation time 57.0 ± 18.1 vs. 63.4 ± 17.5 min; p = 0.191). In the post-test porcine LC, Training had improved GOALS scores (+ 2.84 ± 2.85 points, p < 0.001), while Control did not (+ 0.55 ± 2.34 points, p = 0.154). Operation time in the post-test was shorter for Training vs. Control (40.0 ± 17.0 vs. 55.0 ± 22.2 min; p = 0.012). Junior residents improved GOALS scores to the level of senior residents (pre-test: 13.7 ± 2.7 vs. 18.3 ± 2.9; p = 0.010; post-test: 15.5 ± 3.4 vs. 18.8 ± 3.8; p = 0.120) but senior residents remained faster (50.1 ± 20.6 vs. 25.0 ± 1.9 min; p < 0.001). No differences were found between groups on the post-test VR trainer. Structured multi-modality training is beneficial for novices to improve basics and overcome the initial learning curve in laparoscopy as well as to decrease operation time for LCs in different stages of experience. Future studies should evaluate multi-modality training in comparison with single modalities. German Clinical Trials Register DRKS00011040.

A Hybrid FPGA/Coarse Parallel Processing Architecture for Multi-modal Visual Feature Descriptors

DEFF Research Database (Denmark)

Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Alonso, Javier Díaz

2008-01-01

This paper describes the hybrid architecture developed for speeding up the processing of so-called multi-modal visual primitives which are sparse image descriptors extracted along contours. In the system, the first stages of visual processing are implemented on FPGAs due to their highly parallel...

A hierarchical structure approach to MultiSensor Information Fusion

Energy Technology Data Exchange (ETDEWEB)

Maren, A.J. (Tennessee Univ., Tullahoma, TN (United States). Space Inst.); Pap, R.M.; Harston, C.T. (Accurate Automation Corp., Chattanooga, TN (United States))

1989-01-01

A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

A hierarchical structure approach to MultiSensor Information Fusion

Energy Technology Data Exchange (ETDEWEB)

Maren, A.J. [Tennessee Univ., Tullahoma, TN (United States). Space Inst.; Pap, R.M.; Harston, C.T. [Accurate Automation Corp., Chattanooga, TN (United States)

1989-12-31

A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

Modality prediction of biomedical literature images using multimodal feature representation

Directory of Open Access Journals (Sweden)

Pelka, Obioma

2016-08-01

Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

Information fusion in aquaculture: a state-of the art review

Directory of Open Access Journals (Sweden)

Shahbaz Gul HASSAN,Murtaza HASAN,Daoliang LI

2016-09-01

Full Text Available Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multi-sensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing, computer vision, sensors and wireless sensor networks. Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an industrialized ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, pH, dissolved oxygen, turbidity, temperature etc., necessary for the fish feeding process, are examined. Moreover, the different waste management and fish disease diagnosis techniques using different technologies, expert systems and modeling are also reviewed.

Multi-channel motor evoked potential monitoring during anterior cervical discectomy and fusion

Directory of Open Access Journals (Sweden)

Dong-Gun Kim

Full Text Available Objectives: Anterior cervical discectomy and fusion (ACDF surgery is the most common surgical procedure for the cervical spine with low complication rate. Despite the potential prognostic benefit, intraoperative neurophysiological monitoring (IONM, a method for detecting impending neurological compromise, is not routinely used in ACDF surgery. The present study aimed to identify the potential benefits of monitoring multi-channel motor evoked potentials (MEPs during ACDF surgery. Methods: We retrospectively reviewed 200 consecutive patients who received IONM with multi-channel MEPs and somatosensory evoked potentials (SSEPs. On average, 9.2 muscles per patient were evaluated under MEP monitoring. Results: The rate of MEP change during surgery in the multi-level ACDF group was significantly higher than the single-level group. Two patients from the single-level ACDF group (1.7% and four patients from the multi-level ACDF group (4.9% experienced post-operative motor deficits. Multi-channel MEPs monitoring during single and multi-level ACDF surgery demonstrated higher sensitivity, specificity, positive predictive and negative predictive value than SSEP monitoring. Conclusions: Multi-channel MEP monitoring might be beneficial for the detection of segmental injury as well as long tract injury during single- and multi-level ACDF surgery. Significance: This is first large scale study to identify the usefulness of multi-channel MEPs in monitoring ACDF surgery. Keywords: Disc disease, Somatosensory evoked potentials, Intraoperative neurophysiological monitoring, Motor evoked potentials, Anterior cervical discectomy and fusion

Solving the problem of imaging resolution: stochastic multi-scale image fusion

Science.gov (United States)

Karsanina, Marina; Mallants, Dirk; Gilyazetdinova, Dina; Gerke, Kiril

2016-04-01

Structural features of porous materials define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, gas exchange between biologically active soil root zone and atmosphere, etc.) and solute transport. To characterize soil and rock microstructure X-ray microtomography is extremely useful. However, as any other imaging technique, this one also has a significant drawback - a trade-off between sample size and resolution. The latter is a significant problem for multi-scale complex structures, especially such as soils and carbonates. Other imaging techniques, for example, SEM/FIB-SEM or X-ray macrotomography can be helpful in obtaining higher resolution or wider field of view. The ultimate goal is to create a single dataset containing information from all scales or to characterize such multi-scale structure. In this contribution we demonstrate a general solution for merging multiscale categorical spatial data into a single dataset using stochastic reconstructions with rescaled correlation functions. The versatility of the method is demonstrated by merging three images representing macro, micro and nanoscale spatial information on porous media structure. Images obtained by X-ray microtomography and scanning electron microscopy were fused into a single image with predefined resolution. The methodology is sufficiently generic for implementation of other stochastic reconstruction techniques, any number of scales, any number of material phases, and any number of images for a given scale. The methodology can be further used to assess effective properties of fused porous media images or to compress voluminous spatial datasets for efficient data storage. Potential practical applications of this method are abundant in soil science, hydrology and petroleum engineering, as well as other geosciences. This work was partially supported by RSF grant 14-17-00658 (X-ray microtomography study of shale

Single or double-level anterior interbody fusion techniques for cervical degenerative disc disease

NARCIS (Netherlands)

Jacobs, Wilco; Willems, Paul C.; van Limbeek, Jacques; Bartels, Ronald; Pavlov, Paul; Anderson, Patricia G.; Oner, Cumhur

2011-01-01

Background The number of surgical techniques for decompression and solid interbody fusion as treatment for cervical spondylosis has increased rapidly, but the rationale for the choice between different techniques remains unclear. Objectives To determine which technique of anterior interbody fusion

Prediction of Quadcopter State through Multi-Microphone Side-Channel Fusion

NARCIS (Netherlands)

Koops, Hendrik Vincent; Garg, Kashish; Kim, Munsung; Li, Jonathan; Volk, Anja; Franchetti, Franz

Improving trust in the state of Cyber-Physical Systems becomes increasingly important as more tasks become autonomous. We present a multi-microphone machine learning fusion approach to accurately predict complex states of a quadcopter drone in flight from the sound it makes using audio content

Numerical analysis of modal tomography for solar multi-conjugate adaptive optics

International Nuclear Information System (INIS)

Dong Bing; Ren Deqing; Zhang Xi

2012-01-01

Multi-conjugate adaptive optics (MCAO) can considerably extend the corrected field of view with respect to classical adaptive optics, which will benefit solar observation in many aspects. In solar MCAO, the Sun structure is utilized to provide multiple guide stars and a modal tomography approach is adopted to implement three-dimensional wavefront restorations. The principle of modal tomography is briefly reviewed and a numerical simulation model is built with three equivalent turbulent layers and a different number of guide stars. Our simulation results show that at least six guide stars are required for an accurate wavefront reconstruction in the case of three layers, and only three guide stars are needed in the two layer case. Finally, eigenmode analysis results are given to reveal the singular modes that cannot be precisely retrieved in the tomography process.

Multi-modal TiO2-LaFeO3 composite films with high photocatalytic activity and hydrophilicity

International Nuclear Information System (INIS)

Gao Kun; Li Shudan

2012-01-01

In this paper, a series of multi-modal TiO 2 -LaFeO 3 composite films have been successfully synthesized through a two-step method. The resultant films were characterized in detail by several testing techniques, such as X-ray diffraction (XRD), ultraviolet-visible diffuse reflection spectrum (UV-vis DRS), photoluminescence spectrum (PL), surface photovoltage spectroscopy (SPS) and water contact angle measurements. The photocatalytic activity of different films was evaluated for degrading Methylene Blue (MB) aqueous solution. Hydrophilicity of the obtained TiO 2 -LaFeO 3 composite films was also investigated. The results show that TL film and LT film exhibited superior photocatalytic activity and hydrophilicity.

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

Science.gov (United States)

Hearn, Tristan A.

2015-01-01

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

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

Science.gov (United States)

Fan, Lei

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

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Science.gov (United States)

Faizrahnemoon, Mahsa; Schlote, Arieh; Maggi, Lorenzo; Crisostomi, Emanuele; Shorten, Robert

2015-11-01

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

CONTEXT-CAPTURE MULTI-VALUED DECISION FUSION WITH FAULT TOLERANT CAPABILITY FOR WIRELESS SENSOR NETWORKS

OpenAIRE

Jun Wu; Shigeru Shimamoto

2011-01-01

Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in pract...

(In)Flexibility of Constituency in Japanese in Multi-Modal Categorial Grammar with Structured Phonology

Science.gov (United States)

Kubota, Yusuke

2010-01-01

This dissertation proposes a theory of categorial grammar called Multi-Modal Categorial Grammar with Structured Phonology. The central feature that distinguishes this theory from the majority of contemporary syntactic theories is that it decouples (without completely segregating) two aspects of syntax--hierarchical organization (reflecting…

Game of Objects: vicarious causation and multi-modal media

Directory of Open Access Journals (Sweden)

Aaron Pedinotti

2013-09-01

Full Text Available This paper applies philosopher Graham Harman's object-oriented theory of "vicarious causation" to an analysis of the multi-modal media phenomenon known as "Game of Thrones." Examining the manner in which George R.R. Martin's best-selling series of fantasy novels has been adapted into a board game, a video game, and a hit HBO television series, it uses the changes entailed by these processes to trace the contours of vicariously generative relations. In the course of the resulting analysis, it provides new suggestions concerning the eidetic dimensions of Harman's causal model, particularly with regard to causation in linear networks and in differing types of game systems.

Detection of relationships among multi-modal brain imaging meta-features via information flow.

Science.gov (United States)

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is

Linked statistical shape models for multi-modal segmentation: application to prostate CT-MR segmentation in radiotherapy planning

Science.gov (United States)

Chowdhury, Najeeb; Chappelow, Jonathan; Toth, Robert; Kim, Sung; Hahn, Stephen; Vapiwala, Neha; Lin, Haibo; Both, Stefan; Madabhushi, Anant

2011-03-01

We present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This framework is particularly relevant in scenarios where accurate delineations of a SOI's boundary on one of the modalities may not be readily available, or difficult to obtain, for training a SSM. We apply the LSSM in the context of multi-modal prostate segmentation for radiotherapy planning, where we segment the prostate on MRI and CT simultaneously. Prostate capsule segmentation is a critical step in prostate radiotherapy planning, where dose plans have to be formulated on CT. Since accurate delineations of the prostate boundary are very difficult to obtain on CT, pre-treatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to do compared to CT. Hence, our framework incorporates multi-modal registration of MRI and CT to map 2D boundary delineations of prostate (obtained from an expert radiation oncologist) on MR training images onto corresponding CT images. The delineations of the prostate capsule on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the building of the LSSM. We acquired 7 MRI-CT patient studies and used the leave-one-out strategy to train and evaluate our LSSM (fLSSM), built using expert ground truth delineations on MRI and MRI-CT fusion derived capsule delineations on CT. A unique attribute of our fLSSM is that it does not require expert delineations of the capsule on CT. In order to perform prostate MRI segmentation using the fLSSM, we employed a regionbased approach where we deformed the evolving prostate boundary to optimize a mutual information based cost criterion, which took into account region-based intensity statistics of the image being segmented. The final prostate segmentation was then

The effectiveness of multi modal representation text books to improve student's scientific literacy of senior high school students

Science.gov (United States)

Zakiya, Hanifah; Sinaga, Parlindungan; Hamidah, Ida

2017-05-01

The results of field studies showed the ability of science literacy of students was still low. One root of the problem lies in the books used in learning is not oriented toward science literacy component. This study focused on the effectiveness of the use of textbook-oriented provisioning capability science literacy by using multi modal representation. The text books development method used Design Representational Approach Learning to Write (DRALW). Textbook design which was applied to the topic of "Kinetic Theory of Gases" is implemented in XI grade students of high school learning. Effectiveness is determined by consideration of the effect and the normalized percentage gain value, while the hypothesis was tested using Independent T-test. The results showed that the textbooks which were developed using multi-mode representation science can improve the literacy skills of students. Based on the size of the effect size textbooks developed with representation multi modal was found effective in improving students' science literacy skills. The improvement was occurred in all the competence and knowledge of scientific literacy. The hypothesis testing showed that there was a significant difference on the ability of science literacy between class that uses textbooks with multi modal representation and the class that uses the regular textbook used in schools.

Application of principal component analysis and information fusion technique to detect hotspots in NOAA/AVHRR images of Jharia coalfield, India - article no. 013523

Energy Technology Data Exchange (ETDEWEB)

Gautam, R.S.; Singh, D.; Mittal, A. [Indian Institute of Technology Roorkee, Roorkee (India)

2007-07-01

Present paper proposes an algorithm for hotspot (sub-surface fire) detection in NOAA/AVHRR images in Jharia region of India by employing Principal Component Analysis (PCA) and fusion technique. Proposed technique is very simple to implement and is more adaptive in comparison to thresholding, multi-thresholding and contextual algorithms. The algorithm takes into account the information of AVHRR channels 1, 2, 3, 4 and vegetation indices NDVI and MSAVI for the required purpose. Proposed technique consists of three steps: (1) detection and removal of cloud and water pixels from preprocessed AVHRR image and screening out the noise of channel 3, (2) application of PCA on multi-channel information along with vegetation index information of NOAA/AVHRR image to obtain principal components, and (3) fusion of information obtained from principal component 1 and 2 to classify image pixels as hotspots. Image processing techniques are applied to fuse information in first two principal component images and no absolute threshold is incorporated to specify whether particular pixel belongs to hotspot class or not, hence, proposed method is adaptive in nature and works successfully for most of the AVHRR images with average 87.27% detection accuracy and 0.201% false alarm rate while comparing with ground truth points in Jharia region of India.

AUTOMATIC REGISTRATION OF MULTI-SOURCE DATA USING MUTUAL INFORMATION

Directory of Open Access Journals (Sweden)

E. G. Parmehr

2012-07-01

Full Text Available Automatic image registration is a basic step in multi-sensor data integration in remote sensing and photogrammetric applications such as data fusion. The effectiveness of Mutual Information (MI as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications. In this paper, a new General Weighted MI (GWMI approach that improves the robustness of MI to local maxima, particularly in the case of registering optical imagery and 3D point clouds, is presented. Two different methods including a Gaussian Mixture Model (GMM and Kernel Density Estimation have been used to define the weight function of joint probability, regardless of the modality of the data being registered. The Expectation Maximizing method is then used to estimate parameters of GMM, and in order to reduce the cost of computation, a multi-resolution strategy has been used. The performance of the proposed GWMI method for the registration of aerial orthotoimagery and LiDAR range and intensity information has been experimentally evaluated and the results obtained are presented.

Object discrimination using optimized multi-frequency auditory cross-modal haptic feedback.

Science.gov (United States)

Gibson, Alison; Artemiadis, Panagiotis

2014-01-01

As the field of brain-machine interfaces and neuro-prosthetics continues to grow, there is a high need for sensor and actuation mechanisms that can provide haptic feedback to the user. Current technologies employ expensive, invasive and often inefficient force feedback methods, resulting in an unrealistic solution for individuals who rely on these devices. This paper responds through the development, integration and analysis of a novel feedback architecture where haptic information during the neural control of a prosthetic hand is perceived through multi-frequency auditory signals. Through representing force magnitude with volume and force location with frequency, the feedback architecture can translate the haptic experiences of a robotic end effector into the alternative sensory modality of sound. Previous research with the proposed cross-modal feedback method confirmed its learnability, so the current work aimed to investigate which frequency map (i.e. frequency-specific locations on the hand) is optimal in helping users distinguish between hand-held objects and tasks associated with them. After short use with the cross-modal feedback during the electromyographic (EMG) control of a prosthetic hand, testing results show that users are able to use audial feedback alone to discriminate between everyday objects. While users showed adaptation to three different frequency maps, the simplest map containing only two frequencies was found to be the most useful in discriminating between objects. This outcome provides support for the feasibility and practicality of the cross-modal feedback method during the neural control of prosthetics.

Integration of Fiber-Optic Sensor Arrays into a Multi-Modal Tactile Sensor Processing System for Robotic End-Effectors

Directory of Open Access Journals (Sweden)

Peter Kampmann

2014-04-01

Full Text Available With the increasing complexity of robotic missions and the development towards long-term autonomous systems, the need for multi-modal sensing of the environment increases. Until now, the use of tactile sensor systems has been mostly based on sensing one modality of forces in the robotic end-effector. The use of a multi-modal tactile sensory system is motivated, which combines static and dynamic force sensor arrays together with an absolute force measurement system. This publication is focused on the development of a compact sensor interface for a fiber-optic sensor array, as optic measurement principles tend to have a bulky interface. Mechanical, electrical and software approaches are combined to realize an integrated structure that provides decentralized data pre-processing of the tactile measurements. Local behaviors are implemented using this setup to show the effectiveness of this approach.

Fusion alpha loss diagnostic for ITER using activation technique

Czech Academy of Sciences Publication Activity Database

Bonheure, G.; Hult, M.; González de Orduña, R.; Vermaercke, P.; Murari, A.; Popovichev, S.; Mlynář, Jan

2011-01-01

Roč. 86, 6-8 (2011), s. 1298-1301 ISSN 0920-3796. [Symposium on Fusion Technology (SOFT) /26th./. Port o, 27.09.2010-01.10.2010] Institutional research plan: CEZ:AV0Z20430508 Keywords : ITER * fusion product * burning plasma diagnostics * alpha losses * activation technique Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 1.490, year: 2011 http://www.sciencedirect.com/science/article/pii/S0920379611002778

A software framework for real-time multi-modal detection of microsleeps.

Science.gov (United States)

Knopp, Simon J; Bones, Philip J; Weddell, Stephen J; Jones, Richard D

2017-09-01

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

Development of positron sensor for multi-modal endoscopy

Energy Technology Data Exchange (ETDEWEB)

Shimazoe, Kenji, E-mail: shimazoe@it-club.jp [Department of Bioengineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan); Takahashi, Hiroyuki [Department of Nuclear Engineering and Management, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan); Fujita, Kaoru [Japan Atomic Energy Agency, 4-29 Tokaimura, 319-1184 Ibaraki (Japan); Mori, Hiroshi; Momose, Toshimitsu [Department of Bioengineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan)

2011-08-21

Endoscopy is an important inspection device to detect cancers in the human body, but there exists the case of cancer that is hard to detect with only an optical device. Double inspection with optical and radio images is preferable for high accuracy diagnosis, and real time radio imaging is also promising for real time surgery with an endoscope. We have simulated, designed and fabricated a Si-based positron imaging probe for more accurate cancer detection in multi-modality endoscope systems. The fabricated Si-based detector with 2 mm diameter and 1 mm thickness was tested with gamma and positron sources, and also tested to detect cancers in a tumor bearing mouse. The direct positron imaging could have an advantage over gamma imaging in its high sensitivity and resolution.

Modal testing and analysis of NOVA laser structures

International Nuclear Information System (INIS)

Burdick, R.B.; Weaver, H.J.; Pastrnak, J.W.

1984-09-01

NOVA, currently the world's most powerful laser system, is an ongoing project at the Lawrence Livermore National Laboratory in California. The project seeks to develop a feasible method of achieving controlled fusion reaction, initiated by multiple laser beams targeted on a tiny fuel pellet. The NOVA system consists of several large steel framed structures, the largest of which is the Target Chamber Tower. In conjunction with design engineers, the tower was first modelled and analyzed by sophisticated finite element techniques. A modal test was then conducted on the tower structure to evaluate its vibrational characteristics and seismic integrity as well as for general comparison to the finite element results. This paper will discuss the procedure used in the experimental modal analysis and the results obtained from that test

Obstacle traversal and self-righting of bio-inspired robots reveal the physics of multi-modal locomotion

Science.gov (United States)

Li, Chen; Fearing, Ronald; Full, Robert

Most animals move in nature in a variety of locomotor modes. For example, to traverse obstacles like dense vegetation, cockroaches can climb over, push across, reorient their bodies to maneuver through slits, or even transition among these modes forming diverse locomotor pathways; if flipped over, they can also self-right using wings or legs to generate body pitch or roll. By contrast, most locomotion studies have focused on a single mode such as running, walking, or jumping, and robots are still far from capable of life-like, robust, multi-modal locomotion in the real world. Here, we present two recent studies using bio-inspired robots, together with new locomotion energy landscapes derived from locomotor-environment interaction physics, to begin to understand the physics of multi-modal locomotion. (1) Our experiment of a cockroach-inspired legged robot traversing grass-like beam obstacles reveals that, with a terradynamically ``streamlined'' rounded body like that of the insect, robot traversal becomes more probable by accessing locomotor pathways that overcome lower potential energy barriers. (2) Our experiment of a cockroach-inspired self-righting robot further suggests that body vibrations are crucial for exploring locomotion energy landscapes and reaching lower barrier pathways. Finally, we posit that our new framework of locomotion energy landscapes holds promise to better understand and predict multi-modal biological and robotic movement.

Percolated microstructures for multi-modal transport enhancement in porous active materials

Energy Technology Data Exchange (ETDEWEB)

McKay, Ian Salmon; Yang, Sungwoo; Wang, Evelyn N.; Kim, Hyunho

2018-03-13

A method of forming a composite material for use in multi-modal transport includes providing three-dimensional graphene having hollow channels, enabling a polymer to wick into the hollow channels of the three-dimensional graphene, curing the polymer to form a cured three-dimensional graphene, adding an active material to the cured three-dimensional graphene to form a composite material, and removing the polymer from within the hollow channels. A composite material formed according to the method is also provided.

MEDCIS: Multi-Modality Epilepsy Data Capture and Integration System.

Science.gov (United States)

Zhang, Guo-Qiang; Cui, Licong; Lhatoo, Samden; Schuele, Stephan U; Sahoo, Satya S

2014-01-01

Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy-related death and is most common in patients with intractable, frequent, and continuing seizures. A statistically significant cohort of patients for SUDEP study requires meticulous, prospective follow up of a large population that is at an elevated risk, best represented by the Epilepsy Monitoring Unit (EMU) patient population. Multiple EMUs need to collaborate, share data for building a larger cohort of potential SUDEP patient using a state-of-the-art informatics infrastructure. To address the challenges of data integration and data access from multiple EMUs, we developed the Multi-Modality Epilepsy Data Capture and Integration System (MEDCIS) that combines retrospective clinical free text processing using NLP, prospective structured data capture using an ontology-driven interface, interfaces for cohort search and signal visualization, all in a single integrated environment. A dedicated Epilepsy and Seizure Ontology (EpSO) has been used to streamline the user interfaces, enhance its usability, and enable mappings across distributed databases so that federated queries can be executed. MEDCIS contained 936 patient data sets from the EMUs of University Hospitals Case Medical Center (UH CMC) in Cleveland and Northwestern Memorial Hospital (NMH) in Chicago. Patients from UH CMC and NMH were stored in different databases and then federated through MEDCIS using EpSO and our mapping module. More than 77GB of multi-modal signal data were processed using the Cloudwave pipeline and made available for rendering through the web-interface. About 74% of the 40 open clinical questions of interest were answerable accurately using the EpSO-driven VISual AGregagator and Explorer (VISAGE) interface. Questions not directly answerable were either due to their inherent computational complexity, the unavailability of primary information, or the scope of concept that has been formulated in the existing Ep

Dual Channel Pulse Coupled Neural Network Algorithm for Fusion of Multimodality Brain Images with Quality Analysis

Directory of Open Access Journals (Sweden)

Kavitha SRINIVASAN

2014-09-01

Full Text Available Background: In the review of medical imaging techniques, an important fact that emerged is that radiologists and physicians still are in a need of high-resolution medical images with complementary information from different modalities to ensure efficient analysis. This requirement should have been sorted out using fusion techniques with the fused image being used in image-guided surgery, image-guided radiotherapy and non-invasive diagnosis. Aim: This paper focuses on Dual Channel Pulse Coupled Neural Network (PCNN Algorithm for fusion of multimodality brain images and the fused image is further analyzed using subjective (human perception and objective (statistical measures for the quality analysis. Material and Methods: The modalities used in fusion are CT, MRI with subtypes T1/T2/PD/GAD, PET and SPECT, since the information from each modality is complementary to one another. The objective measures selected for evaluation of fused image were: Information Entropy (IE - image quality, Mutual Information (MI – deviation in fused to the source images and Signal to Noise Ratio (SNR – noise level, for analysis. Eight sets of brain images with different modalities (T2 with T1, T2 with CT, PD with T2, PD with GAD, T2 with GAD, T2 with SPECT-Tc, T2 with SPECT-Ti, T2 with PET are chosen for experimental purpose and the proposed technique is compared with existing fusion methods such as the Average method, the Contrast pyramid, the Shift Invariant Discrete Wavelet Transform (SIDWT with Harr and the Morphological pyramid, using the selected measures to ascertain relative performance. Results: The IE value and SNR value of the fused image derived from dual channel PCNN is higher than other fusion methods, shows that the quality is better with less noise. Conclusion: The fused image resulting from the proposed method retains the contrast, shape and texture as in source images without false information or information loss.

Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

OpenAIRE

Park, Chanoh; Moghadam, Peyman; Kim, Soohwan; Elfes, Alberto; Fookes, Clinton; Sridharan, Sridha

2017-01-01

The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these advantages, its offline property caused by the requirement of global batch optimization is critically hindering its relevance for real-time and life-long applications. In this paper, we present a dense map-centric SLAM method based on a continuous-time trajectory to cope with this problem. The proposed system locally f...

Tomotherapeutic stereotactic body radiation therapy: Techniques and comparison between modalities

International Nuclear Information System (INIS)

Fuss, Martin; Chengyu Shi; Papanikolaou, Niko

2006-01-01

Presentation and comparison of tomotherapeutic intensity-modulated techniques for planning and delivery of stereotactic body radiation therapy. Serial tomotherapeutic SBRT has been planned and delivered at our institution since 8/2001. Since 12/2005, 12 patients have been treated using a helical tomotherapy unit. For these 12 patients both helical and serial tomotherapy plans were computed and clinically approved. Techniques and considerations of tomotherapy SBRT planning, associated image-guidance, and delivery are presented. The respective treatment plans were compared based on dosimetric parameters as well as time to develop a treatment plan and delivery times. Also the associated quality of megavoltage CT (MVCT) image-guidance inherent to the helical tomotherapy unit was assessed. Tumor volumes averaged 9.3, 9.8, and 58.7 cm 3 for liver, lung, and spinal targets. Helical and serial tomotherapy plans showed comparable plan quality with respect to maximum and average doses to the gross tumor and planning target volumes. Time to develop helical tomotherapy plans averaged 3.5 h while serial tomotherapy planning consistently required less than one hour. Treatment delivery was also slower using helical tomotherapy, with differences of less than 10 min between modalities. MVCT image-guidance proved satisfactory for bony and lung targets, but failed to depict liver lesions, owing to poor soft-tissue contrast. SBRT planning and delivery is clinically feasible using either tomotherapeutic modality. While treatment planning time was consistently shorter and more readily accomplished in a standardized approach using the serial tomotherapy modality, actual plan quality and treatment delivery times are grossly comparable between the modalities. MVCT volumetric image-guidance, was observed to be valuable for thoracic and spinal target volumes, whereas it proved challenging for liver targets

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

Science.gov (United States)

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

2014-04-01

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

A multi-modal approach to assessing recovery in youth athletes following concussion.

Science.gov (United States)

Reed, Nick; Murphy, James; Dick, Talia; Mah, Katie; Paniccia, Melissa; Verweel, Lee; Dobney, Danielle; Keightley, Michelle

2014-09-25

Concussion is one of the most commonly reported injuries amongst children and youth involved in sport participation. Following a concussion, youth can experience a range of short and long term neurobehavioral symptoms (somatic, cognitive and emotional/behavioral) that can have a significant impact on one's participation in daily activities and pursuits of interest (e.g., school, sports, work, family/social life, etc.). Despite this, there remains a paucity in clinically driven research aimed specifically at exploring concussion within the youth sport population, and more specifically, multi-modal approaches to measuring recovery. This article provides an overview of a novel and multi-modal approach to measuring recovery amongst youth athletes following concussion. The presented approach involves the use of both pre-injury/baseline testing and post-injury/follow-up testing to assess performance across a wide variety of domains (post-concussion symptoms, cognition, balance, strength, agility/motor skills and resting state heart rate variability). The goal of this research is to gain a more objective and accurate understanding of recovery following concussion in youth athletes (ages 10-18 years). Findings from this research can help to inform the development and use of improved approaches to concussion management and rehabilitation specific to the youth sport community.

Interactive natural language acquisition in a multi-modal recurrent neural architecture

Science.gov (United States)

Heinrich, Stefan; Wermter, Stefan

2018-01-01

For the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural network, where parts have different leakage characteristics and thus operate on multiple timescales for every modality and the association of the higher level nodes of all modalities into cell assemblies. The model is capable of learning language production grounded in both, temporal dynamic somatosensation and vision, and features hierarchical concept abstraction, concept decomposition, multi-modal integration, and self-organisation of latent representations.

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

Science.gov (United States)

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

2015-01-01

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

Computer simulation of multi-elemental fusion reactor materials

International Nuclear Information System (INIS)

Voertler, K.

2011-01-01

Thermonuclear fusion is a sustainable energy solution, in which energy is produced using similar processes as in the sun. In this technology hydrogen isotopes are fused to gain energy and consequently to produce electricity. In a fusion reactor hydrogen isotopes are confined by magnetic fields as ionized gas, the plasma. Since the core plasma is millions of degrees hot, there are special needs for the plasma-facing materials. Moreover, in the plasma the fusion of hydrogen isotopes leads to the production of high energetic neutrons which sets demanding abilities for the structural materials of the reactor. This thesis investigates the irradiation response of materials to be used in future fusion reactors. Interactions of the plasma with the reactor wall leads to the removal of surface atoms, migration of them, and formation of co-deposited layers such as tungsten carbide. Sputtering of tungsten carbide and deuterium trapping in tungsten carbide was investigated in this thesis. As the second topic the primary interaction of the neutrons in the structural material steel was examined. As model materials for steel iron chromium and iron nickel were used. This study was performed theoretically by the means of computer simulations on the atomic level. In contrast to previous studies in the field, in which simulations were limited to pure elements, in this work more complex materials were used, i.e. they were multi-elemental including two or more atom species. The results of this thesis are in the microscale. One of the results is a catalogue of atom species, which were removed from tungsten carbide by the plasma. Another result is e.g. the atomic distributions of defects in iron chromium caused by the energetic neutrons. These microscopic results are used in data bases for multiscale modelling of fusion reactor materials, which has the aim to explain the macroscopic degradation in the materials. This thesis is therefore a relevant contribution to investigate the

vECTlab-A fully integrated multi-modality Monte Carlo simulation framework for the radiological imaging sciences

International Nuclear Information System (INIS)

Peter, Joerg; Semmler, Wolfhard

2007-01-01

Alongside and in part motivated by recent advances in molecular diagnostics, the development of dual-modality instruments for patient and dedicated small animal imaging has gained attention by diverse research groups. The desire for such systems is high not only to link molecular or functional information with the anatomical structures, but also for detecting multiple molecular events simultaneously at shorter total acquisition times. While PET and SPECT have been integrated successfully with X-ray CT, the advance of optical imaging approaches (OT) and the integration thereof into existing modalities carry a high application potential, particularly for imaging small animals. A multi-modality Monte Carlo (MC) simulation approach at present has been developed that is able to trace high-energy (keV) as well as optical (eV) photons concurrently within identical phantom representation models. We show that the involved two approaches for ray-tracing keV and eV photons can be integrated into a unique simulation framework which enables both photon classes to be propagated through various geometry models representing both phantoms and scanners. The main advantage of such integrated framework for our specific application is the investigation of novel tomographic multi-modality instrumentation intended for in vivo small animal imaging through time-resolved MC simulation upon identical phantom geometries. Design examples are provided for recently proposed SPECT-OT and PET-OT imaging systems

Biometric image enhancement using decision rule based image fusion techniques

Science.gov (United States)

Sagayee, G. Mary Amirtha; Arumugam, S.

2010-02-01

Introducing biometrics into information systems may result in considerable benefits. Most of the researchers confirmed that the finger print is widely used than the iris or face and more over it is the primary choice for most privacy concerned applications. For finger prints applications, choosing proper sensor is at risk. The proposed work deals about, how the image quality can be improved by introducing image fusion technique at sensor levels. The results of the images after introducing the decision rule based image fusion technique are evaluated and analyzed with its entropy levels and root mean square error.

Modal parameter identification based on combining transmissibility functions and blind source separation techniques

Science.gov (United States)

Araújo, Iván Gómez; Sánchez, Jesús Antonio García; Andersen, Palle

2018-05-01

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility-based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios.

P-Link: A method for generating multicomponent cytochrome P450 fusions with variable linker length

DEFF Research Database (Denmark)

Belsare, Ketaki D.; Ruff, Anna Joelle; Martinez, Ronny

2014-01-01

Fusion protein construction is a widely employed biochemical technique, especially when it comes to multi-component enzymes such as cytochrome P450s. Here we describe a novel method for generating fusion proteins with variable linker lengths, protein fusion with variable linker insertion (P...

Semiotic foundation for multisensor-multilook fusion

Science.gov (United States)

Myler, Harley R.

1998-07-01

This paper explores the concept of an application of semiotic principles to the design of a multisensor-multilook fusion system. Semiotics is an approach to analysis that attempts to process media in a united way using qualitative methods as opposed to quantitative. The term semiotic refers to signs, or signatory data that encapsulates information. Semiotic analysis involves the extraction of signs from information sources and the subsequent processing of the signs into meaningful interpretations of the information content of the source. The multisensor fusion problem predicated on a semiotic system structure and incorporating semiotic analysis techniques is explored and the design for a multisensor system as an information fusion system is explored. Semiotic analysis opens the possibility of using non-traditional sensor sources and modalities in the fusion process, such as verbal and textual intelligence derived from human observers. Examples of how multisensor/multimodality data might be analyzed semiotically is shown and discussion on how a semiotic system for multisensor fusion could be realized is outlined. The architecture of a semiotic multisensor fusion processor that can accept situational awareness data is described, although an implementation has not as yet been constructed.

Architecture of the Multi-Modal Organizational Research and Production Heterogeneous Network (MORPHnet)

Energy Technology Data Exchange (ETDEWEB)

Aiken, R.J.; Carlson, R.A.; Foster, I.T. [and others

1997-01-01

The research and education (R&E) community requires persistent and scaleable network infrastructure to concurrently support production and research applications as well as network research. In the past, the R&E community has relied on supporting parallel network and end-node infrastructures, which can be very expensive and inefficient for network service managers and application programmers. The grand challenge in networking is to provide support for multiple, concurrent, multi-layer views of the network for the applications and the network researchers, and to satisfy the sometimes conflicting requirements of both while ensuring one type of traffic does not adversely affect the other. Internet and telecommunications service providers will also benefit from a multi-modal infrastructure, which can provide smoother transitions to new technologies and allow for testing of these technologies with real user traffic while they are still in the pre-production mode. The authors proposed approach requires the use of as much of the same network and end system infrastructure as possible to reduce the costs needed to support both classes of activities (i.e., production and research). Breaking the infrastructure into segments and objects (e.g., routers, switches, multiplexors, circuits, paths, etc.) gives the capability to dynamically construct and configure the virtual active networks to address these requirements. These capabilities must be supported at the campus, regional, and wide-area network levels to allow for collaboration by geographically dispersed groups. The Multi-Modal Organizational Research and Production Heterogeneous Network (MORPHnet) described in this report is an initial architecture and framework designed to identify and support the capabilities needed for the proposed combined infrastructure and to address related research issues.

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

Science.gov (United States)

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.

Diagnostic technique for measuring fusion reaction rate for inertial confinement fusion experiments at Shen Guang-III prototype laser facility

International Nuclear Information System (INIS)

Wang Feng; Peng Xiao-Shi; Liu Shen-Ye; Xu Tao; Kang Dong-Guo

2013-01-01

A study is conducted using a two-dimensional simulation program (Lared-s) with the goal of developing a technique to evaluate the effect of Rayleigh-Taylor growth in a neutron fusion reaction region. Two peaks of fusion reaction rate are simulated by using a two-dimensional simulation program (Lared-s) and confirmed by the experimental results. A neutron temporal diagnostic (NTD) system is developed with a high temporal resolution of ∼ 30 ps at the Shen Guang-III (SG-III) prototype laser facility in China, to measure the fusion reaction rate history. With the shape of neutron reaction rate curve and the spherical harmonic function in this paper, the degree of Rayleigh-Taylor growth and the main source of the neutron yield in our experiment can be estimated qualitatively. This technique, including the diagnostic system and the simulation program, may provide important information for obtaining a higher neutron yield in implosion experiments of inertial confinement fusion

Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique

International Nuclear Information System (INIS)

Hou Zhijian; Lian Zhiwei; Yao Ye; Yuan Xinjian

2006-01-01

A novel method integrating rough sets (RS) theory and an artificial neural network (ANN) based on data-fusion technique is presented to forecast an air-conditioning load. Data-fusion technique is the process of combining multiple sensors data or related information to estimate or predict entity states. In this paper, RS theory is applied to find relevant factors to the load, which are used as inputs of an artificial neural-network to predict the cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load-prediction model, by synthesizing multi-RSAN (MRAN), is presented so as to make full use of redundant information. The optimum principle is employed to deduce the weights of each RSAN model. Actual prediction results from a real air-conditioning system show that, the MRAN forecasting model is better than the individual RSAN and moving average (AMIMA) ones, whose relative error is within 4%. In addition, individual RSAN forecasting results are better than that of ARIMA

The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion.

Science.gov (United States)

Ehrenfeld, Stephan; Butz, Martin V

2013-02-01

Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.

Pellet bed reactor for multi-modal space power

International Nuclear Information System (INIS)

Buden, D.; Williams, K.; Mast, P.; Mims, J.

1987-01-01

A review of forthcoming space power needs for both civil and military missions indicates that power requirements will be in the tens of megawatts. The electrical power requirements are envisioned to be twofold: long-duration lower power levels will be needed for station keeping, communications, and/or surveillance; short-duration higher power levels will be required for pulsed power devices. These power characteristics led to the proposal of a multi-modal space power reactor using a pellet bed design. Characteristics desired for such a multimegawatt reactor power source are standby, alert, and pulsed power modes; high-thermal output heat source (approximately 1000 MWt peak power); long lifetime station keeping power (10 to 30 years); high temperature output (1500 K to 1800 K); rapid-burst power transition; high reliability (above 95 percent); and stringent safety standards compliance. The proposed pellet bed reactor is designed to satisfy these characteristics

Fusion Imaging for Procedural Guidance.

Science.gov (United States)

Wiley, Brandon M; Eleid, Mackram F; Thaden, Jeremy J

2018-05-01

The field of percutaneous structural heart interventions has grown tremendously in recent years. This growth has fueled the development of new imaging protocols and technologies in parallel to help facilitate these minimally-invasive procedures. Fusion imaging is an exciting new technology that combines the strength of 2 imaging modalities and has the potential to improve procedural planning and the safety of many commonly performed transcatheter procedures. In this review we discuss the basic concepts of fusion imaging along with the relative strengths and weaknesses of static vs dynamic fusion imaging modalities. This review will focus primarily on echocardiographic-fluoroscopic fusion imaging and its application in commonly performed transcatheter structural heart procedures. Copyright © 2017 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

ESTIMATION OF MULTI-MODAL ANALGESIA ADEQUACY IN THE PERIOPERATIVE PERIOD AT LONG-TERMED TRAUMATIZING ABDOMINAL OPERATIVE INTERVENTIONS

Directory of Open Access Journals (Sweden)

V. Kh. Sharipova

2015-01-01

Full Text Available PURPOSE OF THE STUDY. Improvement of perioperative multimodal analgesia at long­termed traumatizing abdominal interventions with estimation of its effectiveness.MATERIALS AND METHODS. Eighty six patients have been examined and divided into 3 groups depending on anesthesia and postoperative pain relief methods.RESULTS. The effectiveness of perioperative multi­modal analgesia using methods affecting the whole pathogenesis of pain has been revealed. Minimal stress of central and peripheral hemodynamics parameters, less evident pain syndrome in the post­operative period, economic effect shown up by the decrease of the use of narcotic analgesics both in intra­ and post­operative period have been observed.CONCLUSION. Algorithm of perioperative multi­modal analgesia at long­termed and traumatizing abdominal operative interventions has been developed. 

Multi-rate sensor fusion-based adaptive discrete finite-time synergetic control for flexible-joint mechanical systems

International Nuclear Information System (INIS)

Xue Guang-Yue; Ren Xue-Mei; Xia Yuan-Qing

2013-01-01

This paper proposes an adaptive discrete finite-time synergetic control (ADFTSC) scheme based on a multi-rate sensor fusion estimator for flexible-joint mechanical systems in the presence of unmeasured states and dynamic uncertainties. Multi-rate sensors are employed to observe the system states which cannot be directly obtained by encoders due to the existence of joint flexibilities. By using an extended Kalman filter (EKF), the finite-time synergetic controller is designed based on a sensor fusion estimator which estimates states and parameters of the mechanical system with multi-rate measurements. The proposed controller can guarantee the finite-time convergence of tracking errors by the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach. (general)

A novel APD-based detector module for multi-modality PET/SPECT/CT scanners

International Nuclear Information System (INIS)

Saoudi, A.; Lecomte, R.

1999-01-01

The lack of anatomical information in SPECT and PET images is one of the major factors limiting the ability to localize and accurately quantify radionuclide uptake in small regions of interest. This problem could be resolved by using multi-modality scanners having the capability to acquire anatomical and functional images simultaneously. The feasibility of a novel detector suitable for measuring high-energy annihilation radiation in PET, medium-energy γ-rays in SPECT and low-energy X-rays in transmission CT is demonstrated and its performance is evaluated for potential use in multi-modality PET/SPECT/CT imaging. The proposed detector consists of a thin CsI(Tl) scintillator sitting on top of a deep GSO/LSO pair read out by an avalanche photodiode. The GSO/LOS pair provides depth-of-interaction information for 511 keV detection in PET, while the thin CsI(Tl) that is essentially transparent to annihilation radiation is used for detecting lower energy X- and γ-rays. The detector performance is compared to that of an LSO/YSO phoswich. Although the implementation of the proposed GSO/LSO/CsI(Tl) detector raises special problems that increase complexity, it generally outperforms the LSO/YSO phoswich for simultaneous PET, SPECT and CT imaging

Operational Modal Analysis Tutorial

DEFF Research Database (Denmark)

Brincker, Rune; Andersen, Palle

of modal parameters of practical interest - including the mode shape scaling factor - with a high degree of accuracy. It is also argued that the operational technology offers the user a number of advantages over traditional modal testing. The operational modal technology allows the user to perform a modal......In this paper the basic principles in operational modal testing and analysis are presented and discussed. A brief review of the techniques for operational modal testing and identification is presented, and it is argued, that there is now a wide range of techniques for effective identification...

Multi-modal intervention improved oral intake in hospitalized patients

DEFF Research Database (Denmark)

Holst, M; Beermann, T; Mortensen, M N

2015-01-01

BACKGROUND: Good nutritional practice (GNP) includes screening, nutrition plan and monitoring, and is mandatory for targeted treatment of malnourished patients in hospital. AIMS: To optimize energy- and protein-intake in patients at nutritional risk and to improve GNP in a hospital setting. METHODS......: A 12-months observational multi-modal intervention study was done, using the top-down and bottom-up principle. All hospitalized patients (>3 days) were included. Setting: A university hospital with 758 beds and all specialities. Measurements: Record audit of GNP, energy- and protein-intake by 24-h...... recall, patient interviews and staff questionnaire before and after the intervention. Interventions: Based on pre-measurements, nutrition support teams in each department made targeted action plans, supervised by an expert team. Education, diagnose-specific nutrition plans, improved menus and eating...

Variance-reduction technique for Coulomb-nuclear thermalization of energetic fusion products in hot plasmas

International Nuclear Information System (INIS)

DeVeaux, J.C.; Miley, G.H.

1982-01-01

A variance-reduction technique involving use of exponential transform and angular-biasing methods has been developed. Its purpose is to minimize the variance and computer time involved in estimating the mean fusion product (fp) energy deposited in a hot, multi-region plasma under the influence of small-energy transfer Coulomb collisions and large-energy transfer nuclear elastic scattering (NES) events. This technique is applicable to high-temperature D- 3 He, Cat. D and D-T plasmas which have highly energetic fps capable of undergoing NES. A first application of this technique is made to a D- 3 He Field Reversed Mirror (FRM) where the Larmor radius of the 14.7 MeV protons are typically comparable to the plasma radius (plasma radius approx. 2 fp gyroradii) and the optimistic fp confinement (approx. 45% of 14.7 MeV protons) previously predicted is vulnerable to large orbit perturbations induced by NES. In the FRM problem, this variance reduction technique is used to estimate the fractional difference in the average fp energy deposited in the closed-field region, E/sub cf/, with and without NES collisions

Multi-modal highlight generation for sports videos using an information-theoretic excitability measure

Science.gov (United States)

Hasan, Taufiq; Bořil, Hynek; Sangwan, Abhijeet; L Hansen, John H.

2013-12-01

The ability to detect and organize `hot spots' representing areas of excitement within video streams is a challenging research problem when techniques rely exclusively on video content. A generic method for sports video highlight selection is presented in this study which leverages both video/image structure as well as audio/speech properties. Processing begins where the video is partitioned into small segments and several multi-modal features are extracted from each segment. Excitability is computed based on the likelihood of the segmental features residing in certain regions of their joint probability density function space which are considered both exciting and rare. The proposed measure is used to rank order the partitioned segments to compress the overall video sequence and produce a contiguous set of highlights. Experiments are performed on baseball videos based on signal processing advancements for excitement assessment in the commentators' speech, audio energy, slow motion replay, scene cut density, and motion activity as features. Detailed analysis on correlation between user excitability and various speech production parameters is conducted and an effective scheme is designed to estimate the excitement level of commentator's speech from the sports videos. Subjective evaluation of excitability and ranking of video segments demonstrate a higher correlation with the proposed measure compared to well-established techniques indicating the effectiveness of the overall approach.

Advances of operational modal identification

International Nuclear Information System (INIS)

Zhang, L.

2001-01-01

Operational modal analysis has shown many advantages compared to the traditional one. In this paper, the development of ambient modal identification in time domain is summarized. The mathematical models for modal identification have been presented as unified framework for time domain (TD) System realization algorithms, such as polyrefence (PRCE), extended Ibrahim time domain (EITD) and eigensystem realization algorithm (ERA), etc., and recently developed Stochastic subspace technique (SST). The latest technique named as frequency domain decomposition (FDD) is introduced for operational modal identification, which has many advantages as a frequency domain (FD) technique. Applications of the operational modal analysis in civil and mechanical engineering have shown the success and accuracy of the advanced operational modal identification algorithms- FDD and SST techniques. The major issues of TD and FD operational modal identification are also discussed. (author)

Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

DEFF Research Database (Denmark)

Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

2018-01-01

, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data......PAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance...... recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate....

On modal cross-coupling in the asymptotic modal limit

Science.gov (United States)

Culver, Dean; Dowell, Earl

2018-03-01

The conditions under which significant modal cross-coupling occurs in dynamical systems responding to high-frequency, broadband forcing that excites many modes is studied. The modal overlap factor plays a key role in the analysis of these systems as the modal density (the ratio of number of modes to the frequency bandwidth) becomes large. The modal overlap factor is effectively the ratio of the width of a resonant peak (the damping ratio times the resonant frequency) to the average frequency interval between resonant peaks (or rather, the inverse of the modal density). It is shown that this parameter largely determines whether substantial modal cross-coupling occurs in a given system's response. Here, two prototypical systems are considered. The first is a simple rectangular plate whose significant modal cross-coupling is the exception rather than the norm. The second is a pair of rectangular plates attached at a point where significant modal cross-coupling is more likely to occur. We show that, for certain cases of modal density and damping, non-negligible cross coupling occurs in both systems. Under similar circumstances, the constraint force between the two plates in the latter system becomes broadband. The implications of this for using Asymptotic Modal Analysis (AMA) in multi-component systems are discussed.

A Bayes-Maximum Entropy method for multi-sensor data fusion

Energy Technology Data Exchange (ETDEWEB)

Beckerman, M.

1991-01-01

In this paper we introduce a Bayes-Maximum Entropy formalism for multi-sensor data fusion, and present an application of this methodology to the fusion of ultrasound and visual sensor data as acquired by a mobile robot. In our approach the principle of maximum entropy is applied to the construction of priors and likelihoods from the data. Distances between ultrasound and visual points of interest in a dual representation are used to define Gibbs likelihood distributions. Both one- and two-dimensional likelihoods are presented, and cast into a form which makes explicit their dependence upon the mean. The Bayesian posterior distributions are used to test a null hypothesis, and Maximum Entropy Maps used for navigation are updated using the resulting information from the dual representation. 14 refs., 9 figs.

Study on Efficiency of Fusion Techniques for IKONOS Images

International Nuclear Information System (INIS)

Liu, Yanmei; Yu, Haiyang; Guijun, Yang; Nie, Chenwei; Yang, Xiaodong; Ren, Dong

2014-01-01

Many image fusion techniques have been proposed to achieve optimal resolution in the spatial and spectral domains. Six different merging methods were listed in this paper and the efficiency of fusion techniques was assessed in qualitative and quantitative aspect. Both local and global evaluation parameters were used in the spectral quality and a Laplace filter method was used in spatial quality assessment. By simulation, the spectral quality of the images merged by Brovery was demonstrated to be the worst. In contrast, GS and PCA algorithms, especially the Pansharpening provided higher spectral quality than the standard Brovery, wavelet and CN methods. In spatial quality assessment, the CN method represented best compared with that of others, while the Brovery algorithm was worst. The wavelet parameters that performed best achieved acceptable spectral and spatial quality compared to the others

Optimal Face-Iris Multimodal Fusion Scheme

Directory of Open Access Journals (Sweden)

Omid Sharifi

2016-06-01

Full Text Available Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA, a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

Sustainable Multi-Modal Sensing by a Single Sensor Utilizing the Passivity of an Elastic Actuator

Directory of Open Access Journals (Sweden)

Takashi Takuma

2014-05-01

Full Text Available When a robot equipped with compliant joints driven by elastic actuators contacts an object and its joints are deformed, multi-modal information, including the magnitude and direction of the applied force and the deformation of the joint, is used to enhance the performance of the robot such as dexterous manipulation. In conventional approaches, some types of sensors used to obtain the multi-modal information are attached to the point of contact where the force is applied and at the joint. However, this approach is not sustainable for daily use in robots, i.e., not durable or robust, because the sensors can undergo damage due to the application of excessive force and wear due to repeated contacts. Further, multiple types of sensors are required to measure such physical values, which add to the complexity of the device system of the robot. In our approach, a single type of sensor is used and it is located at a point distant from the contact point and the joint, and the information is obtained indirectly by the measurement of certain physical parameters that are influenced by the applied force and the joint deformation. In this study, we employ the McKibben pneumatic actuator whose inner pressure changes passively when a force is applied to the actuator. We derive the relationships between information and the pressures of a two-degrees-of-freedom (2-DoF joint mechanism driven by four pneumatic actuators. Experimental results show that the multi-modal information can be obtained by using the set of pressures measured before and after the force is applied. Further, we apply our principle to obtain the stiffness values of certain contacting objects that can subsequently be categorized by using the aforementioned relationships.

Risk factors for insufficient perioperative oral nutrition after hip fracture surgery within a multi-modal rehabilitation programme

DEFF Research Database (Denmark)

Foss, Nicolai B; Jensen, Pia S; Kehlet, Henrik

2007-01-01

To examine oral nutritional intake in the perioperative phase in elderly hip fracture patients treated according to a well-defined multi-modal rehabilitation program, including unselected oral nutritional supplementation, and to identify independent risk factors for insufficient nutritional intake....

Video Surveillance using a Multi-Camera Tracking and Fusion System

OpenAIRE

Zhang , Zhong; Scanlon , Andrew; Yin , Weihong; Yu , Li; Venetianer , Péter L.

2008-01-01

International audience; Usage of intelligent video surveillance (IVS) systems is spreading rapidly. These systems are being utilized in a wide range of applications. In most cases, even in multi-camera installations, the video is processed independently in each feed. This paper describes a system that fuses tracking information from multiple cameras, thus vastly expanding its capabilities. The fusion relies on all cameras being calibrated to a site map, while the individual sensors remain lar...

Efficacy and safety of a modular multi-modal exercise program in prostate cancer patients with bone metastases: a randomized controlled trial

International Nuclear Information System (INIS)

Galvão, Daniel A; Groom, Geoff; Newton, Robert U; Taaffe, Dennis R; Cormie, Prue; Spry, Nigel; Chambers, Suzanne K; Peddle-McIntyre, Carolyn; Baker, Michael; Denham, James; Joseph, David

2011-01-01

The presence of bone metastases has excluded participation of prostate cancer patients in exercise intervention studies to date and is also a relative contraindication to supervised exercise in the community setting because of concerns of fragility fracture. However, this group of patients often have developed significant muscle atrophy and functional impairments from prior and continuing androgen deprivation that is exacerbated by subsequent and more intensive interventions such as chemotherapy. The aim of this study is to determine the efficacy and safety of a modular multi-modal exercise program in prostate cancer patients with bone metastases. Multi-site randomized controlled trial in Western Australia and New South Wales to examine the efficacy and safety of a modular multi-modal physical exercise program in 90 prostate cancer survivors with bone metastases. Participants will be randomized to (1) modular multi-modal exercise intervention group or (2) usual medical care group. The modular multi-modal exercise group will receive a 3-month supervised exercise program based on bone lesion location/extent. Measurements for primary and secondary endpoints will take place at baseline, 3 months (end of the intervention) and 6 months follow-up. Delaying or preventing skeletal complication and improving physical function for men with bone metastases would provide clinically meaningful benefits to patients. However, exercise programs must be designed and executed with careful consideration of the skeletal complications associated with bone metastatic disease and cumulative toxicities from androgen deprivation such as osteoporosis and increased risk of fractures. The results from this study will form the basis for the development of a specific exercise prescription in this patient group in order to alleviate disease burden, counteract the adverse treatment related side-effects and enhance quality of life. ACTRN: http://www.anzctr.org.au/ACTRN12611001158954.aspx

Distributed flow estimation and closed-loop control of an underwater vehicle with a multi-modal artificial lateral line.

Science.gov (United States)

DeVries, Levi; Lagor, Francis D; Lei, Hong; Tan, Xiaobo; Paley, Derek A

2015-03-25

Bio-inspired sensing modalities enhance the ability of autonomous vehicles to characterize and respond to their environment. This paper concerns the lateral line of cartilaginous and bony fish, which is sensitive to fluid motion and allows fish to sense oncoming flow and the presence of walls or obstacles. The lateral line consists of two types of sensing modalities: canal neuromasts measure approximate pressure gradients, whereas superficial neuromasts measure local flow velocities. By employing an artificial lateral line, the performance of underwater sensing and navigation strategies is improved in dark, cluttered, or murky environments where traditional sensing modalities may be hindered. This paper presents estimation and control strategies enabling an airfoil-shaped unmanned underwater vehicle to assimilate measurements from a bio-inspired, multi-modal artificial lateral line and estimate flow properties for feedback control. We utilize potential flow theory to model the fluid flow past a foil in a uniform flow and in the presence of an upstream obstacle. We derive theoretically justified nonlinear estimation strategies to estimate the free stream flowspeed, angle of attack, and the relative position of an upstream obstacle. The feedback control strategy uses the estimated flow properties to execute bio-inspired behaviors including rheotaxis (the tendency of fish to orient upstream) and station-holding (the tendency of fish to position behind an upstream obstacle). A robotic prototype outfitted with a multi-modal artificial lateral line composed of ionic polymer metal composite and embedded pressure sensors experimentally demonstrates the distributed flow sensing and closed-loop control strategies.

Crack Identification in CFRP Laminated Beams Using Multi-Resolution Modal Teager–Kaiser Energy under Noisy Environments

Science.gov (United States)

Xu, Wei; Cao, Maosen; Ding, Keqin; Radzieński, Maciej; Ostachowicz, Wiesław

2017-01-01

Carbon fiber reinforced polymer laminates are increasingly used in the aerospace and civil engineering fields. Identifying cracks in carbon fiber reinforced polymer laminated beam components is of considerable significance for ensuring the integrity and safety of the whole structures. With the development of high-resolution measurement technologies, mode-shape-based crack identification in such laminated beam components has become an active research focus. Despite its sensitivity to cracks, however, this method is susceptible to noise. To address this deficiency, this study proposes a new concept of multi-resolution modal Teager–Kaiser energy, which is the Teager–Kaiser energy of a mode shape represented in multi-resolution, for identifying cracks in carbon fiber reinforced polymer laminated beams. The efficacy of this concept is analytically demonstrated by identifying cracks in Timoshenko beams with general boundary conditions; and its applicability is validated by diagnosing cracks in a carbon fiber reinforced polymer laminated beam, whose mode shapes are precisely acquired via non-contact measurement using a scanning laser vibrometer. The analytical and experimental results show that multi-resolution modal Teager–Kaiser energy is capable of designating the presence and location of cracks in these beams under noisy environments. This proposed method holds promise for developing crack identification systems for carbon fiber reinforced polymer laminates. PMID:28773016

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

Science.gov (United States)

Umeta, Ricardo S G; Avanzi, Osmar

2011-07-01

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

Roles of multi-step transfer in fusion process induced by heavy-ion reactions

International Nuclear Information System (INIS)

Imanishi, B.; Oertzen, W. von.

1993-06-01

In nucleus-nucleus collisions of the systems, 12 C+ 13 C and 13 C+ 16 O- 12 C+ 17 O, the effects of the multi-step transfers and inelastic excitations on the fusion cross sections are investigated in the framework of the coupled-reaction-channel (CRC) method. Strong CRC effects of the multi-step processes are observed. Namely, the valence neutron in 13 C or 17 O plays an important role in the enhancement of the fusion. The potential barrier is effectively lowered with the formation of the covalent molecule of the configuration, 12 C+n+ 12 C or 12 C+n+ 16 O. In the analyses of the system 12 C+ 13 C, however, it is still required to introduce core-core optical potential of lower barrier height in the state of the positive total parity. This could be due to the neck formation with the nucleons contained in two core nuclei. (author)

Multi-agent control system with information fusion based comfort model for smart buildings

International Nuclear Information System (INIS)

Wang, Zhu; Wang, Lingfeng; Dounis, Anastasios I.; Yang, Rui

2012-01-01

Highlights: ► Proposed a model to manage indoor energy and comfort for smart buildings. ► Developed a control system to maximize comfort with minimum energy consumption. ► Information fusion with ordered weighted averaging aggregation is used. ► Multi-agent technology and heuristic intelligent optimization are deployed in developing the control system. -- Abstract: From the perspective of system control, a smart and green building is a large-scale dynamic system with high complexity and a huge amount of information. Proper combination of the available information and effective control of the overall building system turns out to be a big challenge. In this study, we proposed a building indoor energy and comfort management model based on information fusion using ordered weighted averaging (OWA) aggregation. A multi-agent control system with heuristic intelligent optimization is developed to achieve a high level of comfort with the minimum power consumption. Case studies and simulation results are presented and discussed in this paper.

[Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain].

Science.gov (United States)

Fiedler, E; Platsch, G; Schwarz, A; Schmiedehausen, K; Tomandl, B; Huk, W; Rupprecht, Th; Rahn, N; Kuwert, T

2003-10-01

Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. PATIENTS, MATERIAL AND METHOD: In 32 patients regional cerebral blood flow was measured using (99m)Tc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

Joining and fabrication techniques for high temperature structures including the first wall in fusion reactor

International Nuclear Information System (INIS)

Lee, Ho Jin; Lee, B. S.; Kim, K. B.

2003-09-01

The materials for PFC's (Plasma Facing Components) in a fusion reactor are severely irradiated with fusion products in facing the high temperature plasma during the operation. The refractory materials can be maintained their excellent properties in severe operating condition by lowering surface temperature by bonding them to the high thermal conducting materials of heat sink. Hence, the joining and bonding techniques between dissimilar materials is considered to be important in case of the fusion reactor or nuclear reactor which is operated at high temperature. The first wall in the fusion reactor is heated to approximately 1000 .deg. C and irradiated severely by the plasma. In ITER, beryllium is expected as the primary armour candidate for the PFC's; other candidates including W, Mo, SiC, B4C, C/C and Si 3 N 4 . Since the heat affected zones in the PFC's processed by conventional welding are reported to have embrittlement and degradation in the sever operation condition, both brazing and diffusion bonding are being considered as prime candidates for the joining technique. In this report, both the materials including ceramics and the fabrication techniques including joining technique between dissimilar materials for PFC's are described. The described joining technique between the refractory materials and the dissimilar materials may be applicable for the fusion reactor and Generation-4 future nuclear reactor which are operated at high temperature and high irradiation

Multi-detector CT imaging in the postoperative orthopedic patient with metal hardware

International Nuclear Information System (INIS)

Vande Berg, Bruno; Malghem, Jacques; Maldague, Baudouin; Lecouvet, Frederic

2006-01-01

Multi-detector CT imaging (MDCT) becomes routine imaging modality in the assessment of the postoperative orthopedic patients with metallic instrumentation that degrades image quality at MR imaging. This article reviews the physical basis and CT appearance of such metal-related artifacts. It also addresses the clinical value of MDCT in postoperative orthopedic patients with emphasis on fracture healing, spinal fusion or arthrodesis, and joint replacement. MDCT imaging shows limitations in the assessment of the bone marrow cavity and of the soft tissues for which MR imaging remains the imaging modality of choice despite metal-related anatomic distortions and signal alteration

Scenarios for multi-unit inertial fusion energy plants producing hydrogen fuel

International Nuclear Information System (INIS)

Logan, B.G.

1993-12-01

This work describes: (a) the motivation for considering fusion in general, and Inertial Fusion Energy (IFE) in particular, to produce hydrogen fuel powering low-emission vehicles; (b) the general requirements for any fusion electric plant to produce hydrogen by water electrolysis at costs competitive with present consumer gasoline fuel costs per passenger mile, for advanced car architectures meeting President Clinton's 80 mpg advanced car goal, and (c) a comparative economic analysis for the potential cost of electricity (CoE) and corresponding cost of hydrogen (CoH) from a variety of multi-unit IFE plants with one to eight target chambers sharing a common driver and target fab facility. Cases with either heavy-ion or diode-pumped, solid-state laser drivers are considered, with ''conventional'' indirect drive target gains versus ''advanced, e.g. Fast Ignitor'' direct drive gain assumptions, and with conventional steam balance-of-plant (BoP) versus advanced MHD plus steam combined cycle BoP, to contrast the potential economics under ''conventional'' and ''advanced'' IFE assumptions, respectively

Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles.

Science.gov (United States)

Xing, Boyang; Zhu, Quanmin; Pan, Feng; Feng, Xiaoxue

2018-05-25

A novel multi-sensor fusion indoor localization algorithm based on ArUco marker is designed in this paper. The proposed ArUco mapping algorithm can build and correct the map of markers online with Grubbs criterion and K-mean clustering, which avoids the map distortion due to lack of correction. Based on the conception of multi-sensor information fusion, the federated Kalman filter is utilized to synthesize the multi-source information from markers, optical flow, ultrasonic and the inertial sensor, which can obtain a continuous localization result and effectively reduce the position drift due to the long-term loss of markers in pure marker localization. The proposed algorithm can be easily implemented in a hardware of one Raspberry Pi Zero and two STM32 micro controllers produced by STMicroelectronics (Geneva, Switzerland). Thus, a small-size and low-cost marker-based localization system is presented. The experimental results show that the speed estimation result of the proposed system is better than Px4flow, and it has the centimeter accuracy of mapping and positioning. The presented system not only gives satisfying localization precision, but also has the potential to expand other sensors (such as visual odometry, ultra wideband (UWB) beacon and lidar) to further improve the localization performance. The proposed system can be reliably employed in Micro Aerial Vehicle (MAV) visual localization and robotics control.

Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles

Directory of Open Access Journals (Sweden)

Boyang Xing

2018-05-01

Full Text Available A novel multi-sensor fusion indoor localization algorithm based on ArUco marker is designed in this paper. The proposed ArUco mapping algorithm can build and correct the map of markers online with Grubbs criterion and K-mean clustering, which avoids the map distortion due to lack of correction. Based on the conception of multi-sensor information fusion, the federated Kalman filter is utilized to synthesize the multi-source information from markers, optical flow, ultrasonic and the inertial sensor, which can obtain a continuous localization result and effectively reduce the position drift due to the long-term loss of markers in pure marker localization. The proposed algorithm can be easily implemented in a hardware of one Raspberry Pi Zero and two STM32 micro controllers produced by STMicroelectronics (Geneva, Switzerland. Thus, a small-size and low-cost marker-based localization system is presented. The experimental results show that the speed estimation result of the proposed system is better than Px4flow, and it has the centimeter accuracy of mapping and positioning. The presented system not only gives satisfying localization precision, but also has the potential to expand other sensors (such as visual odometry, ultra wideband (UWB beacon and lidar to further improve the localization performance. The proposed system can be reliably employed in Micro Aerial Vehicle (MAV visual localization and robotics control.

Discovery and fusion of salient multimodal features toward news story segmentation

Science.gov (United States)

Hsu, Winston; Chang, Shih-Fu; Huang, Chih-Wei; Kennedy, Lyndon; Lin, Ching-Yung; Iyengar, Giridharan

2003-12-01

In this paper, we present our new results in news video story segmentation and classification in the context of TRECVID video retrieval benchmarking event 2003. We applied and extended the Maximum Entropy statistical model to effectively fuse diverse features from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to automatically discover relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features -- asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measures up to 0.76 in ABC news and 0.73 in CNN news), present how these multi-level multi-modal features construct the probabilistic framework, and more importantly observe an interesting opportunity for further improvement.

Rapid fabricating technique for multi-layered human hepatic cell sheets by forceful contraction of the fibroblast monolayer.

Directory of Open Access Journals (Sweden)

Yusuke Sakai

Full Text Available Cell sheet engineering is attracting attention from investigators in various fields, from basic research scientists to clinicians focused on regenerative medicine. However, hepatocytes have a limited proliferation potential in vitro, and it generally takes a several days to form a sheet morphology and multi-layered sheets. We herein report our rapid and efficient technique for generating multi-layered human hepatic cell (HepaRG® cell sheets using pre-cultured fibroblast monolayers derived from human skin (TIG-118 cells as a feeder layer on a temperature-responsive culture dish. Multi-layered TIG-118/HepaRG cell sheets with a thick morphology were harvested on day 4 of culturing HepaRG cells by forceful contraction of the TIG-118 cells, and the resulting sheet could be easily handled. In addition, the human albumin and alpha 1-antitrypsin synthesis activities of TIG-118/HepaRG cells were approximately 1.2 and 1.3 times higher than those of HepaRG cells, respectively. Therefore, this technique is considered to be a promising modality for rapidly fabricating multi-layered human hepatocyte sheets from cells with limited proliferation potential, and the engineered cell sheet could be used for cell transplantation with highly specific functions.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

Science.gov (United States)

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

RadMAP: The Radiological Multi-sensor Analysis Platform

International Nuclear Information System (INIS)

Bandstra, Mark S.; Aucott, Timothy J.; Brubaker, Erik; Chivers, Daniel H.; Cooper, Reynold J.; Curtis, Joseph C.; Davis, John R.; Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J.; Srinivasan, Shreyas; Zakhor, Avideh; Zhang, Richard; Vetter, Kai

2016-01-01

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

RadMAP: The Radiological Multi-sensor Analysis Platform

Energy Technology Data Exchange (ETDEWEB)

Bandstra, Mark S., E-mail: msbandstra@lbl.gov [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Aucott, Timothy J. [Department of Nuclear Engineering, University of California Berkeley, CA (United States); Brubaker, Erik [Sandia National Laboratory, Livermore, CA (United States); Chivers, Daniel H.; Cooper, Reynold J. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Curtis, Joseph C. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Department of Nuclear Engineering, University of California Berkeley, CA (United States); Davis, John R. [Department of Nuclear Engineering, University of California Berkeley, CA (United States); Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J. [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Srinivasan, Shreyas [Department of Nuclear Engineering, University of California Berkeley, CA (United States); Department of Electrical Engineering and Computer Science, University of California Berkeley, CA (United States); Zakhor, Avideh; Zhang, Richard [Department of Electrical Engineering and Computer Science, University of California Berkeley, CA (United States); Vetter, Kai [Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Department of Nuclear Engineering, University of California Berkeley, CA (United States)

2016-12-21

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images.

Science.gov (United States)

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Gilmore, John H; Lin, Weili; Shen, Dinggang

2015-03-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. Copyright © 2014 Elsevier Inc. All rights reserved.

Modal Techniques for Remote Identification of Nonlinear Reactions at Gap-Supported Tubes Under Turbulent Excitation

International Nuclear Information System (INIS)

Delaune, X.; Piteau, Ph.; Borsoi, L.; Antunes, J.; Debut, V.

2010-01-01

Predictive computation of the nonlinear dynamical responses of gap-supported tubes subjected to flow excitation has been the subject of very active research. Nevertheless, experimental results are still very important, for validation of the theoretical predictions as well as for asserting the integrity of field components. Because carefully instrumented test tubes and tube-supports are seldom possible, due to space limitations and to the severe environment conditions, there is a need for robust techniques capable of extracting, from the actual vibratory response data, information that is relevant for asserting the components integrity. The dynamical contact/impact (vibro-impact) forces are of paramount significance, as are the tube/support gaps. Following our previous studies in this area using wave-propagation techniques (De Araujo, Antunes, and Piteau, 1998, 'Remote Identification of Impact Forces on Loosely Supported Tubes: Part 1-Basic Theory and Experiments', J. Sound Vib., 215, pp. 1015-1041; Antunes, Paulino, and Piteau, 1998, 'Remote Identification of Impact Forces on Loosely Supported Tubes: Part 2-Complex Vibro-Impact Motions', J. Sound Vib., 215, pp. 1043-1064; Paulino, Antunes, and Izquierdo, 1999, 'Remote Identification of Impact Forces on Loosely Supported Tubes: Analysis of Multi-Supported Systems', ASME J. Pressure Vessel Technol., 121, pp. 61-70), we apply modal methods in the present paper for extracting such information. The dynamical support forces, as well as the vibratory responses at the support locations, are identified from one or several vibratory response measurements at remote transducers, from which the support gaps can be inferred. As for most inverse problems, the identification results may prove quite sensitive to noise and modeling errors. Therefore, topics discussed in the paper include regularization techniques to mitigate the effects of non-measured noise perturbations. In particular, a method is proposed to improve the

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

Science.gov (United States)

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°) (Plordosis 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.

Spinal fusion-hardware construct: Basic concepts and imaging review

Science.gov (United States)

Nouh, Mohamed Ragab

2012-01-01

The interpretation of spinal images fixed with metallic hardware forms an increasing bulk of daily practice in a busy imaging department. Radiologists are required to be familiar with the instrumentation and operative options used in spinal fixation and fusion procedures, especially in his or her institute. This is critical in evaluating the position of implants and potential complications associated with the operative approaches and spinal fixation devices used. Thus, the radiologist can play an important role in patient care and outcome. This review outlines the advantages and disadvantages of commonly used imaging methods and reports on the best yield for each modality and how to overcome the problematic issues associated with the presence of metallic hardware during imaging. Baseline radiographs are essential as they are the baseline point for evaluation of future studies should patients develop symptoms suggesting possible complications. They may justify further imaging workup with computed tomography, magnetic resonance and/or nuclear medicine studies as the evaluation of a patient with a spinal implant involves a multi-modality approach. This review describes imaging features of potential complications associated with spinal fusion surgery as well as the instrumentation used. This basic knowledge aims to help radiologists approach everyday practice in clinical imaging. PMID:22761979

Three-Dimensional Model Retrieval Using Dynamic Multi-Descriptor Fusion

Institute of Scientific and Technical Information of China (English)

Jau-Ling Shi; Chang-Hsing Lee; Yao-Wen Hou; Po-Ting Yeh

2017-01-01

In this paper, we propose a dynamic multi-descriptor fusion (DMDF) approach to improving the retrieval accuracy of 3-dimensional (3D) model retrieval systems. First, an independent retrieval list is generated by using each individual descriptor. Second, we propose an automatic relevant/irrelevant models selection (ARMS) approach to selecting the relevant and irrelevant 3D models automatically without any user interaction. A weighted distance, in which the weight associated with each individual descriptor is learnt by using the selected relevant and irrelevant models, is used to measure the similarity between two 3D models. Furthermore, a descriptor-dependent adaptive query point movement (AQPM) approach is employed to update every feature vector. This set of new feature vectors is used to index 3D models in the next search process. Four 3D model databases are used to compare the retrieval accuracy of our proposed DMDF approach with several descriptors as well as some well-known information fusion methods. Experimental results have shown that our proposed DMDF approach provides a promising retrieval result and always yields the best retrieval accuracy.

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

Science.gov (United States)

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.

Joining and fabrication techniques for high temperature structures including the first wall in fusion reactor

Energy Technology Data Exchange (ETDEWEB)

Lee, Ho Jin; Lee, B. S.; Kim, K. B

2003-09-01

The materials for PFC's (Plasma Facing Components) in a fusion reactor are severely irradiated with fusion products in facing the high temperature plasma during the operation. The refractory materials can be maintained their excellent properties in severe operating condition by lowering surface temperature by bonding them to the high thermal conducting materials of heat sink. Hence, the joining and bonding techniques between dissimilar materials is considered to be important in case of the fusion reactor or nuclear reactor which is operated at high temperature. The first wall in the fusion reactor is heated to approximately 1000 .deg. C and irradiated severely by the plasma. In ITER, beryllium is expected as the primary armour candidate for the PFC's; other candidates including W, Mo, SiC, B4C, C/C and Si{sub 3}N{sub 4}. Since the heat affected zones in the PFC's processed by conventional welding are reported to have embrittlement and degradation in the sever operation condition, both brazing and diffusion bonding are being considered as prime candidates for the joining technique. In this report, both the materials including ceramics and the fabrication techniques including joining technique between dissimilar materials for PFC's are described. The described joining technique between the refractory materials and the dissimilar materials may be applicable for the fusion reactor and Generation-4 future nuclear reactor which are operated at high temperature and high irradiation.

Prediction of the microsurgical window for skull-base tumors by advanced three-dimensional multi-fusion volumetric imaging

International Nuclear Information System (INIS)

Oishi, Makoto; Fukuda, Masafumi; Saito, Akihiko; Hiraishi, Tetsuya; Fujii, Yukihiko; Ishida, Go

2011-01-01

The surgery of skull base tumors (SBTs) is difficult due to the complex and narrow surgical window that is restricted by the cranium and important structures. The utility of three-dimensional multi-fusion volumetric imaging (3-D MFVI) for visualizing the predicted window for SBTs was evaluated. Presurgical simulation using 3-D MFVI was performed in 32 patients with SBTs. Imaging data were collected from computed tomography, magnetic resonance imaging, and digital subtraction angiography. Skull data was processed to imitate actual bone resection and integrated with various structures extracted from appropriate imaging modalities by image-analyzing software. The simulated views were compared with the views obtained during surgery. All craniotomies and bone resections except opening of the acoustic canal in 2 patients were performed as simulated. The simulated window allowed observation of the expected microsurgical anatomies including tumors, vasculatures, and cranial nerves, through the predicted operative window. We could not achieve the planned tumor removal in only 3 patients. 3-D MFVI afforded high quality images of the relevant microsurgical anatomies during the surgery of SBTs. The intraoperative deja-vu effect of the simulation increased the confidence of the surgeon in the planned surgical procedures. (author)

A Review of Data Fusion Techniques

Science.gov (United States)

2013-01-01

The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion. PMID:24288502

Visual servoing in medical robotics: a survey. Part II: tomographic imaging modalities--techniques and applications.

Science.gov (United States)

Azizian, Mahdi; Najmaei, Nima; Khoshnam, Mahta; Patel, Rajni

2015-03-01

Intraoperative application of tomographic imaging techniques provides a means of visual servoing for objects beneath the surface of organs. The focus of this survey is on therapeutic and diagnostic medical applications where tomographic imaging is used in visual servoing. To this end, a comprehensive search of the electronic databases was completed for the period 2000-2013. Existing techniques and products are categorized and studied, based on the imaging modality and their medical applications. This part complements Part I of the survey, which covers visual servoing techniques using endoscopic imaging and direct vision. The main challenges in using visual servoing based on tomographic images have been identified. 'Supervised automation of medical robotics' is found to be a major trend in this field and ultrasound is the most commonly used tomographic modality for visual servoing. Copyright © 2014 John Wiley & Sons, Ltd.

A novel method of range measuring for a mobile robot based on multi-sensor information fusion

International Nuclear Information System (INIS)

Zhang Yi; Luo Yuan; Wang Jifeng

2005-01-01

The traditional measuring range for a mobile robot is based on a sonar sensor. Because of different working environments, it is very difficult to obtain high precision by using just one single method of range measurement. So, a hybrid sonar sensor and laser scanner method is put forward to overcome these shortcomings. A novel fusion model is proposed based on basic theory and a method of information fusion. An optimal measurement result has been obtained with information fusion from different sensors. After large numbers of experiments and performance analysis, a conclusion can be drawn that the laser scanner and sonar sensor method with multi-sensor information fusion have a higher precision than the single method of sonar. It can also be the same with different environments

Research on multi-source image fusion technology in haze environment

Science.gov (United States)

Ma, GuoDong; Piao, Yan; Li, Bing

2017-11-01

In the haze environment, the visible image collected by a single sensor can express the details of the shape, color and texture of the target very well, but because of the haze, the sharpness is low and some of the target subjects are lost; Because of the expression of thermal radiation and strong penetration ability, infrared image collected by a single sensor can clearly express the target subject, but it will lose detail information. Therefore, the multi-source image fusion method is proposed to exploit their respective advantages. Firstly, the improved Dark Channel Prior algorithm is used to preprocess the visible haze image. Secondly, the improved SURF algorithm is used to register the infrared image and the haze-free visible image. Finally, the weighted fusion algorithm based on information complementary is used to fuse the image. Experiments show that the proposed method can improve the clarity of the visible target and highlight the occluded infrared target for target recognition.

A framework of region-based dynamic image fusion

Institute of Scientific and Technical Information of China (English)

WANG Zhong-hua; QIN Zheng; LIU Yu

2007-01-01

A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions. Then different fusion rules are employed in different regions so that the target information is preserved as much as possible. In addition, steerable non-separable wavelet frame transform is used in the process of multi-resolution analysis, so the system achieves favorable characters of orientation and invariant shift. Compared with other image fusion methods, experimental results showed that the proposed method has better capabilities of target recognition and preserves clear background information.

Investigations of image fusion

Science.gov (United States)

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

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

Science.gov (United States)

Chirayath, Ved

2018-01-01

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with

Association between Gene Polymorphisms and Pain Sensitivity Assessed in a Multi-Modal Multi-Tissue Human Experimental Model - An Explorative Study

DEFF Research Database (Denmark)

Nielsen, Lecia Møller; Olesen, Anne Estrup; Sato, Hiroe

2016-01-01

The genetic influence on sensitivity to noxious stimuli (pain sensitivity) remains controversial and needs further investigation. In the present study, the possible influence of polymorphisms in three opioid receptor (OPRM, OPRD and OPRK) genes and the catechol-O-methyltransferase (COMT) gene...... on pain sensitivity in healthy participants was investigated. Catechol-O-methyltransferase has an indirect effect on the mu opioid receptor by changing its activity through an altered endogenous ligand effect. Blood samples for genetic analysis were withdrawn in a multi-modal and multi-tissue experimental......, electrical and thermal visceral stimulations. A cold pressor test was also conducted. DNA was available from 38 of 40 participants. Compared to non-carriers of the COMT rs4680A allele, carriers reported higher bone pressure pain tolerance threshold (i.e. less pain) by up to 23.8% (p

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

Science.gov (United States)

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

2017-01-01

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

Seismic qualification using digital signal processing/modal testing and finite element techniques

International Nuclear Information System (INIS)

Steedman, J.B.; Edelstein, A.

1983-01-01

A systematic procedure in which digital signal processing, modal testing and finite element techniques can be used to seismically qualify Class IE equipment for use in nuclear generating stations is presented. A new method was also developed in which measured transmissibility functions and Fourier transformation techniques were combined to compute instrument response spectra. As an illustrative example of the qualification method, the paper follows the qualification of a safety related Class IE Heating, Ventilating, and Air Conditioning (HVAC) Control Panel subjected to both seismic and hydrodynamic loading conditions

A Single Session of rTMS Enhances Small-Worldness in Writer’s Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph

Directory of Open Access Journals (Sweden)

Rose D. Bharath

2017-09-01

Full Text Available Background and Purpose: Repetitive transcranial magnetic stimulation (rTMS induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI.Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1 and after (R2 a single session of rTMS in 14 patients with Writer’s Cramp (WC. Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI. Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients.Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05. Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe.Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not “noise”.

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

Science.gov (United States)

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

2016-07-15

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

Uni-, bi- and tri-modal warning signals: effects of temporal parameters and sensory modality on perceived urgency

NARCIS (Netherlands)

van Erp, Johannes Bernardus Fransiscus; Toet, Alexander; Janssen, Joris B.

Multi-sensory warnings can potentially enhance risk communication. Hereto we investigated how temporal signal parameters affect perceived urgency within and across modalities. In an experiment, 78 observers rated the perceived urgency of uni-, bi-, and/or tri-modal stimuli as function of 25

Uni-, bi- and tri-modal warning signals : Effects of temporal parameters and sensory modality on perceived urgency

NARCIS (Netherlands)

Erp, J.B.F. van; Toet, A.; Janssen, J.B.

2015-01-01

Multi-sensory warnings can potentially enhance risk communication. Hereto we investigated how temporal signal parameters affect perceived urgency within and across modalities. In an experiment, 78 observers rated the perceived urgency of uni-, bi-, and/or tri-modal stimuli as function of 25

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

Science.gov (United States)

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 hybrid SEA/modal technique for modeling structural-acoustic interior noise in rotorcraft.

Science.gov (United States)

Jayachandran, V; Bonilha, M W

2003-03-01

This paper describes a hybrid technique that combines Statistical Energy Analysis (SEA) predictions for structural vibration with acoustic modal summation techniques to predict interior noise levels in rotorcraft. The method was applied for predicting the sound field inside a mock-up of the interior panel system of the Sikorsky S-92 helicopter. The vibration amplitudes of the frame and panel systems were predicted using a detailed SEA model and these were used as inputs to the model of the interior acoustic space. The spatial distribution of the vibration field on individual panels, and their coupling to the acoustic space were modeled using stochastic techniques. Leakage and nonresonant transmission components were accounted for using space-averaged values obtained from a SEA model of the complete structural-acoustic system. Since the cabin geometry was quite simple, the modeling of the interior acoustic space was performed using a standard modal summation technique. Sound pressure levels predicted by this approach at specific microphone locations were compared with measured data. Agreement within 3 dB in one-third octave bands above 40 Hz was observed. A large discrepancy in the one-third octave band in which the first acoustic mode is resonant (31.5 Hz) was observed. Reasons for such a discrepancy are discussed in the paper. The developed technique provides a method for modeling helicopter cabin interior noise in the frequency mid-range where neither FEA nor SEA is individually effective or accurate.

Mass acceleration in a multi-module plasma jet for impact fusion. Final report, 21 May 1984-21 May 1985

International Nuclear Information System (INIS)

Burton, R.L.; Goldstein, S.A.; Tidman, D.A.; Massey, D.W.; Winsor, N.K.; Witherspoon, F.D.

1985-07-01

GT-Devices began work on multi-module mass accelerators for impact fusion in 1981. The technique employs sequentially switched high pressure plasma jets to accelerate a lightweight projectile in a circular barrel. The purpose of the work of the past 12 months was to improve the understanding of the plasma jet acceleration process, and to translate that understanding into verifiable results. Both goals have been accomplished. During the past year we conceived, designed, built and fired 325 shots on the Module Test Facility (MTF). This facility provided sufficient diagnostics to investigate a wide variety of geometries, plasmas and current pulses, so that rapid progress was made

Osteoclast Fusion

DEFF Research Database (Denmark)

Marie Julie Møller, Anaïs; Delaissé, Jean-Marie; Søe, Kent

2017-01-01

on the nuclearity of fusion partners. While CD47 promotes cell fusions involving mono-nucleated pre-osteoclasts, syncytin-1 promotes fusion of two multi-nucleated osteoclasts, but also reduces the number of fusions between mono-nucleated pre-osteoclasts. Furthermore, CD47 seems to mediate fusion mostly through...... individual fusion events using time-lapse and antagonists of CD47 and syncytin-1. All time-lapse recordings have been studied by two independent observers. A total of 1808 fusion events were analyzed. The present study shows that CD47 and syncytin-1 have different roles in osteoclast fusion depending...... broad contact surfaces between the partners' cell membrane while syncytin-1 mediate fusion through phagocytic-cup like structure. J. Cell. Physiol. 9999: 1-8, 2016. © 2016 Wiley Periodicals, Inc....

Collision of H- with Aq+ ions and their relevance for fusion

International Nuclear Information System (INIS)

Salzborn, E.

1993-01-01

Employing the crossed-beams technique, we have measured absolute cross sections for single- and double-electron removal from H - in collisions with ions Ar q+ and Xe q+ (q ≤ 8) at cm-energies between 20 keV and 200 keV. The single-electron removal cross sections are in excellent agreement with quantum calculations by Presnyakov and Uskov based on a generalization of the Keldysh theory for multi-photon ionization. The data allows, for the first time, a realistic modelling of plasma neutralizers proposed for efficient production of powerful H 0 beams via neutralization of energetic H - ion beams. Multi-megawatt neutral beam injection is a proven technique for auxiliary heating of magnetically confined fusion plasmas. The next fusion tokamak ITER calls for a total of 75 MW neutral beam heating based on 1.3 MeV D 0 beams

A New Linearization Technique Using Multi-sinh Doublet

Directory of Open Access Journals (Sweden)

CEHAN, V.

2009-06-01

Full Text Available In this paper a new linearization technique using multi-sinh doublet, implemented with a second generation current conveyor is presented. This new linearization technique is compared with the one based on multi-tanh doublets with linearization series connected diodes on the branches. The comparative study of the two linearization techniques is carried out using both dynamic range analysis, expressed by linearity error and the THD value calculation of output current, and the noise behavior of the two analyzed doublets. For the multi-sinh linearization technique proposed in the paper a method which assures the increase of the dynamic range, keeping the transconductance value constant is presented. This is done by using two design parameters: the number of series connected diodes N, which specifies the desired linear operating range and the k emitters areas ratio of the input stage transistors, which establishes the transconductance value. In the paper is also shown that if the transconductances of the two analyzed doublets are identical, and for the same values of N and k parameters, respectively, the current consumption of the multi-sinh doublet is always smaller than for the multi-tanh doublet.

Multi-focus image fusion based on area-based standard deviation in dual tree contourlet transform domain

Science.gov (United States)

Dong, Min; Dong, Chenghui; Guo, Miao; Wang, Zhe; Mu, Xiaomin

2018-04-01

Multiresolution-based methods, such as wavelet and Contourlet are usually used to image fusion. This work presents a new image fusion frame-work by utilizing area-based standard deviation in dual tree Contourlet trans-form domain. Firstly, the pre-registered source images are decomposed with dual tree Contourlet transform; low-pass and high-pass coefficients are obtained. Then, the low-pass bands are fused with weighted average based on area standard deviation rather than the simple "averaging" rule. While the high-pass bands are merged with the "max-absolute' fusion rule. Finally, the modified low-pass and high-pass coefficients are used to reconstruct the final fused image. The major advantage of the proposed fusion method over conventional fusion is the approximately shift invariance and multidirectional selectivity of dual tree Contourlet transform. The proposed method is compared with wavelet- , Contourletbased methods and other the state-of-the art methods on common used multi focus images. Experiments demonstrate that the proposed fusion framework is feasible and effective, and it performs better in both subjective and objective evaluation.

Fusion blanket design and optimization techniques

International Nuclear Information System (INIS)

Gohar, Y.

2005-01-01

In fusion reactors, the blanket design and its characteristics have a major impact on the reactor performance, size, and economics. The selection and arrangement of the blanket materials, dimensions of the different blanket zones, and different requirements of the selected materials for a satisfactory performance are the main parameters, which define the blanket performance. These parameters translate to a large number of variables and design constraints, which need to be simultaneously considered in the blanket design process. This represents a major design challenge because of the lack of a comprehensive design tool capable of considering all these variables to define the optimum blanket design and satisfying all the design constraints for the adopted figure of merit and the blanket design criteria. The blanket design techniques of the First Wall/Blanket/Shield Design and Optimization System (BSDOS) have been developed to overcome this difficulty and to provide the state-of-the-art techniques and tools for performing blanket design and analysis. This report describes some of the BSDOS techniques and demonstrates its use. In addition, the use of the optimization technique of the BSDOS can result in a significant blanket performance enhancement and cost saving for the reactor design under consideration. In this report, examples are presented, which utilize an earlier version of the ITER solid breeder blanket design and a high power density self-cooled lithium blanket design for demonstrating some of the BSDOS blanket design techniques

A modified random decrement technique for modal identification from nonstationary ambient response data only

International Nuclear Information System (INIS)

Lin, Chang Sheng; Chiang, Dar Yun

2012-01-01

Modal identification is considered from response data of structural system under nonstationary ambient vibration. In a previous paper, we showed that by assuming the ambient excitation to be nonstationary white noise in the form of a product model, the nonstationary response signals can be converted into free-vibration data via the correlation technique. In the present paper, if the ambient excitation can be modeled as a nonstationary white noise in the form of a product model, then the nonstationary cross random decrement signatures of structural response evaluated at any fixed time instant are shown theoretically to be proportional to the nonstationary cross-correlation functions. The practical problem of insufficient data samples available for evaluating nonstationary random decrement signatures can be approximately resolved by first extracting the amplitude-modulating function from the response and then transforming the nonstationary responses into stationary ones. Modal-parameter identification can then be performed using the Ibrahim time-domain technique, which is effective at identifying closely spaced modes. The theory proposed can be further extended by using the filtering concept to cover the case of nonstationary color excitations. Numerical simulations confirm the validity of the proposed method for identification of modal parameters from nonstationary ambient response data

A MULTI-RESOLUTION FUSION MODEL INCORPORATING COLOR AND ELEVATION FOR SEMANTIC SEGMENTATION

Directory of Open Access Journals (Sweden)

W. Zhang

2017-05-01

Full Text Available In recent years, the developments for Fully Convolutional Networks (FCN have led to great improvements for semantic segmentation in various applications including fused remote sensing data. There is, however, a lack of an in-depth study inside FCN models which would lead to an understanding of the contribution of individual layers to specific classes and their sensitivity to different types of input data. In this paper, we address this problem and propose a fusion model incorporating infrared imagery and Digital Surface Models (DSM for semantic segmentation. The goal is to utilize heterogeneous data more accurately and effectively in a single model instead of to assemble multiple models. First, the contribution and sensitivity of layers concerning the given classes are quantified by means of their recall in FCN. The contribution of different modalities on the pixel-wise prediction is then analyzed based on visualization. Finally, an optimized scheme for the fusion of layers with color and elevation information into a single FCN model is derived based on the analysis. Experiments are performed on the ISPRS Vaihingen 2D Semantic Labeling dataset. Comprehensive evaluations demonstrate the potential of the proposed approach.

A review on machine learning principles for multi-view biological data integration.

Science.gov (United States)

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

A Study on Elastic Guided Wave Modal Characteristics in Multi-Layered Structures

International Nuclear Information System (INIS)

Cho, Youn Ho; Lee, Chong Myoung

2008-01-01

In this study, we have developed a program which can calculate phase and group velocities, attenuation and wave structures of each mode in multi-layered plates. The wave structures of each mode are obtained, varying material properties and number of layers. The key in the success of guided wave NDE is how to optimize the mode selection scheme by minimizing energy loss when a structure is in contact with liquid. In this study, the normalized out-of-plane displacements at the surface of a free plate are used to predict the variation of modal attenuation and verily the correlation between attenuation and wave structure. It turns out that the guided wave attenuation can be efficiently obtain from the out-of-plane displacement variation of a free wave guide alleviating such mathematical difficulties in extracting complex roots for the eigenvalue problem of a liquid loaded wave guide. Through this study, the concert to optimize guided wave mode selection is accomplished to enhance sensitivity and efficiency in nondestructive evaluation for multi-layered structures.

Multi-modal MRI of mild traumatic brain injury

Directory of Open Access Journals (Sweden)

Ponnada A. Narayana

2015-01-01

Full Text Available Multi-modal magnetic resonance imaging (MRI that included high resolution structural imaging, diffusion tensor imaging (DTI, magnetization transfer ratio (MTR imaging, and magnetic resonance spectroscopic imaging (MRSI were performed in mild traumatic brain injury (mTBI patients with negative computed tomographic scans and in an orthopedic-injured (OI group without concomitant injury to the brain. The OI group served as a comparison group for mTBI. MRI scans were performed both in the acute phase of injury (~24 h and at follow-up (~90 days. DTI data was analyzed using tract based spatial statistics (TBSS. Global and regional atrophies were calculated using tensor-based morphometry (TBM. MTR values were calculated using the standard method. MRSI was analyzed using LC Model. At the initial scan, the mean diffusivity (MD was significantly higher in the mTBI cohort relative to the comparison group in several white matter (WM regions that included internal capsule, external capsule, superior corona radiata, anterior corona radiata, posterior corona radiata, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, forceps major and forceps minor of the corpus callosum, superior longitudinal fasciculus, and corticospinal tract in the right hemisphere. TBSS analysis failed to detect significant differences in any DTI measures between the initial and follow-up scans either in the mTBI or OI group. No significant differences were found in MRSI, MTR or morphometry between the mTBI and OI cohorts either at the initial or follow-up scans with or without family wise error (FWE correction. Our study suggests that a number of WM tracts are affected in mTBI in the acute phase of injury and that these changes disappear by 90 days. This study also suggests that none of the MRI-modalities used in this study, with the exception of DTI, is sensitive in detecting changes in the acute phase of mTBI.

Remote welding and cutting techniques for fusion experimental reactors

International Nuclear Information System (INIS)

Onozuka, M.; Ishide, T.; Oda, Y.; Nagaoka, E.; Ue, K.; Kamei, H.

1995-01-01

Experimental investigation of the YAG laser cutting/welding and plasma gouging techniques has been conducted to examine their suitability for remote maintenance systems in future fusion experimental reactors. Using a hybrid beam coupling system, two laser beams of 500W and 740W powers were successfully combined to provide a 1,240W beam power. The combined laser was transmitted through the optical fiber for cutting and welding. The transmission loss for the beams is in the range of 13% to 14%, which is low. As for plasma gouging, the shallow gouging made a groove measuring 10 mm in width and 4 mm in depth on the stainless steel plates at a traversing speed of 75 cm/min, while the deep gouging made a groove of 12 mm in width and 7.5 mm in depth at a traversing speed of 50 cm/min. In addition, it was found that the shallow gouging did not leave byproducts from the material, providing a clean surface. Based on the findings, it is shown that the YAG laser cutting/welding and plasma gouging techniques can be us3ed for remote welding and cutting in future fusion experimental reactors

Remote welding and cutting techniques for fusion experimental reactors

Energy Technology Data Exchange (ETDEWEB)

Onozuka, M.; Ishide, T.; Oda, Y.; Nagaoka, E.; Ue, K.; Kamei, H. [Mitsubishi Heavy Industries, Ltd., Yokohama (Japan)

1995-12-31

Experimental investigation of the YAG laser cutting/welding and plasma gouging techniques has been conducted to examine their suitability for remote maintenance systems in future fusion experimental reactors. Using a hybrid beam coupling system, two laser beams of 500W and 740W powers were successfully combined to provide a 1,240W beam power. The combined laser was transmitted through the optical fiber for cutting and welding. The transmission loss for the beams is in the range of 13% to 14%, which is low. As for plasma gouging, the shallow gouging made a groove measuring 10 mm in width and 4 mm in depth on the stainless steel plates at a traversing speed of 75 cm/min, while the deep gouging made a groove of 12 mm in width and 7.5 mm in depth at a traversing speed of 50 cm/min. In addition, it was found that the shallow gouging did not leave byproducts from the material, providing a clean surface. Based on the findings, it is shown that the YAG laser cutting/welding and plasma gouging techniques can be us3ed for remote welding and cutting in future fusion experimental reactors.

Accuracy and reproducibility of tumor positioning during prolonged and multi-modality animal imaging studies

International Nuclear Information System (INIS)

Zhang Mutian; Huang Minming; Le, Carl; Zanzonico, Pat B; Ling, C Clifton; Koutcher, Jason A; Humm, John L; Claus, Filip; Kolbert, Katherine S; Martin, Kyle

2008-01-01

Dedicated small-animal imaging devices, e.g. positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI) scanners, are being increasingly used for translational molecular imaging studies. The objective of this work was to determine the positional accuracy and precision with which tumors in situ can be reliably and reproducibly imaged on dedicated small-animal imaging equipment. We designed, fabricated and tested a custom rodent cradle with a stereotactic template to facilitate registration among image sets. To quantify tumor motion during our small-animal imaging protocols, 'gold standard' multi-modality point markers were inserted into tumor masses on the hind limbs of rats. Three types of imaging examination were then performed with the animals continuously anesthetized and immobilized: (i) consecutive microPET and MR images of tumor xenografts in which the animals remained in the same scanner for 2 h duration, (ii) multi-modality imaging studies in which the animals were transported between distant imaging devices and (iii) serial microPET scans in which the animals were repositioned in the same scanner for subsequent images. Our results showed that the animal tumor moved by less than 0.2-0.3 mm over a continuous 2 h microPET or MR imaging session. The process of transporting the animal between instruments introduced additional errors of ∼0.2 mm. In serial animal imaging studies, the positioning reproducibility within ∼0.8 mm could be obtained.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

Science.gov (United States)

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Clinical outcomes associated with evolving treatment modalities and radiation techniques for base-of-tongue carcinoma: thirty years of institutional experience

International Nuclear Information System (INIS)

Chen, Leechuan Andy; Anker, Christopher J; Hunt, Jason P; Buchmann, Luke O; Grossmann, Kenneth F; Boucher, Kenneth; Fang, Li-Ming Christine; Shrieve, Dennis C; Hitchcock, Ying J

2015-01-01

Curative treatment for base-of-tongue squamous cell carcinoma (BOT SCC) has evolved over time; however, comparative outcomes analysis for various treatment strategies is lacking. The authors reviewed the evolution of treatment modality and radiotherapy (RT) technique for 231 consecutive BOT SCC patients at our institution between 1981 and 2011. Treatment modalities included definitive chemoradiotherapy (chemoRT) (42%), definitive RT (33%), surgery followed by RT (20%), and surgery alone (5%). RT techniques included external beam plus interstitial brachytherapy (EBRT + IB) (37%), conventional EBRT (29%), intensity-modulated radiation therapy ± simultaneous integrated boost (IMRT ± SIB) (34%). Clinical characteristics and outcomes were stratified by modality or RT technique. Treatment modality evolved from definitive RT (1980s–1990s) to definitive chemoRT (1990s–2000s). RT technique evolved from EBRT + IB (1980s–1990s) to conventional EBRT (1990s–2000s) to IMRT + SIB (2000s). With median alive follow-up of 6 years (0.3–28 years), the 5-year LC, LRC, and OS rates were 80%, 73%, and 51%. There was no difference in distribution of gender, age, stage among treatment modalities. Definitive chemoRT had improved LRC (HR 1.6) and OS (HR 1.7) compared to definitive RT. IMRT + SIB had improved LRC (HR 3.2), DFS (HR 3.4), and OS (HR 3.0) compared to conventional EBRT. Over the past 30 years, BOT SCC treatment has undergone major paradigm shifts that incorporate nonsurgical functional preservation, concurrent chemotherapy, and advanced RT techniques. Excellent locoregional control and survival outcomes are associated with accelerated IMRT with chemotherapy

Sex in the Curriculum: The Effect of a Multi-Modal Sexual History-Taking Module on Medical Student Skills

Science.gov (United States)

Lindau, Stacy Tessler; Goodrich, Katie G.; Leitsch, Sara A.; Cook, Sandy

2008-01-01

Purpose: The objective of this study was to determine the effect of a multi-modal curricular intervention designed to teach sexual history-taking skills to medical students. The Association of Professors of Gynecology and Obstetrics, the National Board of Medical Examiners, and others, have identified sexual history-taking as a learning objective…

The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna

NARCIS (Netherlands)

Weimar Acerbi, F.; Clevers, J.G.P.W.; Schaepman, M.E.

2006-01-01

Multi-sensor image fusion using the wavelet approach provides a conceptual framework for the improvement of the spatial resolution with minimal distortion of the spectral content of the source image. This paper assesses whether images with a large ratio of spatial resolution can be fused, and

Computer-based image analysis in radiological diagnostics and image-guided therapy: 3D-Reconstruction, contrast medium dynamics, surface analysis, radiation therapy and multi-modal image fusion

International Nuclear Information System (INIS)

Beier, J.

2001-01-01

This book deals with substantial subjects of postprocessing and analysis of radiological image data, a particular emphasis was put on pulmonary themes. For a multitude of purposes the developed methods and procedures can directly be transferred to other non-pulmonary applications. The work presented here is structured in 14 chapters, each describing a selected complex of research. The chapter order reflects the sequence of the processing steps starting from artefact reduction, segmentation, visualization, analysis, therapy planning and image fusion up to multimedia archiving. In particular, this includes virtual endoscopy with three different scene viewers (Chap. 6), visualizations of the lung disease bronchiectasis (Chap. 7), surface structure analysis of pulmonary tumors (Chap. 8), quantification of contrast medium dynamics from temporal 2D and 3D image sequences (Chap. 9) as well as multimodality image fusion of arbitrary tomographical data using several visualization techniques (Chap. 12). Thus, the software systems presented cover the majority of image processing applications necessary in radiology and were entirely developed, implemented and validated in the clinical routine of a university medical school. (orig.) [de

A borax fusion technique for quantitative X-ray fluorescence analysis

NARCIS (Netherlands)

van Willigen, J.H.H.G.; Kruidhof, H.; Dahmen, E.A.M.F.

1971-01-01

A borax fusion technique to cast glass discs for quantitative X-ray analysis is described in detail. The method is based on the “nonwetting” properties of a Pt/Au alloy towards molten borax, on the favourable composition of the flux and finally on the favourable form of the casting mould. The

Robust site security using smart seismic array technology and multi-sensor data fusion

Science.gov (United States)

Hellickson, Dean; Richards, Paul; Reynolds, Zane; Keener, Joshua

2010-04-01

Traditional site security systems are susceptible to high individual sensor nuisance alarm rates that reduce the overall system effectiveness. Visual assessment of intrusions can be intensive and manually difficult as cameras are slewed by the system to non intrusion areas or as operators respond to nuisance alarms. Very little system intrusion performance data are available other than discrete sensor alarm indications that provide no real value. This paper discusses the system architecture, integration and display of a multi-sensor data fused system for wide area surveillance, local site intrusion detection and intrusion classification. The incorporation of a novel seismic array of smart sensors using FK Beamforming processing that greatly enhances the overall system detection and classification performance of the system is discussed. Recent test data demonstrates the performance of the seismic array within several different installations and its ability to classify and track moving targets at significant standoff distances with exceptional immunity to background clutter and noise. Multi-sensor data fusion is applied across a suite of complimentary sensors eliminating almost all nuisance alarms while integrating within a geographical information system to feed a visual-fusion display of the area being secured. Real-time sensor detection and intrusion classification data is presented within a visual-fusion display providing greatly enhanced situational awareness, system performance information and real-time assessment of intrusions and situations of interest with limited security operator involvement. This approach scales from a small local perimeter to very large geographical area and can be used across multiple sites controlled at a single command and control station.

Coronary plaque morphology on multi-modality imagining and periprocedural myocardial infarction after percutaneous coronary intervention

Directory of Open Access Journals (Sweden)

Akira Sato

2016-06-01

Full Text Available Percutaneous coronary intervention (PCI may be complicated by periprocedural myocardial infarction (PMI as manifested by elevated cardiac biomarkers such as creatine kinase (CK-MB or troponin T. The occurrence of PMI has been shown to be associated with worse short- and long-term clinical outcome. However, recent studies suggest that PMI defined by biomarker levels alone is a marker of atherosclerosis burden and procedural complexity but in most cases does not have independent prognostic significance. Diagnostic multi-modality imaging such as intravascular ultrasound, optical coherence tomography, coronary angioscopy, near-infrared spectroscopy, multidetector computed tomography, and magnetic resonance imaging can be used to closely investigate the atherosclerotic lesion in order to detect morphological markers of unstable and vulnerable plaques in the patients undergoing PCI. With the improvement of technical aspects of multimodality coronary imaging, clinical practice and research are increasingly shifting toward defining the clinical implication of plaque morphology and patients outcomes. There were numerous published data regarding the relationship between pre-PCI lesion subsets on multi-modality imaging and post-PCI biomarker levels. In this review, we discuss the relationship between coronary plaque morphology estimated by invasive or noninvasive coronary imaging and the occurrence of PMI. Furthermore, this review underlies that the value of the multimodality coronary imaging approach will become the gold standard for invasive or noninvasive prediction of PMI in clinical practice.

Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain

International Nuclear Information System (INIS)

Fiedler, E.; Platsch, G.; Schwarz, A.; Schmiedehausen, K.; Kuwert, T.; Tomandl, B.; Huk, W.; Rupprecht, Th.; Rahn, N.

2003-01-01

Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and method: In 32 patients regional cerebral blood flow was measured using 99m Tc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3 D-T1 w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use. (orig.) [de

NIMROD: A Customer Focused, Team Driven Approach for Fusion Code Development

Science.gov (United States)

Karandikar, H. M.; Schnack, D. D.

1996-11-01

NIMROD is a new code that will be used for the analysis of existing fusion experiments, prediction of operational limits, and design of future devices. An approach called Integrated Product Development (IPD) is being used for the development of NIMROD. It is a dramatic departure from existing practice in the fusion program. Code development is being done by a self-directed, multi-disciplinary, multi-institutional team that consists of experts in plasma theory, experiment, computational physics, and computer science. Customer representatives (ITER, US experiments) are an integral part of the team. The team is using techniques such as Quality Function Deployment (QFD), Pugh Concept Selection, Rapid Prototyping, and Risk Management, during the design phase of NIMROD. Extensive use is made of communication and internet technology to support collaborative work. Our experience with using these team techniques for such a complex software development project will be reported.

Development of a low activation concrete shielding wall by multi-layered structure for a fusion reactor

International Nuclear Information System (INIS)

Sato, Satoshi; Maegawa, Toshio; Yoshimatsu, Kenji; Sato, Koichi; Nonaka, Akira; Takakura, Kosuke; Ochiai, Kentaro; Konno, Chikara

2011-01-01

A multi-layered concrete structure has been developed to reduce induced activity in the shielding for neutron generating facilities such as a fusion reactor. The multi-layered concrete structure is composed of: (1) an inner low activation concrete, (2) a boron-doped low activation concrete as the second layer, and (3) ordinary concrete as the outer layer of the neutron shield. With the multi-layered concrete structure the volume of boron is drastically decreased compared to a monolithic boron-doped concrete. A 14 MeV neutron shielding experiment with multi-layered concrete structure mockups was performed at FNS and several reaction rates and induced activity in the mockups were measured. This demonstrated that the multi-layered concrete effectively reduced low energy neutrons and induced activity.

Integrating Iris and Signature Traits for Personal Authentication Using User-SpecificWeighting

Directory of Open Access Journals (Sweden)

Serestina Viriri

2012-03-01

Full Text Available Biometric systems based on uni-modal traits are characterized by noisy sensor data, restricted degrees of freedom, non-universality and are susceptible to spoof attacks. Multi-modal biometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. In this paper, a user-score-based weighting technique for integrating the iris and signature traits is presented. This user-specific weighting technique has proved to be an efficient and effective fusion scheme which increases the authentication accuracy rate of multi-modal biometric systems. The weights are used to indicate the importance of matching scores output by each biometrics trait. The experimental results show that our biometric system based on the integration of iris and signature traits achieve a false rejection rate (FRR of 0.08% and a false acceptance rate (FAR of 0.01%.

Conceptual structure within and between modalities

Directory of Open Access Journals (Sweden)

Katia eDilkina

2013-01-01

Full Text Available Current views of semantic memory share the assumption that conceptual representations are based on multi-modal experience, which activates distinct modality-specific brain regions. This proposition is widely accepted, yet little is known about how each modality contributes to conceptual knowledge and how the structure of this contribution varies across these multiple information sources. We used verbal feature lists, features from drawings and verbal co-occurrence statistics from latent semantic analysis to examine the informational structure in four domains of knowledge: perceptual, functional, encyclopedic and verbal. The goals of the analysis were three-fold: (1 to assess the structure within individual modalities; (2 to compare structures between modalities; and (3 to assess the degree to which concepts organize categorically or randomly.Our results indicated significant and unique structure in all four modalities: perceptually, concepts organize based on prominent features such as shape, size, color and parts; functionally, they group based on use and interaction; encyclopedically, they arrange based on commonality in location or behavior; and verbally, they group associatively or relationally. Visual/perceptual knowledge gives rise to the strongest hierarchical organization and is closest to classic taxonomic structure. Information is organized somewhat similarly in the perceptual and encyclopedic domains, which differs significantly from the structure in the functional and verbal domains. Notably, the verbal modality has the most unique organization, which is not at all categorical but also not random. The idiosyncrasy and complexity of conceptual structure across modalities begs the question of how all of these modality-specific experiences are fused together into coherent, multi-faceted yet unified concepts. Accordingly, both methodological and theoretical implications of the present findings are discussed.

The Analysis of Product Traits and Innovation Process of Multi Level Marketing Business at Talk Fusion

OpenAIRE

Suwarno, Sutrisno

2013-01-01

Multi Level Marketing Business nowadays is rapidly growing. In recent years, there have been so many new Multi Level Marketing Business coming out with their own specific offers and products. One of the advantages of MLM Business is the contribution it makes to economic growth. It makes it possible for the national income to continuously grow. The objectives can be achieved from this research are to examine product traits and innovation process of Talk Fusion. Theories supporting research are...

Dust in fusion devices-a multi-faceted problem connecting high- and low-temperature plasma physics

International Nuclear Information System (INIS)

Winter, J

2004-01-01

Small particles with sizes between a few nanometers and a few 10 μm (dust) are formed in fusion devices by plasma-surface interaction processes. Though it is not a major problem today, dust is considered a problem that could arise in future long pulse fusion devices. This is primarily due to its radioactivity and due to its very high chemical reactivity. Dust formation is particularly pronounced when carbonaceous wall materials are used. Dust particles can be transported in the tokamak over significant distances. Radioactivity leads to electrical charging of dust and to its interaction with plasmas and electric fields. This may cause interference with the discharge but may also result in options for particle removal. This paper discusses some of the multi-faceted problems using information both from fusion research and from low-temperature dusty plasma work

Automated Modal Parameter Estimation for Operational Modal Analysis of Large Systems

DEFF Research Database (Denmark)

Andersen, Palle; Brincker, Rune; Goursat, Maurice

2007-01-01

In this paper the problems of doing automatic modal parameter extraction and how to account for large number of data to process are considered. Two different approaches for obtaining the modal parameters automatically using OMA are presented: The Frequency Domain Decomposition (FDD) technique and...

Palmprint and face multi-modal biometric recognition based on SDA-GSVD and its kernelization.

Science.gov (United States)

Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu

2012-01-01

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.

Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization

Science.gov (United States)

Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu

2012-01-01

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance. PMID:22778600

Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications

Science.gov (United States)

Paramanandham, Nirmala; Rajendiran, Kishore

2018-01-01

A novel image fusion technique is presented for integrating infrared and visible images. Integration of images from the same or various sensing modalities can deliver the required information that cannot be delivered by viewing the sensor outputs individually and consecutively. In this paper, a swarm intelligence based image fusion technique using discrete cosine transform (DCT) domain is proposed for surveillance application which integrates the infrared image with the visible image for generating a single informative fused image. Particle swarm optimization (PSO) is used in the fusion process for obtaining the optimized weighting factor. These optimized weighting factors are used for fusing the DCT coefficients of visible and infrared images. Inverse DCT is applied for obtaining the initial fused image. An enhanced fused image is obtained through adaptive histogram equalization for a better visual understanding and target detection. The proposed framework is evaluated using quantitative metrics such as standard deviation, spatial frequency, entropy and mean gradient. The experimental results demonstrate the outperformance of the proposed algorithm over many other state- of- the- art techniques reported in literature.

Thin layer activation techniques in research and industry

International Nuclear Information System (INIS)

Conlon, T.W.

1993-01-01

The following key application of thin layer activation technique (TLA) are discussed: ion-erosion in fusion tokamaks, bio-engineering technology, automobile industry. Future developments of the techniques, such as fission fragment TLA, multi-layer TLA and recoil implantation are discussed as well. 7 refs, 6 figs, 1 tab

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis

International Nuclear Information System (INIS)

Jesneck, Jonathan L.; Nolte, Loren W.; Baker, Jay A.; Floyd, Carey E.; Lo, Joseph Y.

2006-01-01

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p<0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets

Design and Implementation of Multi Agentbased Information Fusion System for Decision Making Support (A Case Study on Military Operation

Directory of Open Access Journals (Sweden)

Arwin Datunaya Wahyudi Sumari

2013-09-01

Full Text Available Quick, accurate, and complete information is highly required for supporting strategically impact decision making in a Military Operation (MO in order to reduce the decision cycle and to minimize the loss. For that purpose, we propose, design and implement a hierarchical Multi Agentbased Information Fusion System for Decision Making Support (MAIFSDMS. The information fusion is implemented by applying Maximum Score of the Total Sum of Joint Probabilities (MSJP fusion method and is done by a collection of Information Fusion Agents (IFA that forms a multiagent system. MAIFS uses a combination of generalization of Dasarathy and Joint Director’s Laboratory (JDL process models for information fusion mechanism. Information fusion products that are displayed in graphical forms provide comprehensive information regarding the MO’s area dynamics. By observing the graphics resulted from the information fusion, the commandant will have situational awareness and knowledge in order to make the most accurate strategic de cision as fast as possible.

Verification Modal Summation Technique for Synthetic and Observation Seismogram for Pidie Jaya Earthquake M6.5

Science.gov (United States)

Irwandi, Irwandi; Fashbir; Daryono

2018-04-01

Neo-Deterministic Seismic Hazard Assessment (NDSHA) method is a seismic hazard assessment method that has an advantage on realistic physical simulation of the source, propagation, and geological-geophysical structure. This simulation is capable on generating the synthetics seismograms at the sites that being observed. At the regional NDSHA scale, calculation of the strong ground motion is based on 1D modal summation technique because it is more efficient in computation. In this article, we verify the result of synthetic seismogram calculations with the result of field observations when Pidie Jaya earthquake on 7 December 2016 occurred with the moment magnitude of M6.5. Those data were recorded by broadband seismometers installed by BMKG (Indonesian Agency for Meteorology, Climatology and Geophysics). The result of the synthetic seismogram calculations verifies that some stations well show the suitability with observation while some other stations show the discrepancies with observation results. Based on the results of the observation of some stations, evidently 1D modal summation technique method has been well verified for thin sediment region (near the pre-tertiary basement), but less suitable for thick sediment region. The reason is that the 1D modal summation technique excludes the amplification effect of seismic wave occurring within thick sediment region. So, another approach is needed, e.g., 2D finite difference hybrid method, which is a part of local scale NDSHA method.

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

Directory of Open Access Journals (Sweden)

Chenhui Qiu

2017-01-01

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

Ultrasound-guided image fusion with computed tomography and magnetic resonance imaging. Clinical utility for imaging and interventional diagnostics of hepatic lesions

International Nuclear Information System (INIS)

Clevert, D.A.; Helck, A.; Paprottka, P.M.; Trumm, C.; Reiser, M.F.; Zengel, P.

2012-01-01

Abdominal ultrasound is often the first-line imaging modality for assessing focal liver lesions. Due to various new ultrasound techniques, such as image fusion, global positioning system (GPS) tracking and needle tracking guided biopsy, abdominal ultrasound now has great potential regarding detection, characterization and treatment of focal liver lesions. Furthermore, these new techniques will help to improve the clinical management of patients before and during interventional procedures. This article presents the principle and clinical impact of recently developed techniques in the field of ultrasound, e.g. image fusion, GPS tracking and needle tracking guided biopsy and discusses the results based on a feasibility study on 20 patients with focal hepatic lesions. (orig.) [de

Application of data fusion techniques and technologies for wearable health monitoring.

Science.gov (United States)

King, Rachel C; Villeneuve, Emma; White, Ruth J; Sherratt, R Simon; Holderbaum, William; Harwin, William S

2017-04-01

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market. Copyright © 2017. Published by Elsevier Ltd.

Integration of vibro-acoustography imaging modality with the traditional mammography.

Science.gov (United States)

Hosseini, H Gholam; Alizad, A; Fatemi, M

2007-01-01

Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Integrating unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we establish correspondence between the VA images and traditional X-ray mammogram by adopting a flexible control-point selection technique for image registration. A modified second-order polynomial, which simply leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the X-ray mammography with a registration error of less than 1.65 mm. The fused image is defined as a linear integration of the VA and X-ray images. Moreover, a color-based fusion technique was employed to integrate the images for better visualization of structural information.

Study on a multi-component palladium alloy membrane for the fusion fuel cycle

International Nuclear Information System (INIS)

Yoshida, Hiroshi; Okuno, Kenji; Nagasaki, Takanori; Noda, Kenji; Ishii, Yoshinobu; Takeshita, Hidefumi.

1985-11-01

A feasibility study on the material integrity with respect to the hydride formation and helium damage of the palladium alloy membrane was performed for an application of the palladium diffuser to a fusion fuel cleanup process. This study was conducted under the Japan/US Fusion Cooperation Program. Experimental works on the crystallography, hydrogen solubility and 3 He release characteristics were carried out with a multi-component palladium alloy(Pd-25Ag.Au.Ru). The excellent hydrogen permeability and mechanical properties of the membrane made of this alloy had been confirmed by authors' previous study. Based on the present study, this alloy membrane has high resistivity to the hydrogen embrittlement, and swelling and fracture due to the helium bubble formation under the practical operating conditions of the diffuser. (author)

Pulsed fusion space propulsion: Computational Magneto-Hydro Dynamics of a multi-coil parabolic reaction chamber

Science.gov (United States)

Romanelli, Gherardo; Mignone, Andrea; Cervone, Angelo

2017-10-01

Pulsed fusion propulsion might finally revolutionise manned space exploration by providing an affordable and relatively fast access to interplanetary destinations. However, such systems are still in an early development phase and one of the key areas requiring further investigations is the operation of the magnetic nozzle, the device meant to exploit the fusion energy and generate thrust. One of the last pulsed fusion magnetic nozzle design is the so called multi-coil parabolic reaction chamber: the reaction is thereby ignited at the focus of an open parabolic chamber, enclosed by a series of coaxial superconducting coils that apply a magnetic field. The field, beside confining the reaction and preventing any contact between hot fusion plasma and chamber structure, is also meant to reflect the explosion and push plasma out of the rocket. Reflection is attained thanks to electric currents induced in conductive skin layers that cover each of the coils, the change of plasma axial momentum generates thrust in reaction. This working principle has yet to be extensively verified and computational Magneto-Hydro Dynamics (MHD) is a viable option to achieve that. This work is one of the first detailed ideal-MHD analysis of a multi-coil parabolic reaction chamber of this kind and has been completed employing PLUTO, a freely distributed computational code developed at the Physics Department of the University of Turin. The results are thus a preliminary verification of the chamber's performance. Nonetheless, plasma leakage through the chamber structure has been highlighted. Therefore, further investigations are required to validate the chamber design. Implementing a more accurate physical model (e.g. Hall-MHD or relativistic-MHD) is thus mandatory, and PLUTO shows the capabilities to achieve that.

Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

Energy Technology Data Exchange (ETDEWEB)

Ariyarit, Atthaphon; Kanazaki, Masahiro [Tokyo Metropolitan University, Tokyo (Japan)

2015-04-15

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

International Nuclear Information System (INIS)

Ariyarit, Atthaphon; Kanazaki, Masahiro

2015-01-01

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

Evidence for a supra-modal representation of emotion from cross-modal adaptation.

Science.gov (United States)

Pye, Annie; Bestelmeyer, Patricia E G

2015-01-01

Successful social interaction hinges on accurate perception of emotional signals. These signals are typically conveyed multi-modally by the face and voice. Previous research has demonstrated uni-modal contrastive aftereffects for emotionally expressive faces or voices. Here we were interested in whether these aftereffects transfer across modality as theoretical models predict. We show that adaptation to facial expressions elicits significant auditory aftereffects. Adaptation to angry facial expressions caused ambiguous vocal stimuli drawn from an anger-fear morphed continuum to be perceived as less angry and more fearful relative to adaptation to fearful faces. In a second experiment, we demonstrate that these aftereffects are not dependent on learned face-voice congruence, i.e. adaptation to one facial identity transferred to an unmatched voice identity. Taken together, our findings provide support for a supra-modal representation of emotion and suggest further that identity and emotion may be processed independently from one another, at least at the supra-modal level of the processing hierarchy. Copyright © 2014 Elsevier B.V. All rights reserved.

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

Science.gov (United States)

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.

Application of the level set method for multi-phase flow computation in fusion engineering

International Nuclear Information System (INIS)

Luo, X-Y.; Ni, M-J.; Ying, A.; Abdou, M.

2006-01-01

Numerical simulation of multi-phase flow is essential to evaluate the feasibility of a liquid protection scheme for the power plant chamber. The level set method is one of the best methods for computing and analyzing the motion of interface among the multi-phase flow. This paper presents a general formula for the second-order projection method combined with the level set method to simulate unsteady incompressible multi-phase flow with/out phase change flow encountered in fusion science and engineering. The third-order ENO scheme and second-order semi-implicit Crank-Nicholson scheme is used to update the convective and diffusion term. The numerical results show this method can handle the complex deformation of the interface and the effect of liquid-vapor phase change will be included in the future work

Multi-terawatt fusion laser systems

International Nuclear Information System (INIS)

Holzrichter, J.F.

1993-01-01

The evolution of laser fusion systems started with a description of the basic principles of the laser in 1959, then a physical demonstration showing 1000 Watts of peak optical power in 1961 to the present systems that deliver 10 14 watts of peak optical power, are presented. Physical limits to large systems are reviewed: thermal limits, material stress limits, structural limits and stability, parasitic coupling, measurement precision and diagnostics. The various steps of the fusion laser-system development process are then discussed through an historical presentation. 3 figs., 8 refs

MIMO wireless networks channels, techniques and standards for multi-antenna, multi-user and multi-cell systems

CERN Document Server

Clerckx, Bruno

2013-01-01

This book is unique in presenting channels, techniques and standards for the next generation of MIMO wireless networks. Through a unified framework, it emphasizes how propagation mechanisms impact the system performance under realistic power constraints. Combining a solid mathematical analysis with a physical and intuitive approach to space-time signal processing, the book progressively derives innovative designs for space-time coding and precoding as well as multi-user and multi-cell techniques, taking into consideration that MIMO channels are often far from ideal. Reflecting developments

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

Energy Technology Data Exchange (ETDEWEB)

Timofeev, Andrey V.; Egorov, Dmitry V. [LPP “EqualiZoom”, Astana, 010000 (Kazakhstan)

2016-06-08

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

A multi-modal training programme to improve physical activity, physical fitness and perceived physical ability in obese children.

Science.gov (United States)

Morano, Milena; Colella, Dario; Rutigliano, Irene; Fiore, Pietro; Pettoello-Mantovani, Massimo; Campanozzi, Angelo

2014-01-01

Actual and perceived physical abilities are important correlates of physical activity (PA) and fitness, but little research has explored these relationships over time in obese children. This study was designed: (a) to assess the feasibility of a multi-modal training programme promoting changes in PA, fundamental motor skills and real and perceived physical abilities of obese children; and (b) to explore cross-sectional and longitudinal relationships between real and perceived physical competence in boys and girls. Forty-one participants (9.2 ± 1.2 years) were assessed before and after an 8-month intervention with respect to body composition, physical fitness, self-reported PA and perceived physical ability. After treatment, obese children reported improvements in the body mass index, PA levels, gross motor performance and actual and perceived physical abilities. Real and perceived physical competence was correlated in boys, but not in girls. Results indicate that a multi-modal programme focused on actual and perceived physical competence as associated with the gradual increase in the volume of activity might be an effective strategy to improve adherence of the participants and to increase the lifelong exercise skills of obese children.

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

Science.gov (United States)

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

2018-01-01

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

Scientific Foundations of the Demining by Fusion of Techniques Project

DEFF Research Database (Denmark)

Larsen, Jan

The Nordic Demining Research Forum (NDRF) has initiated a pilot project Demining by Fusion of Techniques (DeFuse), which will investigate the correlation between different methods used in mine action. While the ultimate purpose is to suggest new mine clearance operation practices, DeFuse will foc...... on achieving scientific and general knowledge about the coherence between different methods. This talk provides the scientific foundations for the project....

Design and Implementation of Multi Agent-based Information Fusion System for Supporting Decision Making (A Case Study on Military Operation

Directory of Open Access Journals (Sweden)

Arwin Datumaya Wahyudi Sumari

2008-05-01

Full Text Available Quick, accurate, and complete information is highly required for supporting strategically impact decision making in a Military Operation (MO in order to reduce the decision cycle and to minimize the loss. For that purpose, we propose, design and implement a hierarchical Multi Agent-based Information Fusion System for Decision Making Support (MAIFS-DMS. The information fusion is implemented by applying Maximum Score of the Total Sum of Joint Probabilities (MSJP fusion method and is done by a collection of Information Fusion Agents (IFA that forms a multiagent system. MAIFS uses a combination of generalization of Dasarathy and Joint Director’s Laboratory (JDL process models for information fusion mechanism. Information fusion products that are displayed in graphical forms provide comprehensive information regarding the MO area dynamics. By observing the graphics resulted from the information fusion, the commandant will have situational awareness and knowledge in order to make the most accurate strategic decision as fast as possible

Quantitative multi-modal MRI of the Hippocampus and cognitive ability in community-dwelling older subjects.

Science.gov (United States)

Aribisala, Benjamin S; Royle, Natalie A; Maniega, Susana Muñoz; Valdés Hernández, Maria C; Murray, Catherine; Penke, Lars; Gow, Alan; Starr, John M; Bastin, Mark E; Deary, Ian J; Wardlaw, Joanna M

2014-04-01

Hippocampal structural integrity is commonly quantified using volumetric measurements derived from brain magnetic resonance imaging (MRI). Previously reported associations with cognitive decline have not been consistent. We investigate hippocampal integrity using quantitative MRI techniques and its association with cognitive abilities in older age. Participants from the Lothian Birth Cohort 1936 underwent brain MRI at mean age 73 years. Longitudinal relaxation time (T1), magnetization transfer ratio (MTR), fractional anisotropy (FA) and mean diffusivity (MD) were measured in the hippocampus. General factors of fluid-type intelligence (g), cognitive processing speed (speed) and memory were obtained at age 73 years, as well as childhood IQ test results at age 11 years. Amongst 565 older adults, multivariate linear regression showed that, after correcting for ICV, gender and age 11 IQ, larger left hippocampal volume was significantly associated with better memory ability (β = .11, p = .003), but not with speed or g. Using quantitative MRI and after correcting for multiple testing, higher T1 and MD were significantly associated with lower scores of g (β range = -.11 to -.14, p multi-modal MRI assessments were more sensitive at detecting cognition-hippocampal integrity associations than volumetric measurements, resulting in stronger associations between MRI biomarkers and age-related cognition changes. Copyright © 2014. Published by Elsevier Ltd.

Dual-Modality Prostate Imaging with PET and Transrectal Ultrasound

Science.gov (United States)

2011-09-01

Downloaded on April 07,2010 at 23:12:26 UTC from IEEE Xplore . Restrictions apply. HUBER et al.: MULTI-MODALITY PHANTOM DEVELOPMENT 2723 As soon as...allow Authorized licensed use limited to: Lawrence Berkeley National Laboratory. Downloaded on April 07,2010 at 23:12:26 UTC from IEEE Xplore ...April 07,2010 at 23:12:26 UTC from IEEE Xplore . Restrictions apply. HUBER et al.: MULTI-MODALITY PHANTOM DEVELOPMENT 2725 Fig. 3. Reconstructed

Holographic Raman tweezers controlled by multi-modal natural user interface

International Nuclear Information System (INIS)

Tomori, Zoltán; Keša, Peter; Nikorovič, Matej; Valušová, Eva; Antalík, Marián; Kaňka, Jan; Jákl, Petr; Šerý, Mojmír; Bernatová, Silvie; Zemánek, Pavel

2016-01-01

Holographic optical tweezers provide a contactless way to trap and manipulate several microobjects independently in space using focused laser beams. Although the methods of fast and efficient generation of optical traps are well developed, their user friendly control still lags behind. Even though several attempts have appeared recently to exploit touch tablets, 2D cameras, or Kinect game consoles, they have not yet reached the level of natural human interface. Here we demonstrate a multi-modal ‘natural user interface’ approach that combines finger and gaze tracking with gesture and speech recognition. This allows us to select objects with an operator’s gaze and voice, to trap the objects and control their positions via tracking of finger movement in space and to run semi-automatic procedures such as acquisition of Raman spectra from preselected objects. This approach takes advantage of the power of human processing of images together with smooth control of human fingertips and downscales these skills to control remotely the motion of microobjects at microscale in a natural way for the human operator. (paper)

A study on multi-data source fusion method for petroleum pipeline leak detection

Energy Technology Data Exchange (ETDEWEB)

Liang, Wei; Zhang, Laibin [Research Center of Oil and Gas Safety Engineering Technology, China University of Petroleum, Beijing, (China)

2010-07-01

The detection of leaks on petroleum pipeline is a very important safety issue. Several studies were commissioned to develop new monitoring procedures for leakage detection. This paper sets out a new leak detection process. The approach developed took into consideration steady and transient states. The study investigated leak diagnosis problems in product pipelines using multi-sensor measurements (pressure, flux, density and temperature). The information collected from each sensor was considered as pieces of evidence that describe the operational conditions of the pipeline. The Dempster-Shafer (D-S) theory is used to associate multi-sensor data to pipe health indices. Experimental pressure and flow rate data were recorded using a Pipeline Leak Detection System (PLDS) acquisition card and used to verify the accuracy and reliability of this new detection method. The results showed that the degree of credibility was a high as 0.877. It was also found that multi-feature information fusion improves recognition of pipeline conditions.

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

Science.gov (United States)

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.

Fusion neutronics

CERN Document Server

Wu, Yican

2017-01-01

This book provides a systematic and comprehensive introduction to fusion neutronics, covering all key topics from the fundamental theories and methodologies, as well as a wide range of fusion system designs and experiments. It is the first-ever book focusing on the subject of fusion neutronics research. Compared with other nuclear devices such as fission reactors and accelerators, fusion systems are normally characterized by their complex geometry and nuclear physics, which entail new challenges for neutronics such as complicated modeling, deep penetration, low simulation efficiency, multi-physics coupling, etc. The book focuses on the neutronics characteristics of fusion systems and introduces a series of theories and methodologies that were developed to address the challenges of fusion neutronics, and which have since been widely applied all over the world. Further, it introduces readers to neutronics design’s unique principles and procedures, experimental methodologies and technologies for fusion systems...

Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory

Directory of Open Access Journals (Sweden)

Qian Ding

2014-10-01

Full Text Available Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature, gas and moisture, the range of fire monitoring in space and time is expanded compared with a single-sensor system. Then, the D-S evidence theory is applied to fuse the information from the multi-sensor with the specific fire model, and the fire alarm is more accurate and timely. The proposed method can avoid the failure of the monitoring data effectively, deal with the conflicting evidence from the multi-sensor robustly and improve the reliability of fire warning significantly.

Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization

Directory of Open Access Journals (Sweden)

Jing-Yu Yang

2012-04-01

Full Text Available When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA. Specifically, one person’s different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.

MPLM On-Orbit Interface Dynamic Flexibility Modal Test

Science.gov (United States)

Bookout, Paul S.; Rodriguez, Pedro I.; Tinson, Ian; Fleming, Paolo

2001-01-01

Now that the International Space Station (ISS) is being constructed, payload developers have to not only verify the Shuttle-to-payload interface, but also the interfaces their payload will have with the ISS. The Multi Purpose Logistic Module (MPLM) being designed and built by Alenia Spazio in Torino, Italy is one such payload. The MPLM is the primary carrier for the ISS Payload Racks, Re-supply Stowage Racks, and the Resupply Stowage Platforms to re-supply the ISS with food, water, experiments, maintenance equipment and etc. During the development of the MPLM there was no requirement for verification of the on-orbit interfaces with the ISS. When this oversight was discovered, all the dynamic test stands had already been disassembled. A method was needed that would not require an extensive testing stand and could be completed in a short amount of time. The residual flexibility testing technique was chosen. The residual flexibility modal testing method consists of measuring the free-free natural frequencies and mode shapes along with the interface frequency response functions (FRF's). Analytically, the residual flexibility method has been investigated in detail by, MacNeal, Martinez, Carne, and Miller, and Rubin, but has not been implemented extensively for model correlation due to difficulties in data acquisition. In recent years improvement of data acquisition equipment has made possible the implementation of the residual flexibility method as in Admire, Tinker, and Ivey, and Klosterman and Lemon. The residual flexibility modal testing technique is applicable to a structure with distinct points (DOF) of contact with its environment, such as the MPLM-to-Station interface through the Common Berthing Mechanism (CBM). The CBM is bolted to a flange on the forward cone of the MPLM. During the fixed base test (to verify Shuttle interfaces) some data was gathered on the forward cone panels. Even though there was some data on the forward cones, an additional modal test was

Multi-layered nanoparticles for penetrating the endosome and nuclear membrane via a step-wise membrane fusion process.

Science.gov (United States)

Akita, Hidetaka; Kudo, Asako; Minoura, Arisa; Yamaguti, Masaya; Khalil, Ikramy A; Moriguchi, Rumiko; Masuda, Tomoya; Danev, Radostin; Nagayama, Kuniaki; Kogure, Kentaro; Harashima, Hideyoshi

2009-05-01

Efficient targeting of DNA to the nucleus is a prerequisite for effective gene therapy. The gene-delivery vehicle must penetrate through the plasma membrane, and the DNA-impermeable double-membraned nuclear envelope, and deposit its DNA cargo in a form ready for transcription. Here we introduce a concept for overcoming intracellular membrane barriers that involves step-wise membrane fusion. To achieve this, a nanotechnology was developed that creates a multi-layered nanoparticle, which we refer to as a Tetra-lamellar Multi-functional Envelope-type Nano Device (T-MEND). The critical structural elements of the T-MEND are a DNA-polycation condensed core coated with two nuclear membrane-fusogenic inner envelopes and two endosome-fusogenic outer envelopes, which are shed in stepwise fashion. A double-lamellar membrane structure is required for nuclear delivery via the stepwise fusion of double layered nuclear membrane structure. Intracellular membrane fusions to endosomes and nuclear membranes were verified by spectral imaging of fluorescence resonance energy transfer (FRET) between donor and acceptor fluorophores that had been dually labeled on the liposome surface. Coating the core with the minimum number of nucleus-fusogenic lipid envelopes (i.e., 2) is essential to facilitate transcription. As a result, the T-MEND achieves dramatic levels of transgene expression in non-dividing cells.

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

Science.gov (United States)

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.

Research on digital multi-channel pulse height analysis techniques

International Nuclear Information System (INIS)

Xiao Wuyun; Wei Yixiang; Ai Xianyun; Ao Qi

2005-01-01

Multi-channel pulse height analysis techniques are developing in the direction of digitalization. Based on digital signal processing techniques, digital multi-channel analyzers are characterized by powerful pulse processing ability, high throughput, improved stability and flexibility. This paper analyzes key techniques of digital nuclear pulse processing. With MATLAB software, main algorithms are simulated, such as trapezoidal shaping, digital baseline estimation, digital pole-zero/zero-pole compensation, poles and zeros identification. The preliminary general scheme of digital MCA is discussed, as well as some other important techniques about its engineering design. All these lay the foundation of developing homemade digital nuclear spectrometers. (authors)

FuzzyFusion: an application architecture for multisource information fusion

Science.gov (United States)

Fox, Kevin L.; Henning, Ronda R.

2009-04-01

The correlation of information from disparate sources has long been an issue in data fusion research. Traditional data fusion addresses the correlation of information from sources as diverse as single-purpose sensors to all-source multi-media information. Information system vulnerability information is similar in its diversity of sources and content, and in the desire to draw a meaningful conclusion, namely, the security posture of the system under inspection. FuzzyFusionTM, A data fusion model that is being applied to the computer network operations domain is presented. This model has been successfully prototyped in an applied research environment and represents a next generation assurance tool for system and network security.

Hierarchical patch-based co-registration of differently stained histopathology slides

Science.gov (United States)

Yigitsoy, Mehmet; Schmidt, Günter

2017-03-01

Over the past decades, digital pathology has emerged as an alternative way of looking at the tissue at subcellular level. It enables multiplexed analysis of different cell types at micron level. Information about cell types can be extracted by staining sections of a tissue block using different markers. However, robust fusion of structural and functional information from different stains is necessary for reproducible multiplexed analysis. Such a fusion can be obtained via image co-registration by establishing spatial correspondences between tissue sections. Spatial correspondences can then be used to transfer various statistics about cell types between sections. However, the multi-modal nature of images and sparse distribution of interesting cell types pose several challenges for the registration of differently stained tissue sections. In this work, we propose a co-registration framework that efficiently addresses such challenges. We present a hierarchical patch-based registration of intensity normalized tissue sections. Preliminary experiments demonstrate the potential of the proposed technique for the fusion of multi-modal information from differently stained digital histopathology sections.

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

Science.gov (United States)

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

2016-04-01

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

Incidental acquisition of foreign language vocabulary through brief multi-modal exposure.

Science.gov (United States)

Bisson, Marie-Josée; van Heuven, Walter J B; Conklin, Kathy; Tunney, Richard J

2013-01-01

First language acquisition requires relatively little effort compared to foreign language acquisition and happens more naturally through informal learning. Informal exposure can also benefit foreign language learning, although evidence for this has been limited to speech perception and production. An important question is whether informal exposure to spoken foreign language also leads to vocabulary learning through the creation of form-meaning links. Here we tested the impact of exposure to foreign language words presented with pictures in an incidental learning phase on subsequent explicit foreign language learning. In the explicit learning phase, we asked adults to learn translation equivalents of foreign language words, some of which had appeared in the incidental learning phase. Results revealed rapid learning of the foreign language words in the incidental learning phase showing that informal exposure to multi-modal foreign language leads to foreign language vocabulary acquisition. The creation of form-meaning links during the incidental learning phase is discussed.

Real-Time MRI Navigated Ultrasound for Preoperative Tumor Evaluation in Breast Cancer Patients: Technique and Clinical Implementation

Energy Technology Data Exchange (ETDEWEB)

Park, Ah Young; Seo, Bo Kyoung [Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan 15355 (Korea, Republic of)

2016-11-01

Real-time magnetic resonance imaging (MRI) navigated ultrasound is an image fusion technique to display the results of both MRI and ultrasonography on the same monitor. This system is a promising technique to improve lesion detection and analysis, to maximize advantages of each imaging modality, and to compensate the disadvantages of both MRI and ultrasound. In evaluating breast cancer stage preoperatively, MRI and ultrasound are the most representative imaging modalities. However, sometimes difficulties arise in interpreting and correlating the radiological features between these two different modalities. This pictorial essay demonstrates the technical principles of the real-time MRI navigated ultrasound, and clinical implementation of the system in preoperative evaluation of tumor extent, multiplicity, and nodal status in breast cancer patients.

Real-time MRI navigated ultrasound for preoperative tumor evaluation in breast cancer patients: Technique and clinical implementation

Energy Technology Data Exchange (ETDEWEB)

Park, Ah Young; Seo, Bo Kyoung [Dept. of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan (Korea, Republic of)

2016-09-15

Real-time magnetic resonance imaging (MRI) navigated ultrasound is an image fusion technique to display the results of both MRI and ultrasonography on the same monitor. This system is a promising technique to improve lesion detection and analysis, to maximize advantages of each imaging modality, and to compensate the disadvantages of both MRI and ultrasound. In evaluating breast cancer stage preoperatively, MRI and ultrasound are the most representative imaging modalities. However, sometimes difficulties arise in interpreting and correlating the radiological features between these two different modalities. This pictorial essay demonstrates the technical principles of the real-time MRI navigated ultrasound, and clinical implementation of the system in preoperative evaluation of tumor extent, multiplicity, and nodal status in breast cancer patients.

Role of magnetic resonance urography in pediatric renal fusion anomalies

International Nuclear Information System (INIS)

Chan, Sherwin S.; Ntoulia, Aikaterini; Khrichenko, Dmitry; Back, Susan J.; Darge, Kassa; Tasian, Gregory E.; Dillman, Jonathan R.

2017-01-01

Renal fusion is on a spectrum of congenital abnormalities that occur due to disruption of the migration process of the embryonic kidneys from the pelvis to the retroperitoneal renal fossae. Clinically, renal fusion anomalies are often found incidentally and associated with increased risk for complications, such as urinary tract obstruction, infection and urolithiasis. These anomalies are most commonly imaged using ultrasound for anatomical definition and less frequently using renal scintigraphy to quantify differential renal function and assess urinary tract drainage. Functional magnetic resonance urography (fMRU) is an advanced imaging technique that combines the excellent soft-tissue contrast of conventional magnetic resonance (MR) images with the quantitative assessment based on contrast medium uptake and excretion kinetics to provide information on renal function and drainage. fMRU has been shown to be clinically useful in evaluating a number of urological conditions. A highly sensitive and radiation-free imaging modality, fMRU can provide detailed morphological and functional information that can facilitate conservative and/or surgical management of children with renal fusion anomalies. This paper reviews the embryological basis of the different types of renal fusion anomalies, their imaging appearances at fMRU, complications associated with fusion anomalies, and the important role of fMRU in diagnosing and managing children with these anomalies. (orig.)

Role of magnetic resonance urography in pediatric renal fusion anomalies

Energy Technology Data Exchange (ETDEWEB)

Chan, Sherwin S. [Children' s Mercy Hospital, Department of Radiology, Kansas City, MO (United States); Ntoulia, Aikaterini; Khrichenko, Dmitry [The Children' s Hospital of Philadelphia, Division of Body Imaging, Department of Radiology, Philadelphia, PA (United States); Back, Susan J.; Darge, Kassa [The Children' s Hospital of Philadelphia, Division of Body Imaging, Department of Radiology, Philadelphia, PA (United States); University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA (United States); Tasian, Gregory E. [University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA (United States); The Children' s Hospital of Philadelphia, Division of Urology, Department of Surgery, Philadelphia, PA (United States); Dillman, Jonathan R. [Cincinnati Children' s Hospital Medical Center, Division of Thoracoabdominal Imaging, Department of Radiology, Cincinnati, OH (United States)

2017-12-15

Renal fusion is on a spectrum of congenital abnormalities that occur due to disruption of the migration process of the embryonic kidneys from the pelvis to the retroperitoneal renal fossae. Clinically, renal fusion anomalies are often found incidentally and associated with increased risk for complications, such as urinary tract obstruction, infection and urolithiasis. These anomalies are most commonly imaged using ultrasound for anatomical definition and less frequently using renal scintigraphy to quantify differential renal function and assess urinary tract drainage. Functional magnetic resonance urography (fMRU) is an advanced imaging technique that combines the excellent soft-tissue contrast of conventional magnetic resonance (MR) images with the quantitative assessment based on contrast medium uptake and excretion kinetics to provide information on renal function and drainage. fMRU has been shown to be clinically useful in evaluating a number of urological conditions. A highly sensitive and radiation-free imaging modality, fMRU can provide detailed morphological and functional information that can facilitate conservative and/or surgical management of children with renal fusion anomalies. This paper reviews the embryological basis of the different types of renal fusion anomalies, their imaging appearances at fMRU, complications associated with fusion anomalies, and the important role of fMRU in diagnosing and managing children with these anomalies. (orig.)

Multi-photon microscope driven by novel green laser pump

DEFF Research Database (Denmark)

Marti, Dominik; Djurhuus, Martin; Jensen, Ole Bjarlin

2016-01-01

Multi-photon microscopy is extensively used in research due to its superior possibilities when compared to other microscopy modalities. The technique also has the possibility to advance diagnostics in clinical applications, due to its capabilities complementing existing technology in a multimodal...

Exploiting Higher Order and Multi-modal Features for 3D Object Detection

DEFF Research Database (Denmark)

Kiforenko, Lilita

that describe object visual appearance such as shape, colour, texture etc. This thesis focuses on robust object detection and pose estimation of rigid objects using 3D information. The thesis main contributions are novel feature descriptors together with object detection and pose estimation algorithms....... The initial work introduces a feature descriptor that uses edge categorisation in combination with a local multi-modal histogram descriptor in order to detect objects with little or no texture or surface variation. The comparison is performed with a state-of-the-art method, which is outperformed...... of the methods work well for one type of objects in a specific scenario, in another scenario or with different objects they might fail, therefore more robust solutions are required. The typical problem solution is the design of robust feature descriptors, where feature descriptors contain information...

Multisensor data fusion algorithm development

Energy Technology Data Exchange (ETDEWEB)

Yocky, D.A.; Chadwick, M.D.; Goudy, S.P.; Johnson, D.K.

1995-12-01

This report presents a two-year LDRD research effort into multisensor data fusion. We approached the problem by addressing the available types of data, preprocessing that data, and developing fusion algorithms using that data. The report reflects these three distinct areas. First, the possible data sets for fusion are identified. Second, automated registration techniques for imagery data are analyzed. Third, two fusion techniques are presented. The first fusion algorithm is based on the two-dimensional discrete wavelet transform. Using test images, the wavelet algorithm is compared against intensity modulation and intensity-hue-saturation image fusion algorithms that are available in commercial software. The wavelet approach outperforms the other two fusion techniques by preserving spectral/spatial information more precisely. The wavelet fusion algorithm was also applied to Landsat Thematic Mapper and SPOT panchromatic imagery data. The second algorithm is based on a linear-regression technique. We analyzed the technique using the same Landsat and SPOT data.

Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner

OpenAIRE

LABAYRADE, R; ROYERE, C; GRUYER, D; AUBERT, D

2003-01-01

The authors propose in this paper a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features of these two sensors to perform robust, accurate and real-time detection of multi-obstacles in the automotive context. The proposed system is able to estimate the position and the height, width and depth of generic obstacles at video frame rate (25 frames per second). The vehicle pitch, estimated by stereovision, is used to filter laser sca...

Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner

OpenAIRE

LABAYRADE, R; ROYERE, C; GRUYER, D; AUBERT, D

2005-01-01

We propose a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context. The proposed system is able to estimate the position and the height, width and depth of generic obstacles at video frame rate (25 frames per second). The vehicle pitch, estimated by stereovision, is used to filter lase...

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

Science.gov (United States)

Kanbar, Lara J; Shalish, Wissam; Precup, Doina; Brown, Karen; Sant'Anna, Guilherme M; Kearney, Robert E

2017-07-01

In multi-disciplinary studies, different forms of data are often collected for analysis. For example, APEX, a study on the automated prediction of extubation readiness in extremely preterm infants, collects clinical parameters and cardiorespiratory signals. A variety of cardiorespiratory metrics are computed from these signals and used to assign a cardiorespiratory pattern at each time. In such a situation, exploratory analysis requires a visualization tool capable of displaying these different types of acquired and computed signals in an integrated environment. Thus, we developed APEX_SCOPE, a graphical tool for the visualization of multi-modal data comprising cardiorespiratory signals, automated cardiorespiratory metrics, automated respiratory patterns, manually classified respiratory patterns, and manual annotations by clinicians during data acquisition. This MATLAB-based application provides a means for collaborators to view combinations of signals to promote discussion, generate hypotheses and develop features.

Three dimensional image alignment, registration and fusion

International Nuclear Information System (INIS)

Treves, S.T.; Mitchell, K.D.; Habboush, I.H.

1998-01-01

Combined assessment of three dimensional anatomical and functional images (SPECT, PET, MRI, CT) is useful to determine the nature and extent of lesions in many parts of the body. Physicians principally rely on their spatial sense of mentally re-orient and overlap images obtained with different imaging modalities. Objective methods that enable easy and intuitive image registration can help the physician arrive at more optimal diagnoses and better treatment decisions. This review describes a simple, intuitive and robust image registration approach developed in our laboratory. It differs from most other registration techniques in that it allows the user to incorporate all of the available information within the images in the registration process. This method takes full advantage of the ability of knowledgeable operators to achieve image registration and fusion using an intuitive interactive visual approach. It can register images accurately and quickly without the use of elaborate mathematical modeling or optimization techniques. The method provides the operator with tools to manipulate images in three dimensions, including visual feedback techniques to assess the accuracy of registration (grids, overlays, masks, and fusion of images in different colors). Its application is not limited to brain imaging and can be applied to images from any region in the body. The overall effect is a registration algorithm that is easy to implement and can achieve accuracy on the order of one pixel

Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

Science.gov (United States)

Huang, Yawen; Shao, Ling; Frangi, Alejandro F

2018-03-01

Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.

Multi-regional local anesthetic infiltration during laparoscopic cholecystectomy in patients receiving prophylactic multi-modal analgesia: a randomized, double-blinded, placebo-controlled study

DEFF Research Database (Denmark)

Bisgaard, T; Klarskov, B; Kristiansen, V B

1999-01-01

undergoing elective laparoscopic cholecystectomy. In addition, all patients received multi-modal prophylactic analgesic treatment. Fifty-eight patients were randomized to receive a total of 286 mg (66 mL) ropivacaine or 66 mL saline via periportal and intraperitoneal infiltration. During the first 3...... postoperative h, the use of morphine and antiemetics was registered, and pain and nausea were rated hourly. Daily pain intensity, pain localization, and supplemental analgesic consumption were registered the first postoperative week. Ropivacaine reduced overall pain the first two hours and incisional pain...... for the first three postoperative hours (P ropivacaine group (P

Two Phase Non-Rigid Multi-Modal Image Registration Using Weber Local Descriptor-Based Similarity Metrics and Normalized Mutual Information

Directory of Open Access Journals (Sweden)

Feng Yang

2013-06-01

Full Text Available Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Existing image registration methods based on similarity metrics such as mutual information (MI and sum of squared differences (SSD cannot achieve either high registration accuracy or high registration efficiency. To address this problem, we propose a novel two phase non-rigid multi-modal image registration method by combining Weber local descriptor (WLD based similarity metrics with the normalized mutual information (NMI using the diffeomorphic free-form deformation (FFD model. The first phase aims at recovering the large deformation component using the WLD based non-local SSD (wldNSSD or weighted structural similarity (wldWSSIM. Based on the output of the former phase, the second phase is focused on getting accurate transformation parameters related to the small deformation using the NMI. Extensive experiments on T1, T2 and PD weighted MR images demonstrate that the proposed wldNSSD-NMI or wldWSSIM-NMI method outperforms the registration methods based on the NMI, the conditional mutual information (CMI, the SSD on entropy images (ESSD and the ESSD-NMI in terms of registration accuracy and computation efficiency.

The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking.

Science.gov (United States)

Rajpoot, Kashif; Grau, Vicente; Noble, J Alison; Becher, Harald; Szmigielski, Cezary

2011-08-01

Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images. Copyright © 2011 Elsevier B.V. All rights reserved.

Mixed-Modality Stimulation to Evoke Two Modalities Simultaneously in One Channel for Electrocutaneous Sensory Feedback.

Science.gov (United States)

Choi, Kyunghwan; Kim, Pyungkang; Kim, Kyung-Soo; Kim, Soohyun

2017-12-01

One of the long-standing challenges in upper limb prosthetics is restoring the sensory feedback that is missing due to amputation. Two approaches have previously been presented to provide various types of sensory information to users, namely, multi-modality sensory feedback and using an array of single-modality stimulators. However, the feedback systems used in these approaches were too bulky to be embedded in prosthesis sockets. In this paper, we propose an electrocutaneous sensory feedback method that is capable of conveying two modalities simultaneously with only one electrode. The stimulation method, which we call mixed-modality stimulation, utilizes the phenomenon in which the superposition of two electric pulse trains of different frequencies is able to evoke two different modalities (i.e., pressure and tapping) at the same time. We conducted psychophysical experiments in which healthy subjects were required to recognize the intensity of pressure or the frequency of tapping from mixed-modality or two-channel stimulations. The results demonstrated that the subjects were able to discriminate the features of the two modalities in one electrode during mixed-modality stimulation and that the accuracies of successful recognitions (mean ± standard deviation) for the two feedback variables were 84.3 ± 7% for mixed-modality stimulation and 89.5 ± 6% for two-channel dual-modality stimulation, showing no statistically significant difference. Therefore, mixed-modality stimulation is an attractive method for modulating two modalities independently with only one electrode, and it could be used for implementing a compact sensory feedback system that is able to provide two different types of sensory information from prosthetics.

Tissue identification with micro-magnetic resonance imaging in a caprine spinal fusion model

NARCIS (Netherlands)

Uffen, M.; Krijnen, M.; Hoogendoorn, R.; Strijkers, G.; Everts, V.; Wuisman, P.; Smit, T.

2008-01-01

Nonunion is a major complication of spinal interbody fusion. Currently X-ray and computed tomography (CT) are used for evaluating the spinal fusion process. However, both imaging modalities have limitations in judgment of the early stages of this fusion process, as they only visualize mineralized

MISTRAL: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries

International Nuclear Information System (INIS)

Talarico, Luca; Reniers, Genserik; Sörensen, Kenneth; Springael, Johan

2015-01-01

In this paper we present a multi-modal security-transportation model to allocate security resources within a chemical supply chain which is characterized by the use of different transport modes, each having their own security features. We consider security-related risks so as to take measures against terrorist acts which could target critical transportation systems. The idea of addressing security-related issues, by supporting decisions for preventing or mitigating intentional acts on transportation infrastructure, has gained attention in academic research only recently. The decision model presented in this paper is based on game theory and it can be employed to organize intelligence capabilities aimed at securing chemical supply chains. It enables detection and warning against impending attacks on transportation infrastructures and the subsequent adoption of security countermeasures. This is of extreme importance for preventing terrorist attacks and for avoiding (possibly huge) human and economic losses. In our work we also provide data sources and numerical simulations by applying the proposed model to a illustrative multi-modal chemical supply chain. - Highlights: • A model to increase the security in a multimodal chemical supply chain is proposed. • The model considers adaptive opponents having multi-attribute utility functions. • The model is based on game theory using an attacker–defender schema. • The model provides recommendations about where to allocate security measures. • Numerical simulations on a sample multimodal chemical supply chain are shown

Fusion of Geophysical Images in the Study of Archaeological Sites

Science.gov (United States)

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

Clinical significance of creative 3D-image fusion across multimodalities [PET + CT + MR] based on characteristic coregistration

International Nuclear Information System (INIS)

Peng, Matthew Jian-qiao; Ju Xiangyang; Khambay, Balvinder S.; Ayoub, Ashraf F.; Chen, Chin-Tu; Bai Bo

2012-01-01

Objective: To investigate a registration approach for 2-dimension (2D) based on characteristic localization to achieve 3-dimension (3D) fusion from images of PET, CT and MR one by one. Method: A cubic oriented scheme of“9-point and 3-plane” for co-registration design was verified to be geometrically practical. After acquisiting DICOM data of PET/CT/MR (directed by radiotracer 18 F-FDG etc.), through 3D reconstruction and virtual dissection, human internal feature points were sorted to combine with preselected external feature points for matching process. By following the procedure of feature extraction and image mapping, “picking points to form planes” and “picking planes for segmentation” were executed. Eventually, image fusion was implemented at real-time workstation mimics based on auto-fuse techniques so called “information exchange” and “signal overlay”. Result: The 2D and 3D images fused across modalities of [CT + MR], [PET + MR], [PET + CT] and [PET + CT + MR] were tested on data of patients suffered from tumors. Complementary 2D/3D images simultaneously presenting metabolic activities and anatomic structures were created with detectable-rate of 70%, 56%, 54% (or 98%) and 44% with no significant difference for each in statistics. Conclusion: Currently, based on the condition that there is no complete hybrid detector integrated of triple-module [PET + CT + MR] internationally, this sort of multiple modality fusion is doubtlessly an essential complement for the existing function of single modality imaging.

Fusion set selection with surrogate metric in multi-atlas based image segmentation

International Nuclear Information System (INIS)

Zhao, Tingting; Ruan, Dan

2016-01-01

Multi-atlas based image segmentation sees unprecedented opportunities but also demanding challenges in the big data era. Relevant atlas selection before label fusion plays a crucial role in reducing potential performance loss from heterogeneous data quality and high computation cost from extensive data. This paper starts with investigating the image similarity metric (termed ‘surrogate’), an alternative to the inaccessible geometric agreement metric (termed ‘oracle’) in atlas relevance assessment, and probes into the problem of how to select the ‘most-relevant’ atlases and how many such atlases to incorporate. We propose an inference model to relate the surrogates and the oracle geometric agreement metrics. Based on this model, we quantify the behavior of the surrogates in mimicking oracle metrics for atlas relevance ordering. Finally, analytical insights on the choice of fusion set size are presented from a probabilistic perspective, with the integrated goal of including the most relevant atlases and excluding the irrelevant ones. Empirical evidence and performance assessment are provided based on prostate and corpus callosum segmentation. (paper)

Using Enhanced Frequency Domain Decomposition as a Robust Technique to Harmonic Excitation in Operational Modal Analysis

DEFF Research Database (Denmark)

Jacobsen, Niels-Jørgen; Andersen, Palle; Brincker, Rune

2006-01-01

The presence of harmonic components in the measured responses is unavoidable in many applications of Operational Modal Analysis. This is especially true when measuring on mechanical structures containing rotating or reciprocating parts. This paper describes a new method based on the popular...... agreement is found and the method is proven to be an easy-to-use and robust tool for handling responses with deterministic and stochastic content....... Enhanced Frequency Domain Decomposition technique for eliminating the influence of these harmonic components in the modal parameter extraction process. For various experiments, the quality of the method is assessed and compared to the results obtained using broadband stochastic excitation forces. Good...

A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

Science.gov (United States)

Liu, Bailing; Zhang, Fumin; Qu, Xinghua

2015-01-01

An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

Multi-modality image reconstruction for dual-head small-animal PET

International Nuclear Information System (INIS)

Huang, Chang-Han; Chou, Cheng-Ying

2015-01-01

The hybrid positron emission tomography/computed tomography (PET/CT) or positron emission tomography/magnetic resonance imaging (PET/MRI) has become routine practice in clinics. The applications of multi-modality imaging can also benefit research advances. Consequently, dedicated small-imaging system like dual-head small-animal PET (DHAPET) that possesses the advantages of high detection sensitivity and high resolution can exploit the structural information from CT or MRI. It should be noted that the special detector arrangement in DHAPET leads to severe data truncation, thereby degrading the image quality. We proposed to take advantage of anatomical priors and total variation (TV) minimization methods to reconstruct PET activity distribution form incomplete measurement data. The objective is to solve the penalized least-squares function consisted of data fidelity term, TV norm and medium root priors. In this work, we employed the splitting-based fast iterative shrinkage/thresholding algorithm to split smooth and non-smooth functions in the convex optimization problems. Our simulations studies validated that the images reconstructed by use of the proposed method can outperform those obtained by use of conventional expectation maximization algorithms or that without considering the anatomical prior information. Additionally, the convergence rate is also accelerated.

Plasma diagnostic techniques in thermal-barrier tandem-mirror fusion experiments

International Nuclear Information System (INIS)

Silver, E.H.; Clauser, J.F.; Carter, M.R.; Failor, B.H.; Foote, J.H.; Hornady, R.S.; James, R.A.; Lasnier, C.J.; Perkins, D.E.

1986-01-01

We review two classes of plasma diagnostic techniques used in thermal-barrier tandem-mirror fusion experiments. The emphasis of the first class is to study mirror-trapped electrons at the thermal-barrier location. The focus of the second class is to measure the spatial and temporal behavior of the plasma space potential at various axial locations. The design and operation of the instruments in these two categories are discussed and data that are representative of their performance is presented

A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification

CSIR Research Space (South Africa)

Brown, K

2016-05-01

Full Text Available after basic pre-processing, consist- ing of image alignment, pixel normalization and histogram equalization. The following results relate to the various face and fingerprint datasets only. Local Binary Pattern Histogram (LBPH) proved to be a versatile... descriptor by increasing the radius in correlation to the interpolated neighbours. The modified ELBP operator significantly outperformed the histogram equalization and pixel normalization under dynamic lighting conditions. This was used before the LOG filter...

Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion

Directory of Open Access Journals (Sweden)

Chu He

2017-11-01

Full Text Available Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.

A Standard Mammography Unit - Standard 3D Ultrasound Probe Fusion Prototype: First Results.

Science.gov (United States)

Schulz-Wendtland, Rüdiger; Jud, Sebastian M; Fasching, Peter A; Hartmann, Arndt; Radicke, Marcus; Rauh, Claudia; Uder, Michael; Wunderle, Marius; Gass, Paul; Langemann, Hanna; Beckmann, Matthias W; Emons, Julius

2017-06-01

The combination of different imaging modalities through the use of fusion devices promises significant diagnostic improvement for breast pathology. The aim of this study was to evaluate image quality and clinical feasibility of a prototype fusion device (fusion prototype) constructed from a standard tomosynthesis mammography unit and a standard 3D ultrasound probe using a new method of breast compression. Imaging was performed on 5 mastectomy specimens from patients with confirmed DCIS or invasive carcinoma (BI-RADS ™ 6). For the preclinical fusion prototype an ABVS system ultrasound probe from an Acuson S2000 was integrated into a MAMMOMAT Inspiration (both Siemens Healthcare Ltd) and, with the aid of a newly developed compression plate, digital mammogram and automated 3D ultrasound images were obtained. The quality of digital mammogram images produced by the fusion prototype was comparable to those produced using conventional compression. The newly developed compression plate did not influence the applied x-ray dose. The method was not more labour intensive or time-consuming than conventional mammography. From the technical perspective, fusion of the two modalities was achievable. In this study, using only a few mastectomy specimens, the fusion of an automated 3D ultrasound machine with a standard mammography unit delivered images of comparable quality to conventional mammography. The device allows simultaneous ultrasound - the second important imaging modality in complementary breast diagnostics - without increasing examination time or requiring additional staff.

Modal Testing of Mechanical Structures Subject to Operational Excitation Forces

DEFF Research Database (Denmark)

Møller, N.; Brincker, Rune; Herlufsen, H.

2000-01-01

to the Operational Modal Analysis. For Operational Modal Analysis two different estimation techniques are used: a non-parametric technique based on Frequency Domain Decomposition (FDD), and a parametric technique working on the raw data in time domain, a data driven Stochastic Subspace Identification (SSI) algorithm......Operational Modal Analysis also known as Output Only Modal Analysis has in the recent years been used for extracting modal parameters of civil engineering structures and is now becoming popular for mechanical structures. The advantage of the method is that no artificial excitation need...

Performance improvement of developed program by using multi-thread technique

Directory of Open Access Journals (Sweden)

Surasak Jabal

2015-03-01

Full Text Available This research presented how to use a multi-thread programming technique to improve the performance of a program written by Windows Presentation Foundation (WPF. The Computer Assisted Instruction (CAI software, named GAME24, was selected to use as a case study. This study composed of two main parts. The first part was about design and modification of the program structure upon the Object Oriented Programing (OOP approach. The second part was about coding the program using the multi-thread technique which the number of threads were based on the calculated Catalan number. The result showed that the multi-thread programming technique increased the performance of the program 44%-88% compared to the single-thread technique. In addition, it has been found that the number of cores in the CPU also increase the performance of multithreaded program proportionally.

A multi-modal intervention for Activating Patients at Risk for Osteoporosis (APROPOS: Rationale, design, and uptake of online study intervention material

Directory of Open Access Journals (Sweden)

Maria I. Danila

2016-12-01

Conclusion: We developed and implemented a novel tailored multi-modal intervention to improve initiation of osteoporosis therapy. An email address provided on the survey was the most important factor independently associated with accessing the intervention online. The design and uptake of this intervention may have implications for future studies in osteoporosis or other chronic diseases.

Assessment of Closed-Loop Control Using Multi-Mode Sensor Fusion For a High Reynolds Number Transonic Jet

Science.gov (United States)

Low, Kerwin; Elhadidi, Basman; Glauser, Mark

2009-11-01

Understanding the different noise production mechanisms caused by the free shear flows in a turbulent jet flow provides insight to improve ``intelligent'' feedback mechanisms to control the noise. Towards this effort, a control scheme is based on feedback of azimuthal pressure measurements in the near field of the jet at two streamwise locations. Previous studies suggested that noise reduction can be achieved by azimuthal actuators perturbing the shear layer at the jet lip. The closed-loop actuation will be based on a low-dimensional Fourier representation of the hydrodynamic pressure measurements. Preliminary results show that control authority and reduction in the overall sound pressure level was possible. These results provide motivation to move forward with the overall vision of developing innovative multi-mode sensing methods to improve state estimation and derive dynamical systems. It is envisioned that estimating velocity-field and dynamic pressure information from various locations both local and in the far-field regions, sensor fusion techniques can be utilized to ascertain greater overall control authority.

Visualization of intracranial vessel anatomy using high resolution MRI and a simple image fusion technique

International Nuclear Information System (INIS)

Nasel, C.

2005-01-01

A new technique for fusion and 3D viewing of high resolution magnetic resonance (MR) angiography and morphological MR sequences is reported. Scanning and image fusion was possible within 20 min on a standard 1.5 T MR-scanner. The procedure was successfully performed in 10 consecutive cases with excellent visualization of wall and luminal aspects of the intracranial segments of the internal carotid artery, the vertebrobasilar system and the anterior, middle and posterior cerebral artery

Visualization of intracranial vessel anatomy using high resolution MRI and a simple image fusion technique

Energy Technology Data Exchange (ETDEWEB)

Nasel, C. [Division of Neuroradiology, Department of Radiology, Medical University of Vienna, Waehringerguertel 18-20, A-1090 Vienna (Austria)]. E-mail: christian.nasel@perfusion.at

2005-04-01

A new technique for fusion and 3D viewing of high resolution magnetic resonance (MR) angiography and morphological MR sequences is reported. Scanning and image fusion was possible within 20 min on a standard 1.5 T MR-scanner. The procedure was successfully performed in 10 consecutive cases with excellent visualization of wall and luminal aspects of the intracranial segments of the internal carotid artery, the vertebrobasilar system and the anterior, middle and posterior cerebral artery.

Sensing Nepal in a Peer Student Created Multi-sensory Environment

OpenAIRE

Sthapit, Erose; Kansakar, Sajina

2010-01-01

The idea that learning experienced through all the senses is helpful in reinforcing memory has a long history in pedagogy. From the earliest teaching guides, educators have embraced a range of multi-sensory techniques in order to make learning richer and more motivating for learners. The term multisensory is used to refer to any learning activity that combines two or more sensory strategies/ modalities simultaneously to take in or express information. The sensory modalities include visual (si...

Tensor-based fusion of EEG and FMRI to understand neurological changes in Schizophrenia

DEFF Research Database (Denmark)

Evrim, Acar Ataman; Levin-Schwartz, Yuri; Calhoun, Vince D.

2016-01-01

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain...

Composite multi-modal vibration control for a stiffened plate using non-collocated acceleration sensor and piezoelectric actuator

International Nuclear Information System (INIS)

Li, Shengquan; Li, Juan; Mo, Yueping; Zhao, Rong

2014-01-01

A novel active method for multi-mode vibration control of an all-clamped stiffened plate (ACSP) is proposed in this paper, using the extended-state-observer (ESO) approach based on non-collocated acceleration sensors and piezoelectric actuators. Considering the estimated capacity of ESO for system state variables, output superposition and control coupling of other modes, external excitation, and model uncertainties simultaneously, a composite control method, i.e., the ESO based vibration control scheme, is employed to ensure the lumped disturbances and uncertainty rejection of the closed-loop system. The phenomenon of phase hysteresis and time delay, caused by non-collocated sensor/actuator pairs, degrades the performance of the control system, even inducing instability. To solve this problem, a simple proportional differential (PD) controller and acceleration feed-forward with an output predictor design produce the control law for each vibration mode. The modal frequencies, phase hysteresis loops and phase lag values due to non-collocated placement of the acceleration sensor and piezoelectric patch actuator are experimentally obtained, and the phase lag is compensated by using the Smith Predictor technology. In order to improve the vibration control performance, the chaos optimization method based on logistic mapping is employed to auto-tune the parameters of the feedback channel. The experimental control system for the ACSP is tested using the dSPACE real-time simulation platform. Experimental results demonstrate that the proposed composite active control algorithm is an effective approach for suppressing multi-modal vibrations. (paper)

A comparison of techniques for multi-display reaching

NARCIS (Netherlands)

Nacenta, M.A.; Aliakseyeu, D.; Subramanian, S.; Gutwin, C.

2005-01-01

Recent advances in multi-user collaboration have seen a proliferation of interaction techniques for moving digital objects from one device to another. However, little is known about how these techniques work in realistic situations, or how they compare to one another. We conducted a study to compare

Accelerators for heavy ion fusion

International Nuclear Information System (INIS)

Bangerter, R.O.

1985-10-01

Large fusion devices will almost certainly produce net energy. However, a successful commercial fusion energy system must also satisfy important engineering and economic constraints. Inertial confinement fusion power plants driven by multi-stage, heavy-ion accelerators appear capable of meeting these constraints. The reasons behind this promising outlook for heavy-ion fusion are given in this report. This report is based on the transcript of a talk presented at the Symposium on Lasers and Particle Beams for Fusion and Strategic Defense at the University of Rochester on April 17-19, 1985

Investigations of Orchestra Auralizations Using the Multi-Channel Multi-Source Auralization Technique

DEFF Research Database (Denmark)

Vigeant, Michelle; Wang, Lily M.; Rindel, Jens Holger

2008-01-01

a multi-channel multi-source auralization technique, involving individual five-channel anechoic recordings of each instrumental part of two symphonies. In the first study, these auralizations were subjectively compared to orchestra auralizations made using (a) a single omni-directional source, (b......) a surface source, and (c) single-channel multi-source method. Results show that the multi-source auralizations were rated to be more realistic than the surface source ones and to have larger source width than the single omni-directional source auralizations. No significant differences were found between......Room acoustics computer modeling is a tool for generating impulse responses and auralizations from modeled spaces. The auralizations are commonly made from a single-channel anechoic recording of solo instruments. For this investigation, auralizations of an entire orchestra were created using...

Clinical use of digital retrospective image fusion of CT, MRI, FDG-PET and SPECT - fields of indications and results

International Nuclear Information System (INIS)

Lemke, A.J.; Niehues, S.M.; Amthauer, H.; Felix, R.; Rohlfing, T.; Hosten, N.

2004-01-01

Purpose: To evaluate the feasibility and the clinical benefits of retrospective digital image fusion (PET, SPECT, CT and MRI). Materials and methods: In a prospective study, a total of 273 image fusions were performed and evaluated. The underlying image acquisitions (CT, MRI, SPECT and PET) were performed in a way appropriate for the respective clinical question and anatomical region. Image fusion was executed with a software program developed during this study. The results of the image fusion procedure were evaluated in terms of technical feasibility, clinical objective, and therapeutic impact. Results: The most frequent combinations of modalities were CT/PET (n = 156) and MRI/PET (n = 59), followed by MRI/SPECT (n = 28), CT/SPECT (n = 22) and CT/MRI (n = 8). The clinical questions included following regions (more than one region per case possible): neurocranium (n = 42), neck (n = 13), lung and mediastinum (n = 24), abdomen (n = 181), and pelvis (n = 65). In 92.6% of all cases (n = 253), image fusion was technically successful. Image fusion was able to improve sensitivity and specificity of the single modality, or to add important diagnostic information. Image fusion was problematic in cases of different body positions between the two imaging modalities or different positions of mobile organs. In 37.9% of the cases, image fusion added clinically relevant information compared to the single modality. Conclusion: For clinical questions concerning liver, pancreas, rectum, neck, or neurocranium, image fusion is a reliable method suitable for routine clinical application. Organ motion still limits its feasibility and routine use in other areas (e.g., thorax). (orig.)

Inertial fusion: strategy and economic potential

International Nuclear Information System (INIS)

Nuckolls, J.H.

1983-01-01

Inertial fusion must demonstrate that the high target gains required for practical fusion energy can be achieved with driver energies not larger than a few megajoules. Before a multi-megajoule scale driver is constructed, inertial fusion must provide convincing experimental evidence that the required high target gains are feasible. This will be the principal objective of the NOVA laser experiments. Implosions will be conducted with scaled targets which are nearly hydrodynamically equivalent to the high gain target implosions. Experiments which demonstrate high target gains will be conducted in the early nineties when multi-megajoule drivers become available. Efficient drivers will also be demonstrated by this time period. Magnetic fusion may demonstrate high Q at about the same time as inertial fusion demonstrates high gain. Beyond demonstration of high performance fusion, economic considerations will predominate. Fusion energy will achieve full commercial success when it becomes cheaper than fission and coal. Analysis of the ultimate economic potential of inertial fusion suggests its costs may be reduced to half those of fission and coal. Relative cost escalation would increase this advantage. Fusions potential economic advantage derives from two fundamental properties: negligible fuel costs and high quality energy (which makes possible more efficient generation of electricity)

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

Science.gov (United States)

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.

Heterogeneous Multi-Metric Learning for Multi-Sensor Fusion

Science.gov (United States)

2011-07-01

Neural Information Processing Systems, 2010. [18] C.-C. Shen and W.-H. Tsai. Multisensor fusion in smartphones for lifestyle monitoring. In Int. Conf. on...Ministry of Education (708085) of China. REFERENCES [1] C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006. [2] S. Boughhorbel, J

Nuclear measurements, techniques and instrumentation, industrial applications, plasma physics and nuclear fusion 1986-1996. International Atomic Energy Agency publications

International Nuclear Information System (INIS)

1997-03-01

This catalogue lists all sales publications of the International Atomic Energy Agency dealing with Nuclear Measurements, Techniques, and Instrumentation, Industrial Applications, Plasma Physics and Nuclear Fusion, issued during the period 1986-1996. Most publications are in English. Proceedings of conferences, symposia and panels of experts may contain some papers in languages other than English (French, Russian or Spanish), but all of these papers have abstracts in English. Contents cover the three main areas of (i) Nuclear Measurements, Techniques and Instrumentation (Physics, Dosimetry Techniques, Nuclear Analytical Techniques, Research Reactor and Particle Accelerator Applications, and Nuclear Data), (ii) Industrial Applications (Radiation Processing, Radiometry, and Tracers), and (iii) Plasma Physics and Controlled Thermonuclear Fusion

Solving conformal contacts using multi-Hertzian techniques

Science.gov (United States)

Pascal, Jean-Pierre; Soua, Brahim

2016-06-01

Recently, publications aiming at wheel-rail contact surveys let readers think that multi-Hertzian methods present severe drawbacks with respect to 'virtual penetration' methods. These surveys criticise multi-Hertzian solutions mainly because presenting 'larger contacts overlaps' and 'frequent secondary contacts near the border of the first contact', both obvious geometric possibilities of which the practical occurrence and eventual inconvenience would remain purely theoretical unless established over definite methods demonstrating poor practical results. Recent surveys all quote Piotrowski-Chollet 2005 survey of wheel-rail contact models that attempted to illustrate defective multi-Hertzian techniques by concentrating on the method initiated by Sauvage in the 1990s and further developed by Pascal. The 2005 paper not only gives no evidence of practical inconveniences of Sauvage's method but also confuses static geometric contact overlaps with the dynamical overlapping of forces. In reality it mixes Sauvage method up with a quite different technique. Thus a clarification is now necessary by reminding what the proper Sauvage technique really is and by showing some of its practical successful applications. The present paper, focusing on determination of normal contact forces in conformal situations, intends to explain clearly the advantages of the unequivocal localisation of secondary ellipses in that multi-Hertzian method which has been developed in INRETS VOCO codes in the 1990s and successfully used by SNCF and ALSTOM in the INRETS-SNCF code, VOCODYM, and later in Pascal's online calculation of railway elastic contacts code. It proved its effectiveness for studying freight wagons derailments as well as rail wear and head-check, unrounded wheels wear, high-speed lines' deformations or TGV comfort. While simulating American ACELA trainsets' behaviour on the US North-East Corridor tracks, prior to actual tests, as part of the commercial contract. It has been also a

Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

Science.gov (United States)

Audebert, Nicolas; Le Saux, Bertrand; Lefèvre, Sébastien

2018-06-01

In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: (a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, (b) we investigate early and late fusion of Lidar and multispectral data, (c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.

A Weighted Combination Method for Conflicting Evidence in Multi-Sensor Data Fusion

Directory of Open Access Journals (Sweden)

Fuyuan Xiao

2018-05-01

Full Text Available Dempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, how to avoid the counter-intuitive results is an open issue when combining highly conflicting pieces of evidence. In order to handle such a problem, a weighted combination method for conflicting pieces of evidence in multi-sensor data fusion is proposed by considering both the interplay between the pieces of evidence and the impacts of the pieces of evidence themselves. First, the degree of credibility of the evidence is determined on the basis of the modified cosine similarity measure of basic probability assignment. Then, the degree of credibility of the evidence is adjusted by leveraging the belief entropy function to measure the information volume of the evidence. Finally, the final weight of each piece of evidence generated from the above steps is obtained and adopted to modify the bodies of evidence before using Dempster’s combination rule. A numerical example is provided to illustrate that the proposed method is reasonable and efficient in handling the conflicting pieces of evidence. In addition, applications in data classification and motor rotor fault diagnosis validate the practicability of the proposed method with better accuracy.

MO-E-12A-01: Quantitative Imaging: Techniques, Applications, and Challenges

International Nuclear Information System (INIS)

Jackson, E; Jeraj, R; McNitt-Gray, M; Cao, Y

2014-01-01

The first symposium in the Quantitative Imaging Track focused on the introduction of quantitative imaging (QI) by illustrating the potential of QI in diagnostic and therapeutic applications in research and patient care, highlighting key challenges in implementation of such QI applications, and reviewing QI efforts of selected national and international agencies and organizations, including the FDA, NCI, NIST, and RSNA. This second QI symposium will focus more specifically on the techniques, applications, and challenges of QI. The first talk of the session will focus on modalityagnostic challenges of QI, beginning with challenges of the development and implementation of QI applications in single-center, single-vendor settings and progressing to the challenges encountered in the most general setting of multi-center, multi-vendor settings. The subsequent three talks will focus on specific QI challenges and opportunities in the modalityspecific settings of CT, PET/CT, and MR. Each talk will provide information on modality-specific QI techniques, applications, and challenges, including current efforts focused on solutions to such challenges. Learning Objectives: Understand key general challenges of QI application development and implementation, regardless of modality. Understand selected QI techniques and applications in CT, PET/CT, and MR. Understand challenges, and potential solutions for such challenges, for the applications presented for each modality

A Transgenic Tri-Modality Reporter Mouse

OpenAIRE

Yan, Xinrui; Ray, Pritha; Paulmurugan, Ramasamy; Tong, Ricky; Gong, Yongquan; Sathirachinda, Ataya; Wu, Joseph C.; Gambhir, Sanjiv S.

2013-01-01

Transgenic mouse with a stably integrated reporter gene(s) can be a valuable resource for obtaining uniformly labeled stem cells, tissues, and organs for various applications. We have generated a transgenic mouse model that ubiquitously expresses a tri-fusion reporter gene (fluc2-tdTomato-ttk) driven by a constitutive chicken β-actin promoter. This "Tri-Modality Reporter Mouse" system allows one to isolate most cells from this donor mouse and image them for bioluminescent (fluc2), fluorescent...

Fusion imaging of computed tomographic pulmonary angiography and SPECT ventilation/perfusion scintigraphy: initial experience and potential benefit

International Nuclear Information System (INIS)

Harris, Benjamin; Bailey, Dale; Roach, Paul; Bailey, Elizabeth; King, Gregory

2007-01-01

The objective of this study was to examine the feasibility of fusing ventilation and perfusion data from single-photon emission computed tomography (SPECT) ventilation perfusion (V/Q) scintigraphy together with computed tomographic pulmonary angiography (CTPA) data. We sought to determine the accuracy of this fusion process. In addition, we correlated the findings of this technique with the final clinical diagnosis. Thirty consecutive patients (17 female, 13 male) who had undergone both CTPA and SPECT V/Q scintigraphy during their admission for investigation of potential pulmonary embolism were identified retrospectively. Image datasets from these two modalities were co-registered and fused using commercial software. Accuracy of the fusion process was determined subjectively by correlation between modalities of the anatomical boundaries and co-existent pleuro-parenchymal abnormalities. In all 30 cases, SPECT V/Q images were accurately fused with CTPA images. An automated registration algorithm was sufficient alone in 23 cases (77%). Additional linear z-axis scaling was applied in seven cases. There was accurate topographical co-localisation of vascular, parenchymal and pleural disease on the fused images. Nine patients who had positive CTPA performed as an initial investigation had co-localised perfusion defects on the subsequent fused CTPA/SPECT images. Three of the 11 V/Q scans initially reported as intermediate could be reinterpreted as low probability owing to co-localisation of defects with parenchymal or pleural pathology. Accurate fusion of SPECT V/Q scintigraphy to CTPA images is possible. This technique may be clinically useful in patients who have non-diagnostic initial investigations or in whom corroborative imaging is sought. (orig.)

Remote sensing image fusion in the context of Digital Earth

International Nuclear Information System (INIS)

Pohl, C

2014-01-01

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

Fully Realistic Multi-Criteria Multi-Modal Routing

OpenAIRE

Gündling, Felix; Keyhani, Mohammad Hossein; Schnee, Mathias; Weihe, Karsten

2014-01-01

We report on a multi-criteria search system, in which the German long- and short-distance trains, local public transport, walking, private car, private bike, and taxi are incorporated. The system is fully realistic. Three optimization criteria are addressed: travel time, travel cost, and convenience. Our algorithmic approach computes a complete Pareto set of reasonable connections. The computational study demonstrates that, even in such a large-scale, highly complex scenario, approp...

Role of the multi-modality image archival and communication system in nuclear medicine

International Nuclear Information System (INIS)

Bela Kari; Adam Mester; Erno Mako; Zoltan Gyorfi; Bela Mihalik; Zsolt; Hegyi

2004-01-01

Various non-invasive imaging systems produce increasing amount of diagnostic images day by day in digital format. The direct consequence of this tendency places electronic archives and image transfers in spotlight. Moreover, the digital image archives may support any other activities like simultaneous displaying of multi-modality images, telediagnostics, on-line consultation, construction of standard databases for dedicated organs by regional and/or country wide (e.g. myocardial scintigraphy, mammography, etc....) in order to obtain much more exact diagnosis as well as to support education and training. Our institute started similar research and developing activities few years ago, resulting the construction of our PACS systems -MEDISA LINUX Debian and eRAD ImageMedical TM LINUX Red Hat- together with the telecommunication part. Mass storage unit of PACS is based on hard drives connecting in RAID with l.2Tbyte capacity. The on-line telecommunication system consists of an ISDN Multi-Media System (MMS) and Internet based independent units. MMS was dedicated mainly for on-line teleconferencing and consultation by the simultaneously transferred morphological and functional images obtaining from the central archives by DICOM or any other allowable image formats. MMS has been created as a part and requirements of an EU research project - RETRANSPLANT -. The central archives -PACS- can be accessed by DICOM 3.0 protocol on Internet surface through well maintained and secure access rights. Displaying and post-processing of any retrieved images on individual workstations are supported by eRAD ImageMedical TM PracticeBuilder1-2-3 (Window based) image manager with its unique supports and services. The 'real engine' of PracticeBuilder is Ver.5.0 or newer Internet Explorer. The unique feature of PracticelBuilder1-2-3 is the extremely fast patient and image access from the archives even from very 'far distance' (through continents), due to the exceptional image communication

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

Science.gov (United States)

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

[Application of 3D virtual reality technology with multi-modality fusion in resection of glioma located in central sulcus region].

Science.gov (United States)

Chen, T N; Yin, X T; Li, X G; Zhao, J; Wang, L; Mu, N; Ma, K; Huo, K; Liu, D; Gao, B Y; Feng, H; Li, F

2018-05-08

Objective: To explore the clinical and teaching application value of virtual reality technology in preoperative planning and intraoperative guide of glioma located in central sulcus region. Method: Ten patients with glioma in the central sulcus region were proposed to surgical treatment. The neuro-imaging data, including CT, CTA, DSA, MRI, fMRI were input to 3dgo sczhry workstation for image fusion and 3D reconstruction. Spatial relationships between the lesions and the surrounding structures on the virtual reality image were obtained. These images were applied to the operative approach design, operation process simulation, intraoperative auxiliary decision and the training of specialist physician. Results: Intraoperative founding of 10 patients were highly consistent with preoperative simulation with virtual reality technology. Preoperative 3D reconstruction virtual reality images improved the feasibility of operation planning and operation accuracy. This technology had not only shown the advantages for neurological function protection and lesion resection during surgery, but also improved the training efficiency and effectiveness of dedicated physician by turning the abstract comprehension to virtual reality. Conclusion: Image fusion and 3D reconstruction based virtual reality technology in glioma resection is helpful for formulating the operation plan, improving the operation safety, increasing the total resection rate, and facilitating the teaching and training of the specialist physician.

Development of radiation fusion biotechnology

International Nuclear Information System (INIS)

Jung, Uhee; Lee, Ju Woon; Park, Sang Hyun

2012-04-01

Development of Radiation Fusion Technology with Food Technology by the Application of High Dose Irradiation - To develop fundamental technology using high dose irradiation, effects of high dose irradiation on food components, combined effects of irradiation with food engineering, irradiation condition to destroy radiation resistant foodborne bacteria were studied. - To develop E-beam irradiation technology, irradiation conditions for E-beam and domination effects of E-beam irradiation were determined. The physical marker for E beam irradiated foods or not was developed. - To develop purposed foods to extreme environmental, ready to eat foods and low toxic animal feeds were developed. Through the fundamental researches, the legislation for new irradiated foods and application of E-beam was introduced. Development of modulators against degenerative aging using radiation fusion technology - Selection of 20 kinds of degenerative aging biomarkers related to immune/hematopoiesis, oxidative damage, molecular signaling, lipid metabolism - Establishment of optimal radiation application conditions for aging modeling (fractionated irradiation of total 5Gy, a lapse of 4 months or more - Selection of effective aging modulating substances by screening of 800 natural substances - Development of 1 multi-functional and high-efficacy aging modulator by combination of effective substances and evaluation by in vivo models Development of biochips and kits using RI detection technology for life science - Establishment of kinase substrate interaction analysis using RI detection technique (More than 100 times detection sensitivity compared to conventional fluorescence detection techniques). - The RI detection technique reduces the overall experiment time, as the use of blocking agent can be avoided, offer minimum non specific binding, and facilitates a rapid data analysis with a simplify the process of chip manufacturing. - Establishment of multi-channel type Lab on a chip (LOC) using

Development of radiation fusion biotechnology

Energy Technology Data Exchange (ETDEWEB)

Jung, Uhee; Lee, Ju Woon; Park, Sang Hyun

2012-04-15

Development of Radiation Fusion Technology with Food Technology by the Application of High Dose Irradiation - To develop fundamental technology using high dose irradiation, effects of high dose irradiation on food components, combined effects of irradiation with food engineering, irradiation condition to destroy radiation resistant foodborne bacteria were studied. - To develop E-beam irradiation technology, irradiation conditions for E-beam and domination effects of E-beam irradiation were determined. The physical marker for E beam irradiated foods or not was developed. - To develop purposed foods to extreme environmental, ready to eat foods and low toxic animal feeds were developed. Through the fundamental researches, the legislation for new irradiated foods and application of E-beam was introduced. Development of modulators against degenerative aging using radiation fusion technology - Selection of 20 kinds of degenerative aging biomarkers related to immune/hematopoiesis, oxidative damage, molecular signaling, lipid metabolism - Establishment of optimal radiation application conditions for aging modeling (fractionated irradiation of total 5Gy, a lapse of 4 months or more - Selection of effective aging modulating substances by screening of 800 natural substances - Development of 1 multi-functional and high-efficacy aging modulator by combination of effective substances and evaluation by in vivo models Development of biochips and kits using RI detection technology for life science - Establishment of kinase substrate interaction analysis using RI detection technique (More than 100 times detection sensitivity compared to conventional fluorescence detection techniques). - The RI detection technique reduces the overall experiment time, as the use of blocking agent can be avoided, offer minimum non specific binding, and facilitates a rapid data analysis with a simplify the process of chip manufacturing. - Establishment of multi-channel type Lab on a chip (LOC) using

Joint Multi-Focus Fusion and Bayer ImageRestoration

Institute of Scientific and Technical Information of China (English)

Ling Guo; Bin Yang; Chao Yang

2015-01-01

In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer pattern images are fused before colorrestoration. Therefore, the Bayer image restoration operation is only performed one time. Thus, the proposed algorithm is moreefficient than traditional fusion schemes. In detail, a clarity measurement of Bayer pattern image is defined for raw Bayer patternimages, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. Theraw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicingalgorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain betterfused results with more natural appearance and fewer artifacts than the traditional algorithms.

The Markov multi-phase transferable belief model: A data fusion theory for enhancing cyber situational awareness

OpenAIRE

Ioannou, Georgios

2015-01-01

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London. eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with critical information exfiltration from High Valued Targets (HVT's) by terrorists, cyber criminals or enemy states. Existing Cyber Defence frameworks and data fusion models do not adequately address (i) the multi-stage nature of XAPTs and (ii) the uncertainty and conflicting...

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

Science.gov (United States)

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.

Advanced data visualization and sensor fusion: Conversion of techniques from medical imaging to Earth science

Science.gov (United States)

Savage, Richard C.; Chen, Chin-Tu; Pelizzari, Charles; Ramanathan, Veerabhadran

1993-01-01

Hughes Aircraft Company and the University of Chicago propose to transfer existing medical imaging registration algorithms to the area of multi-sensor data fusion. The University of Chicago's algorithms have been successfully demonstrated to provide pixel by pixel comparison capability for medical sensors with different characteristics. The research will attempt to fuse GOES (Geostationary Operational Environmental Satellite), AVHRR (Advanced Very High Resolution Radiometer), and SSM/I (Special Sensor Microwave Imager) sensor data which will benefit a wide range of researchers. The algorithms will utilize data visualization and algorithm development tools created by Hughes in its EOSDIS (Earth Observation SystemData/Information System) prototyping. This will maximize the work on the fusion algorithms since support software (e.g. input/output routines) will already exist. The research will produce a portable software library with documentation for use by other researchers.

Fusion of imaging and nonimaging data for surveillance aircraft

Science.gov (United States)

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).

Fusion systems engineering

International Nuclear Information System (INIS)

Anon.

1978-01-01

Research during this report period has covered the following areas: (1) fusion reactor systems studies, (2) development of blanket processing technology for fusion reactors, (3) safety studies of fusion concepts, (4) MACKLIB-IV, a new library of nuclear response functions, (5) energy storage and power supply requirements for commercial fusion reactors, (6) blanket/shield design evaluation for commercial fusion reactors, and (7) cross section measurements, evaluations, and techniques

Applications of Skyrme energy-density functional to fusion reactions spanning the fusion barriers

International Nuclear Information System (INIS)

Liu Min; Wang, Ning; Li Zhuxia; Wu Xizhen; Zhao Enguang

2006-01-01

The Skyrme energy density functional has been applied to the study of heavy-ion fusion reactions. The barriers for fusion reactions are calculated by the Skyrme energy density functional with proton and neutron density distributions determined by using restricted density variational (RDV) method within the same energy density functional together with semi-classical approach known as the extended semi-classical Thomas-Fermi method. Based on the fusion barrier obtained, we propose a parametrization of the empirical barrier distribution to take into account the multi-dimensional character of real barrier and then apply it to calculate the fusion excitation functions in terms of barrier penetration concept. A large number of measured fusion excitation functions spanning the fusion barriers can be reproduced well. The competition between suppression and enhancement effects on sub-barrier fusion caused by neutron-shell-closure and excess neutron effects is studied

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

Energy Technology Data Exchange (ETDEWEB)

Yan, Shiju [School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China and School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 and Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819 (China); Guan, Yubao [Department of Radiology, Guangzhou Medical University, Guangzhou 510182 (China); Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States)

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 initially 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.

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

International Nuclear Information System (INIS)

Yan, Shiju; Qian, Wei; Guan, Yubao; Zheng, Bin

2016-01-01

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 initially 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.

A dynamically weighted multi-modal biometric security system

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

Full Text Available The face, fingerprint and palmprint feature vectors are automatically extracted and dynamically selected for fusion at the feature-level, toward an improved human identification accuracy. The feature-level has a higher potential accuracy than...

Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma

International Nuclear Information System (INIS)

Liberman, Gilad; Louzoun, Yoram; Aizenstein, Orna; Blumenthal, Deborah T.; Bokstein, Felix; Palmon, Mika; Corn, Benjamin W.; Ben Bashat, Dafna

2013-01-01

Background: Current methods for evaluation of treatment response in glioblastoma are inaccurate, limited and time-consuming. This study aimed to develop a multi-modal MRI automatic classification method to improve accuracy and efficiency of treatment response assessment in patients with recurrent glioblastoma (GB). Materials and methods: A modification of the k-Nearest-Neighbors (kNN) classification method was developed and applied to 59 longitudinal MR data sets of 13 patients with recurrent GB undergoing bevacizumab (anti-angiogenic) therapy. Changes in the enhancing tumor volume were assessed using the proposed method and compared with Macdonald's criteria and with manual volumetric measurements. The edema-like area was further subclassified into peri- and non-peri-tumoral edema, using both the kNN method and an unsupervised method, to monitor longitudinal changes. Results: Automatic classification using the modified kNN method was applicable in all scans, even when the tumors were infiltrative with unclear borders. The enhancing tumor volume obtained using the automatic method was highly correlated with manual measurements (N = 33, r = 0.96, p < 0.0001), while standard radiographic assessment based on Macdonald's criteria matched manual delineation and automatic results in only 68% of cases. A graded pattern of tumor infiltration within the edema-like area was revealed by both automatic methods, showing high agreement. All classification results were confirmed by a senior neuro-radiologist and validated using MR spectroscopy. Conclusion: This study emphasizes the important role of automatic tools based on a multi-modal view of the tissue in monitoring therapy response in patients with high grade gliomas specifically under anti-angiogenic therapy

Phase aberrations and beam cleanup techniques in carbon-dioxide laser fusion systems

International Nuclear Information System (INIS)

Viswanathan, V.K.

1981-01-01

This paper describes the various carbon dioxide laser fusion systems at Los Alamos from the point of view of an optical designer. The types of phase aberrations present in these systems, as well as the beam cleanup techniques that can be used to improve the beam optical quality, are discussed. As this is a review article, some previously published results are also used where relevant

Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

Science.gov (United States)

Alkilani, Amjad; Shirkhodaie, Amir

2013-05-01

Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

Remote Sensing Image Fusion Based on the Combination Grey Absolute Correlation Degree and IHS Transform

Directory of Open Access Journals (Sweden)

Hui LIN

2014-12-01

Full Text Available An improved fusion algorithm for multi-source remote sensing images with high spatial resolution and multi-spectral capacity is proposed based on traditional IHS fusion and grey correlation analysis. Firstly, grey absolute correlation degree is used to discriminate non-edge pixels and edge pixels in high-spatial resolution images, by which the weight of intensity component is identified in order to combine it with high-spatial resolution image. Therefore, image fusion is achieved using IHS inverse transform. The proposed method is applied to ETM+ multi-spectral images and panchromatic image, and Quickbird’s multi-spectral images and panchromatic image respectively. The experiments prove that the fusion method proposed in the paper can efficiently preserve spectral information of the original multi-spectral images while enhancing spatial resolution greatly. By comparison and analysis, the proposed fusion algorithm is better than traditional IHS fusion and fusion method based on grey correlation analysis and IHS transform.

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

Science.gov (United States)

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.

MMX-I: A data-processing software for multi-modal X-ray imaging and tomography

International Nuclear Information System (INIS)

Bergamaschi, A; Medjoubi, K; Somogyi, A; Messaoudi, C; Marco, S

2017-01-01

Scanning hard X-ray imaging allows simultaneous acquisition of multimodal information, including X-ray fluorescence, absorption, phase and dark-field contrasts, providing structural and chemical details of the samples. Combining these scanning techniques with the infrastructure developed for fast data acquisition at Synchrotron Soleil permits to perform multimodal imaging and tomography during routine user experiments at the Nanoscopium beamline. A main challenge of such imaging techniques is the online processing and analysis of the generated very large volume (several hundreds of Giga Bytes) multimodal data-sets. This is especially important for the wide user community foreseen at the user oriented Nanoscopium beamline (e.g. from the fields of Biology, Life Sciences, Geology, Geobiology), having no experience in such data-handling. MMX-I is a new multi-platform open-source freeware for the processing and reconstruction of scanning multi-technique X-ray imaging and tomographic datasets. The MMX-I project aims to offer, both expert users and beginners, the possibility of processing and analysing raw data, either on-site or off-site. Therefore we have developed a multi-platform (Mac, Windows and Linux 64bit) data processing tool, which is easy to install, comprehensive, intuitive, extendable and user-friendly. MMX-I is now routinely used by the Nanoscopium user community and has demonstrated its performance in treating big data. (paper)

MMX-I: A data-processing software for multi-modal X-ray imaging and tomography

Science.gov (United States)

Bergamaschi, A.; Medjoubi, K.; Messaoudi, C.; Marco, S.; Somogyi, A.

2017-06-01

Scanning hard X-ray imaging allows simultaneous acquisition of multimodal information, including X-ray fluorescence, absorption, phase and dark-field contrasts, providing structural and chemical details of the samples. Combining these scanning techniques with the infrastructure developed for fast data acquisition at Synchrotron Soleil permits to perform multimodal imaging and tomography during routine user experiments at the Nanoscopium beamline. A main challenge of such imaging techniques is the online processing and analysis of the generated very large volume (several hundreds of Giga Bytes) multimodal data-sets. This is especially important for the wide user community foreseen at the user oriented Nanoscopium beamline (e.g. from the fields of Biology, Life Sciences, Geology, Geobiology), having no experience in such data-handling. MMX-I is a new multi-platform open-source freeware for the processing and reconstruction of scanning multi-technique X-ray imaging and tomographic datasets. The MMX-I project aims to offer, both expert users and beginners, the possibility of processing and analysing raw data, either on-site or off-site. Therefore we have developed a multi-platform (Mac, Windows and Linux 64bit) data processing tool, which is easy to install, comprehensive, intuitive, extendable and user-friendly. MMX-I is now routinely used by the Nanoscopium user community and has demonstrated its performance in treating big data.

Multi-modal Biomarkers to Discriminate Cognitive State

Science.gov (United States)

2015-11-01

of timing and coordination in vocal and facial expression . 5.1 Audio-video AVEC depression database The 2014 Audio/Video Emotion Challenge (AVEC...signs of emotional experience. Journal of personality and social psychology. 39, 6 (1980), 1125. 37. Gaebel, W. and Wölwer, W. 1992. Facial expression ...Example modalities used in detection of these conditions include voice, facial expression , physiology, eye tracking, gait, and EEG analysis. Toward the

A Multi-Disciplinary University Research Initiative in Hard and Soft Information Fusion: Overview, Research Strategies and Initial Results

Science.gov (United States)

2010-07-01

Multisource Information Fusion ( CMIF ) along with a team including the Pennsylvania State University (PSU), Iona College (Iona), and Tennessee State...License. 14. ABSTRACT The University at Buffalo (UB) Center for Multisource Information Fusion ( CMIF ) along with a team including the Pennsylvania...of CMIF current research on methods for Test and Evaluation ([7], [8]) involving for example large- factor-space experimental design techniques ([9

Optimal Transport for Data Fusion in Remote Sensing

OpenAIRE

Courty , Nicolas; Flamary , Rémi; Tuia , Devis; Corpetti , Thomas

2016-01-01

International audience; One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and computational aspects, we showcase its application to three remote sen...

Acceleration techniques for the direct use of CAD-based geometry in fusion neutronics analysis

International Nuclear Information System (INIS)

Wilson, Paul P.H.; Tautges, Timothy J.; Kraftcheck, Jason A.; Smith, Brandon M.; Henderson, Douglass L.

2010-01-01

The Direct Accelerated Geometry Monte Carlo (DAGMC) software library offers a unique approach to performing neutronics analysis on CAD-based geometries of fusion systems. By employing a number of acceleration techniques, the ray-tracing operations that are fundamental to Monte Carlo radiation transport are implemented efficiently for direct use on the CAD-based solid model, eliminating the need to translate to the native Monte Carlo input language. By forming hierarchical trees of oriented bounding boxes, one for each facet that results from a high-fidelity tessellation of the model, the ray-tracing performance is adequate to permit detailed analysis of large complex systems. In addition to the reduction in human effort and improvement in quality assurance that is found in the translation approaches, the DAGMC approach also permits the analysis of geometries with higher order surfaces that cannot be represented by many native Monte Carlo radiation transport tools. The paper describes the various acceleration techniques and demonstrates the resulting capability in a real fusion neutronics analysis.

DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

Directory of Open Access Journals (Sweden)

Y. C. Lai

2015-05-01

Full Text Available This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS. There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system

Development of a Pedestrian Indoor Navigation System Based on Multi-Sensor Fusion and Fuzzy Logic Estimation Algorithms

Science.gov (United States)

Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.

2015-05-01

This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its

History of Civil Engineering Modal Analysis

DEFF Research Database (Denmark)

Brincker, Rune

2008-01-01

techniques are available for civil engineering modal analysis. The testing of civil structures defers from the traditional modal testing in the sense, that very often it is difficult, or sometimes impossible, to artificially excite a large civil engineering structure. Also, many times, even though...

Automation of fusion first wall design using artificial intelligence technique

International Nuclear Information System (INIS)

Yoshimura, Shinobu; Yagawa, Genki; Mochizuki, Yoshihiko

1990-01-01

This paper describes the application of artificial intelligence techniques to a design automation of the fusion first wall to be operated in the complex environment where huge electromagnetic and thermal loading as well as heavy neutron irradiation occur. As a basic strategy of designing structure shape considering many coupled phenomena, an ordinary design procedure based on the generate and test strategy is adopted because of its simplicity and broad applicability. To automate the design procedure with maintaining its flexibility, extensibility and efficiency, artificial intelligence techniques are utilized in the following. An object-oriented knowledge representation technique is adopted to store knowledge modules, that is, objects, related to the first wall design, while a data-flow processing technique is utilized as an inference mechanism among the knowledge modules. These techniques realize the flexibility and extensibility of the system. Moreover, as an efficient design modification mechanism, which is essential in a design process, an empirical approach based on experts' empirical knowledge and a mathematical approach based on a kind of numerical sensitivity analysis are introduced. The developed system is applied to a simple example of the design of a two-dimensional model of the first wall with a cooling channel, and its fundamental performance is clearly demonstrated. (author)

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

Science.gov (United States)

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Directory of Open Access Journals (Sweden)

Zhiqin Zhu

2017-02-01

Full Text Available In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.

Imaging of oxygenation in 3D tissue models with multi-modal phosphorescent probes

Science.gov (United States)

Papkovsky, Dmitri B.; Dmitriev, Ruslan I.; Borisov, Sergei

2015-03-01

Cell-penetrating phosphorescence based probes allow real-time, high-resolution imaging of O2 concentration in respiring cells and 3D tissue models. We have developed a panel of such probes, small molecule and nanoparticle structures, which have different spectral characteristics, cell penetrating and tissue staining behavior. The probes are compatible with conventional live cell imaging platforms and can be used in different detection modalities, including ratiometric intensity and PLIM (Phosphorescence Lifetime IMaging) under one- or two-photon excitation. Analytical performance of these probes and utility of the O2 imaging method have been demonstrated with different types of samples: 2D cell cultures, multi-cellular spheroids from cancer cell lines and primary neurons, excised slices from mouse brain, colon and bladder tissue, and live animals. They are particularly useful for hypoxia research, ex-vivo studies of tissue physiology, cell metabolism, cancer, inflammation, and multiplexing with many conventional fluorophors and markers of cellular function.

Established rheumatoid arthritis - new imaging modalities

DEFF Research Database (Denmark)

McQueen, Fiona M; Østergaard, Mikkel

2007-01-01

in real-time and facilitates diagnostic and therapeutic interventions such as joint aspiration and injection. Exciting experimental modalities are also being developed with the potential to provide not just morphological but functional imaging. Techniques such as positron emission tomography (PET......) and high-resolution computerized tomography. Erosions are very clearly depicted using these modalities and MRI also allows imaging of soft tissues with assessment of joint inflammation. High-resolution ultrasound is a convenient clinical technique for the assessment of erosions, synovitis and tenosynovitis...

Technique for thick polymer coating of inertial-confinement-fusion targets

International Nuclear Information System (INIS)

Lee, M.C.; Feng, I.; Wang, T.G.; Kim, H.

1983-01-01

A novel technique has been developed to coat a thick layer (15--50 μm) of polymer materials on inertial-confinement-fusion (ICF) targets. In this technique, the target and the coating material are independently positioned and manipulated. The coating material is first dissolved in an appropriate solvent to form a polymer solution. The solution is then atomized, transported, and allowed to coalesce into a droplet in a stable acoustic levitating field. The ICF target mounted on a stalk is moved into the acoustic field by manipulating a three-dimensional (3-D) positioner to penetrate the surface membrane of the droplet and thus the target is immersed in the levitated coating solution. The 3-D coordinates of the target inside the droplet are obtained using two orthogonally placed television cameras. The target is positioned at the geometric center of the droplet and maintained at that location by continuously manipulating the 3-D device until the coating layer is dried. Tests of this technique using a polymer solution have been highly successful

Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

Science.gov (United States)

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.

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods.

Directory of Open Access Journals (Sweden)

Alexander Toet

Full Text Available The fusion and enhancement of multiband nighttime imagery for surveillance and navigation has been the subject of extensive research for over two decades. Despite the ongoing efforts in this area there is still only a small number of static multiband test images available for the development and evaluation of new image fusion and enhancement methods. Moreover, dynamic multiband imagery is also currently lacking. To fill this gap we present the TRICLOBS dynamic multi-band image data set containing sixteen registered visual (0.4-0.7μm, near-infrared (NIR, 0.7-1.0μm and long-wave infrared (LWIR, 8-14μm motion sequences. They represent different military and civilian surveillance scenarios registered in three different scenes. Scenes include (military and civilian people that are stationary, walking or running, or carrying various objects. Vehicles, foliage, and buildings or other man-made structures are also included in the scenes. This data set is primarily intended for the development and evaluation of image fusion, enhancement and color mapping algorithms for short-range surveillance applications. The imagery was collected during several field trials with our newly developed TRICLOBS (TRI-band Color Low-light OBServation all-day all-weather surveillance system. This system registers a scene in the Visual, NIR and LWIR part of the electromagnetic spectrum using three optically aligned sensors (two digital image intensifiers and an uncooled long-wave infrared microbolometer. The three sensor signals are mapped to three individual RGB color channels, digitized, and stored as uncompressed RGB (false color frames. The TRICLOBS data set enables the development and evaluation of (both static and dynamic image fusion, enhancement and color mapping algorithms. To allow the development of realistic color remapping procedures, the data set also contains color photographs of each of the three scenes. The color statistics derived from these photographs

Multi-modal distraction. Using technology to combat pain in young children with burn injuries.

Science.gov (United States)

Miller, Kate; Rodger, Sylvia; Bucolo, Sam; Greer, Ristan; Kimble, Roy M

2010-08-01

The use of non-pharmacological pain management remains adhoc within acute paediatric burns pain management protocols despite ongoing acknowledgement of its role. Advancements in adult based pain services including the integration of virtual reality has been adapted to meet the needs of children in pain, as exemplified by the development of multi-modal distraction (MMD). This easy to use, hand held interactive device uses customized programs designed to inform the child about the procedure he/she is about to experience and to distract the child during dressing changes. (1) To investigate if either MMD procedural preparation (MMD-PP) or distraction (MMD-D) has a greater impact on child pain reduction compared to standard distraction (SD) or hand held video game distraction (VG), (2) to understand the impact of MMD-PP and MMD-D on clinic efficiency by measuring length of treatment across groups, and lastly, (3) to assess the efficacy of distraction techniques over three dressing change procedures. A prospective randomised control trial was completed in a paediatric tertiary hospital Burns Outpatient Clinic. Eighty participants were recruited and studied over their first three dressing changes. Pain was assessed using validated child report, caregiver report, nursing observation and physiological measures. MMD-D and MMD-PP were both shown to significantly relieve reported pain (ppositive effects of both MMD-D and MMD-PP were sustained with subsequent dressing changes. The use of MMD as a preparatory or a distraction tool in an outpatient burns clinic offered superior pain reduction across three dressing changes to children when compared to standard practices or hand held video games. This device has the potential to improve clinic efficiency with reductions in treatment lengths.

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

Science.gov (United States)

Deng, Xinyang; Jiang, Wen

2017-09-12

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

Nuclear measurements, techniques and instrumentation industrial applications plasma physics and nuclear fusion. 1980-1994. International Atomic Energy Agency publications

International Nuclear Information System (INIS)

1995-04-01

This catalogue lists all sales publications of the International Atomic Energy Agency dealing with Nuclear Measurements, Techniques and Instrumentation, with Industrial Applications (of Nuclear Physics and Engineering), and with Plasma Physics and Nuclear Fusion, issued during the period 1980-1994. Most publications are in English. Proceedings of conferences, symposia, and panels of experts may contain some papers in other languages (French, Russian, or Spanish), but all papers have abstracts in English. Price quotes are in Austrian Schillings, do not include local taxes, and are subject to change without notice. Contents cover the three main categories of (i) Nuclear Measurements, Techniques and Instrumentation (Physics, Chemistry, Dosimetry Techniques, Nuclear Analytical Techniques, Research Reactors and Particle Accelerator Applications, Nuclear Data); (ii) Industrial Applications (Radiation Processing, Radiometry, Tracers); and (iii) Plasma Physics and Nuclear Fusion

Nuclear measurements, techniques and instrumentation industrial applications plasma physics and nuclear fusion, 1980-1993. International Atomic Energy Agency publications

International Nuclear Information System (INIS)

1994-01-01

This catalogue lists all sales publications of the International Atomic Energy Agency dealing with Nuclear Measurements, Techniques and Instrumentation, with Industrial Applications (of Nuclear Physics and Engineering), and with Plasma Physics and Nuclear Fusion, issued during the period 1980-1993. Most publications are in English. Proceedings of conferences, symposia, and panels of experts may contain some papers in other languages (French, Russian, or Spanish), but all papers have abstracts in English. Price quotes are in Austrian Schillings, do not include local taxes, and are subject to change without notice. Contents cover the three main categories of (I) Nuclear Measurements, Techniques and Instrumentation (Physics, Chemistry, Dosimetry Techniques, Nuclear Analytical Techniques, Research Reactors and Particle Accelerator Applications, Nuclear Data); (ii) Industrial Applications (Radiation Processing, Radiometry, Tracers); and (iii) Plasma Physics and Nuclear Fusion

Automated double-cone-beam CT fusion technique. Enhanced evaluation of glue distribution in cases of spinal dural arteriovenous fistula (SDAVF) embolisation

International Nuclear Information System (INIS)

Farago, Giuseppe; Caldiera, V.; Antozzi, C.; Bellino, A.; Innocenti, A.; Ciceri, E.

2017-01-01

Spinal dural arteriovenous fistulas (SDAVFs) are acquired diseases that represent the majority of all arteriovenous spinal shunts, leading to progressive and disabling myelopathy. Treatment is focused on accurately disconnecting the fistula point. We present our experience with the double-cone-beam CT fusion technique successfully applied to evaluate treatment results in a series of SDAVFs. Between November 2011 and December 2015 we performed double-DynaCT acquisition (pre- and post-embolisation) in 12 cases of SDAVF. A successful DynaCT fusion technique was only achieved in the group of patients with pre- and post-treatment images acquired at the same time as the treatment session, under general anaesthesia (4/12). DynaCT performed on different days proved to be inadequate for the automated fusion technique because of changes in the body position (8/12). A pre-treatment flat-panel cone-beam CT with contrast, at the time of diagnostic angiography, can be very helpful to detect the correct level of the fistula and the relationship between the fistula and the surrounding structures. In case of the endovascular approach, additional post-treatment native acquisition merged with the pre-treatment acquisition (double-cone-beam CT fusion technique) permits to immediately evaluate the distribution of the glue cast and to confirm the success of the procedure. (orig.)

Automated double-cone-beam CT fusion technique. Enhanced evaluation of glue distribution in cases of spinal dural arteriovenous fistula (SDAVF) embolisation

Energy Technology Data Exchange (ETDEWEB)

Farago, Giuseppe [Foundation Neurological Institute ' ' C. Besta' ' , Department of Interventional Neuroradiology, Milan (Italy); Fondazione IRCCS Istituto Neurologico Carlo Besta, Department of Interventional Neuroradiology, Milan (Italy); Caldiera, V. [Foundation Neurological Institute ' ' C. Besta' ' , Department of Interventional Neuroradiology, Milan (Italy); Antozzi, C.; Bellino, A. [Foundation Neurological Institute ' ' C. Besta' ' , Department of Neuroimmunology and Neuromuscular Diseases, Milan (Italy); Innocenti, A. [Foundation Neurological Institute ' ' C. Besta' ' , Department of Neuro-Oncology, Milan (Italy); Ciceri, E. [Foundation Neurological Institute ' ' C. Besta' ' , Department of Interventional Neuroradiology, Milan (Italy); Azienda Ospedaliera Universitaria Integrata Borgo Trento, Department of Neuroradiology, Verona (Italy)

2017-05-15

Spinal dural arteriovenous fistulas (SDAVFs) are acquired diseases that represent the majority of all arteriovenous spinal shunts, leading to progressive and disabling myelopathy. Treatment is focused on accurately disconnecting the fistula point. We present our experience with the double-cone-beam CT fusion technique successfully applied to evaluate treatment results in a series of SDAVFs. Between November 2011 and December 2015 we performed double-DynaCT acquisition (pre- and post-embolisation) in 12 cases of SDAVF. A successful DynaCT fusion technique was only achieved in the group of patients with pre- and post-treatment images acquired at the same time as the treatment session, under general anaesthesia (4/12). DynaCT performed on different days proved to be inadequate for the automated fusion technique because of changes in the body position (8/12). A pre-treatment flat-panel cone-beam CT with contrast, at the time of diagnostic angiography, can be very helpful to detect the correct level of the fistula and the relationship between the fistula and the surrounding structures. In case of the endovascular approach, additional post-treatment native acquisition merged with the pre-treatment acquisition (double-cone-beam CT fusion technique) permits to immediately evaluate the distribution of the glue cast and to confirm the success of the procedure. (orig.)

Feature level fusion of hand and face biometrics

Science.gov (United States)

Ross, Arun A.; Govindarajan, Rohin

2005-03-01

Multibiometric systems utilize the evidence presented by multiple biometric sources (e.g., face and fingerprint, multiple fingers of a user, multiple matchers, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in several distinct levels, including the feature extraction level, match score level and decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. In this paper we discuss fusion at the feature level in 3 different scenarios: (i) fusion of PCA and LDA coefficients of face; (ii) fusion of LDA coefficients corresponding to the R,G,B channels of a face image; (iii) fusion of face and hand modalities. Preliminary results are encouraging and help in highlighting the pros and cons of performing fusion at this level. The primary motivation of this work is to demonstrate the viability of such a fusion and to underscore the importance of pursuing further research in this direction.

Fast modal decomposition for optical fibers using digital holography.

Science.gov (United States)

Lyu, Meng; Lin, Zhiquan; Li, Guowei; Situ, Guohai

2017-07-26

Eigenmode decomposition of the light field at the output end of optical fibers can provide fundamental insights into the nature of electromagnetic-wave propagation through the fibers. Here we present a fast and complete modal decomposition technique for step-index optical fibers. The proposed technique employs digital holography to measure the light field at the output end of the multimode optical fiber, and utilizes the modal orthonormal property of the basis modes to calculate the modal coefficients of each mode. Optical experiments were carried out to demonstrate the proposed decomposition technique, showing that this approach is fast, accurate and cost-effective.

Coal ash fusion temperatures -- New characterization techniques, and associations with phase equilibria

Energy Technology Data Exchange (ETDEWEB)

Wall, T.F.; Gupta, R.P.; Gupta, S. [Univ. of Newcastle, New South Wales (Australia). Dept. of Chemical Engineering; Creelman, R.A. [R.A. Creelman and Associates, Epping, New South Wales (Australia); Coin, C. [ACIRL Ipswich, Booval, Queensland (Australia); Lowe, A. [Pacific Power, Sydney, New South Wales (Australia)

1996-12-31

The well-documented shortcomings of the standard technique for estimating the fusion temperature of coal ash are its subjective nature and poor accuracy. Alternative measurements based on the shrinkage and electrical conductivity of heating samples are therefore examined with laboratory ash prepared at about 800 C in crucibles, as well as combustion ash sampled from power stations. Sensitive shrinkage measurements indicate temperatures of rapid change which correspond to the formation of liquid phases that can be identified on ternary phase diagrams. The existence and extent of formation of these phases, as quantified by the magnitude of peaks in the test, provide alternative ash fusion temperatures. The peaks from laboratory ashes and corresponding combustion ashes derived from the same coals show clear differences which may be related to the evaporation of potassium during combustion and the reactions of the mineral residues to form combustion ash.

Measuring situational awareness and resolving inherent high-level fusion obstacles

Science.gov (United States)

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

2006-04-01

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

Multi-modal MRI analysis with disease-specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer’s disease

Directory of Open Access Journals (Sweden)

Kenichi eOishi

2011-08-01

Full Text Available Background: Alterations of the gray and white matter have been identified in Alzheimer’s disease (AD by structural MRI and diffusion tensor imaging (DTI. However, whether the combination of these modalities could increase the diagnostic performance is unknown.Methods: Participants included 19 AD patients, 22 amnestic mild cognitive impairment (aMCI patients, and 22 cognitively normal elderly (NC. The aMCI group was further divided into an aMCI-converter group (converted to AD dementia within three years, and an aMCI-stable group who did not convert in this time period. A T1-weighted image, a T2 map, and a DTI of each participant were normalized, and voxel-based comparisons between AD and NC groups were performed. Regions-of-interest, which defined the areas with significant differences between AD and NC, were created for each modality and named disease-specific spatial filters (DSF. Linear discriminant analysis was used to optimize the combination of multiple MRI measurements extracted by DSF to effectively differentiate AD from NC. The resultant DSF and the discriminant function were applied to the aMCI group to investigate the power to differentiate the aMCI-converters from the aMCI-stable patients. Results: The multi-modal approach with AD-specific filters led to a predictive model with an area under the receiver operating characteristic curve (AUC of 0.93, in differentiating aMCI-converters from aMCI-stable patients. This AUC was better than that of a single-contrast-based approach, such as T1-based morphometry or diffusion anisotropy analysis. Conclusion: The multi-modal approach has the potential to increase the value of MRI in predicting conversion from aMCI to AD.

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

Directory of Open Access Journals (Sweden)

Yan Guo

2018-01-01

Full Text Available A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.

Modal Identification from Ambient Responses using Frequency Domain Decomposition

DEFF Research Database (Denmark)

Brincker, Rune; Zhang, L.; Andersen, P.

2000-01-01

In this paper a new frequency domain technique is introduced for the modal identification from ambient responses, ie. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical ...

Respiratory gating and multi field technique radiotherapy for esophageal cancer

International Nuclear Information System (INIS)

Ohta, Atsushi; Kaidu, Motoki; Tanabe, Satoshi

2017-01-01

To investigate the effects of a respiratory gating and multi field technique on the dose-volume histogram (DVH) in radiotherapy for esophageal cancer. Twenty patients who underwent four-dimensional computed tomography for esophageal cancer were included. We retrospectively created the four treatment plans for each patient, with or without the respiratory gating and multi field technique: No gating-2-field, No gating-4-field, Gating-2-field, and Gating-4-field plans. We compared the DVH parameters of the lung and heart in the No gating-2-field plan with the other three plans.Result In the comparison of the parameters in the No gating-2-field plan, there are significant differences in the Lung V 5Gy , V 20Gy , mean dose with all three plans and the Heart V 25Gy -V 40Gy with Gating-2-field plan, V 35Gy , V 40Gy , mean dose with No Gating-4-field plan and V 30Gy -V 40Gy , and mean dose with Gating-4-field plan. The lung parameters were smaller in the Gating-2-field plan and larger in the No gating-4-field and Gating-4-field plans. The heart parameters were all larger in the No gating-2-field plan. The lung parameters were reduced by the respiratory gating technique and increased by the multi field technique. The heart parameters were reduced by both techniques. It is important to select the optimal technique according to the risk of complications. (author)

Anticipation by multi-modal association through an artificial mental imagery process

Science.gov (United States)

Gaona, Wilmer; Escobar, Esaú; Hermosillo, Jorge; Lara, Bruno

2015-01-01

Mental imagery has become a central issue in research laboratories seeking to emulate basic cognitive abilities in artificial agents. In this work, we propose a computational model to produce an anticipatory behaviour by means of a multi-modal off-line hebbian association. Unlike the current state of the art, we propose to apply hebbian learning during an internal sensorimotor simulation, emulating a process of mental imagery. We associate visual and tactile stimuli re-enacted by a long-term predictive simulation chain motivated by covert actions. As a result, we obtain a neural network which provides a robot with a mechanism to produce a visually conditioned obstacle avoidance behaviour. We developed our system in a physical Pioneer 3-DX robot and realised two experiments. In the first experiment we test our model on one individual navigating in two different mazes. In the second experiment we assess the robustness of the model by testing in a single environment five individuals trained under different conditions. We believe that our work offers an underpinning mechanism in cognitive robotics for the study of motor control strategies based on internal simulations. These strategies can be seen analogous to the mental imagery process known in humans, opening thus interesting pathways to the construction of upper-level grounded cognitive abilities.

Evaluating the efficacy of an integrated motivational interviewing and multi-modal exercise intervention for youth with major depression: Healthy Body, Healthy Mind randomised controlled trial protocol.

Science.gov (United States)

Nasstasia, Yasmina; Baker, Amanda L; Halpin, Sean A; Hides, Leanne; Lewin, Terry J; Kelly, Brian J; Callister, Robin

2018-03-01

Recent meta-analytic reviews suggest exercise can reduce depression severity among adults with major depressive disorder (MDD); however, efficacy studies with depressed youth are limited. Few studies have investigated the efficacy of multi-modal exercise interventions in this population, addressed treatment engagement, or explored the differential effects of exercise on depressive symptom profiles. This paper describes the study protocol and recruitment pattern for an assessor blinded, two-arm randomised controlled trial investigating the efficacy of an integrated motivational interviewing (MI) and multi-modal exercise intervention in youth diagnosed with MDD. Associations between depressive symptom profiles (cognitive, somatic and affective) and psychological, physiological (fitness), and biological (blood biomarker) outcomes will also be examined. Participants aged 15-25 years with current MDD were recruited. Eligible participants were randomised and stratified according to gender and depression severity to either an immediate or delayed (control) group. The immediate group received a brief MI intervention followed by a 12-week small group exercise intervention (3 times per week for 1 h), all delivered by personal trainers. The delayed control group received the same intervention 12-weeks later. Both groups were reassessed at mid-treatment or mid-control, post-treatment or post-control, and follow-up (12 weeks post-treatment). 68 participants were recruited and randomly allocated to an intervention group. This trial will increase our understanding of the efficacy of multi-modal exercise interventions for depression and the specific effects of exercise on depressive symptom profiles. It also offers a novel contribution by addressing treatment engagement in exercise efficacy trials in youth with MDD.

Complete proof systems for weighted modal logic

DEFF Research Database (Denmark)

Larsen, Kim G.; Mardare, Radu

2014-01-01

(WML) is a multi-modal logic that expresses qualitative and quantitative properties of WTSs. While WML has been studied in various contexts and for various application domains, no proof system has been developed for it. In this paper we solve this open problem and propose both weak-complete and strong......The weighted transition systems (WTS) considered in this paper are transition systems having both states and transitions labeled with real numbers: the state labels denote quantitative resources, while the transition labels denote costs of transitions in terms of resources. Weighted Modal Logic....... This work emphasizes a series of similarities between WML and the probabilistic/stochastic modal logics for Markov processes and Harsanyi type spaces, such as the use of particular infinitary rules to guarantee the strong-completeness....