Relevant bibliographies by topics / Collaborative Fusion

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Collaborative Fusion'

Author: Grafiati
Published: 27 January 2024

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Journal articles on the topic "Collaborative Fusion"

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Liu, Jing, Xuesong Hai, and Keqin Li. "TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts." Future Internet 16, no. 1 (December 25, 2023): 6. http://dx.doi.org/10.3390/fi16010006.

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Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. Firstly, the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models.
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P.C. Cheung, Patti, and Maria L.C. Lau. "From union catalogue to fusion catalogue." Library Management 35, no. 1/2 (January 7, 2014): 88–101. http://dx.doi.org/10.1108/lm-04-2013-0031.

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Purpose – The purpose of this paper is to reflect The Chinese University of Hong Kong Library's catalogue evolution as a result of electronic resources cataloguing and how collaborative cataloguing could be implemented in the context of Hong Kong. Design/methodology/approach – The paper outlines the challenges faced by The Chinese University of Hong Kong Library and the need to find alternative way to catalogue e-books come in large batches. It describes in particular the cataloguing of Chinese e-books in collaboration with the China Academic Library and Information System (CALIS). Findings – Different cataloguing data set are inevitably blended into the library catalogue to be used by users. Still, collaboration is feasible when libraries are ready to make compromise and accept variances in the library catalogue. Originality/value – The Chinese University of Hong Kong Library is the first library in Hong Kong to work collaboratively with CALIS to batch convert its records for cataloguing of Chinese e-books. The paper is useful for librarians exploring new source for Chinese cataloguing or collaborative initiatives with libraries in China.
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Arshad, Kamran, Muhammad Ali Imran, and Klaus Moessner. "Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/424036.

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The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.
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Benli, Emrah, Richard Lee Spidalieri, and Yuichi Motai. "Thermal Multisensor Fusion for Collaborative Robotics." IEEE Transactions on Industrial Informatics 15, no. 7 (July 2019): 3784–95. http://dx.doi.org/10.1109/tii.2019.2908626.

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Biswas, Pratik K., Hairong Qi, and Yingyue Xu. "Mobile-agent-based collaborative sensor fusion." Information Fusion 9, no. 3 (July 2008): 399–411. http://dx.doi.org/10.1016/j.inffus.2007.09.001.

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Liu, Guohua, Juan Guan, Haiying Liu, and Chenlin Wang. "Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/5819409.

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Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.
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Kant, Surya, and Tripti Mahara. "Nearest biclusters collaborative filtering framework with fusion." Journal of Computational Science 25 (March 2018): 204–12. http://dx.doi.org/10.1016/j.jocs.2017.03.018.

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Hongxing Wang and Junsong Yuan. "Collaborative Multifeature Fusion for Transductive Spectral Learning." IEEE Transactions on Cybernetics 45, no. 3 (March 2015): 451–61. http://dx.doi.org/10.1109/tcyb.2014.2327960.

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Wang, Heyong, Ming Hong, and Jinjiong Lan. "Study on Collaborative Filtering Recommendation Model Fusing User Reviews." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 5 (September 20, 2019): 864–73. http://dx.doi.org/10.20965/jaciii.2019.p0864.

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The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.
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Chen Cheng, Haibin Lv, and Zhihan Lv. "Sensing fusion in vehicular network digital twins for 6G smart city." ITU Journal on Future and Evolving Technologies 3, no. 2 (June 3, 2022): 342–58. http://dx.doi.org/10.52953/cofv5663.

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The aims demonstrated in this article are to effectively monitor the complex road environment in smart city transportation using the sixth generation mobile communication technology (6G) Digital Twins (DTs), to perceive the complex road environment of smart city traffic. Vehicular Networks (VN) in the smart transportation system have been selected as the research object, and the multi-sensor collaboration and fusion technology in the network is explored, so as to meet the active control requirements of intelligent vehicles. A lidar and camera fusion-based segmentation network C-LNet is proposed. The structure of a C-LNet multi-sensing data fusion segmentation network is double encoder-single decoder. Two encoders are used to extract image features and lidar features respectively. The same heterogeneous data is realized through the synchronization of lidar point cloud data and image data in sensor space. For multimodal information, a multiscale feature fusion-based vehicle collaboration method is designed. In the simulation experiment part, the C-LNet multi-sensing data fusion segmentation network is verified on the KITTI data set. The accuracy, F1 value, and MIoU of C-LNet are 98.4%, 96.7%, and 94.51%, respectively, which are better than those of an RGB network and lidar network. In summary, the smart transportation system supported by DTs in a 6G environment is explored. The proposed VN sensing fusion method can effectively realize the collaborative positioning perception of multiple vehicles, which lays the foundation for the realization of complex collaborative decision-making and control in smart transportation.
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Dissertations / Theses on the topic "Collaborative Fusion"

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Haj, Chhadé Hiba. "Data fusion and collaborative state estimation in wireless sensor networks." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2207/document.

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L'objectif de la thèse est de développer des algorithmes de fusion de données recueillies à l’aide d'un réseau de capteurs sans fil afin de localiser plusieurs sources émettant un agent chimique ou biologique dans l'air. Ces capteurs détectent la concentration de la substance émise, transportée par advection et diffusion, au niveau de leurs positions et de communiquer cette information à un centre de traitement. L’information recueillie de façon collaborative est d'abord utilisée pour localiser les capteurs déployés au hasard et ensuite pour localiser les sources. Les applications comprennent, entre autres, la surveillance environnementale et la surveillance de sites sensibles ainsi que des applications de sécurité dans le cas d'une libération accidentelle ou intentionnelle d'un agent toxique. Toutefois, l'application considérée dans la thèse est celle de la détection et la localisation de mines terrestres. Dans cette approche, les mines sont considérées comme des sources émettrices de produits chimiques explosifs.La thèse comprend une contribution théorique où nous étendons l'algorithme de propagation de la croyance, un algorithme de fusion de données bien connu et largement utilisé pour l'estimation collaborative d'état dans les réseaux de capteurs, au cadre des méthodes à erreurs bornées. Le nouvel algorithme est testé sur le problème de l'auto-localisation dans les réseaux de capteurs statiques ainsi que l'application de suivi d'un objet mobile en utilisant un réseau de capteurs de distance. Autres contributions comprennent l'utilisation d'une approche probabiliste bayésienne avec des techniques d'analyse de données pour localiser un nombre inconnu de sources émettrices de vapeur
The aim of the thesis is to develop fusion algorithms for data collected from a wireless sensor network in order to locate multiple sources emitting some chemical or biological agent in the air. These sensors detect the concentration of the emitted substance, transported by advection and diffusion, at their positions and communicate this information to a treatment center. The information collected in a collaborative manner is used first to locate the randomly deployed sensors and second to locate the sources. Applications include, amongst others, environmental monitoring and surveillance of sensitive sites as well as security applications in the case of an accidental or intentional release of a toxic agent. However, the application we consider in the thesis is that of landmine detection and localization. In this approach, the land mines are considered as sources emitting explosive chemicals. The thesis includes a theoretical contribution where we extend the Belief Propagation algorithm, a well-known data fusion algorithm that is widely used for collaborative state estimation in sensor networks, to the bounded error framework. The novel algorithm is tested on the self-localization problem in static sensor networks as well as the application of tracking a mobile object using a network of range sensors. Other contributions include the use of a Bayesian probabilistic approach along with data analysis techniques to locate an unknown number of vapor emitting sources
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Narayanan, Siddharth. "Cinemacraft: Exploring Fidelity Cues in Collaborative Virtual World Interactions." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/82142.

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The research presented in this thesis concerns the contribution of virtual human (or avatar) fidelity to social interaction in virtual environments (VEs) and how sensory fusion can improve these interactions. VEs present new possibilities for mediated communication by placing people in a shared 3D context. However, there are technical constraints in creating photo realistic and behaviorally realistic avatars capable of mimicking a person's actions or intentions in real time. At the same time, previous research findings indicate that virtual humans can elicit social responses even with minimal cues, suggesting that full realism may not be essential for effective social interaction. This research explores the impact of avatar behavioral realism on people's experience of interacting with virtual humans by varying the interaction fidelity. This is accomplished through the creation of Cinemacraft, a technology-mediated immersive platform for collaborative human-computer interaction in a virtual 3D world and the incorporation of sensory fusion to improve the fidelity of interactions and realtime collaboration. It investigates interaction techniques within the context of a multiplayer sandbox voxel game engine and proposes how interaction qualities of the shared virtual 3D space can be used to further involve a user as well as simultaneously offer a stimulating experience. The primary hypothesis of the study is that embodied interactions result in a higher degree of presence and co-presence, and that sensory fusion can improve the quality of presence and co-presence. The argument is developed through research justification, followed by a user-study to demonstrate the qualitative results and quantitative metrics.This research comprises of an experiment involving 24 participants. Experiment tasks focus on distinct but interrelated questions as higher levels of interaction fidelity are introduced.The outcome of this research is the generation of an interactive and accessible sensory fusion platform capable of delivering compelling live collaborative performances and empathetic musical storytelling that uses low fidelity avatars to successfully sidestep the 'uncanny valley'. This research contributes to the field of immersive collaborative interaction by making transparent the methodology, instruments and code. Further, it is presented in non-technical terminology making it accessible for developers aspiring to use interactive 3D media to pro-mote further experimentation and conceptual discussions, as well as team members with less technological expertise.
Master of Science
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Taher, Razan. "Recherche d'Information Collaborative." Phd thesis, Université Joseph Fourier (Grenoble), 2004. http://tel.archives-ouvertes.fr/tel-00006500.

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L'arrivée « World Wide Web » et la grande utilisation d'Internet ont ouvert de nouvelles perspectives pour la diffusion et le partage d'information. Nous présentons dans cette thèse une approche collaborative pour la recherche d'information. Nous définissons un environnement collaboratif dédié à un groupe d'utilisateurs désirant effectuer ensemble des recherches d'information sur un sujet commun. Cet environnement aide l'utilisateur à obtenir une meilleure qualité de l'information, et à « bénéficier » efficacement des expériences des autres membres du groupe. Différents contextes dans lesquels le soutien s'avère nécessaire sont analysés : l'aide à la formulation de requête, l'évaluation de sa recherche par rapport aux autres membres du groupe et une vision globale des résultats obtenus. Nous avons conçu et validé expérimentalement un modèle de soutien souple et adapté à la typologie de l'utilisateur pris individuellement et à la typologie du groupe d'utilisateur pris dans son ensemble. Le soutien est aussi personnalisé selon ses désirs et souhaits exprimés au moyen, entre autres, de critères de personnalisation de soutien.
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Nasman, James M. "Deployed virtual consulting : the fusion of wearable computing, collaborative technology, augmented reality and intelligent agents to support fleet aviation maintenance /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FNasman.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2004.
Thesis advisor(s): Alex Bordetsky, Gurminder Singh. Includes bibliographical references (p. 49). Also available online.
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Al, Hage Joelle. "Fusion de données tolérante aux défaillances : application à la surveillance de l’intégrité d’un système de localisation." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10074/document.

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L'intérêt des recherches dans le domaine de la fusion de données multi-capteurs est en plein essor en raison de la diversité de ses secteurs d'applications. Plus particulièrement, dans le domaine de la robotique et de la localisation, l'exploitation des différentes informations fournies par les capteurs constitue une étape primordiale afin d'assurer une estimation fiable de la position. Dans ce contexte de fusion de données multi-capteurs, nous nous attachons à traiter le diagnostic, menant à l'identification de la cause d'une défaillance, et la tolérance de l'approche proposée aux défauts de capteurs, peu abordés dans la littérature.Nous avons fait le choix de développer une approche basée sur un formalisme purement informationnel : filtre informationnel d'une part, et outils de la théorie de l'information d'autre part. Des résidus basés sur la divergence de Kullback-Leibler sont développés. Via des méthodes optimisées de seuillage, ces résidus conduisent à la détection et à l'exclusion de ces défauts capteurs. La théorie proposée est éprouvée sur deux applications de localisation. La première application concerne la localisation collaborative, tolérante aux défauts d'un système multi-robots. La seconde application traite de la localisation en milieu ouvert utilisant un couplage serré GNSS/odométrie tolérant aux défauts
The interest of research in the multi-sensor data fusion field is growing because of its various applications sectors. Particularly, in the field of robotics and localization, the use of different sensors informations is a vital step to ensure a reliable position estimation. In this context of multi-sensor data fusion, we consider the diagnosis, leading to the identification of the cause of a failure, and the sensors faults tolerance aspect, discussed in limited work in the literature. We chose to develop an approach based on a purely informational formalism: information filter on the one hand and tools of the information theory on the other. Residuals based on the Kullback-Leibler divergence are developed. These residuals allow to detect and to exclude the faulty sensors through optimized thresholding methods. This theory is tested in two applications. The first application is the fault tolerant collaborative localization of a multi-robot system. The second application is the localization in outdoor environments using a tightly coupled GNSS/odometer with a fault tolerant aspect
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Coyle, Timothy P. "Eyes of the storm: can fusion centers play a crucial role during the response phase of natural disasters through collaborative relationships with emergency operations centers?" Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/43896.

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Approved for public release; distribution is unlimited
CHDS State/Local
Through the maturation of the national network of fusion centers, processes and capabilities originally designed to detect and thwart terrorist attacks are now applied to disaster responses. The fusion process, which involves the synthesis and analysis of streams of data, can create incident specific intelligence. The sharing of this information can enhance the operating picture that is critical to key decision makers and the discipline of emergency management. This thesis examined three case studies of fusion center disaster responses through a collaborative-based analytical framework. The resulting analysis of the case studies identified the crucial role played by fusion centers in responding to disaster events in a collaborative effort with emergency operations centers. This thesis concludes that fusion centers offer the greatest impact through enabling information sharing throughout the response phase. The specific benefits of the sharing of information directly influence executive briefings and the deployment of resources. This thesis also modeled a collaborative response. The research determined that the depth and breadth of these relationships involving cooperative responses must be proportionate to the incident and include a level of redundancy. Through a system design model, overconnectivity through efficiency was shown to increase the likelihood of fracturing cooperative relationships.
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Daass, Bilal. "Approches informationnelles pour une navigation autonome collaborative de robots d'exploration de zones à risques." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I054.

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Ces dernières années, de nombreux travaux ont été menés afin de fournir une estimation précise de l’état d’un système dynamique. Dans cette thèse, nous ciblons les systèmes composés de sous-systèmes collaboratifs possédant une multitude de capteurs. Nous proposons un filtre combinant les avantages du filtre de Kalman et du filtre informationnel, nécessitant une charge de calculs bien moins élevée. Afin de prendre en compte la méconnaissance des covariances des mesures, une fusion multi-capteurs basée sur l'intersection des covariances est analysée en termes de charge calculatoire. Trois architectures de fusion multi-capteurs sont dès lors considérées. On réalise, sur les différents composants de ces architectures, une analyse fine de la répartition de la charge calculatoire du filtre et de l’algorithme d’intersection des covariances. Dans l’objectif de rendre un système tolérant aux défauts, des méthodes statistiques informationnelles sont développées. Elles sont applicables à toute méthode basée sur le rapport de vraisemblance généralisé, entraînant un seuillage adaptatif de ce rapport. Leurs mises en œuvre à travers deux types de cartes de contrôle permettent une détection rapide des défaillances des capteurs. Nos approches théoriques sont validées à travers un système de robots mobiles collaboratifs. Nous intégrons une phase de diagnostic et de détection de défauts des capteurs. Celle-ci est basée sur l’intégration de ces méthodes statistiques informationnelles dans le processus de fusion et d’estimation composé d’un filtre bayésien et de l’intersection des covariances. L’objectif est d’assurer une navigation autonome sûre, précise et tolérante aux défaillances des capteurs. Enfin, nous présentons une preuve de concept d'une méthode de contrôle et d'évaluation non destructive des matériaux dans l’environnement immédiat des robots. En particulier, il s'agit d'introduire un capteur hyperfréquence pour l'interaction entre l'onde électromagnétique propagée et le matériau sous investigation. Cette méthode, connue sous le vocable radar, a connu un essor grandissant dans les laboratoires de recherche et dans les applications courantes liées notamment à la mesure de vitesse. Néanmoins, sa transposition sur des robots mobiles collaboratifs demeure un challenge pour adresser l'évaluation sans contact de matériaux, notamment en environnement sévère. Elle consiste à déterminer les caractéristiques du matériau sous test à l'aide de capteurs micro-ondes embarqués
In the recent years, there was a growing interest to provide an accurate estimate of the state of a dynamic system for a wide range of applications. In this work, we target systems built up with several collaborative subsystems integrating various heterogeneous sensors. We introduce a filter concept that combines the advantages of both Kalman and informational filters to achieve low computational load. To consider any system whose measurement covariances are incomplete or unknown, a multi-sensor fusion based on the covariance intersection is analyzed in terms of calculation burden. Three multi-sensor fusion architectures are then considered. A fine analysis of the calculation load distribution of the filter and the covariance intersection algorithm is performed on the different components of these architectures. To make the system fault tolerant, informational statistical methods are developed. They are applicable to any method based on the generalized likelihood ratio. They lead to an adaptive threshold of this ratio. The technique has been implemented considering two types of control charts for the fast detection of sensor failures. Our theoretical approaches are validated through a system of collaborative mobile robots. We integrate a diagnosis and fault detection phase, which is based on the integration of these informational statistical methods into the fusion and estimation process, the latter being composed of a Bayesian filter and the covariance intersection. The main objective is to ensure that this system provides safe, accurate and fault-tolerant autonomous navigation. Finally, we present a proof-of-concept method for nondestructive and evaluation of materials in close proximity of the robot environment. In particular, we introduce a microwave sensor to characterize the electromagnetic wave to material under test interaction. This technique, known under the name radar, had a growing interest in academic laboratories and for usual applications related to speed measurements. Nevertheless, its adaptation to collaborative mobile robots remains a challenging task to address contactless characterization of materials, especially in harsh environments. This latter consists to determine the material characteristics from embedded microwave sensors
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Liu, Zhenjiao. "Incomplete multi-view data clustering with hidden data mining and fusion techniques." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS011.

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Le regroupement de données multivues incomplètes est un axe de recherche majeur dans le domaines de l'exploration de données et de l'apprentissage automatique. Dans les applications pratiques, nous sommes souvent confrontés à des situations où seule une partie des données modales peut être obtenue ou lorsqu'il y a des valeurs manquantes. La fusion de données est une méthode clef pour l'exploration d'informations multivues incomplètes. Résoudre le problème de l'extraction d'informations multivues incomplètes de manière ciblée, parvenir à une collaboration flexible entre les vues visibles et les vues cachées partagées, et améliorer la robustesse sont des défis. Cette thèse se concentre sur trois aspects : l'exploration de données cachées, la fusion collaborative et l'amélioration de la robustesse du regroupement. Les principales contributions sont les suivantes:1) Exploration de données cachées pour les données multi-vues incomplètes : les algorithmes existants ne peuvent pas utiliser pleinement l'observation des informations dans et entre les vues, ce qui entraîne la perte d'une grande quantité d'informations. Nous proposons donc un nouveau modèle de regroupement multi-vues incomplet IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) basé sur la factorisation de matrices non négatives et la fusion de tenseurs de faible rang. IMC-NLT utilise d'abord un tenseur de faible rang pour conserver les caractéristiques des vues avec une dimension unifiée. En utilisant une mesure de cohérence, IMC-NLT capture une représentation cohérente à travers plusieurs vues. Enfin, IMC-NLT intègre plusieurs apprentissages dans un modèle unifié afin que les informations cachées puissent être extraites efficacement à partir de vues incomplètes. Des expériences sur cinq jeux de données ont validé les performances d'IMC-NLT.2) Fusion collaborative pour les données multivues incomplètes : notre approche pour résoudre ce problème est le regroupement multivues incomplet par représentation à faible rang. L'algorithme est basé sur une représentation éparse de faible rang et une représentation de sous-espace, dans laquelle les données manquantes sont complétées en utilisant les données d'une modalité et les données connexes d'autres modalités. Pour améliorer la stabilité des résultats de clustering pour des données multi-vues avec différents degrés de manquants, CCIM-SLR utilise le modèle Γ-norm, qui est une méthode de représentation à faible rang ajustable. CCIM-SLR peut alterner entre l'apprentissage de la vue cachée partagée, la vue visible et les partitions de clusters au sein d'un cadre d'apprentissage collaboratif. Un algorithme itératif avec convergence garantie est utilisé pour optimiser la fonction objective proposée.3) Amélioration de la robustesse du regroupement pour les données multivues incomplètes : nous proposons une fusion de la convolution graphique et des goulots d'étranglement de l'information (apprentissage de la représentation multivues incomplète via le goulot d'étranglement de l'information). Nous introduisons la théorie du goulot d'étranglement de l'information afin de filtrer les données parasites contenant des détails non pertinents et de ne conserver que les éléments les plus pertinents. Nous intégrons les informations sur la structure du graphe basées sur les points d'ancrage dans les informations sur le graphe local. Le modèle intègre des représentations multiples à l'aide de goulets d'étranglement de l'information, réduisant ainsi l'impact des informations redondantes dans les données. Des expériences approfondies sont menées sur plusieurs ensembles de données du monde réel, et les résultats démontrent la supériorité de IMRL-AGI. Plus précisément, IMRL-AGI montre des améliorations significatives dans la précision du clustering et de la classification, même en présence de taux élevés de données manquantes par vue (par exemple, 10,23 % et 24,1% respectivement sur l'ensemble de données ORL)
Incomplete multi-view data clustering is a research direction that attracts attention in the fields of data mining and machine learning. In practical applications, we often face situations where only part of the modal data can be obtained or there are missing values. Data fusion is an important method for incomplete multi-view information mining. Solving incomplete multi-view information mining in a targeted manner, achieving flexible collaboration between visible views and shared hidden views, and improving the robustness have become quite challenging. This thesis focuses on three aspects: hidden data mining, collaborative fusion, and enhancing the robustness of clustering. The main contributions are as follows:1. Hidden data mining for incomplete multi-view data: existing algorithms cannot make full use of the observation of information within and between views, resulting in the loss of a large amount of valuable information, and so we propose a new incomplete multi-view clustering model IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) based on non-negative matrix factorization and low-rank tensor fusion. IMC-NLT first uses a low-rank tensor to retain view features with a unified dimension. Using a consistency measure, IMC-NLT captures a consistent representation across multiple views. Finally, IMC-NLT incorporates multiple learning into a unified model such that hidden information can be extracted effectively from incomplete views. We conducted comprehensive experiments on five real-world datasets to validate the performance of IMC-NLT. The overall experimental results demonstrate that the proposed IMC-NLT performs better than several baseline methods, yielding stable and promising results.2. Collaborative fusion for incomplete multi-view data: our approach to address this issue is Incomplete Multi-view Co-Clustering by Sparse Low-Rank Representation (CCIM-SLR). The algorithm is based on sparse low-rank representation and subspace representation, in which jointly missing data is filled using data within a modality and related data from other modalities. To improve the stability of clustering results for multi-view data with different missing degrees, CCIM-SLR uses the Γ-norm model, which is an adjustable low-rank representation method. CCIM-SLR can alternate between learning the shared hidden view, visible view, and cluster partitions within a co-learning framework. An iterative algorithm with guaranteed convergence is used to optimize the proposed objective function. Compared with other baseline models, CCIM-SLR achieved the best performance in the comprehensive experiments on the five benchmark datasets, particularly on those with varying degrees of incompleteness.3. Enhancing the clustering robustness for incomplete multi-view data: we offer a fusion of graph convolution and information bottlenecks (Incomplete Multi-view Representation Learning Through Anchor Graph-based GCN and Information Bottleneck - IMRL-AGI). First, we introduce the information bottleneck theory to filter out the noise data with irrelevant details and retain only the most relevant feature items. Next, we integrate the graph structure information based on anchor points into the local graph information of the state fused into the shared information representation and the information representation learning process of the local specific view, a process that can balance the robustness of the learned features and improve the robustness. Finally, the model integrates multiple representations with the help of information bottlenecks, reducing the impact of redundant information in the data. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI. Specifically, IMRL-AGI shows significant improvements in clustering and classification accuracy, even in the presence of high view missing rates (e.g. 10.23% and 24.1% respectively on the ORL dataset)
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Vissière, David. "Solution de guidage-navigation-pilotage pour véhicules autonomes hétérogènes en vue d'une mission collaborative." Phd thesis, École Nationale Supérieure des Mines de Paris, 2008. http://pastel.archives-ouvertes.fr/pastel-00004492.

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Le thème de la « navigation bas-coût », caractérisé par le transfert de performance des capteurs vers les algorithmes de fusion de données et de commande, est un thème central dans de nombreuses applications militaires liées en particulier aux besoins nouveaux des troupes légères. L'utilisation de capteurs inertiels ou magnétométriques type MEMS et de GPS civils (notamment), optimise les critères d'encombrement, de masse et de consommation. Mais elle requiert, en compensation des performances relativement médiocres des capteurs, des algorithmes de guidage-navigation-pilotage spécifiques aux applications considérées. Dans ce mémoire, nous considérons trois scénarios d'utilisation et exposons des techniques innovantes pour la localisation et le contrôle. Nous considérons d'abord le cas d'un robot terrestre équipé de capteurs de proximité, d'un gyroscope, d'odomètres et d'un GPS. Nous implémentons expérimentalement avec succès un algorithme d'évitement d'obstacles dont nous établissons une preuve de convergence, ainsi qu'un algorithme de planification de trajectoires hors ligne dont nous utilisons les résultats en temps réel pour réaliser, via un estimateur non linéaire, un bouclage par retour dynamique. Nous étudions ensuite le cas du vol autonome d'une plate-forme aérienne instable de type hélicoptère. Nous développons et implémentons, à bord sur un système de mesure et de calcul temps-réel de notre conception, un estimateur d'état incluant un modèle de la dynamique du vol de l'engin, recalé par les capteurs inertiels, barométriques, et GPS ainsi qu'un bouclage par retour d'état. En utilisant les résultats de filtrage sur les vols effectués, les paramètres du modèle sont précisément identifiés : la qualité et la robustesse de l'estimation obtenues grâce au modèle permettent de réaliser un vol stationnaire autonome en extérieur. Enfin nous considérons le problème d'un piéton évoluant à l'intérieur de bâtiments. Les erreurs d'estimation du cap lors de l'utilisation des différentes plate-formes (terrestre comme aérienne) nous guident vers une utilisation nouvelle du champ magnétique par l'inspection de ses gradients. Par une technique que nous exposons, nous montrons comment utiliser les perturbations (inconnues) du champ magnétique pour améliorer considérablement l'estimation de position d'une centrale inertielle bas-coût au point qu'elle devienne un instrument de localisation.
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Kidon, Jonathan Goldberg. "Fusion Tables : new ways to collaborate on structured data." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60999.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 55).
Fusion Tables allows data collaborators to create, merge, navigate and set access control permissions on structured data. This thesis focuses on the collaboration tools that were added to Googles Fusion Tables. The collaboration tools provided additional functionality: first, the ability to view, sort and filter all the threaded discussions on the different granularities of the data set; second, the ability to take Snaps, dynamic state bookmarking that allows collaborators to save queries and visualizations and share them with other users. In addition, this thesis initiates a discussion about data collaboration on different platforms outside the Data Management System (DMS), and the implementation of the Fusion Table - Google Wave gadget that provides this functionality. To evaluate these added features, we conducted a user survey based on three sources: Google Analytics, field study of experienced Fusion Tables users, and a user study to evaluate the UI and the collaboration tools. The results showed that approximately 40% of the visitors to the site use the collaboration features . Based on the user study, it appears that UI improvements can increase exposure to these features, and some additional functionality can be added to improve the collaboration features and provide a better collaboration system.
by Jonathan Goldberg Kidon.
M.Eng.
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Books on the topic "Collaborative Fusion"

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Wang, Rong, Zhi Xiong, and Jianye Liu. Resilient Fusion Navigation Techniques: Collaboration in Swarm. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9.

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Interface cultures: Artistic aspects of interaction. Bielefeld: Transcript, 2008.

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Deployed Virtual Consulting: The Fusion of Wearable Computing, Collaborative Technology, Augmented Reality and Intelligent Agents to Support Fleet Aviation Maintenance. Storming Media, 2004.

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Mumford, Christine L. Computational Intelligence: Collaboration, Fusion and Emergence. Springer London, Limited, 2009.

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Mumford, Christine L. Computational Intelligence: Collaboration, Fusion and Emergence. Springer, 2011.

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Xiong, Zhi, Jianye Liu, and Rong Wang. Resilient Fusion Navigation Techniques: Collaboration in Swarm. Springer, 2023.

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Leong, Daphne. Performing Knowledge. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190653545.001.0001.

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This book brings a theorist and performers together to examine the interface of analysis and performance in music of the twentieth century. Nine case studies, of music by Ravel, Schoenberg, Bartók, Schnittke, Milhaud, Messiaen, Babbitt, Carter, and Morris, are co-authored with performers (or composers) of those works. The case studies revolve around musical structure, broadly defined to comprise relations among parts and whole created in the process of making music, whether by composers, performers, listeners, or analysts. Knowledge that is produced in the course of relating analysis and performance is conceived in three dimensions: wissen, können, and kennen. The collaborative process itself is viewed through three constructs that facilitate cross-disciplinary collaboration: shared items, shared objectives (activity objects and epistemic objects), and shared agents. The book’s collaborations “thicken” the description of analysis and performance by illuminating key issues around (a) the implicit identity of a work: the role of embodiment, the affordances of a score, the cultural understanding of notation; (b) the use of metaphor in interpretation: here metaphors of memory, of poetry, and of ritual and drama; and (c) the relation of analysis and performance itself: its antagonisms, its fusion, and—rounding out the perspectives of theorist and performer with those of composer and listener—the role of structure in audience response. Along with these broader insights, each collaboration exemplifies processes of analysis and of performance, in grappling with and interpreting particular pieces. Video performances, demonstrations, and interviews; audio recordings; and photographs partner with the book’s written text.
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Meigh, Abigail E., Ingrid A. Fitz-James Antoine, and Veronica Carullo. Pediatric Spine Surgery. Edited by David E. Traul and Irene P. Osborn. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190850036.003.0016.

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In children, the most common indication for spinal fusion is significant scoliotic curvature, either idiopathic or as a result of neuromuscular disease. Spinal fusion is high-risk surgery, which can be further complicated by comorbid disease. It carries substantial risk for significant fluid shifts, high intraoperative blood loss, physiologic strain secondary to duration and positioning, severe postoperative pain, and potential spinal cord injury. To mitigate risk and optimize outcomes, these patients should be carefully evaluated by the anesthetic team preoperatively and a comprehensive perioperative plan established. To protect the spinal cord and predict poor neurologic outcomes, the majority of these cases employ intraoperative neuromonitoring. The specific anesthetic agents to allow maximal neuromonitoring signals while ensuring adequate anesthetic depth and pain control should also be established collaboratively. These patients experience severe postoperative pain, and a multimodal approach to therapy should be employed to allow for expedited recovery and decreased length of stay.
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Yildiz. Technical Excellence Framework for Innovative Digital Transformation Leadership: Transform Enterprise with Technical Excellence, Innovation, Simplicity, Agility, Fusion, and Collaboration. Independently Published, 2019.

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Gipps, Richard G. T. Cognitive Behavior Therapy. Edited by K. W. M. Fulford, Martin Davies, Richard G. T. Gipps, George Graham, John Z. Sadler, Giovanni Stanghellini, and Tim Thornton. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199579563.013.0072.

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Cognitive behavior therapy (CBT) theorists propose that disturbances in cognition underlie and maintain much emotional disturbance. Accordingly the cognitive addition to behavioral therapy typically consists in collaboratively noticing, restructuring, de-fusing from, and challenging these cognitions by the therapist and the patient. With the right group of problems, patients, and therapists, the practice of CBT is well known to possess therapeutic efficacy. This chapter, however, primarily considers the theory rather than the therapy of CBT; in particular it looks at the central significance it gives tocognitionin healthy and disturbed emotional function. It suggests that if "cognition" is used to mean merely ourbelief and thought, then CBT theory provides an implausible model of much emotional distress. If, on the other hand, "cognition" refers to the processing ofmeaning, then CBT risks losing its distinctiveness from all therapies other than the most blandly behavioral. The chapter also suggests: (a) that the appearance, in CBT's causal models of psychopathology, of what seem to be distinct causal processes and multiple discrete intervention sites may owe more to the formalism of the theory than to the structure of the well or troubled mind; (b) that CBT theorists sometimes unhelpfully assimilate the having of thoughts to episodes of thinking; (c) that CBT models may sometimes overemphasize the significance of belief and thought in psychopathology because they have unhelpfully theorized meaning as belonging more properly to these, rather than to emotional, functions; (d) that CBT approaches can also misconstrue the nature and value of acknowledgement and self-knowledge-thereby underplaying the value of some of the CBT therapist's own interventions. The theoretical and clinical implications of these critiques is discussed-such as that there are reasons to doubt that CBT always works, when it does, in the manner it tends to describe for itself.
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Book chapters on the topic "Collaborative Fusion"

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Li, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Sparse/Collaborative Representation." In Information Fusion, 13–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_2.

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Zhang, Zeqi, Ying Liu, and Fengli Sun. "Fusion Graph Convolutional Collaborative Filtering." In PRICAI 2021: Trends in Artificial Intelligence, 574–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89363-7_43.

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Wang, Rong, Zhi Xiong, and Jianye Liu. "Collaborative Resilient Navigation Frameworks." In Resilient Fusion Navigation Techniques: Collaboration in Swarm, 19–28. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9_2.

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Wang, Rong, Zhi Xiong, and Jianye Liu. "Collaborative Observation-Based Resilient Navigation Fusion." In Resilient Fusion Navigation Techniques: Collaboration in Swarm, 93–117. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9_5.

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Wang, Rong, Zhi Xiong, and Jianye Liu. "Collaborative Localization-Based Resilient Navigation Fusion." In Resilient Fusion Navigation Techniques: Collaboration in Swarm, 65–92. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9_4.

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Wang, Rong, Zhi Xiong, and Jianye Liu. "Collaborative Integrity Augmentation in Resilient Navigation." In Resilient Fusion Navigation Techniques: Collaboration in Swarm, 149–71. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9_7.

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Wang, Rong, Zhi Xiong, and Jianye Liu. "Collaborative Geometry Optimization in Resilient Navigation." In Resilient Fusion Navigation Techniques: Collaboration in Swarm, 119–48. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9_6.

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Wang, Rong, Zhi Xiong, and Jianye Liu. "Collaborative Fault Detection in Resilient Navigation." In Resilient Fusion Navigation Techniques: Collaboration in Swarm, 173–206. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8371-9_8.

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Alexakis, Spiros, Markus Bauer, Albina Pace, Alexa Schumacher, Andreas Friesen, Athanassios Bouras, and Dimitrios Kourtesis. "Application of the Fusion Approach for Assisted Composition of Web Services." In Establishing the Foundation of Collaborative Networks, 531–38. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-73798-0_57.

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Zhao, Jiayu, and Kuizhi Mei. "Cascaded Bilinear Mapping Collaborative Hybrid Attention Modality Fusion Model." In Pattern Recognition and Computer Vision, 287–98. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8435-0_23.

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Conference papers on the topic "Collaborative Fusion"

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Gipson, Jonathon S., and Robert C. Leishman. "Resilient Collaborative All-source Navigation." In 2021 IEEE 24th International Conference on Information Fusion (FUSION). IEEE, 2021. http://dx.doi.org/10.23919/fusion49465.2021.9626892.

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Weerakoon, Dulanga, Kasthuri Jayarajah, Randy Tandriansyah, and Archan Misra. "Resilient Collaborative Intelligence for Adversarial IoT Environments." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011397.

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Zamani, Mohammad, Jochen Trumpf, and Chris Manzie. "Collaborative Bearing Estimation Using Set Membership Methods." In 2023 26th International Conference on Information Fusion (FUSION). IEEE, 2023. http://dx.doi.org/10.23919/fusion52260.2023.10224173.

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Burks, Luke, and Nisar Ahmed. "Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011299.

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Yue, Yufeng, P. G. C. N. Senarathne, Chule Yang, Jun Zhang, Mingxing Wen, and Danwei Wang. "Probabilistic Fusion Framework for Collaborative Robots 3D Mapping." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455670.

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Escourrou, Maxime, Joelle Al Hage, and Philippe Bonnifait. "Decentralized Collaborative Localization with Map Update using Schmidt-Kalman Filter." In 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841349.

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Hubers, Hans J. C., Michela Turrin, Irem Erbas, and Ioannis Chatzikonstantinou. "pCOLAD: online sharing of parameters for collaborative architectural design." In eCAADe 2014: Fusion. eCAADe, 2014. http://dx.doi.org/10.52842/conf.ecaade.2014.2.039.

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Pedroche, David Sánchez, Daniel Amigo, Jesús García, José Manuel Molina, and Juan Pedro Llerena. "UAV airframe classification based on trajectory data in UTM collaborative environments." In 2023 26th International Conference on Information Fusion (FUSION). IEEE, 2023. http://dx.doi.org/10.23919/fusion52260.2023.10224152.

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Kushwaha, M., and X. Koutsoukos. "Collaborative target tracking using multiple visual features in smart camera networks." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711894.

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Ollander, Simon, Florian A. Schiegg, Friedrich-Wilhelm Bode, and Marcus Baum. "Dual-frequency Collaborative Positioning for Minimization of GNSS Errors in Urban Canyons." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190612.

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Reports on the topic "Collaborative Fusion"

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Chang, Choong Seock. COLLABORATIVE: FUSION SIMULATION PROGRAM. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1061482.

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Corkill, Daniel D. Collaborative Software for Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada437538.

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Corkill, Daniel. An Enhanced Collaborative-Software Environment for Information Fusion at the Unit of Action. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada474879.

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Rowcliffe, A. F. US/Japan collaborative program on fusion reactor materials: Summary of the tenth DOE/JAERI Annex I technical progress meeting on neutron irradiation effects in first wall and blanket structural materials. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/6355827.

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Slough, John. Magnetized Target Fusion Collaboration. Final report. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1038663.

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Slough, John. Final report on the Magnetized Target Fusion Collaboration. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/963718.

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Trujillo, Sharon, Zachary Parks, and Thomas Weber. Magneto Inertial Fusion Plasma Target Collaboration: CRADA Final Report. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1330817.

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Zhang, Jingyuan. Development of Enhanced Interactive Datawalls for Data Fusion and Collaboration. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada484697.

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none,. Review of the Strategic Plan for International Collaboration on Fusion Science and Technology Research. Fusion Energy Sciences Advisory Committee (FESAC). Office of Scientific and Technical Information (OSTI), January 1998. http://dx.doi.org/10.2172/1206546.

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Gershoni, Jonathan M., David E. Swayne, Tal Pupko, Shimon Perk, Alexander Panshin, Avishai Lublin, and Natalia Golander. Discovery and reconstitution of cross-reactive vaccine targets for H5 and H9 avian influenza. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7699854.bard.

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Research objectives: Identification of highly conserved B-cell epitopes common to either H5 or H9 subtypes of AI Reconstruction of conserved epitopes from (1) as recombinantimmunogens, and testing their suitability to be used as universal vaccine components by measuring their binding to Influenza vaccinated sera of birds Vaccination of chickens with reconstituted epitopes and evaluation of successful vaccination, clinical protection and viral replication Development of a platform to investigate the dynamics of immune response towards infection or an epitope based vaccine Estimate our ability to focus the immune response towards an epitope-based vaccine using the tool we have developed in (D) Summary: This study is a multi-disciplinary study of four-way collaboration; The SERPL, USDA, Kimron-Israel, and two groups at TAU with the purpose of evaluating the production and implementation of epitope based vaccines against avian influenza (AI). Systematic analysis of the influenza viral spike led to the production of a highly conserved epitope situated at the hinge of the HA antigen designated “cluster 300” (c300). This epitope consists of a total of 31 residues and was initially expressed as a fusion protein of the Protein 8 major protein of the bacteriophagefd. Two versions of the c300 were produced to correspond to the H5 and H9 antigens respectively as well as scrambled versions that were identical with regard to amino acid composition yet with varied linear sequence (these served as negative controls). The recombinantimmunogens were produced first as phage fusions and then subsequently as fusions with maltose binding protein (MBP) or glutathioneS-transferase (GST). The latter were used to immunize and boost chickens at SERPL and Kimron. Furthermore, vaccinated and control chickens were challenged with concordant influenza strains at Kimron and SEPRL. Polyclonal sera were obtained for further analyses at TAU and computational bioinformatics analyses in collaboration with Prof. Pupko. Moreover, the degree of protection afforded by the vaccination was determined. Unfortunately, no protection could be demonstrated. In parallel to the main theme of the study, the TAU team (Gershoni and Pupko) designed and developed a novel methodology for the systematic analysis of the antibody composition of polyclonal sera (Deep Panning) which is essential for the analyses of the humoral response towards vaccination and challenge. Deep Panning is currently being used to monitor the polyclonal sera derived from the vaccination studies conducted at the SEPRL and Kimron.
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