Realistic terrain visualization based on 3D virtual world technology

NASA Astrophysics Data System (ADS)

Huang, Fengru; Lin, Hui; Chen, Bin; Xiao, Cai

2009-09-01

The rapid advances in information technologies, e.g., network, graphics processing, and virtual world, have provided challenges and opportunities for new capabilities in information systems, Internet applications, and virtual geographic environments, especially geographic visualization and collaboration. In order to achieve meaningful geographic capabilities, we need to explore and understand how these technologies can be used to construct virtual geographic environments to help to engage geographic research. The generation of three-dimensional (3D) terrain plays an important part in geographical visualization, computer simulation, and virtual geographic environment applications. The paper introduces concepts and technologies of virtual worlds and virtual geographic environments, explores integration of realistic terrain and other geographic objects and phenomena of natural geographic environment based on SL/OpenSim virtual world technologies. Realistic 3D terrain visualization is a foundation of construction of a mirror world or a sand box model of the earth landscape and geographic environment. The capabilities of interaction and collaboration on geographic information are discussed as well. Further virtual geographic applications can be developed based on the foundation work of realistic terrain visualization in virtual environments.

Realistic terrain visualization based on 3D virtual world technology

NASA Astrophysics Data System (ADS)

Huang, Fengru; Lin, Hui; Chen, Bin; Xiao, Cai

2010-11-01

The rapid advances in information technologies, e.g., network, graphics processing, and virtual world, have provided challenges and opportunities for new capabilities in information systems, Internet applications, and virtual geographic environments, especially geographic visualization and collaboration. In order to achieve meaningful geographic capabilities, we need to explore and understand how these technologies can be used to construct virtual geographic environments to help to engage geographic research. The generation of three-dimensional (3D) terrain plays an important part in geographical visualization, computer simulation, and virtual geographic environment applications. The paper introduces concepts and technologies of virtual worlds and virtual geographic environments, explores integration of realistic terrain and other geographic objects and phenomena of natural geographic environment based on SL/OpenSim virtual world technologies. Realistic 3D terrain visualization is a foundation of construction of a mirror world or a sand box model of the earth landscape and geographic environment. The capabilities of interaction and collaboration on geographic information are discussed as well. Further virtual geographic applications can be developed based on the foundation work of realistic terrain visualization in virtual environments.

Toward the establishment of design guidelines for effective 3D perspective interfaces

NASA Astrophysics Data System (ADS)

Fitzhugh, Elisabeth; Dixon, Sharon; Aleva, Denise; Smith, Eric; Ghrayeb, Joseph; Douglas, Lisa

2009-05-01

The propagation of information operation technologies, with correspondingly vast amounts of complex network information to be conveyed, significantly impacts operator workload. Information management research is rife with efforts to develop schemes to aid operators to identify, review, organize, and retrieve the wealth of available data. Data may take on such distinct forms as intelligence libraries, logistics databases, operational environment models, or network topologies. Increased use of taxonomies and semantic technologies opens opportunities to employ network visualization as a display mechanism for diverse information aggregations. The broad applicability of network visualizations is still being tested, but in current usage, the complexity of densely populated abstract networks suggests the potential utility of 3D. Employment of 2.5D in network visualization, using classic perceptual cues, creates a 3D experience within a 2D medium. It is anticipated that use of 3D perspective (2.5D) will enhance user ability to visually inspect large, complex, multidimensional networks. Current research for 2.5D visualizations demonstrates that display attributes, including color, shape, size, lighting, atmospheric effects, and shadows, significantly impact operator experience. However, guidelines for utilization of attributes in display design are limited. This paper discusses pilot experimentation intended to identify potential problem areas arising from these cues and determine how best to optimize perceptual cue settings. Development of optimized design guidelines will ensure that future experiments, comparing network displays with other visualizations, are not confounded or impeded by suboptimal attribute characterization. Current experimentation is anticipated to support development of cost-effective, visually effective methods to implement 3D in military applications.

Generalized Cartographic and Simultaneous Representation of Utility Networks for Decision-Support Systems and Crisis Management in Urban Environments

NASA Astrophysics Data System (ADS)

Becker, T.; König, G.

2015-10-01

Cartographic visualizations of crises are used to create a Common Operational Picture (COP) and enforce Situational Awareness by presenting relevant information to the involved actors. As nearly all crises affect geospatial entities, geo-data representations have to support location-specific analysis throughout the decision-making process. Meaningful cartographic presentation is needed for coordinating the activities of crisis manager in a highly dynamic situation, since operators' attention span and their spatial memories are limiting factors during the perception and interpretation process. Situational Awareness of operators in conjunction with a COP are key aspects in decision-making process and essential for making well thought-out and appropriate decisions. Considering utility networks as one of the most complex and particularly frequent required systems in urban environment, meaningful cartographic presentation of multiple utility networks with respect to disaster management do not exist. Therefore, an optimized visualization of utility infrastructure for emergency response procedures is proposed. The article will describe a conceptual approach on how to simplify, aggregate, and visualize multiple utility networks and their components to meet the requirements of the decision-making process and to support Situational Awareness.

Ultrascale collaborative visualization using a display-rich global cyberinfrastructure.

PubMed

Jeong, Byungil; Leigh, Jason; Johnson, Andrew; Renambot, Luc; Brown, Maxine; Jagodic, Ratko; Nam, Sungwon; Hur, Hyejung

2010-01-01

The scalable adaptive graphics environment (SAGE) is high-performance graphics middleware for ultrascale collaborative visualization using a display-rich global cyberinfrastructure. Dozens of sites worldwide use this cyberinfrastructure middleware, which connects high-performance-computing resources over high-speed networks to distributed ultraresolution displays.

A collaborative interaction and visualization multi-modal environment for surgical planning.

PubMed

Foo, Jung Leng; Martinez-Escobar, Marisol; Peloquin, Catherine; Lobe, Thom; Winer, Eliot

2009-01-01

The proliferation of virtual reality visualization and interaction technologies has changed the way medical image data is analyzed and processed. This paper presents a multi-modal environment that combines a virtual reality application with a desktop application for collaborative surgical planning. Both visualization applications can function independently but can also be synced over a network connection for collaborative work. Any changes to either application is immediately synced and updated to the other. This is an efficient collaboration tool that allows multiple teams of doctors with only an internet connection to visualize and interact with the same patient data simultaneously. With this multi-modal environment framework, one team working in the VR environment and another team from a remote location working on a desktop machine can both collaborate in the examination and discussion for procedures such as diagnosis, surgical planning, teaching and tele-mentoring.

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Robust visual tracking via multiscale deep sparse networks

NASA Astrophysics Data System (ADS)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Neural practice effect during cross-modal selective attention: Supra-modal and modality-specific effects.

PubMed

Xia, Jing; Zhang, Wei; Jiang, Yizhou; Li, You; Chen, Qi

2018-05-16

Practice and experiences gradually shape the central nervous system, from the synaptic level to large-scale neural networks. In natural multisensory environment, even when inundated by streams of information from multiple sensory modalities, our brain does not give equal weight to different modalities. Rather, visual information more frequently receives preferential processing and eventually dominates consciousness and behavior, i.e., visual dominance. It remains unknown, however, the supra-modal and modality-specific practice effect during cross-modal selective attention, and moreover whether the practice effect shows similar modality preferences as the visual dominance effect in the multisensory environment. To answer the above two questions, we adopted a cross-modal selective attention paradigm in conjunction with the hybrid fMRI design. Behaviorally, visual performance significantly improved while auditory performance remained constant with practice, indicating that visual attention more flexibly adapted behavior with practice than auditory attention. At the neural level, the practice effect was associated with decreasing neural activity in the frontoparietal executive network and increasing activity in the default mode network, which occurred independently of the modality attended, i.e., the supra-modal mechanisms. On the other hand, functional decoupling between the auditory and the visual system was observed with the progress of practice, which varied as a function of the modality attended. The auditory system was functionally decoupled with both the dorsal and ventral visual stream during auditory attention while was decoupled only with the ventral visual stream during visual attention. To efficiently suppress the irrelevant visual information with practice, auditory attention needs to additionally decouple the auditory system from the dorsal visual stream. The modality-specific mechanisms, together with the behavioral effect, thus support the visual dominance model in terms of the practice effect during cross-modal selective attention. Copyright © 2018 Elsevier Ltd. All rights reserved.

Tools and Methods to Create Scenarios for Experimental Research in the Network Science Research Laboratory (NSRL)

DTIC Science & Technology

2015-12-01

research areas in network science. 15. SUBJECT TERMS scenario creation , emulation environment, NSRL 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...mobility aspect of the emulated environment, the development and creation of scenarios play an integral part. By creating scenarios that model certain...during the visualization phase. We examine these 3 phases in detail by describing the creation of a scenario based upon a vignette from the Multi-Level

VRML and Collaborative Environments: New Tools for Networked Visualization

NASA Astrophysics Data System (ADS)

Crutcher, R. M.; Plante, R. L.; Rajlich, P.

We present two new applications that engage the network as a tool for astronomical research and/or education. The first is a VRML server which allows users over the Web to interactively create three-dimensional visualizations of FITS images contained in the NCSA Astronomy Digital Image Library (ADIL). The server's Web interface allows users to select images from the ADIL, fill in processing parameters, and create renderings featuring isosurfaces, slices, contours, and annotations; the often extensive computations are carried out on an NCSA SGI supercomputer server without the user having an individual account on the system. The user can then download the 3D visualizations as VRML files, which may be rotated and manipulated locally on virtually any class of computer. The second application is the ADILBrowser, a part of the NCSA Horizon Image Data Browser Java package. ADILBrowser allows a group of participants to browse images from the ADIL within a collaborative session. The collaborative environment is provided by the NCSA Habanero package which includes text and audio chat tools and a white board. The ADILBrowser is just an example of a collaborative tool that can be built with the Horizon and Habanero packages. The classes provided by these packages can be assembled to create custom collaborative applications that visualize data either from local disk or from anywhere on the network.

Scientific Assistant Virtual Laboratory (SAVL)

NASA Astrophysics Data System (ADS)

Alaghband, Gita; Fardi, Hamid; Gnabasik, David

2007-03-01

The Scientific Assistant Virtual Laboratory (SAVL) is a scientific discovery environment, an interactive simulated virtual laboratory, for learning physics and mathematics. The purpose of this computer-assisted intervention is to improve middle and high school student interest, insight and scores in physics and mathematics. SAVL develops scientific and mathematical imagination in a visual, symbolic, and experimental simulation environment. It directly addresses the issues of scientific and technological competency by providing critical thinking training through integrated modules. This on-going research provides a virtual laboratory environment in which the student directs the building of the experiment rather than observing a packaged simulation. SAVL: * Engages the persistent interest of young minds in physics and math by visually linking simulation objects and events with mathematical relations. * Teaches integrated concepts by the hands-on exploration and focused visualization of classic physics experiments within software. * Systematically and uniformly assesses and scores students by their ability to answer their own questions within the context of a Master Question Network. We will demonstrate how the Master Question Network uses polymorphic interfaces and C# lambda expressions to manage simulation objects.

Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

PubMed Central

Yue, Bo; Wang, Shuang; Liang, Xuefeng; Jiao, Licheng; Xu, Caijin

2016-01-01

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception. PMID:26927114

Simple Smartphone-Based Guiding System for Visually Impaired People

PubMed Central

Lin, Bor-Shing; Lee, Cheng-Che; Chiang, Pei-Ying

2017-01-01

Visually impaired people are often unaware of dangers in front of them, even in familiar environments. Furthermore, in unfamiliar environments, such people require guidance to reduce the risk of colliding with obstacles. This study proposes a simple smartphone-based guiding system for solving the navigation problems for visually impaired people and achieving obstacle avoidance to enable visually impaired people to travel smoothly from a beginning point to a destination with greater awareness of their surroundings. In this study, a computer image recognition system and smartphone application were integrated to form a simple assisted guiding system. Two operating modes, online mode and offline mode, can be chosen depending on network availability. When the system begins to operate, the smartphone captures the scene in front of the user and sends the captured images to the backend server to be processed. The backend server uses the faster region convolutional neural network algorithm or the you only look once algorithm to recognize multiple obstacles in every image, and it subsequently sends the results back to the smartphone. The results of obstacle recognition in this study reached 60%, which is sufficient for assisting visually impaired people in realizing the types and locations of obstacles around them. PMID:28608811

Simple Smartphone-Based Guiding System for Visually Impaired People.

PubMed

Lin, Bor-Shing; Lee, Cheng-Che; Chiang, Pei-Ying

2017-06-13

Visually impaired people are often unaware of dangers in front of them, even in familiar environments. Furthermore, in unfamiliar environments, such people require guidance to reduce the risk of colliding with obstacles. This study proposes a simple smartphone-based guiding system for solving the navigation problems for visually impaired people and achieving obstacle avoidance to enable visually impaired people to travel smoothly from a beginning point to a destination with greater awareness of their surroundings. In this study, a computer image recognition system and smartphone application were integrated to form a simple assisted guiding system. Two operating modes, online mode and offline mode, can be chosen depending on network availability. When the system begins to operate, the smartphone captures the scene in front of the user and sends the captured images to the backend server to be processed. The backend server uses the faster region convolutional neural network algorithm or the you only look once algorithm to recognize multiple obstacles in every image, and it subsequently sends the results back to the smartphone. The results of obstacle recognition in this study reached 60%, which is sufficient for assisting visually impaired people in realizing the types and locations of obstacles around them.

Emerging CAE technologies and their role in Future Ambient Intelligence Environments

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2011-03-01

Dramatic improvements are on the horizon in Computer Aided Engineering (CAE) and various simulation technologies. The improvements are due, in part, to the developments in a number of leading-edge technologies and their synergistic combinations/convergence. The technologies include ubiquitous, cloud, and petascale computing; ultra high-bandwidth networks, pervasive wireless communication; knowledge based engineering; networked immersive virtual environments and virtual worlds; novel human-computer interfaces; and powerful game engines and facilities. This paper describes the frontiers and emerging simulation technologies, and their role in the future virtual product creation and learning/training environments. The environments will be ambient intelligence environments, incorporating a synergistic combination of novel agent-supported visual simulations (with cognitive learning and understanding abilities); immersive 3D virtual world facilities; development chain management systems and facilities (incorporating a synergistic combination of intelligent engineering and management tools); nontraditional methods; intelligent, multimodal and human-like interfaces; and mobile wireless devices. The Virtual product creation environment will significantly enhance the productivity and will stimulate creativity and innovation in future global virtual collaborative enterprises. The facilities in the learning/training environment will provide timely, engaging, personalized/collaborative and tailored visual learning.

Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration.

PubMed

Kim, Heejung; Hahm, Jarang; Lee, Hyekyoung; Kang, Eunjoo; Kang, Hyejin; Lee, Dong Soo

2015-05-01

The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

ERIC Educational Resources Information Center

Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

2011-01-01

Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

Photogrammetric point cloud compression for tactical networks

NASA Astrophysics Data System (ADS)

Madison, Andrew C.; Massaro, Richard D.; Wayant, Clayton D.; Anderson, John E.; Smith, Clint B.

2017-05-01

We report progress toward the development of a compression schema suitable for use in the Army's Common Operating Environment (COE) tactical network. The COE facilitates the dissemination of information across all Warfighter echelons through the establishment of data standards and networking methods that coordinate the readout and control of a multitude of sensors in a common operating environment. When integrated with a robust geospatial mapping functionality, the COE enables force tracking, remote surveillance, and heightened situational awareness to Soldiers at the tactical level. Our work establishes a point cloud compression algorithm through image-based deconstruction and photogrammetric reconstruction of three-dimensional (3D) data that is suitable for dissimination within the COE. An open source visualization toolkit was used to deconstruct 3D point cloud models based on ground mobile light detection and ranging (LiDAR) into a series of images and associated metadata that can be easily transmitted on a tactical network. Stereo photogrammetric reconstruction is then conducted on the received image stream to reveal the transmitted 3D model. The reported method boasts nominal compression ratios typically on the order of 250 while retaining tactical information and accurate georegistration. Our work advances the scope of persistent intelligence, surveillance, and reconnaissance through the development of 3D visualization and data compression techniques relevant to the tactical operations environment.

Visual identification and similarity measures used for on-line motion planning of autonomous robots in unknown environments

NASA Astrophysics Data System (ADS)

Martínez, Fredy; Martínez, Fernando; Jacinto, Edwar

2017-02-01

In this paper we propose an on-line motion planning strategy for autonomous robots in dynamic and locally observable environments. In this approach, we first visually identify geometric shapes in the environment by filtering images. Then, an ART-2 network is used to establish the similarity between patterns. The proposed algorithm allows that a robot establish its relative location in the environment, and define its navigation path based on images of the environment and its similarity to reference images. This is an efficient and minimalist method that uses the similarity of landmark view patterns to navigate to the desired destination. Laboratory tests on real prototypes demonstrate the performance of the algorithm.

Investigation of Spatial Data with Open Source Social Network Analysis and Geographic Information Systems Applications

NASA Astrophysics Data System (ADS)

Sabah, L.; Şimşek, M.

2017-11-01

Social networks are the real social experience of individuals in the online environment. In this environment, people use symbolic gestures and mimics, sharing thoughts and content. Social network analysis is the visualization of complex and large quantities of data to ensure that the overall picture appears. It is the understanding, development, quantitative and qualitative analysis of the relations in the social networks of Graph theory. Social networks are expressed in the form of nodes and edges. Nodes are people/organizations, and edges are relationships between nodes. Relations are directional, non-directional, weighted, and weightless. The purpose of this study is to examine the effects of social networks on the evaluation of person data with spatial coordinates. For this, the cluster size and the effect on the geographical area of the circle where the placements of the individual are influenced by the frequently used placeholder feature in the social networks have been studied.

Radiological tele-immersion for next generation networks.

PubMed

Ai, Z; Dech, F; Rasmussen, M; Silverstein, J C

2000-01-01

Since the acquisition of high-resolution three-dimensional patient images has become widespread, medical volumetric datasets (CT or MR) larger than 100 MB and encompassing more than 250 slices are common. It is important to make this patient-specific data quickly available and usable to many specialists at different geographical sites. Web-based systems have been developed to provide volume or surface rendering of medical data over networks with low fidelity, but these cannot adequately handle stereoscopic visualization or huge datasets. State-of-the-art virtual reality techniques and high speed networks have made it possible to create an environment for clinicians geographically distributed to immersively share these massive datasets in real-time. An object-oriented method for instantaneously importing medical volumetric data into Tele-Immersive environments has been developed at the Virtual Reality in Medicine Laboratory (VRMedLab) at the University of Illinois at Chicago (UIC). This networked-VR setup is based on LIMBO, an application framework or template that provides the basic capabilities of Tele-Immersion. We have developed a modular general purpose Tele-Immersion program that automatically combines 3D medical data with the methods for handling the data. For this purpose a DICOM loader for IRIS Performer has been developed. The loader was designed for SGI machines as a shared object, which is executed at LIMBO's runtime. The loader loads not only the selected DICOM dataset, but also methods for rendering, handling, and interacting with the data, bringing networked, real-time, stereoscopic interaction with radiological data to reality. Collaborative, interactive methods currently implemented in the loader include cutting planes and windowing. The Tele-Immersive environment has been tested on the UIC campus over an ATM network. We tested the environment with 3 nodes; one ImmersaDesk at the VRMedLab, one CAVE at the Electronic Visualization Laboratory (EVL) on east campus, and a CT scan machine in UIC Hospital. CT data was pulled directly from the scan machine to the Tele-Immersion server in our Laboratory, and then the data was synchronously distributed by our Onyx2 Rack server to all the VR setups. Instead of permitting medical volume visualization at one VR device, by combining teleconferencing, tele-presence, and virtual reality, the Tele-Immersive environment will enable geographically distributed clinicians to intuitively interact with the same medical volumetric models, point, gesture, converse, and see each other. This environment will bring together clinicians at different geographic locations to participate in Tele-Immersive consultation and collaboration.

VISUAL3D - An EIT network on visualization of geomodels

NASA Astrophysics Data System (ADS)

Bauer, Tobias

2017-04-01

When it comes to interpretation of data and understanding of deep geological structures and bodies at different scales then modelling tools and modelling experience is vital for deep exploration. Geomodelling provides a platform for integration of different types of data, including new kinds of information (e.g., new improved measuring methods). EIT Raw Materials, initiated by the EIT (European Institute of Innovation and Technology) and funded by the European Commission, is the largest and strongest consortium in the raw materials sector worldwide. The VISUAL3D network of infrastructure is an initiative by EIT Raw Materials and aims at bringing together partners with 3D-4D-visualisation infrastructure and 3D-4D-modelling experience. The recently formed network collaboration interlinks hardware, software and expert knowledge in modelling visualization and output. A special focus will be the linking of research, education and industry and integrating multi-disciplinary data and to visualize the data in three and four dimensions. By aiding network collaborations we aim at improving the combination of geomodels with differing file formats and data characteristics. This will create an increased competency in modelling visualization and the ability to interchange and communicate models more easily. By combining knowledge and experience in geomodelling with expertise in Virtual Reality visualization partners of EIT Raw Materials but also external parties will have the possibility to visualize, analyze and validate their geomodels in immersive VR-environments. The current network combines partners from universities, research institutes, geological surveys and industry with a strong background in geological 3D-modelling and 3D visualization and comprises: Luleå University of Technology, Geological Survey of Finland, Geological Survey of Denmark and Greenland, TUBA Freiberg, Uppsala University, Geological Survey of France, RWTH Aachen, DMT, KGHM Cuprum, Boliden, Montan Universität Leoben, Slovenian National Building and Civil Engineering Institute, Tallinn University of Technology and Turku University. The infrastructure within the network comprises different types of capturing and visualization hardware, ranging from high resolution cubes, VR walls, VR goggle solutions, high resolution photogrammetry, UAVs, lidar-scanners, and many more.

Do you see what I hear: experiments in multi-channel sound and 3D visualization for network monitoring?

NASA Astrophysics Data System (ADS)

Ballora, Mark; Hall, David L.

2010-04-01

Detection of intrusions is a continuing problem in network security. Due to the large volumes of data recorded in Web server logs, analysis is typically forensic, taking place only after a problem has occurred. This paper describes a novel method of representing Web log information through multi-channel sound, while simultaneously visualizing network activity using a 3-D immersive environment. We are exploring the detection of intrusion signatures and patterns, utilizing human aural and visual pattern recognition ability to detect intrusions as they occur. IP addresses and return codes are mapped to an informative and unobtrusive listening environment to act as a situational sound track of Web traffic. Web log data is parsed and formatted using Python, then read as a data array by the synthesis language SuperCollider [1], which renders it as a sonification. This can be done either for the study of pre-existing data sets or in monitoring Web traffic in real time. Components rendered aurally include IP address, geographical information, and server Return Codes. Users can interact with the data, speeding or slowing the speed of representation (for pre-existing data sets) or "mixing" sound components to optimize intelligibility for tracking suspicious activity.

A Three Pronged Approach for Improved Data Understanding: 3-D Visualization, Use of Gaming Techniques, and Intelligent Advisory Agents

DTIC Science & Technology

2006-10-01

Pronged Approach for Improved Data Understanding: 3-D Visualization, Use of Gaming Techniques, and Intelligent Advisory Agents. In Visualising Network...University at the start of each fall semester, when numerous new students arrive on campus and begin downloading extensive amounts of audio and...SIGGRAPH ’92 • C. Cruz-Neira, D.J. Sandin, T.A. DeFanti, R.V. Kenyon and J.C. Hart, "The CAVE: Audio Visual Experience Automatic Virtual Environment

Chemistry in Second Life

PubMed Central

Lang, Andrew SID; Bradley, Jean-Claude

2009-01-01

This review will focus on the current level on chemistry research, education, and visualization possible within the multi-user virtual environment of Second Life. We discuss how Second Life has been used as a platform for the interactive and collaborative visualization of data from molecules and proteins to spectra and experimental data. We then review how these visualizations can be scripted for immersive educational activities and real-life collaborative research. We also discuss the benefits of the social networking affordances of Second Life for both chemists and chemistry students. PMID:19852781

Chemistry in second life.

PubMed

Lang, Andrew S I D; Bradley, Jean-Claude

2009-10-23

This review will focus on the current level on chemistry research, education, and visualization possible within the multi-user virtual environment of Second Life. We discuss how Second Life has been used as a platform for the interactive and collaborative visualization of data from molecules and proteins to spectra and experimental data. We then review how these visualizations can be scripted for immersive educational activities and real-life collaborative research. We also discuss the benefits of the social networking affordances of Second Life for both chemists and chemistry students.

Air-Sense: indoor environment monitoring evaluation system based on ZigBee network

NASA Astrophysics Data System (ADS)

Huang, Yang; Hu, Liang; Yang, Disheng; Liu, Hengchang

2017-08-01

In the modern life, people spend most of their time indoors. However, indoor environmental quality problems have always been affecting people’s social activities. In general, indoor environmental quality is also related to our indoor activities. Since most of the organic irritants and volatile gases are colorless, odorless and too tiny to be seen, because we have been unconsciously overlooked indoor environment quality. Consequently, our body suffer a great health problem. In this work, we propose Air-Sense system which utilizes the platform of ZigBee Network to collect and detect the real-time indoor environment quality. What’s more, Air-Sense system can also provide data analysis, and visualizing the results of the indoor environment to the user.

DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts.

PubMed

Czerwinska, Urszula; Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

2015-08-14

Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases. We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network. DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/.

The Virtual Pelvic Floor, a tele-immersive educational environment.

PubMed Central

Pearl, R. K.; Evenhouse, R.; Rasmussen, M.; Dech, F.; Silverstein, J. C.; Prokasy, S.; Panko, W. B.

1999-01-01

This paper describes the development of the Virtual Pelvic Floor, a new method of teaching the complex anatomy of the pelvic region utilizing virtual reality and advanced networking technology. Virtual reality technology allows improved visualization of three-dimensional structures over conventional media because it supports stereo vision, viewer-centered perspective, large angles of view, and interactivity. Two or more ImmersaDesk systems, drafting table format virtual reality displays, are networked together providing an environment where teacher and students share a high quality three-dimensional anatomical model, and are able to converse, see each other, and to point in three dimensions to indicate areas of interest. This project was realized by the teamwork of surgeons, medical artists and sculptors, computer scientists, and computer visualization experts. It demonstrates the future of virtual reality for surgical education and applications for the Next Generation Internet. Images Figure 1 Figure 2 Figure 3 PMID:10566378

Vroom: designing an augmented environment for remote collaboration in digital cinema production

NASA Astrophysics Data System (ADS)

Margolis, Todd; Cornish, Tracy

2013-03-01

As media technologies become increasingly affordable, compact and inherently networked, new generations of telecollaborative platforms continue to arise which integrate these new affordances. Virtual reality has been primarily concerned with creating simulations of environments that can transport participants to real or imagined spaces that replace the "real world". Meanwhile Augmented Reality systems have evolved to interleave objects from Virtual Reality environments into the physical landscape. Perhaps now there is a new class of systems that reverse this precept to enhance dynamic media landscapes and immersive physical display environments to enable intuitive data exploration through collaboration. Vroom (Virtual Room) is a next-generation reconfigurable tiled display environment in development at the California Institute for Telecommunications and Information Technology (Calit2) at the University of California, San Diego. Vroom enables freely scalable digital collaboratories, connecting distributed, high-resolution visualization resources for collaborative work in the sciences, engineering and the arts. Vroom transforms a physical space into an immersive media environment with large format interactive display surfaces, video teleconferencing and spatialized audio built on a highspeed optical network backbone. Vroom enables group collaboration for local and remote participants to share knowledge and experiences. Possible applications include: remote learning, command and control, storyboarding, post-production editorial review, high resolution video playback, 3D visualization, screencasting and image, video and multimedia file sharing. To support these various scenarios, Vroom features support for multiple user interfaces (optical tracking, touch UI, gesture interface, etc.), support for directional and spatialized audio, giga-pixel image interactivity, 4K video streaming, 3D visualization and telematic production. This paper explains the design process that has been utilized to make Vroom an accessible and intuitive immersive environment for remote collaboration specifically for digital cinema production.

VERS: a virtual environment for reconstructive surgery planning

NASA Astrophysics Data System (ADS)

Montgomery, Kevin N.

1997-05-01

The virtual environment for reconstructive surgery (VERS) project at the NASA Ames Biocomputation Center is applying virtual reality technology to aid surgeons in planning surgeries. We are working with a craniofacial surgeon at Stanford to assemble and visualize the bone structure of patients requiring reconstructive surgery either through developmental abnormalities or trauma. This project is an extension of our previous work in 3D reconstruction, mesh generation, and immersive visualization. The current VR system, consisting of an SGI Onyx RE2, FakeSpace BOOM and ImmersiveWorkbench, Virtual Technologies CyberGlove and Ascension Technologies tracker, is currently in development and has already been used to visualize defects preoperatively. In the near future it will be used to more fully plan the surgery and compute the projected result to soft tissue structure. This paper presents the work in progress and details the production of a high-performance, collaborative, and networked virtual environment.

Building University Capacity to Visualize Solutions to Complex Problems in the Arctic

NASA Astrophysics Data System (ADS)

Broderson, D.; Veazey, P.; Raymond, V. L.; Kowalski, K.; Prakash, A.; Signor, B.

2016-12-01

Rapidly changing environments are creating complex problems across the globe, which are particular magnified in the Arctic. These worldwide challenges can best be addressed through diverse and interdisciplinary research teams. It is incumbent on such teams to promote co-production of knowledge and data-driven decision-making by identifying effective methods to communicate their findings and to engage with the public. Decision Theater North (DTN) is a new semi-immersive visualization system that provides a space for teams to collaborate and develop solutions to complex problems, relying on diverse sets of skills and knowledge. It provides a venue to synthesize the talents of scientists, who gather information (data); modelers, who create models of complex systems; artists, who develop visualizations; communicators, who connect and bridge populations; and policymakers, who can use the visualizations to develop sustainable solutions to pressing problems. The mission of Decision Theater North is to provide a cutting-edge visual environment to facilitate dialogue and decision-making by stakeholders including government, industry, communities and academia. We achieve this mission by adopting a multi-faceted approach reflected in the theater's design, technology, networking capabilities, user support, community relationship building, and strategic partnerships. DTN is a joint project of Alaska's National Science Foundation Experimental Program to Stimulate Competitive Research (NSF EPSCoR) and the University of Alaska Fairbanks (UAF), who have brought the facility up to full operational status and are now expanding its development space to support larger team science efforts. Based in Fairbanks, Alaska, DTN is uniquely poised to address changes taking place in the Arctic and subarctic, and is connected with a larger network of decision theaters that include the Arizona State University Decision Theater Network and the McCain Institute in Washington, DC.

Mechanisms of Photoreceptor Patterning in Vertebrates and Invertebrates.

PubMed

Viets, Kayla; Eldred, Kiara; Johnston, Robert J

2016-10-01

Across the animal kingdom, visual systems have evolved to be uniquely suited to the environments and behavioral patterns of different species. Visual acuity and color perception depend on the distribution of photoreceptor (PR) subtypes within the retina. Retinal mosaics can be organized into three broad categories: stochastic/regionalized, regionalized, and ordered. We describe here the retinal mosaics of flies, zebrafish, chickens, mice, and humans, and the gene regulatory networks controlling proper PR specification in each. By drawing parallels in eye development between these divergent species, we identify a set of conserved organizing principles and transcriptional networks that govern PR subtype differentiation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mechanisms of photoreceptor patterning in vertebrates and invertebrates

PubMed Central

Johnston, Robert J

2016-01-01

Across the animal kingdom, visual systems have evolved to be uniquely suited to the environments and behavioral patterns of different species. The visual acuity and color perception of organisms depend on the distribution of photoreceptor subtypes within the retina. Retinal mosaics can be organized into three broad categories: stochastic/regionalized, regionalized, and ordered. Here, we describe the retinal mosaics of flies, zebrafish, chickens, mice, and humans and the gene regulatory networks controlling proper photoreceptor specification in each. By drawing parallels in eye development between these divergent species, we identify a set of conserved organizing principles and transcriptional networks that govern photoreceptor subtype differentiation. PMID:27615122

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

Ideal Positions: 3D Sonography, Medical Visuality, Popular Culture.

PubMed

Seiber, Tim

2016-03-01

As digital technologies are integrated into medical environments, they continue to transform the experience of contemporary health care. Importantly, medicine is increasingly visual. In the history of sonography, visibility has played an important role in accessing fetal bodies for diagnostic and entertainment purposes. With the advent of three-dimensional (3D) rendering, sonography presents the fetus visually as already a child. The aesthetics of this process and the resulting imagery, made possible in digital networks, discloses important changes in the relationship between technology and biology, reproductive health and political debates, and biotechnology and culture.

Construction of Blaze at the University of Illinois at Chicago: A Shared, High-Performance, Visual Computer for Next-Generation Cyberinfrastructure-Accelerated Scientific, Engineering, Medical and Public Policy Research

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

Brown, Maxine D.; Leigh, Jason

2014-02-17

The Blaze high-performance visual computing system serves the high-performance computing research and education needs of University of Illinois at Chicago (UIC). Blaze consists of a state-of-the-art, networked, computer cluster and ultra-high-resolution visualization system called CAVE2(TM) that is currently not available anywhere in Illinois. This system is connected via a high-speed 100-Gigabit network to the State of Illinois' I-WIRE optical network, as well as to national and international high speed networks, such as the Internet2, and the Global Lambda Integrated Facility. This enables Blaze to serve as an on-ramp to national cyberinfrastructure, such as the National Science Foundation’s Blue Waters petascalemore » computer at the National Center for Supercomputing Applications at the University of Illinois at Chicago and the Department of Energy’s Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. DOE award # DE-SC005067, leveraged with NSF award #CNS-0959053 for “Development of the Next-Generation CAVE Virtual Environment (NG-CAVE),” enabled us to create a first-of-its-kind high-performance visual computing system. The UIC Electronic Visualization Laboratory (EVL) worked with two U.S. companies to advance their commercial products and maintain U.S. leadership in the global information technology economy. New applications are being enabled with the CAVE2/Blaze visual computing system that is advancing scientific research and education in the U.S. and globally, and help train the next-generation workforce.« less

Atmospheric ammonia and particulate inorganic nitrogen over the United States

EPA Science Inventory

We use in situ observations from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, the Midwest Ammonia Monitoring Project, 11 surface site campaigns as well as Infrared Atmospheric Sounding Interferometer (IASI) satellite measurements with the GEOS-Ch...

Space-Based Telescopes for the Actionable Refinement of Ephemeris Systems and Test Engineering

DTIC Science & Technology

2011-12-01

Space Surveillance Network STARE Space-based Telescopes for the Actionable Refinement of Ephemeris STK Satellite Toolkit SV Space Vehicle TAMU...vacuum bake out and visual inspection. Additionally, it is prescribed that these tests be performed in accordance with GSFC-STD-7000, more commonly...environment that a FV will see in orbit. Tools such as Solid Works and NX-Ideas can be used to build CAD models to visually validate engineering

Shewregdb: Database and visualization environment for experimental and predicted regulatory information in Shewanella oneidensis mr-1

PubMed Central

Syed, Mustafa H; Karpinets, Tatiana V; Leuze, Michael R; Kora, Guruprasad H; Romine, Margaret R; Uberbacher, Edward C

2009-01-01

Shewanella oneidensis MR-1 is an important model organism for environmental research as it has an exceptional metabolic and respiratory versatility regulated by a complex regulatory network. We have developed a database to collect experimental and computational data relating to regulation of gene and protein expression, and, a visualization environment that enables integration of these data types. The regulatory information in the database includes predictions of DNA regulator binding sites, sigma factor binding sites, transcription units, operons, promoters, and RNA regulators including non-coding RNAs, riboswitches, and different types of terminators. Availability http://shewanella-knowledgebase.org:8080/Shewanella/gbrowserLanding.jsp PMID:20198195

CytoViz: an artistic mapping of network measurements as living organisms in a VR application

NASA Astrophysics Data System (ADS)

López Silva, Brenda A.; Renambot, Luc

2007-02-01

CytoViz is an artistic, real-time information visualization driven by statistical information gathered during gigabit network transfers to the Scalable Adaptive Graphical Environment (SAGE) at various events. Data streams are mapped to cellular organisms defining their structure and behavior as autonomous agents. Network bandwidth drives the growth of each entity and the latency defines its physics-based independent movements. The collection of entity is bound within the 3D representation of the local venue. This visual and animated metaphor allows the public to experience the complexity of high-speed network streams that are used in the scientific community. Moreover, CytoViz displays the presence of discoverable Bluetooth devices carried by nearby persons. The concept is to generate an event-specific, real-time visualization that creates informational 3D patterns based on actual local presence. The observed Bluetooth traffic is put in opposition of the wide-area networking traffic by overlaying 2D animations on top of the 3D world. Each device is mapped to an animation fading over time while displaying the name of the detected device and its unique physical address. CytoViz was publicly presented at two major international conferences in 2005 (iGrid2005 in San Diego, CA and SC05 in Seattle, WA).

New Applications for the Testing and Visualization of Wireless Networks

NASA Technical Reports Server (NTRS)

Griffin, Robert I.; Cauley, Michael A.; Pleva, Michael A.; Seibert, Marc A.; Lopez, Isaac

2005-01-01

Traditional techniques for examining wireless networks use physical link characteristics such as Signal-to-Noise (SNR) ratios to assess the performance of wireless networks. Such measurements may not be reliable indicators of available bandwidth. This work describes two new software applications developed at NASA Glenn Research Center for the investigation of wireless networks. GPSIPerf combines measurements of Transmission Control Protocol (TCP) throughput with Global Positioning System (GPS) coordinates to give users a map of wireless bandwidth for outdoor environments where a wireless infrastructure has been deployed. GPSIPerfView combines the data provided by GPSIPerf with high-resolution digital elevation maps (DEM) to help users visualize and assess the impact of elevation features on wireless networks in a given sample area. These applications were used to examine TCP throughput in several wireless network configurations at desert field sites near Hanksville, Utah during May of 2004. Use of GPSIPerf and GPSIPerfView provides a geographically referenced picture of the extent and deterioration of TCP throughput in tested wireless network configurations. GPSIPerf results from field-testing in Utah suggest that it can be useful in assessing other wireless network architectures, and may be useful to future human-robotic exploration missions.

Behavior Selection of Mobile Robot Based on Integration of Multimodal Information

NASA Astrophysics Data System (ADS)

Chen, Bin; Kaneko, Masahide

Recently, biologically inspired robots have been developed to acquire the capacity for directing visual attention to salient stimulus generated from the audiovisual environment. On purpose to realize this behavior, a general method is to calculate saliency maps to represent how much the external information attracts the robot's visual attention, where the audiovisual information and robot's motion status should be involved. In this paper, we represent a visual attention model where three modalities, that is, audio information, visual information and robot's motor status are considered, while the previous researches have not considered all of them. Firstly, we introduce a 2-D density map, on which the value denotes how much the robot pays attention to each spatial location. Then we model the attention density using a Bayesian network where the robot's motion statuses are involved. Secondly, the information from both of audio and visual modalities is integrated with the attention density map in integrate-fire neurons. The robot can direct its attention to the locations where the integrate-fire neurons are fired. Finally, the visual attention model is applied to make the robot select the visual information from the environment, and react to the content selected. Experimental results show that it is possible for robots to acquire the visual information related to their behaviors by using the attention model considering motion statuses. The robot can select its behaviors to adapt to the dynamic environment as well as to switch to another task according to the recognition results of visual attention.

On computer vision in wireless sensor networks.

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

Berry, Nina M.; Ko, Teresa H.

Wireless sensor networks allow detailed sensing of otherwise unknown and inaccessible environments. While it would be beneficial to include cameras in a wireless sensor network because images are so rich in information, the power cost of transmitting an image across the wireless network can dramatically shorten the lifespan of the sensor nodes. This paper describe a new paradigm for the incorporation of imaging into wireless networks. Rather than focusing on transmitting images across the network, we show how an image can be processed locally for key features using simple detectors. Contrasted with traditional event detection systems that trigger an imagemore » capture, this enables a new class of sensors which uses a low power imaging sensor to detect a variety of visual cues. Sharing these features among relevant nodes cues specific actions to better provide information about the environment. We report on various existing techniques developed for traditional computer vision research which can aid in this work.« less

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

PubMed

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Development of visual 3D virtual environment for control software

NASA Technical Reports Server (NTRS)

Hirose, Michitaka; Myoi, Takeshi; Amari, Haruo; Inamura, Kohei; Stark, Lawrence

1991-01-01

Virtual environments for software visualization may enable complex programs to be created and maintained. A typical application might be for control of regional electric power systems. As these encompass broader computer networks than ever, construction of such systems becomes very difficult. Conventional text-oriented environments are useful in programming individual processors. However, they are obviously insufficient to program a large and complicated system, that includes large numbers of computers connected to each other; such programming is called 'programming in the large.' As a solution for this problem, the authors are developing a graphic programming environment wherein one can visualize complicated software in virtual 3D world. One of the major features of the environment is the 3D representation of concurrent process. 3D representation is used to supply both network-wide interprocess programming capability (capability for 'programming in the large') and real-time programming capability. The authors' idea is to fuse both the block diagram (which is useful to check relationship among large number of processes or processors) and the time chart (which is useful to check precise timing for synchronization) into a single 3D space. The 3D representation gives us a capability for direct and intuitive planning or understanding of complicated relationship among many concurrent processes. To realize the 3D representation, a technology to enable easy handling of virtual 3D object is a definite necessity. Using a stereo display system and a gesture input device (VPL DataGlove), our prototype of the virtual workstation has been implemented. The workstation can supply the 'sensation' of the virtual 3D space to a programmer. Software for the 3D programming environment is implemented on the workstation. According to preliminary assessments, a 50 percent reduction of programming effort is achieved by using the virtual 3D environment. The authors expect that the 3D environment has considerable potential in the field of software engineering.

Enabling Discoveries in Earth Sciences Through the Geosciences Network (GEON)

NASA Astrophysics Data System (ADS)

Seber, D.; Baru, C.; Memon, A.; Lin, K.; Youn, C.

2005-12-01

Taking advantage of the state-of-the-art information technology resources GEON researchers are building a cyberinfrastructure designed to enable data sharing, semantic data integration, high-end computations and 4D visualization in easy-to-use web-based environments. The GEON Network currently allows users to search and register Earth science resources such as data sets (GIS layers, GMT files, geoTIFF images, ASCII files, relational databases etc), software applications or ontologies. Portal based access mechanisms enable developers to built dynamic user interfaces to conduct advanced processing and modeling efforts across distributed computers and supercomputers. Researchers and educators can access the networked resources through the GEON portal and its portlets that were developed to conduct better and more comprehensive science and educational studies. For example, the SYNSEIS portlet in GEON enables users to access in near-real time seismic waveforms from the IRIS Data Management Center, easily build a 3D geologic model within the area of the seismic station(s) and the epicenter and perform a 3D synthetic seismogram analysis to understand the lithospheric structure and earthquake source parameters for any given earthquake in the US. Similarly, GEON's workbench area enables users to create their own work environment and copy, visualize and analyze any data sets within the network, and create subsets of the data sets for their own purposes. Since all these resources are built as part of a Service-oriented Architecture (SOA), they are also used in other development platforms. One such platform is Kepler Workflow system which can access web service based resources and provides users with graphical programming interfaces to build a model to conduct computations and/or visualization efforts using the networked resources. Developments in the area of semantic integration of the networked datasets continue to advance and prototype studies can be accessed via the GEON portal at www.geongrid.org

Neural nets for aligning optical components in harsh environments: Beam smoothing spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.

1991-01-01

The goal is to develop an approach to automating the alignment and adjustment of optical measurement, visualization, inspection, and control systems. Classical controls, expert systems, and neural networks are three approaches to automating the alignment of an optical system. Neural networks were chosen for this project and the judgements that led to this decision are presented. Neural networks were used to automate the alignment of the ubiquitous laser-beam-smoothing spatial filter. The results and future plans of the project are presented.

Behavior and neural basis of near-optimal visual search

PubMed Central

Ma, Wei Ji; Navalpakkam, Vidhya; Beck, Jeffrey M; van den Berg, Ronald; Pouget, Alexandre

2013-01-01

The ability to search efficiently for a target in a cluttered environment is one of the most remarkable functions of the nervous system. This task is difficult under natural circumstances, as the reliability of sensory information can vary greatly across space and time and is typically a priori unknown to the observer. In contrast, visual-search experiments commonly use stimuli of equal and known reliability. In a target detection task, we randomly assigned high or low reliability to each item on a trial-by-trial basis. An optimal observer would weight the observations by their trial-to-trial reliability and combine them using a specific nonlinear integration rule. We found that humans were near-optimal, regardless of whether distractors were homogeneous or heterogeneous and whether reliability was manipulated through contrast or shape. We present a neural-network implementation of near-optimal visual search based on probabilistic population coding. The network matched human performance. PMID:21552276

Distributed Observer Network

NASA Technical Reports Server (NTRS)

2008-01-01

NASA s advanced visual simulations are essential for analyses associated with life cycle planning, design, training, testing, operations, and evaluation. Kennedy Space Center, in particular, uses simulations for ground services and space exploration planning in an effort to reduce risk and costs while improving safety and performance. However, it has been difficult to circulate and share the results of simulation tools among the field centers, and distance and travel expenses have made timely collaboration even harder. In response, NASA joined with Valador Inc. to develop the Distributed Observer Network (DON), a collaborative environment that leverages game technology to bring 3-D simulations to conventional desktop and laptop computers. DON enables teams of engineers working on design and operations to view and collaborate on 3-D representations of data generated by authoritative tools. DON takes models and telemetry from these sources and, using commercial game engine technology, displays the simulation results in a 3-D visual environment. Multiple widely dispersed users, working individually or in groups, can view and analyze simulation results on desktop and laptop computers in real time.

Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments †

PubMed Central

Hoshiba, Kotaro; Washizaki, Kai; Wakabayashi, Mizuho; Ishiki, Takahiro; Bando, Yoshiaki; Gabriel, Daniel; Nakadai, Kazuhiro; Okuno, Hiroshi G.

2017-01-01

In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators. PMID:29099790

Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments.

PubMed

Hoshiba, Kotaro; Washizaki, Kai; Wakabayashi, Mizuho; Ishiki, Takahiro; Kumon, Makoto; Bando, Yoshiaki; Gabriel, Daniel; Nakadai, Kazuhiro; Okuno, Hiroshi G

2017-11-03

In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.

Envision: An interactive system for the management and visualization of large geophysical data sets

NASA Technical Reports Server (NTRS)

Searight, K. R.; Wojtowicz, D. P.; Walsh, J. E.; Pathi, S.; Bowman, K. P.; Wilhelmson, R. B.

1995-01-01

Envision is a software project at the University of Illinois and Texas A&M, funded by NASA's Applied Information Systems Research Project. It provides researchers in the geophysical sciences convenient ways to manage, browse, and visualize large observed or model data sets. Envision integrates data management, analysis, and visualization of geophysical data in an interactive environment. It employs commonly used standards in data formats, operating systems, networking, and graphics. It also attempts, wherever possible, to integrate with existing scientific visualization and analysis software. Envision has an easy-to-use graphical interface, distributed process components, and an extensible design. It is a public domain package, freely available to the scientific community.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

Using artificial intelligence strategies for process-related automated inspection in the production environment

NASA Astrophysics Data System (ADS)

Anding, K.; Kuritcyn, P.; Garten, D.

2016-11-01

In this paper a new method for the automatic visual inspection of metallic surfaces is proposed by using Convolutional Neural Networks (CNN). The different combinations of network parameters were developed and tested. The obtained results of CNN were analysed and compared with the results of our previous investigations with color and texture features as input parameters for a Support Vector Machine. Advantages and disadvantages of the different classifying methods are explained.

Public Health Analysis Transport Optimization Model v. 1.0

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

Beyeler, Walt; Finley, Patrick; Walser, Alex

PHANTOM models logistic functions of national public health systems. The system enables public health officials to visualize and coordinate options for public health surveillance, diagnosis, response and administration in an integrated analytical environment. Users may simulate and analyze system performance applying scenarios that represent current conditions or future contingencies what-if analyses of potential systemic improvements. Public health networks are visualized as interactive maps, with graphical displays of relevant system performance metrics as calculated by the simulation modeling components.

Learning spatially coherent properties of the visual world in connectionist networks

NASA Astrophysics Data System (ADS)

Becker, Suzanna; Hinton, Geoffrey E.

1991-10-01

In the unsupervised learning paradigm, a network of neuron-like units is presented with an ensemble of input patterns from a structured environment, such as the visual world, and learns to represent the regularities in that input. The major goal in developing unsupervised learning algorithms is to find objective functions that characterize the quality of the network's representation without explicitly specifying the desired outputs of any of the units. The sort of objective functions considered cause a unit to become tuned to spatially coherent features of visual images (such as texture, depth, shading, and surface orientation), by learning to predict the outputs of other units which have spatially adjacent receptive fields. Simulations show that using an information-theoretic algorithm called IMAX, a network can be trained to represent depth by observing random dot stereograms of surfaces with continuously varying disparities. Once a layer of depth-tuned units has developed, subsequent layers are trained to perform surface interpolation of curved surfaces, by learning to predict the depth of one image region based on depth measurements in surrounding regions. An extension of the basic model allows a population of competing neurons to learn a distributed code for disparity, which naturally gives rise to a representation of discontinuities.

Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

EPA Science Inventory

Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

Distributed and collaborative synthetic environments

NASA Technical Reports Server (NTRS)

Bajaj, Chandrajit L.; Bernardini, Fausto

1995-01-01

Fast graphics workstations and increased computing power, together with improved interface technologies, have created new and diverse possibilities for developing and interacting with synthetic environments. A synthetic environment system is generally characterized by input/output devices that constitute the interface between the human senses and the synthetic environment generated by the computer; and a computation system running a real-time simulation of the environment. A basic need of a synthetic environment system is that of giving the user a plausible reproduction of the visual aspect of the objects with which he is interacting. The goal of our Shastra research project is to provide a substrate of geometric data structures and algorithms which allow the distributed construction and modification of the environment, efficient querying of objects attributes, collaborative interaction with the environment, fast computation of collision detection and visibility information for efficient dynamic simulation and real-time scene display. In particular, we address the following issues: (1) A geometric framework for modeling and visualizing synthetic environments and interacting with them. We highlight the functions required for the geometric engine of a synthetic environment system. (2) A distribution and collaboration substrate that supports construction, modification, and interaction with synthetic environments on networked desktop machines.

Risk analysis of urban gas pipeline network based on improved bow-tie model

NASA Astrophysics Data System (ADS)

Hao, M. J.; You, Q. J.; Yue, Z.

2017-11-01

Gas pipeline network is a major hazard source in urban areas. In the event of an accident, there could be grave consequences. In order to understand more clearly the causes and consequences of gas pipeline network accidents, and to develop prevention and mitigation measures, the author puts forward the application of improved bow-tie model to analyze risks of urban gas pipeline network. The improved bow-tie model analyzes accident causes from four aspects: human, materials, environment and management; it also analyzes the consequences from four aspects: casualty, property loss, environment and society. Then it quantifies the causes and consequences. Risk identification, risk analysis, risk assessment, risk control, and risk management will be clearly shown in the model figures. Then it can suggest prevention and mitigation measures accordingly to help reduce accident rate of gas pipeline network. The results show that the whole process of an accident can be visually investigated using the bow-tie model. It can also provide reasons for and predict consequences of an unfortunate event. It is of great significance in order to analyze leakage failure of gas pipeline network.

Louisiana: a model for advancing regional e-Research through cyberinfrastructure.

PubMed

Katz, Daniel S; Allen, Gabrielle; Cortez, Ricardo; Cruz-Neira, Carolina; Gottumukkala, Raju; Greenwood, Zeno D; Guice, Les; Jha, Shantenu; Kolluru, Ramesh; Kosar, Tevfik; Leger, Lonnie; Liu, Honggao; McMahon, Charlie; Nabrzyski, Jarek; Rodriguez-Milla, Bety; Seidel, Ed; Speyrer, Greg; Stubblefield, Michael; Voss, Brian; Whittenburg, Scott

2009-06-28

Louisiana researchers and universities are leading a concentrated, collaborative effort to advance statewide e-Research through a new cyberinfrastructure: computing systems, data storage systems, advanced instruments and data repositories, visualization environments and people, all linked together by software programs and high-performance networks. This effort has led to a set of interlinked projects that have started making a significant difference in the state, and has created an environment that encourages increased collaboration, leading to new e-Research. This paper describes the overall effort, the new projects and environment and the results to date.

CollaborationViz: Interactive Visual Exploration of Biomedical Research Collaboration Networks

PubMed Central

Bian, Jiang; Xie, Mengjun; Hudson, Teresa J.; Eswaran, Hari; Brochhausen, Mathias; Hanna, Josh; Hogan, William R.

2014-01-01

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization. PMID:25405477

Self-Orientation Modulates the Neural Correlates of Global and Local Processing

PubMed Central

Liddell, Belinda J.; Das, Pritha; Battaglini, Eva; Malhi, Gin S.; Felmingham, Kim L.; Whitford, Thomas J.; Bryant, Richard A.

2015-01-01

Differences in self-orientation (or “self-construal”) may affect how the visual environment is attended, but the neural and cultural mechanisms that drive this remain unclear. Behavioral studies have demonstrated that people from Western backgrounds with predominant individualistic values are perceptually biased towards local-level information; whereas people from non-Western backgrounds that support collectivist values are preferentially focused on contextual and global-level information. In this study, we compared two groups differing in predominant individualistic (N = 15) vs collectivistic (N = 15) self-orientation. Participants completed a global/local perceptual conflict task whilst undergoing functional Magnetic Resonance Imaging (fMRI) scanning. When participants high in individualistic values attended to the global level (ignoring the local level), greater activity was observed in the frontoparietal and cingulo-opercular networks that underpin attentional control, compared to the match (congruent) baseline. Participants high in collectivistic values activated similar attentional control networks o only when directly compared with global processing. This suggests that global interference was stronger than local interference in the conflict task in the collectivistic group. Both groups showed increased activity in dorsolateral prefrontal regions involved in resolving perceptual conflict during heightened distractor interference. The findings suggest that self-orientation may play an important role in driving attention networks to facilitate interaction with the visual environment. PMID:26270820

Self-Orientation Modulates the Neural Correlates of Global and Local Processing.

PubMed

Liddell, Belinda J; Das, Pritha; Battaglini, Eva; Malhi, Gin S; Felmingham, Kim L; Whitford, Thomas J; Bryant, Richard A

2015-01-01

Differences in self-orientation (or "self-construal") may affect how the visual environment is attended, but the neural and cultural mechanisms that drive this remain unclear. Behavioral studies have demonstrated that people from Western backgrounds with predominant individualistic values are perceptually biased towards local-level information; whereas people from non-Western backgrounds that support collectivist values are preferentially focused on contextual and global-level information. In this study, we compared two groups differing in predominant individualistic (N = 15) vs collectivistic (N = 15) self-orientation. Participants completed a global/local perceptual conflict task whilst undergoing functional Magnetic Resonance Imaging (fMRI) scanning. When participants high in individualistic values attended to the global level (ignoring the local level), greater activity was observed in the frontoparietal and cingulo-opercular networks that underpin attentional control, compared to the match (congruent) baseline. Participants high in collectivistic values activated similar attentional control networks o only when directly compared with global processing. This suggests that global interference was stronger than local interference in the conflict task in the collectivistic group. Both groups showed increased activity in dorsolateral prefrontal regions involved in resolving perceptual conflict during heightened distractor interference. The findings suggest that self-orientation may play an important role in driving attention networks to facilitate interaction with the visual environment.

A robust low-rate coding scheme for packet video

NASA Technical Reports Server (NTRS)

Chen, Y. C.; Sayood, Khalid; Nelson, D. J.; Arikan, E. (Editor)

1991-01-01

Due to the rapidly evolving field of image processing and networking, video information promises to be an important part of telecommunication systems. Although up to now video transmission has been transported mainly over circuit-switched networks, it is likely that packet-switched networks will dominate the communication world in the near future. Asynchronous transfer mode (ATM) techniques in broadband-ISDN can provide a flexible, independent and high performance environment for video communication. For this paper, the network simulator was used only as a channel in this simulation. Mixture blocking coding with progressive transmission (MBCPT) has been investigated for use over packet networks and has been found to provide high compression rate with good visual performance, robustness to packet loss, tractable integration with network mechanics and simplicity in parallel implementation.

Automated visual inspection system based on HAVNET architecture

NASA Astrophysics Data System (ADS)

Burkett, K.; Ozbayoglu, Murat A.; Dagli, Cihan H.

1994-10-01

In this study, the HAusdorff-Voronoi NETwork (HAVNET) developed at the UMR Smart Engineering Systems Lab is tested in the recognition of mounted circuit components commonly used in printed circuit board assembly systems. The automated visual inspection system used consists of a CCD camera, a neural network based image processing software and a data acquisition card connected to a PC. The experiments are run in the Smart Engineering Systems Lab in the Engineering Management Dept. of the University of Missouri-Rolla. The performance analysis shows that the vision system is capable of recognizing different components under uncontrolled lighting conditions without being effected by rotation or scale differences. The results obtained are promising and the system can be used in real manufacturing environments. Currently the system is being customized for a specific manufacturing application.

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

PubMed Central

Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

2012-01-01

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. PMID:23193391

A spiking neural network based cortex-like mechanism and application to facial expression recognition.

PubMed

Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

2012-01-01

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

PubMed

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

A Computational framework for telemedicine.

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

Foster, I.; von Laszewski, G.; Thiruvathukal, G. K.

1998-07-01

Emerging telemedicine applications require the ability to exploit diverse and geographically distributed resources. Highspeed networks are used to integrate advanced visualization devices, sophisticated instruments, large databases, archival storage devices, PCs, workstations, and supercomputers. This form of telemedical environment is similar to networked virtual supercomputers, also known as metacomputers. Metacomputers are already being used in many scientific application areas. In this article, we analyze requirements necessary for a telemedical computing infrastructure and compare them with requirements found in a typical metacomputing environment. We will show that metacomputing environments can be used to enable a more powerful and unified computational infrastructure formore » telemedicine. The Globus metacomputing toolkit can provide the necessary low level mechanisms to enable a large scale telemedical infrastructure. The Globus toolkit components are designed in a modular fashion and can be extended to support the specific requirements for telemedicine.« less

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

PubMed Central

Ruiz-Rizzo, Adriana L.; Neitzel, Julia; Müller, Hermann J.; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity. PMID:29662444

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks.

PubMed

Ruiz-Rizzo, Adriana L; Neitzel, Julia; Müller, Hermann J; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's "theory of visual attention" (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity.

Demonstrating a Web-Design Technique in a Distance-Learning Environment

ERIC Educational Resources Information Center

Zdenek, Sean

2004-01-01

Objective: To lead a brief training session over a distance-learning network. Type of speech: Informative. Point value: 20% of course grade. Requirements: (a) References: Not specified; (b) Length: 15 minutes; (c) Visual aid: Yes; (d) Outline: No; (e) Prerequisite reading: Chapters 12-16, 18 (Bailey, 2002); (f) Additional requirements: None. This…

Modeling Computer Communication Networks in a Realistic 3D Environment

DTIC Science & Technology

2010-03-01

50 2. Comparison of visualization tools . . . . . . . . . . . . . . . . . 75 xi List of Abbreviations Abbreviation Page 2D two-dimensional...International Conference on, 77 –84, 2001. 20. National Defense and the Canadian Forces. “Joint Fires Support”. URL http: //www.cfd-cdf.forces.gc.ca/sites/ page ...Table of Contents Page Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Acknowledgements

Visual analysis and exploration of complex corporate shareholder networks

NASA Astrophysics Data System (ADS)

Tekušová, Tatiana; Kohlhammer, Jörn

2008-01-01

The analysis of large corporate shareholder network structures is an important task in corporate governance, in financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of shareholder relationships, showing its usefulness for effective visual analysis.

Sonification of network traffic flow for monitoring and situational awareness

PubMed Central

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators’ situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen. PMID:29672543

Sonification of network traffic flow for monitoring and situational awareness.

PubMed

Debashi, Mohamed; Vickers, Paul

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators' situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen.

Visual sensory networks and effective information transfer in animal groups.

PubMed

Strandburg-Peshkin, Ariana; Twomey, Colin R; Bode, Nikolai W F; Kao, Albert B; Katz, Yael; Ioannou, Christos C; Rosenthal, Sara B; Torney, Colin J; Wu, Hai Shan; Levin, Simon A; Couzin, Iain D

2013-09-09

Social transmission of information is vital for many group-living animals, allowing coordination of motion and effective response to complex environments. Revealing the interaction networks underlying information flow within these groups is a central challenge. Previous work has modeled interactions between individuals based directly on their relative spatial positions: each individual is considered to interact with all neighbors within a fixed distance (metric range), a fixed number of nearest neighbors (topological range), a 'shell' of near neighbors (Voronoi range), or some combination (Figure 1A). However, conclusive evidence to support these assumptions is lacking. Here, we employ a novel approach that considers individual movement decisions to be based explicitly on the sensory information available to the organism. In other words, we consider that while spatial relations do inform interactions between individuals, they do so indirectly, through individuals' detection of sensory cues. We reconstruct computationally the visual field of each individual throughout experiments designed to investigate information propagation within fish schools (golden shiners, Notemigonus crysoleucas). Explicitly considering visual sensing allows us to more accurately predict the propagation of behavioral change in these groups during leadership events. Furthermore, we find that structural properties of visual interaction networks differ markedly from those of metric and topological counterparts, suggesting that previous assumptions may not appropriately reflect information flow in animal groups. Copyright © 2013 Elsevier Ltd. All rights reserved.

An Intelligent Pinger Network for Solid Glacier Environments

NASA Astrophysics Data System (ADS)

Schönitz, S.; Reuter, S.; Henke, C.; Jeschke, S.; Ewert, D.; Eliseev, D.; Heinen, D.; Linder, P.; Scholz, F.; Weinstock, L.; Wickmann, S.; Wiebusch, C.; Zierke, S.

2016-12-01

This talk presents a novel approach for an intelligent, agent-based pinger network in an extraterrestrial glacier environment. Because of recent findings of the Cassini spacecraft, a mission to Saturn's moon Enceladus is planned in order search for extraterrestrial life within the ocean beneath Enceladus' ice crust. Therefore, a maneuverable melting probe, the EnEx probe, was developed to melt into Enceladus' ice and take liquid samples from water-filled crevasses. Hence, the probe collecting the samples has to be able to navigate in ice which is a hard problem, because neither visual nor gravitational methods can be used. To enhance the navigability of the probe, a network of autonomous pinger units (APU) is in development that is able to extract a map of the ice environment via ultrasonic soundwaves. A network of these APUs will be deployed on the surface of Enceladus, melt into the ice and form a network to help guide the probe safely to its destination. The APU network is able to form itself fully autonomously and to compensate system failures of individual APUs. The agents controlling the single APU are realized by rule-based expert systems implemented in CLIPS. The rule-based expert system evaluates available information of the environment, decides for actions to take to achieve the desired goal (e.g. a specific network topology), and executes and monitors such actions. In general, it encodes certain situations that are evaluated whenever an APU is currently idle, and then decides for a next action to take. It bases this decision on its internal world model that is shared with the other APUs. The optimal network topology that defines each agents position is iteratively determined by mixed-integer nonlinear programming. Extensive simulations studies show that the proposed agent design enables the APUs to form a robust network topology that is suited to create a reliable 3D map of the ice environment.

Combining Multiple Forms Of Visual Information To Specify Contact Relations In Spatial Layout

NASA Astrophysics Data System (ADS)

Sedgwick, Hal A.

1990-03-01

An expert system, called Layout2, has been described, which models a subset of available visual information for spatial layout. The system is used to examine detailed interactions between multiple, partially redundant forms of information in an environment-centered geometrical model of an environment obeying certain rather general constraints. This paper discusses the extension of Layout2 to include generalized contact relations between surfaces. In an environment-centered model, the representation of viewer-centered distance is replaced by the representation of environmental location. This location information is propagated through the representation of the environment by a network of contact relations between contiguous surfaces. Perspective information interacts with other forms of information to specify these contact relations. The experimental study of human perception of contact relations in extended spatial layouts is also discussed. Differences between human results and Layout2 results reveal limitations in the human ability to register available information; they also point to the existence of certain forms of information not yet formalized in Layout2.

A remote instruction system empowered by tightly shared haptic sensation

NASA Astrophysics Data System (ADS)

Nishino, Hiroaki; Yamaguchi, Akira; Kagawa, Tsuneo; Utsumiya, Kouichi

2007-09-01

We present a system to realize an on-line instruction environment among physically separated participants based on a multi-modal communication strategy. In addition to visual and acoustic information, commonly used communication modalities in network environments, our system provides a haptic channel to intuitively conveying partners' sense of touch. The human touch sensation, however, is very sensitive for delays and jitters in the networked virtual reality (NVR) systems. Therefore, a method to compensate for such negative factors needs to be provided. We show an NVR architecture to implement a basic framework that can be shared by various applications and effectively deals with the problems. We take a hybrid approach to implement both data consistency by client-server and scalability by peer-to-peer models. As an application system built on the proposed architecture, a remote instruction system targeted at teaching handwritten characters and line patterns on a Korea-Japan high-speed research network also is mentioned.

Wavelet and Multiresolution Analysis for Finite Element Networking Paradigms

NASA Technical Reports Server (NTRS)

Kurdila, Andrew J.; Sharpley, Robert C.

1999-01-01

This paper presents a final report on Wavelet and Multiresolution Analysis for Finite Element Networking Paradigms. The focus of this research is to derive and implement: 1) Wavelet based methodologies for the compression, transmission, decoding, and visualization of three dimensional finite element geometry and simulation data in a network environment; 2) methodologies for interactive algorithm monitoring and tracking in computational mechanics; and 3) Methodologies for interactive algorithm steering for the acceleration of large scale finite element simulations. Also included in this report are appendices describing the derivation of wavelet based Particle Image Velocity algorithms and reduced order input-output models for nonlinear systems by utilizing wavelet approximations.

Demonstration of NICT Space Weather Cloud --Integration of Supercomputer into Analysis and Visualization Environment--

NASA Astrophysics Data System (ADS)

Watari, S.; Morikawa, Y.; Yamamoto, K.; Inoue, S.; Tsubouchi, K.; Fukazawa, K.; Kimura, E.; Tatebe, O.; Kato, H.; Shimojo, S.; Murata, K. T.

2010-12-01

In the Solar-Terrestrial Physics (STP) field, spatio-temporal resolution of computer simulations is getting higher and higher because of tremendous advancement of supercomputers. A more advanced technology is Grid Computing that integrates distributed computational resources to provide scalable computing resources. In the simulation research, it is effective that a researcher oneself designs his physical model, performs calculations with a supercomputer, and analyzes and visualizes for consideration by a familiar method. A supercomputer is far from an analysis and visualization environment. In general, a researcher analyzes and visualizes in the workstation (WS) managed at hand because the installation and the operation of software in the WS are easy. Therefore, it is necessary to copy the data from the supercomputer to WS manually. Time necessary for the data transfer through long delay network disturbs high-accuracy simulations actually. In terms of usefulness, integrating a supercomputer and an analysis and visualization environment seamlessly with a researcher's familiar method is important. NICT has been developing a cloud computing environment (NICT Space Weather Cloud). In the NICT Space Weather Cloud, disk servers are located near its supercomputer and WSs for data analysis and visualization. They are connected to JGN2plus that is high-speed network for research and development. Distributed virtual high-capacity storage is also constructed by Grid Datafarm (Gfarm v2). Huge-size data output from the supercomputer is transferred to the virtual storage through JGN2plus. A researcher can concentrate on the research by a familiar method without regard to distance between a supercomputer and an analysis and visualization environment. Now, total 16 disk servers are setup in NICT headquarters (at Koganei, Tokyo), JGN2plus NOC (at Otemachi, Tokyo), Okinawa Subtropical Environment Remote-Sensing Center, and Cybermedia Center, Osaka University. They are connected on JGN2plus, and they constitute 1PB (physical size) virtual storage by Gfarm v2. These disk servers are connected with supercomputers of NICT and Osaka University. A system that data output from the supercomputers are automatically transferred to the virtual storage had been built up. Transfer rate is about 50 GB/hrs by actual measurement. It is estimated that the performance is reasonable for a certain simulation and analysis for reconstruction of coronal magnetic field. This research is assumed an experiment of the system, and the verification of practicality is advanced at the same time. Herein we introduce an overview of the space weather cloud system so far we have developed. We also demonstrate several scientific results using the space weather cloud system. We also introduce several web applications of the cloud as a service of the space weather cloud, which is named as "e-SpaceWeather" (e-SW). The e-SW provides with a variety of space weather online services from many aspects.

Concepts to Support HRP Integration Using Publications and Modeling

NASA Technical Reports Server (NTRS)

Mindock, J.; Lumpkins, S.; Shelhamer, M.

2014-01-01

Initial efforts are underway to enhance the Human Research Program (HRP)'s identification and support of potential cross-disciplinary scientific collaborations. To increase the emphasis on integration in HRP's science portfolio management, concepts are being explored through the development of a set of tools. These tools are intended to enable modeling, analysis, and visualization of the state of the human system in the spaceflight environment; HRP's current understanding of that state with an indication of uncertainties; and how that state changes due to HRP programmatic progress and design reference mission definitions. In this talk, we will discuss proof-of-concept work performed using a subset of publications captured in the HRP publications database. The publications were tagged in the database with words representing factors influencing health and performance in spaceflight, as well as with words representing the risks HRP research is reducing. Analysis was performed on the publication tag data to identify relationships between factors and between risks. Network representations were then created as one type of visualization of these relationships. This enables future analyses of the structure of the networks based on results from network theory. Such analyses can provide insights into HRP's current human system knowledge state as informed by the publication data. The network structure analyses can also elucidate potential improvements by identifying network connections to establish or strengthen for maximized information flow. The relationships identified in the publication data were subsequently used as inputs to a model captured in the Systems Modeling Language (SysML), which functions as a repository for relationship information to be gleaned from multiple sources. Example network visualization outputs from a simple SysML model were then also created to compare to the visualizations based on the publication data only. We will also discuss ideas for building upon this proof-of-concept work to further support an integrated approach to human spaceflight risk reduction.

Availability Issues in Wireless Visual Sensor Networks

PubMed Central

Costa, Daniel G.; Silva, Ivanovitch; Guedes, Luiz Affonso; Vasques, Francisco; Portugal, Paulo

2014-01-01

Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks. PMID:24526301

Research on the framework and key technologies of panoramic visualization for smart distribution network

NASA Astrophysics Data System (ADS)

Du, Jian; Sheng, Wanxing; Lin, Tao; Lv, Guangxian

2018-05-01

Nowadays, the smart distribution network has made tremendous progress, and the business visualization becomes even more significant and indispensable. Based on the summarization of traditional visualization technologies and demands of smart distribution network, a panoramic visualization application is proposed in this paper. The overall architecture, integrated architecture and service architecture of panoramic visualization application is firstly presented. Then, the architecture design and main functions of panoramic visualization system are elaborated in depth. In addition, the key technologies related to the application is discussed briefly. At last, two typical visualization scenarios in smart distribution network, which are risk warning and fault self-healing, proves that the panoramic visualization application is valuable for the operation and maintenance of the distribution network.

Louisiana: a model for advancing regional e-Research through cyberinfrastructure

PubMed Central

Katz, Daniel S.; Allen, Gabrielle; Cortez, Ricardo; Cruz-Neira, Carolina; Gottumukkala, Raju; Greenwood, Zeno D.; Guice, Les; Jha, Shantenu; Kolluru, Ramesh; Kosar, Tevfik; Leger, Lonnie; Liu, Honggao; McMahon, Charlie; Nabrzyski, Jarek; Rodriguez-Milla, Bety; Seidel, Ed; Speyrer, Greg; Stubblefield, Michael; Voss, Brian; Whittenburg, Scott

2009-01-01

Louisiana researchers and universities are leading a concentrated, collaborative effort to advance statewide e-Research through a new cyberinfrastructure: computing systems, data storage systems, advanced instruments and data repositories, visualization environments and people, all linked together by software programs and high-performance networks. This effort has led to a set of interlinked projects that have started making a significant difference in the state, and has created an environment that encourages increased collaboration, leading to new e-Research. This paper describes the overall effort, the new projects and environment and the results to date. PMID:19451102

Adapting Advanced Information Technology Network Training for Adults with Visual Impairments

ERIC Educational Resources Information Center

Armstrong, Helen L.; Murray, Iain D.

2010-01-01

This article describes an accessible e-learning environment that was designed to deliver advanced IT skills to legally blind students in preparation for employment. The aim was to convert industry-standard training materials in print into accessible formats and to deliver the learning materials in ways that are more suited to adult students with…

A GPU-accelerated cortical neural network model for visually guided robot navigation.

PubMed

Beyeler, Michael; Oros, Nicolas; Dutt, Nikil; Krichmar, Jeffrey L

2015-12-01

Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Fuzzy-Based Approach for Sensing, Coding and Transmission Configuration of Visual Sensors in Smart City Applications

PubMed Central

Costa, Daniel G.; Collotta, Mario; Pau, Giovanni; Duran-Faundez, Cristian

2017-01-01

The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field. PMID:28067777

A Fuzzy-Based Approach for Sensing, Coding and Transmission Configuration of Visual Sensors in Smart City Applications.

PubMed

Costa, Daniel G; Collotta, Mario; Pau, Giovanni; Duran-Faundez, Cristian

2017-01-05

The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field.

CytoscapeRPC: a plugin to create, modify and query Cytoscape networks from scripting languages.

PubMed

Bot, Jan J; Reinders, Marcel J T

2011-09-01

CytoscapeRPC is a plugin for Cytoscape which allows users to create, query and modify Cytoscape networks from any programming language which supports XML-RPC. This enables them to access Cytoscape functionality and visualize their data interactively without leaving the programming environment with which they are familiar. Install through the Cytoscape plugin manager or visit the web page: http://wiki.nbic.nl/index.php/CytoscapeRPC for the user tutorial and download. j.j.bot@tudelft.nl; j.j.bot@tudelft.nl.

Global Precipitation Mission Visualization Tool

NASA Technical Reports Server (NTRS)

Schwaller, Mathew

2011-01-01

The Global Precipitation Mission (GPM) software provides graphic visualization tools that enable easy comparison of ground- and space-based radar observations. It was initially designed to compare ground radar reflectivity from operational, ground-based, S- and C-band meteorological radars with comparable measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite's precipitation radar instrument. This design is also applicable to other groundbased and space-based radars, and allows both ground- and space-based radar data to be compared for validation purposes. The tool creates an operational system that routinely performs several steps. It ingests satellite radar data (precipitation radar data from TRMM) and groundbased meteorological radar data from a number of sources. Principally, the ground radar data comes from national networks of weather radars (see figure). The data ingested by the visualization tool must conform to the data formats used in GPM Validation Network Geometry-matched data product generation. The software also performs match-ups of the radar volume data for the ground- and space-based data, as well as statistical and graphical analysis (including two-dimensional graphical displays) on the match-up data. The visualization tool software is written in IDL, and can be operated either in the IDL development environment or as a stand-alone executable function.

Virtual Vision

NASA Astrophysics Data System (ADS)

Terzopoulos, Demetri; Qureshi, Faisal Z.

Computer vision and sensor networks researchers are increasingly motivated to investigate complex multi-camera sensing and control issues that arise in the automatic visual surveillance of extensive, highly populated public spaces such as airports and train stations. However, they often encounter serious impediments to deploying and experimenting with large-scale physical camera networks in such real-world environments. We propose an alternative approach called "Virtual Vision", which facilitates this type of research through the virtual reality simulation of populated urban spaces, camera sensor networks, and computer vision on commodity computers. We demonstrate the usefulness of our approach by developing two highly automated surveillance systems comprising passive and active pan/tilt/zoom cameras that are deployed in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. The easily reconfigurable virtual cameras distributed in this environment generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring public spaces. The novel multi-camera control strategies that we describe enable the cameras to collaborate in persistently observing pedestrians of interest and in acquiring close-up videos of pedestrians in designated areas.

Contributions of local speech encoding and functional connectivity to audio-visual speech perception

PubMed Central

Giordano, Bruno L; Ince, Robin A A; Gross, Joachim; Schyns, Philippe G; Panzeri, Stefano; Kayser, Christoph

2017-01-01

Seeing a speaker’s face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by temporally entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioral benefit arising from seeing the speaker’s face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-frontal interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments. DOI: http://dx.doi.org/10.7554/eLife.24763.001 PMID:28590903

Visual Computing Environment

NASA Technical Reports Server (NTRS)

Lawrence, Charles; Putt, Charles W.

1997-01-01

The Visual Computing Environment (VCE) is a NASA Lewis Research Center project to develop a framework for intercomponent and multidisciplinary computational simulations. Many current engineering analysis codes simulate various aspects of aircraft engine operation. For example, existing computational fluid dynamics (CFD) codes can model the airflow through individual engine components such as the inlet, compressor, combustor, turbine, or nozzle. Currently, these codes are run in isolation, making intercomponent and complete system simulations very difficult to perform. In addition, management and utilization of these engineering codes for coupled component simulations is a complex, laborious task, requiring substantial experience and effort. To facilitate multicomponent aircraft engine analysis, the CFD Research Corporation (CFDRC) is developing the VCE system. This system, which is part of NASA's Numerical Propulsion Simulation System (NPSS) program, can couple various engineering disciplines, such as CFD, structural analysis, and thermal analysis. The objectives of VCE are to (1) develop a visual computing environment for controlling the execution of individual simulation codes that are running in parallel and are distributed on heterogeneous host machines in a networked environment, (2) develop numerical coupling algorithms for interchanging boundary conditions between codes with arbitrary grid matching and different levels of dimensionality, (3) provide a graphical interface for simulation setup and control, and (4) provide tools for online visualization and plotting. VCE was designed to provide a distributed, object-oriented environment. Mechanisms are provided for creating and manipulating objects, such as grids, boundary conditions, and solution data. This environment includes parallel virtual machine (PVM) for distributed processing. Users can interactively select and couple any set of codes that have been modified to run in a parallel distributed fashion on a cluster of heterogeneous workstations. A scripting facility allows users to dictate the sequence of events that make up the particular simulation.

A topology visualization early warning distribution algorithm for large-scale network security incidents.

PubMed

He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

2013-01-01

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

PubMed Central

Augustyniak, Piotr; Smoleń, Magdalena; Mikrut, Zbigniew; Kańtoch, Eliasz

2014-01-01

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system. PMID:24787640

Person perception involves functional integration between the extrastriate body area and temporal pole.

PubMed

Greven, Inez M; Ramsey, Richard

2017-02-01

The majority of human neuroscience research has focussed on understanding functional organisation within segregated patches of cortex. The ventral visual stream has been associated with the detection of physical features such as faces and body parts, whereas the theory-of-mind network has been associated with making inferences about mental states and underlying character, such as whether someone is friendly, selfish, or generous. To date, however, it is largely unknown how such distinct processing components integrate neural signals. Using functional magnetic resonance imaging and connectivity analyses, we investigated the contribution of functional integration to social perception. During scanning, participants observed bodies that had previously been associated with trait-based or neutral information. Additionally, we independently localised the body perception and theory-of-mind networks. We demonstrate that when observing someone who cues the recall of stored social knowledge compared to non-social knowledge, a node in the ventral visual stream (extrastriate body area) shows greater coupling with part of the theory-of-mind network (temporal pole). These results show that functional connections provide an interface between perceptual and inferential processing components, thus providing neurobiological evidence that supports the view that understanding the visual environment involves interplay between conceptual knowledge and perceptual processing. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Visualization techniques for malware behavior analysis

NASA Astrophysics Data System (ADS)

Grégio, André R. A.; Santos, Rafael D. C.

2011-06-01

Malware spread via Internet is a great security threat, so studying their behavior is important to identify and classify them. Using SSDT hooking we can obtain malware behavior by running it in a controlled environment and capturing interactions with the target operating system regarding file, process, registry, network and mutex activities. This generates a chain of events that can be used to compare them with other known malware. In this paper we present a simple approach to convert malware behavior into activity graphs and show some visualization techniques that can be used to analyze malware behavior, individually or grouped.

Real-time network security situation visualization and threat assessment based on semi-Markov process

NASA Astrophysics Data System (ADS)

Chen, Junhua

2013-03-01

To cope with a large amount of data in current sensed environments, decision aid tools should provide their understanding of situations in a time-efficient manner, so there is an increasing need for real-time network security situation awareness and threat assessment. In this study, the state transition model of vulnerability in the network based on semi-Markov process is proposed at first. Once events are triggered by an attacker's action or system response, the current states of the vulnerabilities are known. Then we calculate the transition probabilities of the vulnerability from the current state to security failure state. Furthermore in order to improve accuracy of our algorithms, we adjust the probabilities that they exploit the vulnerability according to the attacker's skill level. In the light of the preconditions and post-conditions of vulnerabilities in the network, attack graph is built to visualize security situation in real time. Subsequently, we predict attack path, recognize attack intention and estimate the impact through analysis of attack graph. These help administrators to insight into intrusion steps, determine security state and assess threat. Finally testing in a network shows that this method is reasonable and feasible, and can undertake tremendous analysis task to facilitate administrators' work.

Motion-related resource allocation in dynamic wireless visual sensor network environments.

PubMed

Katsenou, Angeliki V; Kondi, Lisimachos P; Parsopoulos, Konstantinos E

2014-01-01

This paper investigates quality-driven cross-layer optimization for resource allocation in direct sequence code division multiple access wireless visual sensor networks. We consider a single-hop network topology, where each sensor transmits directly to a centralized control unit (CCU) that manages the available network resources. Our aim is to enable the CCU to jointly allocate the transmission power and source-channel coding rates for each node, under four different quality-driven criteria that take into consideration the varying motion characteristics of each recorded video. For this purpose, we studied two approaches with a different tradeoff of quality and complexity. The first one allocates the resources individually for each sensor, whereas the second clusters them according to the recorded level of motion. In order to address the dynamic nature of the recorded scenery and re-allocate the resources whenever it is dictated by the changes in the amount of motion in the scenery, we propose a mechanism based on the particle swarm optimization algorithm, combined with two restarting schemes that either exploit the previously determined resource allocation or conduct a rough estimation of it. Experimental simulations demonstrate the efficiency of the proposed approaches.

Visualizing protein partnerships in living cells and organisms.

PubMed

Lowder, Melissa A; Appelbaum, Jacob S; Hobert, Elissa M; Schepartz, Alanna

2011-12-01

In recent years, scientists have expanded their focus from cataloging genes to characterizing the multiple states of their translated products. One anticipated result is a dynamic map of the protein association networks and activities that occur within the cellular environment. While in vitro-derived network maps can illustrate which of a multitude of possible protein-protein associations could exist, they supply a falsely static picture lacking the subtleties of subcellular location (where) or cellular state (when). Generating protein association network maps that are informed by both subcellular location and cell state requires novel approaches that accurately characterize the state of protein associations in living cells and provide precise spatiotemporal resolution. In this review, we highlight recent advances in visualizing protein associations and networks under increasingly native conditions. These advances include second generation protein complementation assays (PCAs), chemical and photo-crosslinking techniques, and proximity-induced ligation approaches. The advances described focus on background reduction, signal optimization, rapid and reversible reporter assembly, decreased cytotoxicity, and minimal functional perturbation. Key breakthroughs have addressed many challenges and should expand the repertoire of tools useful for generating maps of protein interactions resolved in both time and space. Copyright © 2011 Elsevier Ltd. All rights reserved.

Occam's razor: supporting visual query expression for content-based image queries

NASA Astrophysics Data System (ADS)

Venters, Colin C.; Hartley, Richard J.; Hewitt, William T.

2005-01-01

This paper reports the results of a usability experiment that investigated visual query formulation on three dimensions: effectiveness, efficiency, and user satisfaction. Twenty eight evaluation sessions were conducted in order to assess the extent to which query by visual example supports visual query formulation in a content-based image retrieval environment. In order to provide a context and focus for the investigation, the study was segmented by image type, user group, and use function. The image type consisted of a set of abstract geometric device marks supplied by the UK Trademark Registry. Users were selected from the 14 UK Patent Information Network offices. The use function was limited to the retrieval of images by shape similarity. Two client interfaces were developed for comparison purposes: Trademark Image Browser Engine (TRIBE) and Shape Query Image Retrieval Systems Engine (SQUIRE).

GeoBuilder: a geometric algorithm visualization and debugging system for 2D and 3D geometric computing.

PubMed

Wei, Jyh-Da; Tsai, Ming-Hung; Lee, Gen-Cher; Huang, Jeng-Hung; Lee, Der-Tsai

2009-01-01

Algorithm visualization is a unique research topic that integrates engineering skills such as computer graphics, system programming, database management, computer networks, etc., to facilitate algorithmic researchers in testing their ideas, demonstrating new findings, and teaching algorithm design in the classroom. Within the broad applications of algorithm visualization, there still remain performance issues that deserve further research, e.g., system portability, collaboration capability, and animation effect in 3D environments. Using modern technologies of Java programming, we develop an algorithm visualization and debugging system, dubbed GeoBuilder, for geometric computing. The GeoBuilder system features Java's promising portability, engagement of collaboration in algorithm development, and automatic camera positioning for tracking 3D geometric objects. In this paper, we describe the design of the GeoBuilder system and demonstrate its applications.

Occam"s razor: supporting visual query expression for content-based image queries

NASA Astrophysics Data System (ADS)

Venters, Colin C.; Hartley, Richard J.; Hewitt, William T.

2004-12-01

This paper reports the results of a usability experiment that investigated visual query formulation on three dimensions: effectiveness, efficiency, and user satisfaction. Twenty eight evaluation sessions were conducted in order to assess the extent to which query by visual example supports visual query formulation in a content-based image retrieval environment. In order to provide a context and focus for the investigation, the study was segmented by image type, user group, and use function. The image type consisted of a set of abstract geometric device marks supplied by the UK Trademark Registry. Users were selected from the 14 UK Patent Information Network offices. The use function was limited to the retrieval of images by shape similarity. Two client interfaces were developed for comparison purposes: Trademark Image Browser Engine (TRIBE) and Shape Query Image Retrieval Systems Engine (SQUIRE).

Top-down estimates of biomass burning emissions of black carbon in the western United States

Treesearch

Y. H. Mao; Q. B. Li; D. Chen; L. Zhang; W. -M. Hao; K.-N. Liou

2014-01-01

We estimate biomass burning and anthropogenic emissions of black carbon (BC) in the western US for May-October 2006 by inverting surface BC concentrations from the Interagency Monitoring of PROtected Visual Environment (IMPROVE) network using a global chemical transport model. We first use active fire counts from the Moderate Resolution Imaging Spectroradiometer (MODIS...

GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research

PubMed Central

Yeung, Ka Yee

2016-01-01

Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface. PMID:27045593

GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research.

PubMed

Hung, Ling-Hong; Kristiyanto, Daniel; Lee, Sung Bong; Yeung, Ka Yee

2016-01-01

Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

PubMed

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

Distributed Observer Network

NASA Technical Reports Server (NTRS)

Conroy, Michael; Mazzone, Rebecca; Little, William; Elfrey, Priscilla; Mann, David; Mabie, Kevin; Cuddy, Thomas; Loundermon, Mario; Spiker, Stephen; McArthur, Frank;

Reduction of Complexity: An Aspect of Network Visualization

DTIC Science & Technology

2006-12-01

research is to identify strategies for the visualization of network information. Distinction can be made between visual communication and visual...exploration (MacEachern 1994). Visual communication deals with how to visualize results of different kinds of analysis, i.e., visualization in the case

Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception.

PubMed

Dima, Diana C; Perry, Gavin; Singh, Krish D

2018-06-11

In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at ∼180 ms and are linked to spatial frequency processing. Furthermore, dorsal and ventral stream areas reveal temporally and spatially overlapping representations of low and high-level layer activations extracted from a feedforward neural network. Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential in human visual perception. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Neural dynamics for landmark orientation and angular path integration

PubMed Central

Seelig, Johannes D.; Jayaraman, Vivek

2015-01-01

Summary Many animals navigate using a combination of visual landmarks and path integration. In mammalian brains, head direction cells integrate these two streams of information by representing an animal's heading relative to landmarks, yet maintaining their directional tuning in darkness based on self-motion cues. Here we use two-photon calcium imaging in head-fixed flies walking on a ball in a virtual reality arena to demonstrate that landmark-based orientation and angular path integration are combined in the population responses of neurons whose dendrites tile the ellipsoid body — a toroidal structure in the center of the fly brain. The population encodes the fly's azimuth relative to its environment, tracking visual landmarks when available and relying on self-motion cues in darkness. When both visual and self-motion cues are absent, a representation of the animal's orientation is maintained in this network through persistent activity — a potential substrate for short-term memory. Several features of the population dynamics of these neurons and their circular anatomical arrangement are suggestive of ring attractors — network structures proposed to support the function of navigational brain circuits. PMID:25971509

Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections

PubMed Central

Azpilicueta, Leyre; López-Iturri, Peio; Aguirre, Erik; Martínez, Carlos; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

2016-01-01

With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs) deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network. PMID:27455270

Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections.

PubMed

Azpilicueta, Leyre; López-Iturri, Peio; Aguirre, Erik; Martínez, Carlos; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

2016-07-22

With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs) deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network.

An interactive web-based system using cloud for large-scale visual analytics

NASA Astrophysics Data System (ADS)

Kaseb, Ahmed S.; Berry, Everett; Rozolis, Erik; McNulty, Kyle; Bontrager, Seth; Koh, Youngsol; Lu, Yung-Hsiang; Delp, Edward J.

2015-03-01

Network cameras have been growing rapidly in recent years. Thousands of public network cameras provide tremendous amount of visual information about the environment. There is a need to analyze this valuable information for a better understanding of the world around us. This paper presents an interactive web-based system that enables users to execute image analysis and computer vision techniques on a large scale to analyze the data from more than 65,000 worldwide cameras. This paper focuses on how to use both the system's website and Application Programming Interface (API). Given a computer program that analyzes a single frame, the user needs to make only slight changes to the existing program and choose the cameras to analyze. The system handles the heterogeneity of the geographically distributed cameras, e.g. different brands, resolutions. The system allocates and manages Amazon EC2 and Windows Azure cloud resources to meet the analysis requirements.

A proto-architecture for innate directionally selective visual maps.

PubMed

Adams, Samantha V; Harris, Chris M

2014-01-01

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.

Adaptation disrupts motion integration in the primate dorsal stream

PubMed Central

Patterson, Carlyn A.; Wissig, Stephanie C.; Kohn, Adam

2014-01-01

Summary Sensory systems adjust continuously to the environment. The effects of recent sensory experience—or adaptation—are typically assayed by recording in a relevant subcortical or cortical network. However, adaptation effects cannot be localized to a single, local network. Adjustments in one circuit or area will alter the input provided to others, with unclear consequences for computations implemented in the downstream circuit. Here we show that prolonged adaptation with drifting gratings, which alters responses in the early visual system, impedes the ability of area MT neurons to integrate motion signals in plaid stimuli. Perceptual experiments reveal a corresponding loss of plaid coherence. A simple computational model shows how the altered representation of motion signals in early cortex can derail integration in MT. Our results suggest that the effects of adaptation cascade through the visual system, derailing the downstream representation of distinct stimulus attributes. PMID:24507198

ReNE: A Cytoscape Plugin for Regulatory Network Enhancement

PubMed Central

Politano, Gianfranco; Benso, Alfredo; Savino, Alessandro; Di Carlo, Stefano

2014-01-01

One of the biggest challenges in the study of biological regulatory mechanisms is the integration, americanmodeling, and analysis of the complex interactions which take place in biological networks. Despite post transcriptional regulatory elements (i.e., miRNAs) are widely investigated in current research, their usage and visualization in biological networks is very limited. Regulatory networks are commonly limited to gene entities. To integrate networks with post transcriptional regulatory data, researchers are therefore forced to manually resort to specific third party databases. In this context, we introduce ReNE, a Cytoscape 3.x plugin designed to automatically enrich a standard gene-based regulatory network with more detailed transcriptional, post transcriptional, and translational data, resulting in an enhanced network that more precisely models the actual biological regulatory mechanisms. ReNE can automatically import a network layout from the Reactome or KEGG repositories, or work with custom pathways described using a standard OWL/XML data format that the Cytoscape import procedure accepts. Moreover, ReNE allows researchers to merge multiple pathways coming from different sources. The merged network structure is normalized to guarantee a consistent and uniform description of the network nodes and edges and to enrich all integrated data with additional annotations retrieved from genome-wide databases like NCBI, thus producing a pathway fully manageable through the Cytoscape environment. The normalized network is then analyzed to include missing transcription factors, miRNAs, and proteins. The resulting enhanced network is still a fully functional Cytoscape network where each regulatory element (transcription factor, miRNA, gene, protein) and regulatory mechanism (up-regulation/down-regulation) is clearly visually identifiable, thus enabling a better visual understanding of its role and the effect in the network behavior. The enhanced network produced by ReNE is exportable in multiple formats for further analysis via third party applications. ReNE can be freely installed from the Cytoscape App Store (http://apps.cytoscape.org/apps/rene) and the full source code is freely available for download through a SVN repository accessible at http://www.sysbio.polito.it/tools_svn/BioInformatics/Rene/releases/. ReNE enhances a network by only integrating data from public repositories, without any inference or prediction. The reliability of the introduced interactions only depends on the reliability of the source data, which is out of control of ReNe developers. PMID:25541727

Resting-state Network-specific Breakdown of Functional Connectivity during Ketamine Alteration of Consciousness in Volunteers.

PubMed

Bonhomme, Vincent; Vanhaudenhuyse, Audrey; Demertzi, Athena; Bruno, Marie-Aurélie; Jaquet, Oceane; Bahri, Mohamed Ali; Plenevaux, Alain; Boly, Melanie; Boveroux, Pierre; Soddu, Andrea; Brichant, Jean François; Maquet, Pierre; Laureys, Steven

2016-11-01

Consciousness-altering anesthetic agents disturb connectivity between brain regions composing the resting-state consciousness networks (RSNs). The default mode network (DMn), executive control network, salience network (SALn), auditory network, sensorimotor network (SMn), and visual network sustain mentation. Ketamine modifies consciousness differently from other agents, producing psychedelic dreaming and no apparent interaction with the environment. The authors used functional magnetic resonance imaging to explore ketamine-induced changes in RSNs connectivity. Fourteen healthy volunteers received stepwise intravenous infusions of ketamine up to loss of responsiveness. Because of agitation, data from six subjects were excluded from analysis. RSNs connectivity was compared between absence of ketamine (wake state [W1]), light ketamine sedation, and ketamine-induced unresponsiveness (deep sedation [S2]). Increasing the depth of ketamine sedation from W1 to S2 altered DMn and SALn connectivity and suppressed the anticorrelated activity between DMn and other brain regions. During S2, DMn connectivity, particularly between the medial prefrontal cortex and the remaining network (effect size β [95% CI]: W1 = 0.20 [0.18 to 0.22]; S2 = 0.07 [0.04 to 0.09]), and DMn anticorrelated activity (e.g., right sensory cortex: W1 = -0.07 [-0.09 to -0.04]; S2 = 0.04 [0.01 to 0.06]) were broken down. SALn connectivity was nonuniformly suppressed (e.g., left parietal operculum: W1 = 0.08 [0.06 to 0.09]; S2 = 0.05 [0.02 to 0.07]). Executive control networks, auditory network, SMn, and visual network were minimally affected. Ketamine induces specific changes in connectivity within and between RSNs. Breakdown of frontoparietal DMn connectivity and DMn anticorrelation and sensory and SMn connectivity preservation are common to ketamine and propofol-induced alterations of consciousness.

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Gps-Denied Geo-Localisation Using Visual Odometry

NASA Astrophysics Data System (ADS)

Gupta, Ashish; Chang, Huan; Yilmaz, Alper

2016-06-01

The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modelled as graph edges and typically intersections are modelled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments.

Visual experience sculpts whole-cortex spontaneous infraslow activity patterns through an Arc-dependent mechanism

PubMed Central

Kraft, Andrew W.; Mitra, Anish; Bauer, Adam Q.; Raichle, Marcus E.; Culver, Joseph P.; Lee, Jin-Moo

2017-01-01

Decades of work in experimental animals has established the importance of visual experience during critical periods for the development of normal sensory-evoked responses in the visual cortex. However, much less is known concerning the impact of early visual experience on the systems-level organization of spontaneous activity. Human resting-state fMRI has revealed that infraslow fluctuations in spontaneous activity are organized into stereotyped spatiotemporal patterns across the entire brain. Furthermore, the organization of spontaneous infraslow activity (ISA) is plastic in that it can be modulated by learning and experience, suggesting heightened sensitivity to change during critical periods. Here we used wide-field optical intrinsic signal imaging in mice to examine whole-cortex spontaneous ISA patterns. Using monocular or binocular visual deprivation, we examined the effects of critical period visual experience on the development of ISA correlation and latency patterns within and across cortical resting-state networks. Visual modification with monocular lid suturing reduced correlation between left and right cortices (homotopic correlation) within the visual network, but had little effect on internetwork correlation. In contrast, visual deprivation with binocular lid suturing resulted in increased visual homotopic correlation and increased anti-correlation between the visual network and several extravisual networks, suggesting cross-modal plasticity. These network-level changes were markedly attenuated in mice with genetic deletion of Arc, a gene known to be critical for activity-dependent synaptic plasticity. Taken together, our results suggest that critical period visual experience induces global changes in spontaneous ISA relationships, both within the visual network and across networks, through an Arc-dependent mechanism. PMID:29087327

Use of scientific social networking to improve the research strategies of PubMed readers.

PubMed

Evdokimov, Pavel; Kudryavtsev, Alexey; Ilgisonis, Ekaterina; Ponomarenko, Elena; Lisitsa, Andrey

2016-02-18

Keeping up with journal articles on a daily basis is an important activity of scientists engaged in biomedical research. Usually, journal articles and papers in the field of biomedicine are accessed through the Medline/PubMed electronic library. In the process of navigating PubMed, researchers unknowingly generate user-specific reading profiles that can be shared within a social networking environment. This paper examines the structure of the social networking environment generated by PubMed users. A web browser plugin was developed to map [in Medical Subject Headings (MeSH) terms] the reading patterns of individual PubMed users. We developed a scientific social network based on the personal research profiles of readers of biomedical articles. A browser plugin is used to record the digital object identifier or PubMed ID of web pages. Recorded items are posted on the activity feed and automatically mapped to PubMed abstract. Within the activity feed a user can trace back previously browsed articles and insert comments. By calculating the frequency with which specific MeSH occur, the research interests of PubMed users can be visually represented with a tag cloud. Finally, research profiles can be searched for matches between network users. A social networking environment was created using MeSH terms to map articles accessed through the Medline/PubMed online library system. In-network social communication is supported by the recommendation of articles and by matching users with similar scientific interests. The system is available at http://bioknol.org/en/.

A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

PubMed Central

Hruz, Tomas; Lucas, Christoph; Laule, Oliver; Zimmermann, Philip

2013-01-01

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. PMID:23864855

Alerts Visualization and Clustering in Network-based Intrusion Detection

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

Yang, Dr. Li; Gasior, Wade C; Dasireddy, Swetha

2010-04-01

Today's Intrusion detection systems when deployed on a busy network overload the network with huge number of alerts. This behavior of producing too much raw information makes it less effective. We propose a system which takes both raw data and Snort alerts to visualize and analyze possible intrusions in a network. Then we present with two models for the visualization of clustered alerts. Our first model gives the network administrator with the logical topology of the network and detailed information of each node that involves its associated alerts and connections. In the second model, flocking model, presents the network administratormore » with the visual representation of IDS data in which each alert is represented in different color and the alerts with maximum similarity move together. This gives network administrator with the idea of detecting various of intrusions through visualizing the alert patterns.« less

Model-based analysis of pattern motion processing in mouse primary visual cortex

PubMed Central

Muir, Dylan R.; Roth, Morgane M.; Helmchen, Fritjof; Kampa, Björn M.

2015-01-01

Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features. PMID:26300738

Designing an End-to-End System for Data Storage, Analysis, and Visualization for an Urban Environmental Observatory

NASA Astrophysics Data System (ADS)

McGuire, M. P.; Welty, C.; Gangopadhyay, A.; Karabatis, G.; Chen, Z.

2006-05-01

The urban environment is formed by complex interactions between natural and human dominated systems, the study of which requires the collection and analysis of very large datasets that span many disciplines. Recent advances in sensor technology and automated data collection have improved the ability to monitor urban environmental systems and are making the idea of an urban environmental observatory a reality. This in turn has created a number of potential challenges in data management and analysis. We present the design of an end-to-end system to store, analyze, and visualize data from a prototype urban environmental observatory based at the Baltimore Ecosystem Study, a National Science Foundation Long Term Ecological Research site (BES LTER). We first present an object-relational design of an operational database to store high resolution spatial datasets as well as data from sensor networks, archived data from the BES LTER, data from external sources such as USGS NWIS, EPA Storet, and metadata. The second component of the system design includes a spatiotemporal data warehouse consisting of a data staging plan and a multidimensional data model designed for the spatiotemporal analysis of monitoring data. The system design also includes applications for multi-resolution exploratory data analysis, multi-resolution data mining, and spatiotemporal visualization based on the spatiotemporal data warehouse. Also the system design includes interfaces with water quality models such as HSPF, SWMM, and SWAT, and applications for real-time sensor network visualization, data discovery, data download, QA/QC, and backup and recovery, all of which are based on the operational database. The system design includes both internet and workstation-based interfaces. Finally we present the design of a laboratory for spatiotemporal analysis and visualization as well as real-time monitoring of the sensor network.

Collaborative Visualization Project: shared-technology learning environments for science learning

NASA Astrophysics Data System (ADS)

Pea, Roy D.; Gomez, Louis M.

1993-01-01

Project-enhanced science learning (PESL) provides students with opportunities for `cognitive apprenticeships' in authentic scientific inquiry using computers for data-collection and analysis. Student teams work on projects with teacher guidance to develop and apply their understanding of science concepts and skills. We are applying advanced computing and communications technologies to augment and transform PESL at-a-distance (beyond the boundaries of the individual school), which is limited today to asynchronous, text-only networking and unsuitable for collaborative science learning involving shared access to multimedia resources such as data, graphs, tables, pictures, and audio-video communication. Our work creates user technology (a Collaborative Science Workbench providing PESL design support and shared synchronous document views, program, and data access; a Science Learning Resource Directory for easy access to resources including two-way video links to collaborators, mentors, museum exhibits, media-rich resources such as scientific visualization graphics), and refine enabling technologies (audiovisual and shared-data telephony, networking) for this PESL niche. We characterize participation scenarios for using these resources and we discuss national networked access to science education expertise.

Unsupervised learning of contextual constraints in neural networks for simultaneous visual processing of multiple objects

NASA Astrophysics Data System (ADS)

Marshall, Jonathan A.

1992-12-01

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner, is described. EXIN networks develop efficient representation structures for higher-level visual tasks such as segmentation, grouping, transparency, depth perception, and size perception. Exposure to a perceptual environment during a developmental period serves to configure the network to perform appropriate organization of sensory data. A new anti-Hebbian inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners), while maintaining contextual consistency constraints, instead of forcing winner-take-all pattern classifications. The activations can represent multiple patterns simultaneously and can represent uncertainty. The network performs parallel parsing, credit attribution, and simultaneous constraint satisfaction. EXIN networks can learn to represent multiple oriented edges even where they intersect and can learn to represent multiple transparently overlaid surfaces defined by stereo or motion cues. In the case of stereo transparency, the inhibitory learning implements both a uniqueness constraint and permits coactivation of cells representing multiple disparities at the same image location. Thus two or more disparities can be active simultaneously without interference. This behavior is analogous to that of Prazdny's stereo vision algorithm, with the bonus that each binocular point is assigned a unique disparity. In a large implementation, such a NN would also be able to represent effectively the disparities of a cloud of points at random depths, like human observers, and unlike Prazdny's method

BrainBrowser: distributed, web-based neurological data visualization.

PubMed

Sherif, Tarek; Kassis, Nicolas; Rousseau, Marc-Étienne; Adalat, Reza; Evans, Alan C

2014-01-01

Recent years have seen massive, distributed datasets become the norm in neuroimaging research, and the methodologies used to analyze them have, in response, become more collaborative and exploratory. Tools and infrastructure are continuously being developed and deployed to facilitate research in this context: grid computation platforms to process the data, distributed data stores to house and share them, high-speed networks to move them around and collaborative, often web-based, platforms to provide access to and sometimes manage the entire system. BrainBrowser is a lightweight, high-performance JavaScript visualization library built to provide easy-to-use, powerful, on-demand visualization of remote datasets in this new research environment. BrainBrowser leverages modern web technologies, such as WebGL, HTML5 and Web Workers, to visualize 3D surface and volumetric neuroimaging data in any modern web browser without requiring any browser plugins. It is thus trivial to integrate BrainBrowser into any web-based platform. BrainBrowser is simple enough to produce a basic web-based visualization in a few lines of code, while at the same time being robust enough to create full-featured visualization applications. BrainBrowser can dynamically load the data required for a given visualization, so no network bandwidth needs to be waisted on data that will not be used. BrainBrowser's integration into the standardized web platform also allows users to consider using 3D data visualization in novel ways, such as for data distribution, data sharing and dynamic online publications. BrainBrowser is already being used in two major online platforms, CBRAIN and LORIS, and has been used to make the 1TB MACACC dataset openly accessible.

BrainBrowser: distributed, web-based neurological data visualization

PubMed Central

Sherif, Tarek; Kassis, Nicolas; Rousseau, Marc-Étienne; Adalat, Reza; Evans, Alan C.

2015-01-01

Recent years have seen massive, distributed datasets become the norm in neuroimaging research, and the methodologies used to analyze them have, in response, become more collaborative and exploratory. Tools and infrastructure are continuously being developed and deployed to facilitate research in this context: grid computation platforms to process the data, distributed data stores to house and share them, high-speed networks to move them around and collaborative, often web-based, platforms to provide access to and sometimes manage the entire system. BrainBrowser is a lightweight, high-performance JavaScript visualization library built to provide easy-to-use, powerful, on-demand visualization of remote datasets in this new research environment. BrainBrowser leverages modern web technologies, such as WebGL, HTML5 and Web Workers, to visualize 3D surface and volumetric neuroimaging data in any modern web browser without requiring any browser plugins. It is thus trivial to integrate BrainBrowser into any web-based platform. BrainBrowser is simple enough to produce a basic web-based visualization in a few lines of code, while at the same time being robust enough to create full-featured visualization applications. BrainBrowser can dynamically load the data required for a given visualization, so no network bandwidth needs to be waisted on data that will not be used. BrainBrowser's integration into the standardized web platform also allows users to consider using 3D data visualization in novel ways, such as for data distribution, data sharing and dynamic online publications. BrainBrowser is already being used in two major online platforms, CBRAIN and LORIS, and has been used to make the 1TB MACACC dataset openly accessible. PMID:25628562

Network Visualization Project (NVP)

DTIC Science & Technology

2016-07-01

network visualization, network traffic analysis, network forensics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...shell, is a command-line framework used for network forensic analysis. Dshell processes existing pcap files and filters output information based on

TreeNetViz: revealing patterns of networks over tree structures.

PubMed

Gou, Liang; Zhang, Xiaolong Luke

2011-12-01

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

Large-scale functional brain network changes in taxi drivers: evidence from resting-state fMRI.

PubMed

Wang, Lubin; Liu, Qiang; Shen, Hui; Li, Hong; Hu, Dewen

2015-03-01

Driving a car in the environment is a complex behavior that involves cognitive processing of visual information to generate the proper motor outputs and action controls. Previous neuroimaging studies have used virtual simulation to identify the brain areas that are associated with various driving-related tasks. Few studies, however, have focused on the specific patterns of functional organization in the driver's brain. The aim of this study was to assess differences in the resting-state networks (RSNs) of the brains of drivers and nondrivers. Forty healthy subjects (20 licensed taxi drivers, 20 nondrivers) underwent an 8-min resting-state functional MRI acquisition. Using independent component analysis, three sensory (primary and extrastriate visual, sensorimotor) RSNs and four cognitive (anterior and posterior default mode, left and right frontoparietal) RSNs were retrieved from the data. We then examined the group differences in the intrinsic brain activity of each RSN and in the functional network connectivity (FNC) between the RSNs. We found that the drivers had reduced intrinsic brain activity in the visual RSNs and reduced FNC between the sensory RSNs compared with the nondrivers. The major finding of this study, however, was that the FNC between the cognitive and sensory RSNs became more positively or less negatively correlated in the drivers relative to that in the nondrivers. Notably, the strength of the FNC between the left frontoparietal and primary visual RSNs was positively correlated with the number of taxi-driving years. Our findings may provide new insight into how the brain supports driving behavior. © 2014 Wiley Periodicals, Inc.

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

NASA Astrophysics Data System (ADS)

Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

2013-01-01

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

Scalable Adaptive Graphics Environment (SAGE) Software for the Visualization of Large Data Sets on a Video Wall

NASA Technical Reports Server (NTRS)

Jedlovec, Gary; Srikishen, Jayanthi; Edwards, Rita; Cross, David; Welch, Jon; Smith, Matt

2013-01-01

The use of collaborative scientific visualization systems for the analysis, visualization, and sharing of "big data" available from new high resolution remote sensing satellite sensors or four-dimensional numerical model simulations is propelling the wider adoption of ultra-resolution tiled display walls interconnected by high speed networks. These systems require a globally connected and well-integrated operating environment that provides persistent visualization and collaboration services. This abstract and subsequent presentation describes a new collaborative visualization system installed for NASA's Shortterm Prediction Research and Transition (SPoRT) program at Marshall Space Flight Center and its use for Earth science applications. The system consists of a 3 x 4 array of 1920 x 1080 pixel thin bezel video monitors mounted on a wall in a scientific collaboration lab. The monitors are physically and virtually integrated into a 14' x 7' for video display. The display of scientific data on the video wall is controlled by a single Alienware Aurora PC with a 2nd Generation Intel Core 4.1 GHz processor, 32 GB memory, and an AMD Fire Pro W600 video card with 6 mini display port connections. Six mini display-to-dual DVI cables are used to connect the 12 individual video monitors. The open source Scalable Adaptive Graphics Environment (SAGE) windowing and media control framework, running on top of the Ubuntu 12 Linux operating system, allows several users to simultaneously control the display and storage of high resolution still and moving graphics in a variety of formats, on tiled display walls of any size. The Ubuntu operating system supports the open source Scalable Adaptive Graphics Environment (SAGE) software which provides a common environment, or framework, enabling its users to access, display and share a variety of data-intensive information. This information can be digital-cinema animations, high-resolution images, high-definition video-teleconferences, presentation slides, documents, spreadsheets or laptop screens. SAGE is cross-platform, community-driven, open-source visualization and collaboration middleware that utilizes shared national and international cyberinfrastructure for the advancement of scientific research and education.

Scalable Adaptive Graphics Environment (SAGE) Software for the Visualization of Large Data Sets on a Video Wall

NASA Astrophysics Data System (ADS)

Jedlovec, G.; Srikishen, J.; Edwards, R.; Cross, D.; Welch, J. D.; Smith, M. R.

2013-12-01

The use of collaborative scientific visualization systems for the analysis, visualization, and sharing of 'big data' available from new high resolution remote sensing satellite sensors or four-dimensional numerical model simulations is propelling the wider adoption of ultra-resolution tiled display walls interconnected by high speed networks. These systems require a globally connected and well-integrated operating environment that provides persistent visualization and collaboration services. This abstract and subsequent presentation describes a new collaborative visualization system installed for NASA's Short-term Prediction Research and Transition (SPoRT) program at Marshall Space Flight Center and its use for Earth science applications. The system consists of a 3 x 4 array of 1920 x 1080 pixel thin bezel video monitors mounted on a wall in a scientific collaboration lab. The monitors are physically and virtually integrated into a 14' x 7' for video display. The display of scientific data on the video wall is controlled by a single Alienware Aurora PC with a 2nd Generation Intel Core 4.1 GHz processor, 32 GB memory, and an AMD Fire Pro W600 video card with 6 mini display port connections. Six mini display-to-dual DVI cables are used to connect the 12 individual video monitors. The open source Scalable Adaptive Graphics Environment (SAGE) windowing and media control framework, running on top of the Ubuntu 12 Linux operating system, allows several users to simultaneously control the display and storage of high resolution still and moving graphics in a variety of formats, on tiled display walls of any size. The Ubuntu operating system supports the open source Scalable Adaptive Graphics Environment (SAGE) software which provides a common environment, or framework, enabling its users to access, display and share a variety of data-intensive information. This information can be digital-cinema animations, high-resolution images, high-definition video-teleconferences, presentation slides, documents, spreadsheets or laptop screens. SAGE is cross-platform, community-driven, open-source visualization and collaboration middleware that utilizes shared national and international cyberinfrastructure for the advancement of scientific research and education.

Connectopathy in Autism Spectrum Disorders: A Review of Evidence from Visual Evoked Potentials and Diffusion Magnetic Resonance Imaging

PubMed Central

Yamasaki, Takao; Maekawa, Toshihiko; Fujita, Takako; Tobimatsu, Shozo

2017-01-01

Individuals with autism spectrum disorder (ASD) show superior performance in processing fine details; however, they often exhibit impairments of gestalt face, global motion perception, and visual attention as well as core social deficits. Increasing evidence has suggested that social deficits in ASD arise from abnormal functional and structural connectivities between and within distributed cortical networks that are recruited during social information processing. Because the human visual system is characterized by a set of parallel, hierarchical, multistage network systems, we hypothesized that the altered connectivity of visual networks contributes to social cognition impairment in ASD. In the present review, we focused on studies of altered connectivity of visual and attention networks in ASD using visual evoked potentials (VEPs), event-related potentials (ERPs), and diffusion tensor imaging (DTI). A series of VEP, ERP, and DTI studies conducted in our laboratory have demonstrated complex alterations (impairment and enhancement) of visual and attention networks in ASD. Recent data have suggested that the atypical visual perception observed in ASD is caused by altered connectivity within parallel visual pathways and attention networks, thereby contributing to the impaired social communication observed in ASD. Therefore, we conclude that the underlying pathophysiological mechanism of ASD constitutes a “connectopathy.” PMID:29170625

Design and clinical evaluation of a high-capacity digital image archival library and high-speed network for the replacement of cinefilm in the cardiac angiography environment

NASA Astrophysics Data System (ADS)

Cusma, Jack T.; Spero, Laurence A.; Groshong, Bennett R.; Cho, Teddy; Bashore, Thomas M.

1993-09-01

An economical and practical digital solution for the replacement of 35 mm cine film as the archive media in the cardiac x-ray imaging environment has remained lacking to date due to the demanding requirements of high capacity, high acquisition rate, high transfer rate, and a need for application in a distributed environment. A clinical digital image library and network based on the D2 digital video format has been installed in the Duke University Cardiac Catheterization Laboratory. The system architecture includes a central image library with digital video recorders and robotic tape retrieval, three acquisition stations, and remote review stations connected via a serial image network. The library has a capacity for over 20,000 Gigabytes of uncompressed image data, equivalent to records for approximately 20,000 patients. Image acquisition in the clinical laboratories is via a real-time digital interface between the digital angiography system and a local digital recorder. Images are transferred to the library over the serial network at a rate of 14.3 Mbytes/sec and permanently stored for later review. The image library and network are currently undergoing a clinical comparison with cine film for visual and quantitative assessment of coronary artery disease. At the conclusion of the evaluation, the configuration will be expanded to include four additional catheterization laboratories and remote review stations throughout the hospital.

Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study

PubMed Central

Hale, T. Sigi; Kane, Andrea M.; Kaminsky, Olivia; Tung, Kelly L.; Wiley, Joshua F.; McGough, James J.; Loo, Sandra K.; Kaplan, Jonas T.

2014-01-01

Background: A growing body of research has identified abnormal visual information processing in attention-deficit hyperactivity disorder (ADHD). In particular, slow processing speed and increased reliance on visuo-perceptual strategies have become evident. Objective: The current study used recently developed fMRI methods to replicate and further examine abnormal rightward biased visual information processing in ADHD and to further characterize the nature of this effect; we tested its association with several large-scale distributed network systems. Method: We examined fMRI BOLD response during letter and location judgment tasks, and directly assessed visual network asymmetry and its association with large-scale networks using both a voxelwise and an averaged signal approach. Results: Initial within-group analyses revealed a pattern of left-lateralized visual cortical activity in controls but right-lateralized visual cortical activity in ADHD children. Direct analyses of visual network asymmetry confirmed atypical rightward bias in ADHD children compared to controls. This ADHD characteristic was atypically associated with reduced activation across several extra-visual networks, including the default mode network (DMN). We also found atypical associations between DMN activation and ADHD subjects’ inattentive symptoms and task performance. Conclusion: The current study demonstrated rightward VNA in ADHD during a simple letter discrimination task. This result adds an important novel consideration to the growing literature identifying abnormal visual processing in ADHD. We postulate that this characteristic reflects greater perceptual engagement of task-extraneous content, and that it may be a basic feature of less efficient top-down task-directed control over visual processing. We additionally argue that abnormal DMN function may contribute to this characteristic. PMID:25076915

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state.

PubMed

Yang, Yan-Li; Deng, Hong-Xia; Xing, Gui-Yang; Xia, Xiao-Luan; Li, Hai-Fang

2015-02-01

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

Research Trends in Wireless Visual Sensor Networks When Exploiting Prioritization

PubMed Central

Costa, Daniel G.; Guedes, Luiz Affonso; Vasques, Francisco; Portugal, Paulo

2015-01-01

The development of wireless sensor networks for control and monitoring functions has created a vibrant investigation scenario, where many critical topics, such as communication efficiency and energy consumption, have been investigated in the past few years. However, when sensors are endowed with low-power cameras for visual monitoring, a new scope of challenges is raised, demanding new research efforts. In this context, the resource-constrained nature of sensor nodes has demanded the use of prioritization approaches as a practical mechanism to lower the transmission burden of visual data over wireless sensor networks. Many works in recent years have considered local-level prioritization parameters to enhance the overall performance of those networks, but global-level policies can potentially achieve better results in terms of visual monitoring efficiency. In this paper, we make a broad review of some recent works on priority-based optimizations in wireless visual sensor networks. Moreover, we envisage some research trends when exploiting prioritization, potentially fostering the development of promising optimizations for wireless sensor networks composed of visual sensors. PMID:25599425

Review: visual analytics of climate networks

NASA Astrophysics Data System (ADS)

Nocke, T.; Buschmann, S.; Donges, J. F.; Marwan, N.; Schulz, H.-J.; Tominski, C.

2015-09-01

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

Review: visual analytics of climate networks

NASA Astrophysics Data System (ADS)

Nocke, T.; Buschmann, S.; Donges, J. F.; Marwan, N.; Schulz, H.-J.; Tominski, C.

2015-04-01

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis, relating the multiple visualisation challenges with a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space.

PubMed

Zhou, Guangyan; Xia, Jianguo

2018-06-07

Biological networks play increasingly important roles in omics data integration and systems biology. Over the past decade, many excellent tools have been developed to support creation, analysis and visualization of biological networks. However, important limitations remain: most tools are standalone programs, the majority of them focus on protein-protein interaction (PPI) or metabolic networks, and visualizations often suffer from 'hairball' effects when networks become large. To help address these limitations, we developed OmicsNet - a novel web-based tool that allows users to easily create different types of molecular interaction networks and visually explore them in a three-dimensional (3D) space. Users can upload one or multiple lists of molecules of interest (genes/proteins, microRNAs, transcription factors or metabolites) to create and merge different types of biological networks. The 3D network visualization system was implemented using the powerful Web Graphics Library (WebGL) technology that works natively in most major browsers. OmicsNet supports force-directed layout, multi-layered perspective layout, as well as spherical layout to help visualize and navigate complex networks. A rich set of functions have been implemented to allow users to perform coloring, shading, topology analysis, and enrichment analysis. OmicsNet is freely available at http://www.omicsnet.ca.

Wearable Virtual White Cane Network for navigating people with visual impairment.

PubMed

Gao, Yabiao; Chandrawanshi, Rahul; Nau, Amy C; Tse, Zion Tsz Ho

2015-09-01

Navigating the world with visual impairments presents inconveniences and safety concerns. Although a traditional white cane is the most commonly used mobility aid due to its low cost and acceptable functionality, electronic traveling aids can provide more functionality as well as additional benefits. The Wearable Virtual Cane Network is an electronic traveling aid that utilizes ultrasound sonar technology to scan the surrounding environment for spatial information. The Wearable Virtual Cane Network is composed of four sensing nodes: one on each of the user's wrists, one on the waist, and one on the ankle. The Wearable Virtual Cane Network employs vibration and sound to communicate object proximity to the user. While conventional navigation devices are typically hand-held and bulky, the hands-free design of our prototype allows the user to perform other tasks while using the Wearable Virtual Cane Network. When the Wearable Virtual Cane Network prototype was tested for distance resolution and range detection limits at various displacements and compared with a traditional white cane, all participants performed significantly above the control bar (p < 4.3 × 10(-5), standard t-test) in distance estimation. Each sensor unit can detect an object with a surface area as small as 1 cm(2) (1 cm × 1 cm) located 70 cm away. Our results showed that the walking speed for an obstacle course was increased by 23% on average when subjects used the Wearable Virtual Cane Network rather than the white cane. The obstacle course experiment also shows that the use of the white cane in combination with the Wearable Virtual Cane Network can significantly improve navigation over using either the white cane or the Wearable Virtual Cane Network alone (p < 0.05, paired t-test). © IMechE 2015.

Dismounted Warrior Network Experiments

DTIC Science & Technology

2000-11-01

Foxtrot Locomotion Human Joystick ODT Joystick Foot Pedal + Head Orientation Visual Display Wireless HMD 4 Projection Desktop Single Screens (WISE... Biomechanics DI-Guy JackML DI-Guy Table 1. VICs Comparison Matrix VIC Alpha is the Dismounted Soldier Simulation (DSS) system developed by Veda, Inc. under a...using a pressure sensitive foot pedal . The user’s head is tracked with a magnetic sensor and is used to control steering through the environment. A

Modeling Computer Communication Networks in a Realistic 3D Environment

DTIC Science & Technology

2010-03-01

50 2. Comparison of visualization tools . . . . . . . . . . . . . . . . . 75 xi List of Abbreviations Abbreviation Page 2D two-dimensional...International Conference on, 77 –84, 2001. 20. National Defense and the Canadian Forces. “Joint Fires Support”. URL http: //www.cfd-cdf.forces.gc.ca/sites/ page ...UNLIMITED. Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour

RatLab: an easy to use tool for place code simulations

PubMed Central

Schönfeld, Fabian; Wiskott, Laurenz

2013-01-01

In this paper we present the RatLab toolkit, a software framework designed to set up and simulate a wide range of studies targeting the encoding of space in rats. It provides open access to our modeling approach to establish place and head direction cells within unknown environments and it offers a set of parameters to allow for the easy construction of a variety of enclosures for a virtual rat as well as controlling its movement pattern over the course of experiments. Once a spatial code is formed RatLab can be used to modify aspects of the enclosure or movement pattern and plot the effect of such modifications on the spatial representation, i.e., place and head direction cell activity. The simulation is based on a hierarchical Slow Feature Analysis (SFA) network that has been shown before to establish a spatial encoding of new environments using visual input data only. RatLab encapsulates such a network, generates the visual training data, and performs all sampling automatically—with each of these stages being further configurable by the user. RatLab was written with the intention to make our SFA model more accessible to the community and to that end features a range of elements to allow for experimentation with the model without the need for specific programming skills. PMID:23908627

Note: Design and development of wireless controlled aerosol sampling network for large scale aerosol dispersion experiments.

PubMed

Gopalakrishnan, V; Subramanian, V; Baskaran, R; Venkatraman, B

2015-07-01

Wireless based custom built aerosol sampling network is designed, developed, and implemented for environmental aerosol sampling. These aerosol sampling systems are used in field measurement campaign, in which sodium aerosol dispersion experiments have been conducted as a part of environmental impact studies related to sodium cooled fast reactor. The sampling network contains 40 aerosol sampling units and each contains custom built sampling head and the wireless control networking designed with Programmable System on Chip (PSoC™) and Xbee Pro RF modules. The base station control is designed using graphical programming language LabView. The sampling network is programmed to operate in a preset time and the running status of the samplers in the network is visualized from the base station. The system is developed in such a way that it can be used for any other environment sampling system deployed in wide area and uneven terrain where manual operation is difficult due to the requirement of simultaneous operation and status logging.

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

PubMed

Shannon, Paul; Markiel, Andrew; Ozier, Owen; Baliga, Nitin S; Wang, Jonathan T; Ramage, Daniel; Amin, Nada; Schwikowski, Benno; Ideker, Trey

2003-11-01

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Note: Design and development of wireless controlled aerosol sampling network for large scale aerosol dispersion experiments

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

Gopalakrishnan, V.; Subramanian, V.; Baskaran, R.

2015-07-15

Wireless based custom built aerosol sampling network is designed, developed, and implemented for environmental aerosol sampling. These aerosol sampling systems are used in field measurement campaign, in which sodium aerosol dispersion experiments have been conducted as a part of environmental impact studies related to sodium cooled fast reactor. The sampling network contains 40 aerosol sampling units and each contains custom built sampling head and the wireless control networking designed with Programmable System on Chip (PSoC™) and Xbee Pro RF modules. The base station control is designed using graphical programming language LabView. The sampling network is programmed to operate in amore » preset time and the running status of the samplers in the network is visualized from the base station. The system is developed in such a way that it can be used for any other environment sampling system deployed in wide area and uneven terrain where manual operation is difficult due to the requirement of simultaneous operation and status logging.« less

A Dynamic Three-Dimensional Network Visualization Program for Integration into CyberCIEGE and Other Network Visualization Scenarios

DTIC Science & Technology

2007-06-01

information flow involved in network attacks. This kind of information can be invaluable in learning how to best setup and defend computer networks...administrators, and those interested in learning about securing networks a way to conceptualize this complex system of computing. NTAV3D will provide a three...teaching with visual and other components can make learning more effective” (Baxley et al, 2006). A hyperbox (Alpern and Carter, 1991) is

Default Mode Network (DMN) Deactivation during Odor-Visual Association

PubMed Central

Karunanayaka, Prasanna R.; Wilson, Donald A.; Tobia, Michael J.; Martinez, Brittany; Meadowcroft, Mark; Eslinger, Paul J.; Yang, Qing X.

2017-01-01

Default mode network (DMN) deactivation has been shown to be functionally relevant for goal-directed cognition. In this study, we investigated the DMN’s role during olfactory processing using two complementary functional magnetic resonance imaging (fMRI) paradigms with identical timing, visual-cue stimulation and response monitoring protocols. Twenty-nine healthy, non-smoking, right-handed adults (mean age = 26±4 yrs., 16 females) completed an odor-visual association fMRI paradigm that had two alternating odor+visual and visual-only trial conditions. During odor+visual trials, a visual cue was presented simultaneously with an odor, while during visual-only trial conditions the same visual cue was presented alone. Eighteen of the 29 participants (mean age = 27.0 ± 6.0 yrs.,11 females) also took part in a control no-odor fMRI paradigm that consisted of visual-only trial conditions which were identical to the visual-only trials in the odor-visual association paradigm. We used Independent Component Analysis (ICA), extended unified structural equation modeling (euSEM), and psychophysiological interaction (PPI) to investigate the interplay between the DMN and olfactory network. In the odor-visual association paradigm, DMN deactivation was evoked by both the odor+visual and visual-only trial conditions. In contrast, the visual-only trials in the no-odor paradigm did not evoke consistent DMN deactivation. In the odor-visual association paradigm, the euSEM and PPI analyses identified a directed connectivity between the DMN and olfactory network which was significantly different between odor+visual and visual-only trial conditions. The results support a strong interaction between the DMN and olfactory network and highlights DMN’s role in task-evoked brain activity and behavioral responses during olfactory processing. PMID:27785847

The Brain Circuitry Underlying the Temporal Evolution of Nausea in Humans

PubMed Central

Sheehan, James D.; Kim, Jieun; LaCount, Lauren T.; Park, Kyungmo; Kaptchuk, Ted J.; Rosen, Bruce R.; Kuo, Braden

2013-01-01

Nausea is a universal human experience. It evolves slowly over time, and brain mechanisms underlying this evolution are not well understood. Our functional magnetic resonance imaging (fMRI) approach evaluated brain activity contributing to and arising from increasing motion sickness. Subjects rated transitions to increasing nausea, produced by visually induced vection within the fMRI environment. We evaluated parametrically increasing brain activity 1) precipitating increasing nausea and 2) following transition to stronger nausea. All subjects demonstrated visual stimulus–associated activation (P < 0.01) in primary and extrastriate visual cortices. In subjects experiencing motion sickness, increasing phasic activity preceding nausea was found in amygdala, putamen, and dorsal pons/locus ceruleus. Increasing sustained response following increased nausea was found in a broader network including insular, anterior cingulate, orbitofrontal, somatosensory and prefrontal cortices. Moreover, sustained anterior insula activation to strong nausea was correlated with midcingulate activation (r = 0.87), suggesting a closer linkage between these specific regions within the brain circuitry subserving nausea perception. Thus, while phasic activation in fear conditioning and noradrenergic brainstem regions precipitates transition to strong nausea, sustained activation following this transition occurs in a broader interoceptive, limbic, somatosensory, and cognitive network, reflecting the multiple dimensions of this aversive commonly occurring symptom. PMID:22473843

The brain circuitry underlying the temporal evolution of nausea in humans.

PubMed

Napadow, Vitaly; Sheehan, James D; Kim, Jieun; Lacount, Lauren T; Park, Kyungmo; Kaptchuk, Ted J; Rosen, Bruce R; Kuo, Braden

2013-04-01

Nausea is a universal human experience. It evolves slowly over time, and brain mechanisms underlying this evolution are not well understood. Our functional magnetic resonance imaging (fMRI) approach evaluated brain activity contributing to and arising from increasing motion sickness. Subjects rated transitions to increasing nausea, produced by visually induced vection within the fMRI environment. We evaluated parametrically increasing brain activity 1) precipitating increasing nausea and 2) following transition to stronger nausea. All subjects demonstrated visual stimulus-associated activation (P < 0.01) in primary and extrastriate visual cortices. In subjects experiencing motion sickness, increasing phasic activity preceding nausea was found in amygdala, putamen, and dorsal pons/locus ceruleus. Increasing sustained response following increased nausea was found in a broader network including insular, anterior cingulate, orbitofrontal, somatosensory and prefrontal cortices. Moreover, sustained anterior insula activation to strong nausea was correlated with midcingulate activation (r = 0.87), suggesting a closer linkage between these specific regions within the brain circuitry subserving nausea perception. Thus, while phasic activation in fear conditioning and noradrenergic brainstem regions precipitates transition to strong nausea, sustained activation following this transition occurs in a broader interoceptive, limbic, somatosensory, and cognitive network, reflecting the multiple dimensions of this aversive commonly occurring symptom.

A unified data representation theory for network visualization, ordering and coarse-graining

PubMed Central

Kovács, István A.; Mizsei, Réka; Csermely, Péter

2015-01-01

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form. PMID:26348923

VRML metabolic network visualizer.

PubMed

Rojdestvenski, Igor

2003-03-01

A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.

Rocinante, a virtual collaborative visualizer

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

McDonald, M.J.; Ice, L.G.

1996-12-31

With the goal of improving the ability of people around the world to share the development and use of intelligent systems, Sandia National Laboratories` Intelligent Systems and Robotics Center is developing new Virtual Collaborative Engineering (VCE) and Virtual Collaborative Control (VCC) technologies. A key area of VCE and VCC research is in shared visualization of virtual environments. This paper describes a Virtual Collaborative Visualizer (VCV), named Rocinante, that Sandia developed for VCE and VCC applications. Rocinante allows multiple participants to simultaneously view dynamic geometrically-defined environments. Each viewer can exclude extraneous detail or include additional information in the scene as desired.more » Shared information can be saved and later replayed in a stand-alone mode. Rocinante automatically scales visualization requirements with computer system capabilities. Models with 30,000 polygons and 4 Megabytes of texture display at 12 to 15 frames per second (fps) on an SGI Onyx and at 3 to 8 fps (without texture) on Indigo 2 Extreme computers. In its networked mode, Rocinante synchronizes its local geometric model with remote simulators and sensory systems by monitoring data transmitted through UDP packets. Rocinante`s scalability and performance make it an ideal VCC tool. Users throughout the country can monitor robot motions and the thinking behind their motion planners and simulators.« less

Honeycomb: Visual Analysis of Large Scale Social Networks

NASA Astrophysics Data System (ADS)

van Ham, Frank; Schulz, Hans-Jörg; Dimicco, Joan M.

The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.

Robot Task Commander with Extensible Programming Environment

NASA Technical Reports Server (NTRS)

Hart, Stephen W (Inventor); Wightman, Brian J (Inventor); Dinh, Duy Paul (Inventor); Yamokoski, John D. (Inventor); Gooding, Dustin R (Inventor)

2014-01-01

A system for developing distributed robot application-level software includes a robot having an associated control module which controls motion of the robot in response to a commanded task, and a robot task commander (RTC) in networked communication with the control module over a network transport layer (NTL). The RTC includes a script engine(s) and a GUI, with a processor and a centralized library of library blocks constructed from an interpretive computer programming code and having input and output connections. The GUI provides access to a Visual Programming Language (VPL) environment and a text editor. In executing a method, the VPL is opened, a task for the robot is built from the code library blocks, and data is assigned to input and output connections identifying input and output data for each block. A task sequence(s) is sent to the control module(s) over the NTL to command execution of the task.

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Distributed GPU Computing in GIScience

NASA Astrophysics Data System (ADS)

Jiang, Y.; Yang, C.; Huang, Q.; Li, J.; Sun, M.

2013-12-01

Geoscientists strived to discover potential principles and patterns hidden inside ever-growing Big Data for scientific discoveries. To better achieve this objective, more capable computing resources are required to process, analyze and visualize Big Data (Ferreira et al., 2003; Li et al., 2013). Current CPU-based computing techniques cannot promptly meet the computing challenges caused by increasing amount of datasets from different domains, such as social media, earth observation, environmental sensing (Li et al., 2013). Meanwhile CPU-based computing resources structured as cluster or supercomputer is costly. In the past several years with GPU-based technology matured in both the capability and performance, GPU-based computing has emerged as a new computing paradigm. Compare to traditional computing microprocessor, the modern GPU, as a compelling alternative microprocessor, has outstanding high parallel processing capability with cost-effectiveness and efficiency(Owens et al., 2008), although it is initially designed for graphical rendering in visualization pipe. This presentation reports a distributed GPU computing framework for integrating GPU-based computing within distributed environment. Within this framework, 1) for each single computer, computing resources of both GPU-based and CPU-based can be fully utilized to improve the performance of visualizing and processing Big Data; 2) within a network environment, a variety of computers can be used to build up a virtual super computer to support CPU-based and GPU-based computing in distributed computing environment; 3) GPUs, as a specific graphic targeted device, are used to greatly improve the rendering efficiency in distributed geo-visualization, especially for 3D/4D visualization. Key words: Geovisualization, GIScience, Spatiotemporal Studies Reference : 1. Ferreira de Oliveira, M. C., & Levkowitz, H. (2003). From visual data exploration to visual data mining: A survey. Visualization and Computer Graphics, IEEE Transactions on, 9(3), 378-394. 2. Li, J., Jiang, Y., Yang, C., Huang, Q., & Rice, M. (2013). Visualizing 3D/4D Environmental Data Using Many-core Graphics Processing Units (GPUs) and Multi-core Central Processing Units (CPUs). Computers & Geosciences, 59(9), 78-89. 3. Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879-899.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

PubMed

Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

2012-03-26

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space.

PubMed

Ma, Chihua; Luciani, Timothy; Terebus, Anna; Liang, Jie; Marai, G Elisabeta

2017-02-15

Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

A survey on sensor coverage and visual data capturing/processing/transmission in wireless visual sensor networks.

PubMed

Yap, Florence G H; Yen, Hong-Hsu

2014-02-20

Wireless Visual Sensor Networks (WVSNs) where camera-equipped sensor nodes can capture, process and transmit image/video information have become an important new research area. As compared to the traditional wireless sensor networks (WSNs) that can only transmit scalar information (e.g., temperature), the visual data in WVSNs enable much wider applications, such as visual security surveillance and visual wildlife monitoring. However, as compared to the scalar data in WSNs, visual data is much bigger and more complicated so intelligent schemes are required to capture/process/ transmit visual data in limited resources (hardware capability and bandwidth) WVSNs. WVSNs introduce new multi-disciplinary research opportunities of topics that include visual sensor hardware, image and multimedia capture and processing, wireless communication and networking. In this paper, we survey existing research efforts on the visual sensor hardware, visual sensor coverage/deployment, and visual data capture/ processing/transmission issues in WVSNs. We conclude that WVSN research is still in an early age and there are still many open issues that have not been fully addressed. More new novel multi-disciplinary, cross-layered, distributed and collaborative solutions should be devised to tackle these challenging issues in WVSNs.

A Survey on Sensor Coverage and Visual Data Capturing/Processing/Transmission in Wireless Visual Sensor Networks

PubMed Central

Yap, Florence G. H.; Yen, Hong-Hsu

2014-01-01

Wireless Visual Sensor Networks (WVSNs) where camera-equipped sensor nodes can capture, process and transmit image/video information have become an important new research area. As compared to the traditional wireless sensor networks (WSNs) that can only transmit scalar information (e.g., temperature), the visual data in WVSNs enable much wider applications, such as visual security surveillance and visual wildlife monitoring. However, as compared to the scalar data in WSNs, visual data is much bigger and more complicated so intelligent schemes are required to capture/process/transmit visual data in limited resources (hardware capability and bandwidth) WVSNs. WVSNs introduce new multi-disciplinary research opportunities of topics that include visual sensor hardware, image and multimedia capture and processing, wireless communication and networking. In this paper, we survey existing research efforts on the visual sensor hardware, visual sensor coverage/deployment, and visual data capture/processing/transmission issues in WVSNs. We conclude that WVSN research is still in an early age and there are still many open issues that have not been fully addressed. More new novel multi-disciplinary, cross-layered, distributed and collaborative solutions should be devised to tackle these challenging issues in WVSNs. PMID:24561401

The Worldviews Network: Transformative Global Change Education in Immersive Environments

NASA Astrophysics Data System (ADS)

Hamilton, H.; Yu, K. C.; Gardiner, N.; McConville, D.; Connolly, R.; "Irving, Lindsay", L. S.

2011-12-01

Our modern age is defined by an astounding capacity to generate scientific information. From DNA to dark matter, human ingenuity and technologies create an endless stream of data about ourselves and the world of which we are a part. Yet we largely founder in transforming information into understanding, and understanding into rational action for our society as a whole. Earth and biodiversity scientists are especially frustrated by this impasse because the data they gather often point to a clash between Earth's capacity to sustain life and the decisions that humans make to garner the planet's resources. Immersive virtual environments offer an underexplored link in the translation of scientific data into public understanding, dialogue, and action. The Worldviews Network is a collaboration of scientists, artists, and educators focused on developing best practices for the use of immersive environments for science-based ecological literacy education. A central tenet of the Worldviews Network is that there are multiple ways to know and experience the world, so we are developing scientifically accurate, geographically relevant, and culturally appropriate programming to promote ecological literacy within informal science education programs across the United States. The goal of Worldviews Network is to offer transformative learning experiences, in which participants are guided on a process integrating immersive visual explorations, critical reflection and dialogue, and design-oriented approaches to action - or more simply, seeing, knowing, and doing. Our methods center on live presentations, interactive scientific visualizations, and sustainability dialogues hosted at informal science institutions. Our approach uses datasets from the life, Earth, and space sciences to illuminate the complex conditions that support life on earth and the ways in which ecological systems interact. We are leveraging scientific data from federal agencies, non-governmental organizations, and our own research to develop a library of immersive visualization stories and templates that explore ecological relationships across time at cosmic, global, and bioregional scales, with learning goals aligned to climate and earth science literacy principles. These experiential narratives are used to increase participants' awareness of global change issues as well as to engage them in dialogues and design processes focused on steps they can take within their own communities to systemically address these interconnected challenges. More than 600 digital planetariums in the U.S. collectively represent a pioneering opportunity for distributing Earth systems messages over large geographic areas. By placing the viewer-and Earth itself-within the context of the rest of the universe, digital planetariums can uniquely provide essential transcalar perspectives on the complex interdependencies of Earth's interacting physical and biological systems. The Worldviews Network is creating innovative, data-driven approaches for engaging the American public in dialogues about human-induced global changes.

Bayesian networks and information theory for audio-visual perception modeling.

PubMed

Besson, Patricia; Richiardi, Jonas; Bourdin, Christophe; Bringoux, Lionel; Mestre, Daniel R; Vercher, Jean-Louis

2010-09-01

Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions through a rigorous and systematic data-driven process. This requires considering the general relationships between the physical events or factors involved in the process, not only in quantitative terms, but also in term of the influence of one factor on another. We use tools from information theory and probabilistic reasoning to derive relationships between the random variables of interest, where the central notion is that of conditional independence. Using mutual information analysis to guide the model elicitation process, a probabilistic causal model encoded as a Bayesian network is obtained. We exemplify the method by using data collected in an audio-visual localization task for human subjects, and we show that it yields a well-motivated model with good predictive ability. The model elicitation process offers new prospects for the investigation of the cognitive mechanisms of multisensory perception.

General visual robot controller networks via artificial evolution

NASA Astrophysics Data System (ADS)

Cliff, David; Harvey, Inman; Husbands, Philip

1993-08-01

We discuss recent results from our ongoing research concerning the application of artificial evolution techniques (i.e., an extended form of genetic algorithm) to the problem of developing `neural' network controllers for visually guided robots. The robot is a small autonomous vehicle with extremely low-resolution vision, employing visual sensors which could readily be constructed from discrete analog components. In addition to visual sensing, the robot is equipped with a small number of mechanical tactile sensors. Activity from the sensors is fed to a recurrent dynamical artificial `neural' network, which acts as the robot controller, providing signals to motors governing the robot's motion. Prior to presentation of new results, this paper summarizes our rationale and past work, which has demonstrated that visually guided control networks can arise without any explicit specification that visual processing should be employed: the evolutionary process opportunistically makes use of visual information if it is available.

A Quantitative Visual Mapping and Visualization Approach for Deep Ocean Floor Research

NASA Astrophysics Data System (ADS)

Hansteen, T. H.; Kwasnitschka, T.

2013-12-01

Geological fieldwork on the sea floor is still impaired by our inability to resolve features on a sub-meter scale resolution in a quantifiable reference frame and over an area large enough to reveal the context of local observations. In order to overcome these issues, we have developed an integrated workflow of visual mapping techniques leading to georeferenced data sets which we examine using state-of-the-art visualization technology to recreate an effective working style of field geology. We demonstrate a microbathymetrical workflow, which is based on photogrammetric reconstruction of ROV imagery referenced to the acoustic vehicle track. The advantage over established acoustical systems lies in the true three-dimensionality of the data as opposed to the perspective projection from above produced by downward looking mapping methods. A full color texture mosaic derived from the imagery allows studies at resolutions beyond the resolved geometry (usually one order of magnitude below the image resolution) while color gives additional clues, which can only be partly resolved in acoustic backscatter. The creation of a three-dimensional model changes the working style from the temporal domain of a video recording back to the spatial domain of a map. We examine these datasets using a custom developed immersive virtual visualization environment. The ARENA (Artificial Research Environment for Networked Analysis) features a (lower) hemispherical screen at a diameter of six meters, accommodating up to four scientists at once thus providing the ability to browse data interactively among a group of researchers. This environment facilitates (1) the development of spatial understanding analogue to on-land outcrop studies, (2) quantitative observations of seafloor morphology and physical parameters of its deposits, (3) more effective formulation and communication of working hypotheses.

Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement.

PubMed

Yue, Shigang; Rind, F Claire

2006-05-01

The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds.

Evaluating visual and auditory contributions to the cognitive restoration effect.

PubMed

Emfield, Adam G; Neider, Mark B

2014-01-01

It has been suggested that certain real-world environments can have a restorative effect on an individual, as expressed in changes in cognitive performance and mood. Much of this research builds on Attention Restoration Theory (ART), which suggests that environments that have certain characteristics induce cognitive restoration via variations in attentional demands. Specifically, natural environments that require little top-down processing have a positive effect on cognitive performance, while city-like environments show no effect. We characterized the cognitive restoration effect further by examining (1) whether natural visual stimuli, such as blue spaces, were more likely to provide a restorative effect over urban visual stimuli, (2) if increasing immersion with environment-related sound produces a similar or superior effect, (3) if this effect extends to other cognitive tasks, such as the functional field of view (FFOV), and (4) if we could better understand this effect by providing controls beyond previous works. We had 202 participants complete a cognitive task battery, consisting of a reverse digit span task, the attention network task, and the FFOV task prior to and immediately after a restoration period. In the restoration period, participants were assigned to one of seven conditions in which they listened to natural or urban sounds, watched images of natural or urban environments, or a combination of both. Additionally, some participants were in a control group with exposure to neither picture nor sound. While we found some indication of practice effects, there were no differential effects of restoration observed in any of our cognitive tasks, regardless of condition. We did, however, find evidence that our nature images and sounds were more relaxing than their urban counterparts. Overall, our findings suggest that acute exposure to relaxing pictorial and auditory stimulus is insufficient to induce improvements in cognitive performance.

Toward a Visualization-Supported Workflow for Cyber Alert Management using Threat Models and Human-Centered Design

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

Franklin, Lyndsey; Pirrung, Megan A.; Blaha, Leslie M.

Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated tool support in the effort to make their tasks more efficient, accurate, and timely. This tool support comes in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, often speak of a need for tools which help them merge the data they already have and help them establish appropriate baselines against which to compare potential anomalies. Furthermore, existing threat modelsmore » that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand they analytic process and how one such model, the MITRE ATT&CK Matrix [32], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into the threat model to inform our eventual visualization designs. We examine data mapping for gaps where the threat model is under-supported by either data or tools. We discuss these gaps as potential design spaces for future research efforts. We also discuss the design of a prototype tool that combines machine-learning and visualization components to support cyber analysts working with this threat model.« less

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

PubMed Central

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-01-01

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs. PMID:25587878

Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks.

PubMed

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-11-26

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

Neural codes of seeing architectural styles

PubMed Central

Choo, Heeyoung; Nasar, Jack L.; Nikrahei, Bardia; Walther, Dirk B.

2017-01-01

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture. PMID:28071765

Neural codes of seeing architectural styles.

PubMed

Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

2017-01-10

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

NPSNET: Aural cues for virtual world immersion

NASA Astrophysics Data System (ADS)

Dahl, Leif A.

1992-09-01

NPSNET is a low-cost visual and aural simulation system designed and implemented at the Naval Postgraduate School. NPSNET is an example of a virtual world simulation environment that incorporates real-time aural cues through software-hardware interaction. In the current implementation of NPSNET, a graphics workstation functions in the sound server role which involves sending and receiving networked sound message packets across a Local Area Network, composed of multiple graphics workstations. The network messages contain sound file identification information that is transmitted from the sound server across an RS-422 protocol communication line to a serial to Musical Instrument Digital Interface (MIDI) converter. The MIDI converter, in turn relays the sound byte to a sampler, an electronic recording and playback device. The sampler correlates the hexadecimal input to a specific note or stored sound and sends it as an audio signal to speakers via an amplifier. The realism of a simulation is improved by involving multiple participant senses and removing external distractions. This thesis describes the incorporation of sound as aural cues, and the enhancement they provide in the virtual simulation environment of NPSNET.

Common and distinct brain networks underlying verbal and visual creativity.

PubMed

Zhu, Wenfeng; Chen, Qunlin; Xia, Lingxiang; Beaty, Roger E; Yang, Wenjing; Tian, Fang; Sun, Jiangzhou; Cao, Guikang; Zhang, Qinglin; Chen, Xu; Qiu, Jiang

2017-04-01

Creativity is imperative to the progression of human civilization, prosperity, and well-being. Past creative researches tends to emphasize the default mode network (DMN) or the frontoparietal network (FPN) somewhat exclusively. However, little is known about how these networks interact to contribute to creativity and whether common or distinct brain networks are responsible for visual and verbal creativity. Here, we use functional connectivity analysis of resting-state functional magnetic resonance imaging data to investigate visual and verbal creativity-related regions and networks in 282 healthy subjects. We found that functional connectivity within the bilateral superior parietal cortex of the FPN was negatively associated with visual and verbal creativity. The strength of connectivity between the DMN and FPN was positively related to both creative domains. Visual creativity was negatively correlated with functional connectivity within the precuneus of the pDMN and right middle frontal gyrus of the FPN, and verbal creativity was negatively correlated with functional connectivity within the medial prefrontal cortex of the aDMN. Critically, the FPN mediated the relationship between the aDMN and verbal creativity, and it also mediated the relationship between the pDMN and visual creativity. Taken together, decreased within-network connectivity of the FPN and DMN may allow for flexible between-network coupling in the highly creative brain. These findings provide indirect evidence for the cooperative role of the default and executive control networks in creativity, extending past research by revealing common and distinct brain systems underlying verbal and visual creative cognition. Hum Brain Mapp 38:2094-2111, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Functional network connectivity underlying food processing: disturbed salience and visual processing in overweight and obese adults.

PubMed

Kullmann, Stephanie; Pape, Anna-Antonia; Heni, Martin; Ketterer, Caroline; Schick, Fritz; Häring, Hans-Ulrich; Fritsche, Andreas; Preissl, Hubert; Veit, Ralf

2013-05-01

In order to adequately explore the neurobiological basis of eating behavior of humans and their changes with body weight, interactions between brain areas or networks need to be investigated. In the current functional magnetic resonance imaging study, we examined the modulating effects of stimulus category (food vs. nonfood), caloric content of food, and body weight on the time course and functional connectivity of 5 brain networks by means of independent component analysis in healthy lean and overweight/obese adults. These functional networks included motor sensory, default-mode, extrastriate visual, temporal visual association, and salience networks. We found an extensive modulation elicited by food stimuli in the 2 visual and salience networks, with a dissociable pattern in the time course and functional connectivity between lean and overweight/obese subjects. Specifically, only in lean subjects, the temporal visual association network was modulated by the stimulus category and the salience network by caloric content, whereas overweight and obese subjects showed a generalized augmented response in the salience network. Furthermore, overweight/obese subjects showed changes in functional connectivity in networks important for object recognition, motivational salience, and executive control. These alterations could potentially lead to top-down deficiencies driving the overconsumption of food in the obese population.

Towards a Comprehensive Computational Simulation System for Turbomachinery

NASA Technical Reports Server (NTRS)

Shih, Ming-Hsin

1994-01-01

The objective of this work is to develop algorithms associated with a comprehensive computational simulation system for turbomachinery flow fields. This development is accomplished in a modular fashion. These modules includes grid generation, visualization, network, simulation, toolbox, and flow modules. An interactive grid generation module is customized to facilitate the grid generation process associated with complicated turbomachinery configurations. With its user-friendly graphical user interface, the user may interactively manipulate the default settings to obtain a quality grid within a fraction of time that is usually required for building a grid about the same geometry with a general-purpose grid generation code. Non-Uniform Rational B-Spline formulations are utilized in the algorithm to maintain geometry fidelity while redistributing grid points on the solid surfaces. Bezier curve formulation is used to allow interactive construction of inner boundaries. It is also utilized to allow interactive point distribution. Cascade surfaces are transformed from three-dimensional surfaces of revolution into two-dimensional parametric planes for easy manipulation. Such a transformation allows these manipulated plane grids to be mapped to surfaces of revolution by any generatrix definition. A sophisticated visualization module is developed to al-low visualization for both grid and flow solution, steady or unsteady. A network module is built to allow data transferring in the heterogeneous environment. A flow module is integrated into this system, using an existing turbomachinery flow code. A simulation module is developed to combine the network, flow, and visualization module to achieve near real-time flow simulation about turbomachinery geometries. A toolbox module is developed to support the overall task. A batch version of the grid generation module is developed to allow portability and has been extended to allow dynamic grid generation for pitch changing turbomachinery configurations. Various applications with different characteristics are presented to demonstrate the success of this system.

A neural-visualization IDS for honeynet data.

PubMed

Herrero, Álvaro; Zurutuza, Urko; Corchado, Emilio

2012-04-01

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.

Visualizing and Integrating AFSCN Utilization into a Common Operational Picture

NASA Astrophysics Data System (ADS)

Hays, B.; Carlile, A.; Mitchell, T.

The Department of Defense (DoD) and the 50th Space Network Operations Group Studies and Analysis branch (50th SCS/SCXI), located at Schriever AFB Colorado, face the unique challenge of forecasting the expected near term and future utilization of the Air Force Satellite Control Network (AFSCN). The forecasting timeframe covers the planned load from the current date to ten years out. The various satellite missions, satellite requirements, orbital regions, and ground architecture dynamics provide the model inputs and constraints that are used in generating the forecasted load. The AFSCN is the largest network the Air Force uses to control satellites worldwide. Each day, network personnel perform over 500 scheduled events-from satellite maneuvers to critical data downloads. The Forecasting Objective is to provide leadership with the insights necessary to manage the network today and tomorrow. For both today's needs and future needs, SCXI develops AFSCN utilization forecasts to optimize the ground system's coverage and capacity to meet user satellite requirements. SCXI also performs satellite program specific studies to determine network support feasibility. STK and STK Scheduler form the core of the tools used by SCXI. To establish this tool suite, we had to evaluate, evolve, and validate both the COTS products and our own developed code and processes. This began with calibrating the network model to emulate the real life scheduling environment of the AFSCN. Multiple STK Scheduler optimizing (de-confliction) algorithms, including Multi-Pass, Sequential, Random, and Neural, were evaluated and adjusted to determine applicability to the model and the accuracy of the prediction. Additionally, the scheduling Figure of Merit (FOM), which permits custom weighting of various parameters, was analyzed and tested to achieve the most accurate real life result. With the inherent capabilities of STK and the ability to wrap and automate output, SCXI is now able to visually communicate satellite loads in a manner never seen before in AFSCN management meetings. Scenarios such as regional antenna load stress, satellite missed opportunities, and the overall network "big picture" can be visually displayed in 3D versus the textual and line graph methods used for many years. This is the first step towards an integrated space awareness picture with an operational focus. SCXI is working on taking the visual forecast concept farther and begin fusing multiple sources of data to build a 50 SW Common Operating Picture (COP). The vision is to integrate more effective orbital determination processes, resource outages, current and forecasted satellite mission requirements, and future architectural changes into a real-time visual status to enable quick and responsive decisions. This COP would be utilized in a Wing Operations Center to provide up to the minute network status on where satellites are, which ground resources are in contact with them, and what resources are down. The ability to quickly absorb and process this data will enhance decision analysis and save valuable time in both day to day operations and wartime scenarios.

FROMS3D: New Software for 3-D Visualization of Fracture Network System in Fractured Rock Masses

NASA Astrophysics Data System (ADS)

Noh, Y. H.; Um, J. G.; Choi, Y.

2014-12-01

A new software (FROMS3D) is presented to visualize fracture network system in 3-D. The software consists of several modules that play roles in management of borehole and field fracture data, fracture network modelling, visualization of fracture geometry in 3-D and calculation and visualization of intersections and equivalent pipes between fractures. Intel Parallel Studio XE 2013, Visual Studio.NET 2010 and the open source VTK library were utilized as development tools to efficiently implement the modules and the graphical user interface of the software. The results have suggested that the developed software is effective in visualizing 3-D fracture network system, and can provide useful information to tackle the engineering geological problems related to strength, deformability and hydraulic behaviors of the fractured rock masses.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

Real-Time Visualization of Network Behaviors for Situational Awareness

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

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

PubMed Central

2012-01-01

Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications. PMID:22448851

Visualizing Article Similarities via Sparsified Article Network and Map Projection for Systematic Reviews.

PubMed

Ji, Xiaonan; Machiraju, Raghu; Ritter, Alan; Yen, Po-Yin

2017-01-01

Systematic Reviews (SRs) of biomedical literature summarize evidence from high-quality studies to inform clinical decisions, but are time and labor intensive due to the large number of article collections. Article similarities established from textual features have been shown to assist in the identification of relevant articles, thus facilitating the article screening process efficiently. In this study, we visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns. To prompt an effective visualization in an interpretable, intuitive, and scalable way, we implemented a graph-based network visualization with three network sparsification approaches and a distance-based map projection via dimensionality reduction. We evaluated and compared three network sparsification approaches and the visualization types (article network vs. article map). We demonstrated the effectiveness in revealing article distribution and exhibiting clustering patterns of relevant articles with practical meanings for SRs.

BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3

PubMed Central

Su, Gang; Morris, John H.; Demchak, Barry; Bader, Gary D.

2014-01-01

Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and pathway construction. Cytoscape provides core functionality to load, visualize, search, filter and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the network, visualizing network associated data (attributes) and identifying clusters. It also highlights new features that benefit experienced users. PMID:25199793

RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria.

PubMed

Novichkov, Pavel S; Kazakov, Alexey E; Ravcheev, Dmitry A; Leyn, Semen A; Kovaleva, Galina Y; Sutormin, Roman A; Kazanov, Marat D; Riehl, William; Arkin, Adam P; Dubchak, Inna; Rodionov, Dmitry A

2013-11-01

Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in prokaryotes is one of the critical tasks of modern genomics. Bacteria from different taxonomic groups, whose lifestyles and natural environments are substantially different, possess highly diverged transcriptional regulatory networks. The comparative genomics approaches are useful for in silico reconstruction of bacterial regulons and networks operated by both transcription factors (TFs) and RNA regulatory elements (riboswitches). RegPrecise (http://regprecise.lbl.gov) is a web resource for collection, visualization and analysis of transcriptional regulons reconstructed by comparative genomics. We significantly expanded a reference collection of manually curated regulons we introduced earlier. RegPrecise 3.0 provides access to inferred regulatory interactions organized by phylogenetic, structural and functional properties. Taxonomy-specific collections include 781 TF regulogs inferred in more than 160 genomes representing 14 taxonomic groups of Bacteria. TF-specific collections include regulogs for a selected subset of 40 TFs reconstructed across more than 30 taxonomic lineages. Novel collections of regulons operated by RNA regulatory elements (riboswitches) include near 400 regulogs inferred in 24 bacterial lineages. RegPrecise 3.0 provides four classifications of the reference regulons implemented as controlled vocabularies: 55 TF protein families; 43 RNA motif families; ~150 biological processes or metabolic pathways; and ~200 effectors or environmental signals. Genome-wide visualization of regulatory networks and metabolic pathways covered by the reference regulons are available for all studied genomes. A separate section of RegPrecise 3.0 contains draft regulatory networks in 640 genomes obtained by an conservative propagation of the reference regulons to closely related genomes. RegPrecise 3.0 gives access to the transcriptional regulons reconstructed in bacterial genomes. Analytical capabilities include exploration of: regulon content, structure and function; TF binding site motifs; conservation and variations in genome-wide regulatory networks across all taxonomic groups of Bacteria. RegPrecise 3.0 was selected as a core resource on transcriptional regulation of the Department of Energy Systems Biology Knowledgebase, an emerging software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses, and model interactions in microbes, plants, and their communities.

Integrating Time-Synchronized Video with Other Geospatial and Temporal Data for Remote Science Operations

NASA Technical Reports Server (NTRS)

Cohen, Tamar E.; Lees, David S.; Deans, Matthew C.; Lim, Darlene S. S.; Lee, Yeon Jin Grace

2018-01-01

Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing video in context with map, instrument data visualization, geo-located notes and any other collected data. xGDS is an open source web-based software suite developed at NASA Ames Research Center to support remote science operations in analog missions and prototype solutions for remote planetary exploration. (See Appendix B) Typical video systems are designed to play or stream video only, independent of other data collected in the context of the video. Providing customizable displays for monitoring live video and data as well as replaying recorded video and data helps end users build up a rich situational awareness. xGDS was designed to support remote field exploration with unreliable networks. Commercial digital recording systems operate under the assumption that there is a stable and reliable network between the source of the video and the recording system. In many field deployments and space exploration scenarios, this is not the case - there are both anticipated and unexpected network losses. xGDS' Video Module handles these interruptions, storing the available video, organizing and characterizing the dropouts, and presenting the video for streaming or replay to the end user including visualization of the dropouts. Scientific instruments often require custom or expensive software to analyze and visualize collected data. This limits the speed at which the data can be visualized and limits access to the data to those users with the software. xGDS' Instrument Module integrates with instruments that collect and broadcast data in a single snapshot or that continually collect and broadcast a stream of data. While seeing a visualization of collected instrument data is informative, showing the context for the collected data, other data collected nearby along with events indicating current status helps remote science teams build a better understanding of the environment. Further, sharing geo-located, tagged notes recorded by the scientists and others on the team spurs deeper analysis of the data.

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

DOE PAGES

Pavlopoulos, Georgios A.; Paez-Espino, David; Kyrpides, Nikos C.; ...

2017-07-18

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. Lastly, we comment on their strengths and their weaknesses andmore » empirically discuss their scalability, user friendliness, and postvisualization capabilities.« less

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

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

Pavlopoulos, Georgios A.; Paez-Espino, David; Kyrpides, Nikos C.

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. Lastly, we comment on their strengths and their weaknesses andmore » empirically discuss their scalability, user friendliness, and postvisualization capabilities.« less

A Space and Atmospheric Visualization Science System

NASA Technical Reports Server (NTRS)

Szuszczewicz, E. P.; Blanchard, P.; Mankofsky, A.; Goodrich, C.; Kamins, D.; Kulkarni, R.; Mcnabb, D.; Moroh, M.

1994-01-01

SAVS (a Space and Atmospheric Visualization Science system) is an integrated system with user-friendly functionality that employs a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, analysis, and visualization. All of this is accomplished without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. This report describes SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the earth's thermospheric, ionospheric, and mesospheric domains (TIMED). The final chapters provide a user-oriented description of interface functionalities, hands-on operations, and customized modules, with details of the primary modules presented in the appendices. The overall intent of the report is to reflect the accomplishments of the three-year development effort and to introduce potential users to the power and utility of the integrated data acquisition, analysis, and visualization system.

Web-based hybrid-dimensional Visualization and Exploration of Cytological Localization Scenarios.

PubMed

Kovanci, Gökhan; Ghaffar, Mehmood; Sommer, Björn

2016-12-21

The CELLmicrocosmos 4.2 PathwayIntegration (CmPI) is a tool which provides hybrid-dimensional visualization and analysis of intracellular protein and gene localizations in the context of a virtual 3D environment. This tool is developed based on Java/Java3D/JOGL and provides a standalone application compatible to all relevant operating systems. However, it requires Java and the local installation of the software. Here we present the prototype of an alternative web-based visualization approach, using Three.js and D3.js. In this way it is possible to visualize and explore CmPI-generated localization scenarios including networks mapped to 3D cell components by just providing a URL to a collaboration partner. This publication describes the integration of the different technologies – Three.js, D3.js and PHP – as well as an application case: a localization scenario of the citrate cycle. The CmPI web viewer is available at: http://CmPIweb.CELLmicrocosmos.org.

Web-based hybrid-dimensional Visualization and Exploration of Cytological Localization Scenarios.

PubMed

Kovanci, Gökhan; Ghaffar, Mehmood; Sommer, Björn

2016-10-01

The CELLmicrocosmos 4.2 PathwayIntegration (CmPI) is a tool which provides hybriddimensional visualization and analysis of intracellular protein and gene localizations in the context of a virtual 3D environment. This tool is developed based on Java/Java3D/JOGL and provides a standalone application compatible to all relevant operating systems. However, it requires Java and the local installation of the software. Here we present the prototype of an alternative web-based visualization approach, using Three.js and D3.js. In this way it is possible to visualize and explore CmPI-generated localization scenarios including networks mapped to 3D cell components by just providing a URL to a collaboration partner. This publication describes the integration of the different technologies - Three.js, D3.js and PHP - as well as an application case: a localization scenario of the citrate cycle. The CmPI web viewer is available at: http://CmPIweb.CELLmicrocosmos.org.

Applications of the pipeline environment for visual informatics and genomics computations

PubMed Central

2011-01-01

Background Contemporary informatics and genomics research require efficient, flexible and robust management of large heterogeneous data, advanced computational tools, powerful visualization, reliable hardware infrastructure, interoperability of computational resources, and detailed data and analysis-protocol provenance. The Pipeline is a client-server distributed computational environment that facilitates the visual graphical construction, execution, monitoring, validation and dissemination of advanced data analysis protocols. Results This paper reports on the applications of the LONI Pipeline environment to address two informatics challenges - graphical management of diverse genomics tools, and the interoperability of informatics software. Specifically, this manuscript presents the concrete details of deploying general informatics suites and individual software tools to new hardware infrastructures, the design, validation and execution of new visual analysis protocols via the Pipeline graphical interface, and integration of diverse informatics tools via the Pipeline eXtensible Markup Language syntax. We demonstrate each of these processes using several established informatics packages (e.g., miBLAST, EMBOSS, mrFAST, GWASS, MAQ, SAMtools, Bowtie) for basic local sequence alignment and search, molecular biology data analysis, and genome-wide association studies. These examples demonstrate the power of the Pipeline graphical workflow environment to enable integration of bioinformatics resources which provide a well-defined syntax for dynamic specification of the input/output parameters and the run-time execution controls. Conclusions The LONI Pipeline environment http://pipeline.loni.ucla.edu provides a flexible graphical infrastructure for efficient biomedical computing and distributed informatics research. The interactive Pipeline resource manager enables the utilization and interoperability of diverse types of informatics resources. The Pipeline client-server model provides computational power to a broad spectrum of informatics investigators - experienced developers and novice users, user with or without access to advanced computational-resources (e.g., Grid, data), as well as basic and translational scientists. The open development, validation and dissemination of computational networks (pipeline workflows) facilitates the sharing of knowledge, tools, protocols and best practices, and enables the unbiased validation and replication of scientific findings by the entire community. PMID:21791102

NCWin — A Component Object Model (COM) for processing and visualizing NetCDF data

USGS Publications Warehouse

Liu, Jinxun; Chen, J.M.; Price, D.T.; Liu, S.

2005-01-01

NetCDF (Network Common Data Form) is a data sharing protocol and library that is commonly used in large-scale atmospheric and environmental data archiving and modeling. The NetCDF tool described here, named NCWin and coded with Borland C + + Builder, was built as a standard executable as well as a COM (component object model) for the Microsoft Windows environment. COM is a powerful technology that enhances the reuse of applications (as components). Environmental model developers from different modeling environments, such as Python, JAVA, VISUAL FORTRAN, VISUAL BASIC, VISUAL C + +, and DELPHI, can reuse NCWin in their models to read, write and visualize NetCDF data. Some Windows applications, such as ArcGIS and Microsoft PowerPoint, can also call NCWin within the application. NCWin has three major components: 1) The data conversion part is designed to convert binary raw data to and from NetCDF data. It can process six data types (unsigned char, signed char, short, int, float, double) and three spatial data formats (BIP, BIL, BSQ); 2) The visualization part is designed for displaying grid map series (playing forward or backward) with simple map legend, and displaying temporal trend curves for data on individual map pixels; and 3) The modeling interface is designed for environmental model development by which a set of integrated NetCDF functions is provided for processing NetCDF data. To demonstrate that the NCWin can easily extend the functions of some current GIS software and the Office applications, examples of calling NCWin within ArcGIS and MS PowerPoint for showing NetCDF map animations are given.

A Visual-Based Approach for Indoor Radio Map Construction Using Smartphones.

PubMed

Liu, Tao; Zhang, Xing; Li, Qingquan; Fang, Zhixiang

2017-08-04

Localization of users in indoor spaces is a common issue in many applications. Among various technologies, a Wi-Fi fingerprinting based localization solution has attracted much attention, since it can be easily deployed using the existing off-the-shelf mobile devices and wireless networks. However, the collection of the Wi-Fi radio map is quite labor-intensive, which limits its potential for large-scale application. In this paper, a visual-based approach is proposed for the construction of a radio map in anonymous indoor environments. This approach collects multi-sensor data, e.g., Wi-Fi signals, video frames, inertial readings, when people are walking in indoor environments with smartphones in their hands. Then, it spatially recovers the trajectories of people by using both visual and inertial information. Finally, it estimates the location of fingerprints from the trajectories and constructs a Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m. A weighted k-nearest neighbor method is also used to evaluate the constructed radio map. The average localization error is about 3.2 m, indicating that the quality of the constructed radio map is at the same level as those constructed by site surveying. However, this approach can greatly reduce the human labor cost, which increases the potential for applying it to large indoor environments.

Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation

PubMed Central

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1–V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy. PMID:25157228

Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

PubMed

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

Assessing older adults' perceptions of sensor data and designing visual displays for ambient environments. An exploratory study.

PubMed

Reeder, B; Chung, J; Le, T; Thompson, H; Demiris, G

2014-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records". Our objectives were to: 1) characterize older adult participants' perceived usefulness of in-home sensor data and 2) develop novel visual displays for sensor data from Ambient Assisted Living environments that can become part of electronic health records. Semi-structured interviews were conducted with community-dwelling older adult participants during three and six-month visits. We engaged participants in two design iterations by soliciting feedback about display types and visual displays of simulated data related to a fall scenario. Interview transcripts were analyzed to identify themes related to perceived usefulness of sensor data. Thematic analysis identified three themes: perceived usefulness of sensor data for managing health; factors that affect perceived usefulness of sensor data and; perceived usefulness of visual displays. Visual displays were cited as potentially useful for family members and health care providers. Three novel visual displays were created based on interview results, design guidelines derived from prior AAL research, and principles of graphic design theory. Participants identified potential uses of personal activity data for monitoring health status and capturing early signs of illness. One area for future research is to determine how visual displays of AAL data might be utilized to connect family members and health care providers through shared understanding of activity levels versus a more simplified view of self-management. Connecting informal and formal caregiving networks may facilitate better communication between older adults, family members and health care providers for shared decision-making.

Lifting Scheme DWT Implementation in a Wireless Vision Sensor Network

NASA Astrophysics Data System (ADS)

Ong, Jia Jan; Ang, L.-M.; Seng, K. P.

This paper presents the practical implementation of a Wireless Visual Sensor Network (WVSN) with DWT processing on the visual nodes. WVSN consists of visual nodes that capture video and transmit to the base-station without processing. Limitation of network bandwidth restrains the implementation of real time video streaming from remote visual nodes through wireless communication. Three layers of DWT filters are implemented to process the captured image from the camera. With having all the wavelet coefficients produced, it is possible just to transmit the low frequency band coefficients and obtain an approximate image at the base-station. This will reduce the amount of power required in transmission. When necessary, transmitting all the wavelet coefficients will produce the full detail of image, which is similar to the image captured at the visual nodes. The visual node combines the CMOS camera, Xilinx Spartan-3L FPGA and wireless ZigBee® network that uses the Ember EM250 chip.

Visualizing weighted networks: a performance comparison of adjacency matrices versus node-link diagrams

NASA Astrophysics Data System (ADS)

McIntire, John P.; Osesina, O. Isaac; Bartley, Cecilia; Tudoreanu, M. Eduard; Havig, Paul R.; Geiselman, Eric E.

2012-06-01

Ensuring the proper and effective ways to visualize network data is important for many areas of academia, applied sciences, the military, and the public. Fields such as social network analysis, genetics, biochemistry, intelligence, cybersecurity, neural network modeling, transit systems, communications, etc. often deal with large, complex network datasets that can be difficult to interact with, study, and use. There have been surprisingly few human factors performance studies on the relative effectiveness of different graph drawings or network diagram techniques to convey information to a viewer. This is particularly true for weighted networks which include the strength of connections between nodes, not just information about which nodes are linked to other nodes. We describe a human factors study in which participants performed four separate network analysis tasks (finding a direct link between given nodes, finding an interconnected node between given nodes, estimating link strengths, and estimating the most densely interconnected nodes) on two different network visualizations: an adjacency matrix with a heat-map versus a node-link diagram. The results should help shed light on effective methods of visualizing network data for some representative analysis tasks, with the ultimate goal of improving usability and performance for viewers of network data displays.

Technical note: real-time web-based wireless visual guidance system for radiotherapy.

PubMed

Lee, Danny; Kim, Siyong; Palta, Jatinder R; Kim, Taeho

2017-06-01

Describe a Web-based wireless visual guidance system that mitigates issues associated with hard-wired audio-visual aided patient interactive motion management systems that are cumbersome to use in routine clinical practice. Web-based wireless visual display duplicates an existing visual display of a respiratory-motion management system for visual guidance. The visual display of the existing system is sent to legacy Web clients over a private wireless network, thereby allowing a wireless setting for real-time visual guidance. In this study, active breathing coordinator (ABC) trace was used as an input for visual display, which captured and transmitted to Web clients. Virtual reality goggles require two (left and right eye view) images for visual display. We investigated the performance of Web-based wireless visual guidance by quantifying (1) the network latency of visual displays between an ABC computer display and Web clients of a laptop, an iPad mini 2 and an iPhone 6, and (2) the frame rate of visual display on the Web clients in frames per second (fps). The network latency of visual display between the ABC computer and Web clients was about 100 ms and the frame rate was 14.0 fps (laptop), 9.2 fps (iPad mini 2) and 11.2 fps (iPhone 6). In addition, visual display for virtual reality goggles was successfully shown on the iPhone 6 with 100 ms and 11.2 fps. A high network security was maintained by utilizing the private network configuration. This study demonstrated that a Web-based wireless visual guidance can be a promising technique for clinical motion management systems, which require real-time visual display of their outputs. Based on the results of this study, our approach has the potential to reduce clutter associated with wired-systems, reduce space requirements, and extend the use of medical devices from static usage to interactive and dynamic usage in a radiotherapy treatment vault.

A survey of visualization systems for network security.

PubMed

Shiravi, Hadi; Shiravi, Ali; Ghorbani, Ali A

2012-08-01

Security Visualization is a very young term. It expresses the idea that common visualization techniques have been designed for use cases that are not supportive of security-related data, demanding novel techniques fine tuned for the purpose of thorough analysis. Significant amount of work has been published in this area, but little work has been done to study this emerging visualization discipline. We offer a comprehensive review of network security visualization and provide a taxonomy in the form of five use-case classes encompassing nearly all recent works in this area. We outline the incorporated visualization techniques and data sources and provide an informative table to display our findings. From the analysis of these systems, we examine issues and concerns regarding network security visualization and provide guidelines and directions for future researchers and visual system developers.

Development of a Three Dimensional Wireless Sensor Network for Terrain-Climate Research in Remote Mountainous Environments

NASA Astrophysics Data System (ADS)

Kavanagh, K.; Davis, A.; Gessler, P.; Hess, H.; Holden, Z.; Link, T. E.; Newingham, B. A.; Smith, A. M.; Robinson, P.

2011-12-01

Developing sensor networks that are robust enough to perform in the world's remote regions is critical since these regions serve as important benchmarks compared to human-dominated areas. Paradoxically, the factors that make these remote, natural sites challenging for sensor networking are often what make them indispensable for climate change research. We aim to overcome these challenges by developing a three-dimensional sensor network arrayed across a topoclimatic gradient (1100-1800 meters) in a wilderness area in central Idaho. Development of this sensor array builds upon advances in sensing, networking, and power supply technologies coupled with experiences of the multidisciplinary investigators in conducting research in remote mountainous locations. The proposed gradient monitoring network will provide near real-time data from a three-dimensional (3-D) array of sensors measuring biophysical parameters used in ecosystem process models. The network will monitor atmospheric carbon dioxide concentration, humidity, air and soil temperature, soil water content, precipitation, incoming and outgoing shortwave and longwave radiation, snow depth, wind speed and direction, tree stem growth and leaf wetness at time intervals ranging from seconds to days. The long-term goal of this project is to realize a transformative integration of smart sensor networks adaptively communicating data in real-time to ultimately achieve a 3-D visualization of ecosystem processes within remote mountainous regions. Process models will be the interface between the visualization platforms and the sensor network. This will allow us to better predict how non-human dominated terrestrial and aquatic ecosystems function and respond to climate dynamics. Access to the data will be ensured as part of the Northwest Knowledge Network being developed at the University of Idaho, through ongoing Idaho NSF-funded cyber infrastructure initiatives, and existing data management systems funded by NSF, such as the CUAHSI Hydrologic Information System (HIS). These efforts will enhance cross-disciplinary understanding of natural and anthropogenic influences on ecosystem function and ultimately inform decision-making.

Three-dimensional evidence network plot system: covariate imbalances and effects in network meta-analysis explored using a new software tool.

PubMed

Batson, Sarah; Score, Robert; Sutton, Alex J

2017-06-01

The aim of the study was to develop the three-dimensional (3D) evidence network plot system-a novel web-based interactive 3D tool to facilitate the visualization and exploration of covariate distributions and imbalances across evidence networks for network meta-analysis (NMA). We developed the 3D evidence network plot system within an AngularJS environment using a third party JavaScript library (Three.js) to create the 3D element of the application. Data used to enable the creation of the 3D element for a particular topic are inputted via a Microsoft Excel template spreadsheet that has been specifically formatted to hold these data. We display and discuss the findings of applying the tool to two NMA examples considering multiple covariates. These two examples have been previously identified as having potentially important covariate effects and allow us to document the various features of the tool while illustrating how it can be used. The 3D evidence network plot system provides an immediate, intuitive, and accessible way to assess the similarity and differences between the values of covariates for individual studies within and between each treatment contrast in an evidence network. In this way, differences between the studies, which may invalidate the usual assumptions of an NMA, can be identified for further scrutiny. Hence, the tool facilitates NMA feasibility/validity assessments and aids in the interpretation of NMA results. The 3D evidence network plot system is the first tool designed specifically to visualize covariate distributions and imbalances across evidence networks in 3D. This will be of primary interest to systematic review and meta-analysis researchers and, more generally, those assessing the validity and robustness of an NMA to inform reimbursement decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

NASA Astrophysics Data System (ADS)

Madokoro, H.; Tsukada, M.; Sato, K.

2013-07-01

This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

Advanced Visualization of Experimental Data in Real Time Using LiveView3D

NASA Technical Reports Server (NTRS)

Schwartz, Richard J.; Fleming, Gary A.

2006-01-01

LiveView3D is a software application that imports and displays a variety of wind tunnel derived data in an interactive virtual environment in real time. LiveView3D combines the use of streaming video fed into a three-dimensional virtual representation of the test configuration with networked communications to the test facility Data Acquisition System (DAS). This unified approach to real time data visualization provides a unique opportunity to comprehend very large sets of diverse forms of data in a real time situation, as well as in post-test analysis. This paper describes how LiveView3D has been implemented to visualize diverse forms of aerodynamic data gathered during wind tunnel experiments, most notably at the NASA Langley Research Center Unitary Plan Wind Tunnel (UPWT). Planned future developments of the LiveView3D system are also addressed.

New solutions for climate network visualization

NASA Astrophysics Data System (ADS)

Nocke, Thomas; Buschmann, Stefan; Donges, Jonathan F.; Marwan, Norbert

2016-04-01

An increasing amount of climate and climate impact research methods deals with geo-referenced networks, including energy, trade, supply-chain, disease dissemination and climatic tele-connection networks. At the same time, the size and complexity of these networks increases, resulting in networks of more than hundred thousand or even millions of edges, which are often temporally evolving, have additional data at nodes and edges, and can consist of multiple layers even in real 3D. This gives challenges to both the static representation and the interactive exploration of these networks, first of all avoiding edge clutter ("edge spagetti") and allowing interactivity even for unfiltered networks. Within this presentation, we illustrate potential solutions to these challenges. Therefore, we give a glimpse on a questionnaire performed with climate and complex system scientists with respect to their network visualization requirements, and on a review of available state-of-the-art visualization techniques and tools for this purpose (see as well Nocke et al., 2015). In the main part, we present alternative visualization solutions for several use cases (global, regional, and multi-layered climate networks) including alternative geographic projections, edge bundling, and 3-D network support (based on CGV and GTX tools), and implementation details to reach interactive frame rates. References: Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, and C. Tominski: Review: Visual analytics of climate networks, Nonlinear Processes in Geophysics, 22, 545-570, doi:10.5194/npg-22-545-2015, 2015

A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space

PubMed Central

Zheng, Wei; Zhang, Xiaoya; Lu, Qi

2015-01-01

This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones. PMID:26011618

Planning in subsumption architectures

NASA Technical Reports Server (NTRS)

Chalfant, Eugene C.

1994-01-01

A subsumption planner using a parallel distributed computational paradigm based on the subsumption architecture for control of real-world capable robots is described. Virtual sensor state space is used as a planning tool to visualize the robot's anticipated effect on its environment. Decision sequences are generated based on the environmental situation expected at the time the robot must commit to a decision. Between decision points, the robot performs in a preprogrammed manner. A rudimentary, domain-specific partial world model contains enough information to extrapolate the end results of the rote behavior between decision points. A collective network of predictors operates in parallel with the reactive network forming a recurrrent network which generates plans as a hierarchy. Details of a plan segment are generated only when its execution is imminent. The use of the subsumption planner is demonstrated by a simple maze navigation problem.

Biologically inspired computation and learning in Sensorimotor Systems

NASA Astrophysics Data System (ADS)

Lee, Daniel D.; Seung, H. S.

2001-11-01

Networking systems presently lack the ability to intelligently process the rich multimedia content of the data traffic they carry. Endowing artificial systems with the ability to adapt to changing conditions requires algorithms that can rapidly learn from examples. We demonstrate the application of such learning algorithms on an inexpensive quadruped robot constructed to perform simple sensorimotor tasks. The robot learns to track a particular object by discovering the salient visual and auditory cues unique to that object. The system uses a convolutional neural network that automatically combines color, luminance, motion, and auditory information. The weights of the networks are adjusted using feedback from a teacher to reflect the reliability of the various input channels in the surrounding environment. Additionally, the robot is able to compensate for its own motion by adapting the parameters of a vestibular ocular reflex system.

MoZis: mobile zoo information system: a case study for the city of Osnabrueck

NASA Astrophysics Data System (ADS)

Michel, Ulrich

2007-10-01

This paper describes a new project of the Institute for Geoinformatics and Remote Sensing, funded by the German Federal Foundation for the Environment (DBU, Deutsche Bundesstiftung Umwelt www.dbu.de). The goal of this project is to develop a mobile zoo information system for Pocket PCs and Smart phones. Visitors of the zoo will be able to use their own mobile devices or use Pocket PCs, which could be borrowed from the zoo to navigate around the zoo's facilities. The system will also provide additional multimedia based information such as audio-based material, animal video clips, and maps of their natural habitat. People could have access to the project at the zoo via wireless local area network or by downloading the necessary files using a home internet connection. Our software environment consists of proprietary and non-proprietary software solutions in order to make it as flexible as possible. Our first prototype was developed with Visual Studio 2003 and Visual Basic.Net.

Visual terrain mapping for traversable path planning of mobile robots

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Amrani, Rachida; Tunstel, Edward W.

2004-10-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Soft computing-based terrain visual sensing and data fusion for unmanned ground robotic systems

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir

2006-05-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Microphone Array Phased Processing System (MAPPS): Version 4.0 Manual

NASA Technical Reports Server (NTRS)

Watts, Michael E.; Mosher, Marianne; Barnes, Michael; Bardina, Jorge

1999-01-01

A processing system has been developed to meet increasing demands for detailed noise measurement of individual model components. The Microphone Array Phased Processing System (MAPPS) uses graphical user interfaces to control all aspects of data processing and visualization. The system uses networked parallel computers to provide noise maps at selected frequencies in a near real-time testing environment. The system has been successfully used in the NASA Ames 7- by 10-Foot Wind Tunnel.

A pore-scale study of fracture dynamics in rock using X-ray micro-CT under ambient freeze-thaw cycling.

PubMed

De Kock, Tim; Boone, Marijn A; De Schryver, Thomas; Van Stappen, Jeroen; Derluyn, Hannelore; Masschaele, Bert; De Schutter, Geert; Cnudde, Veerle

2015-03-03

Freeze-thaw cycling stresses many environments which include porous media such as soil, rock and concrete. Climate change can expose new regions and subject others to a changing freeze-thaw frequency. Therefore, understanding and predicting the effect of freeze-thaw cycles is important in environmental science, the built environment and cultural heritage preservation. In this paper, we explore the possibilities of state-of-the-art micro-CT in studying the pore scale dynamics related to freezing and thawing. The experiments show the development of a fracture network in a porous limestone when cooling to -9.7 °C, at which an exothermal temperature peak is a proxy for ice crystallization. The dynamics of the fracture network are visualized with a time frame of 80 s. Theoretical assumptions predict that crystallization in these experiments occurs in pores of 6-20.1 nm under transient conditions. Here, the crystallization-induced stress exceeds rock strength when the local crystal fraction in the pores is 4.3%. The location of fractures is strongly related to preferential water uptake paths and rock texture, which are visually identified. Laboratory, continuous X-ray micro-CT scanning opens new perspectives for the pore-scale study of ice crystallization in porous media as well as for environmental processes related to freeze-thaw fracturing.

Visual Environments for CFD Research

NASA Technical Reports Server (NTRS)

Watson, Val; George, Michael W. (Technical Monitor)

1994-01-01

This viewgraph presentation gives an overview of the visual environments for computational fluid dynamics (CFD) research. It includes details on critical needs from the future computer environment, features needed to attain this environment, prospects for changes in and the impact of the visualization revolution on the human-computer interface, human processing capabilities, limits of personal environment and the extension of that environment with computers. Information is given on the need for more 'visual' thinking (including instances of visual thinking), an evaluation of the alternate approaches for and levels of interactive computer graphics, a visual analysis of computational fluid dynamics, and an analysis of visualization software.

Visual imagery and functional connectivity in blindness: a single-case study

PubMed Central

Boucard, Christine C.; Rauschecker, Josef P.; Neufang, Susanne; Berthele, Achim; Doll, Anselm; Manoliu, Andrej; Riedl, Valentin; Sorg, Christian; Wohlschläger, Afra; Mühlau, Mark

2016-01-01

We present a case report on visual brain plasticity after total blindness acquired in adulthood. SH lost her sight when she was 27. Despite having been totally blind for 43 years, she reported to strongly rely on her vivid visual imagery. Three-Tesla magnetic resonance imaging (MRI) of SH and age-matched controls was performed. The MRI sequence included anatomical MRI, resting-state functional MRI, and task-related functional MRI where SH was instructed to imagine colours, faces, and motion. Compared to controls, voxel-based analysis revealed white matter loss along SH's visual pathway as well as grey matter atrophy in the calcarine sulci. Yet we demonstrated activation in visual areas, including V1, using functional MRI. Of the four identified visual resting-state networks, none showed alterations in spatial extent; hence, SH's preserved visual imagery seems to be mediated by intrinsic brain networks of normal extent. Time courses of two of these networks showed increased correlation with that of the inferior posterior default mode network, which may reflect adaptive changes supporting SH's strong internal visual representations. Overall, our findings demonstrate that conscious visual experience is possible even after years of absence of extrinsic input. PMID:25690326

Visual imagery and functional connectivity in blindness: a single-case study.

PubMed

Boucard, Christine C; Rauschecker, Josef P; Neufang, Susanne; Berthele, Achim; Doll, Anselm; Manoliu, Andrej; Riedl, Valentin; Sorg, Christian; Wohlschläger, Afra; Mühlau, Mark

2016-05-01

We present a case report on visual brain plasticity after total blindness acquired in adulthood. SH lost her sight when she was 27. Despite having been totally blind for 43 years, she reported to strongly rely on her vivid visual imagery. Three-Tesla magnetic resonance imaging (MRI) of SH and age-matched controls was performed. The MRI sequence included anatomical MRI, resting-state functional MRI, and task-related functional MRI where SH was instructed to imagine colours, faces, and motion. Compared to controls, voxel-based analysis revealed white matter loss along SH's visual pathway as well as grey matter atrophy in the calcarine sulci. Yet we demonstrated activation in visual areas, including V1, using functional MRI. Of the four identified visual resting-state networks, none showed alterations in spatial extent; hence, SH's preserved visual imagery seems to be mediated by intrinsic brain networks of normal extent. Time courses of two of these networks showed increased correlation with that of the inferior posterior default mode network, which may reflect adaptive changes supporting SH's strong internal visual representations. Overall, our findings demonstrate that conscious visual experience is possible even after years of absence of extrinsic input.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization.

PubMed

Jung, Sang-Kyu; McDonald, Karen

2011-08-16

Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization

PubMed Central

2011-01-01

Background Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. Results The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. Conclusion Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net. PMID:21846353

Mobile collaborative medical display system.

PubMed

Park, Sanghun; Kim, Wontae; Ihm, Insung

2008-03-01

Because of recent advances in wireless communication technologies, the world of mobile computing is flourishing with a variety of applications. In this study, we present an integrated architecture for a personal digital assistant (PDA)-based mobile medical display system that supports collaborative work between remote users. We aim to develop a system that enables users in different regions to share a working environment for collaborative visualization with the potential for exploring huge medical datasets. Our system consists of three major components: mobile client, gateway, and parallel rendering server. The mobile client serves as a front end and enables users to choose the visualization and control parameters interactively and cooperatively. The gateway handles requests and responses between mobile clients and the rendering server for efficient communication. Through the gateway, it is possible to share working environments between users, allowing them to work together in computer supported cooperative work (CSCW) mode. Finally, the parallel rendering server is responsible for performing heavy visualization tasks. Our experience indicates that some features currently available to our mobile clients for collaborative scientific visualization are limited due to the poor performance of mobile devices and the low bandwidth of wireless connections. However, as mobile devices and wireless network systems are experiencing considerable elevation in their capabilities, we believe that our methodology will be utilized effectively in building quite responsive, useful mobile collaborative medical systems in the very near future.

Representation of visual symbols in the visual word processing network.

PubMed

Muayqil, Taim; Davies-Thompson, Jodie; Barton, Jason J S

2015-03-01

Previous studies have shown that word processing involves a predominantly left-sided occipitotemporal network. Words are a form of symbolic representation, in that they are arbitrary perceptual stimuli that represent other objects, actions or concepts. Lesions of parts of the visual word processing network can cause alexia, which can be associated with difficulty processing other types of symbols such as musical notation or road signs. We investigated whether components of the visual word processing network were also activated by other types of symbols. In 16 music-literate subjects, we defined the visual word network using fMRI and examined responses to four symbolic categories: visual words, musical notation, instructive symbols (e.g. traffic signs), and flags and logos. For each category we compared responses not only to scrambled stimuli, but also to similar stimuli that lacked symbolic meaning. The left visual word form area and a homologous right fusiform region responded similarly to all four categories, but equally to both symbolic and non-symbolic equivalents. Greater response to symbolic than non-symbolic stimuli occurred only in the left inferior frontal and middle temporal gyri, but only for words, and in the case of the left inferior frontal gyri, also for musical notation. A whole-brain analysis comparing symbolic versus non-symbolic stimuli revealed a distributed network of inferior temporooccipital and parietal regions that differed for different symbols. The fusiform gyri are involved in processing the form of many symbolic stimuli, but not specifically for stimuli with symbolic content. Selectivity for stimuli with symbolic content only emerges in the visual word network at the level of the middle temporal and inferior frontal gyri, but is specific for words and musical notation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting state neural networks for visual Chinese word processing in Chinese adults and children.

PubMed

Li, Ling; Liu, Jiangang; Chen, Feiyan; Feng, Lu; Li, Hong; Tian, Jie; Lee, Kang

2013-07-01

This study examined the resting state neural networks for visual Chinese word processing in Chinese children and adults. Both the functional connectivity (FC) and amplitude of low frequency fluctuation (ALFF) approaches were used to analyze the fMRI data collected when Chinese participants were not engaged in any specific explicit tasks. We correlated time series extracted from the visual word form area (VWFA) with those in other regions in the brain. We also performed ALFF analysis in the resting state FC networks. The FC results revealed that, regarding the functionally connected brain regions, there exist similar intrinsically organized resting state networks for visual Chinese word processing in adults and children, suggesting that such networks may already be functional after 3-4 years of informal exposure to reading plus 3-4 years formal schooling. The ALFF results revealed that children appear to recruit more neural resources than adults in generally reading-irrelevant brain regions. Differences between child and adult ALFF results suggest that children's intrinsic word processing network during the resting state, though similar in functional connectivity, is still undergoing development. Further exposure to visual words and experience with reading are needed for children to develop a mature intrinsic network for word processing. The developmental course of the intrinsically organized word processing network may parallel that of the explicit word processing network. Copyright © 2013 Elsevier Ltd. All rights reserved.

On Biological Network Visualization: Understanding Challenges, Measuring the Status Quo, and Estimating Saliency of Visual Attributes

ERIC Educational Resources Information Center

Gopal, Nikhil

2017-01-01

Biomedical research increasingly relies on the analysis and visualization of a wide range of collected data. However, for certain research questions, such as those investigating the interconnectedness of biological elements, the sheer quantity and variety of data results in rather uninterpretable--this is especially true for network visualization,…

Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

PubMed

Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

2010-07-01

The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

Evaluation of Visualization Tools for Computer Network Defense Analysts: Display Design, Methods, and Results for a User Study

DTIC Science & Technology

2016-11-01

Display Design, Methods , and Results for a User Study by Christopher J Garneau and Robert F Erbacher Approved for public...NOV 2016 US Army Research Laboratory Evaluation of Visualization Tools for Computer Network Defense Analysts: Display Design, Methods ...January 2013–September 2015 4. TITLE AND SUBTITLE Evaluation of Visualization Tools for Computer Network Defense Analysts: Display Design, Methods

CerebralWeb: a Cytoscape.js plug-in to visualize networks stratified by subcellular localization.

PubMed

Frias, Silvia; Bryan, Kenneth; Brinkman, Fiona S L; Lynn, David J

2015-01-01

CerebralWeb is a light-weight JavaScript plug-in that extends Cytoscape.js to enable fast and interactive visualization of molecular interaction networks stratified based on subcellular localization or other user-supplied annotation. The application is designed to be easily integrated into any website and is configurable to support customized network visualization. CerebralWeb also supports the automatic retrieval of Cerebral-compatible localizations for human, mouse and bovine genes via a web service and enables the automated parsing of Cytoscape compatible XGMML network files. CerebralWeb currently supports embedded network visualization on the InnateDB (www.innatedb.com) and Allergy and Asthma Portal (allergen.innatedb.com) database and analysis resources. Database tool URL: http://www.innatedb.com/CerebralWeb © The Author(s) 2015. Published by Oxford University Press.

Development of a Bayesian Estimator for Audio-Visual Integration: A Neurocomputational Study

PubMed Central

Ursino, Mauro; Crisafulli, Andrea; di Pellegrino, Giuseppe; Magosso, Elisa; Cuppini, Cristiano

2017-01-01

The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli. Model assumes the presence of two unimodal areas (auditory and visual) topologically organized. Neurons in each area receive an input from the external environment, computed as the inner product of the sensory-specific stimulus and the receptive field synapses, and a cross-modal input from neurons of the other modality. Based on sensory experience, synapses were trained via Hebbian potentiation and a decay term. Aim of this work is to improve the previous model, including a more realistic distribution of visual stimuli: visual stimuli have a higher spatial accuracy at the central azimuthal coordinate and a lower accuracy at the periphery. Moreover, their prior probability is higher at the center, and decreases toward the periphery. Simulations show that, after training, the receptive fields of visual and auditory neurons shrink to reproduce the accuracy of the input (both at the center and at the periphery in the visual case), thus realizing the likelihood estimate of unimodal spatial position. Moreover, the preferred positions of visual neurons contract toward the center, thus encoding the prior probability of the visual input. Finally, a prior probability of the co-occurrence of audio-visual stimuli is encoded in the cross-modal synapses. The model is able to simulate the main properties of a Bayesian estimator and to reproduce behavioral data in all conditions examined. In particular, in unisensory conditions the visual estimates exhibit a bias toward the fovea, which increases with the level of noise. In cross modal conditions, the SD of the estimates decreases when using congruent audio-visual stimuli, and a ventriloquism effect becomes evident in case of spatially disparate stimuli. Moreover, the ventriloquism decreases with the eccentricity. PMID:29046631

3DScapeCS: application of three dimensional, parallel, dynamic network visualization in Cytoscape

PubMed Central

2013-01-01

Background The exponential growth of gigantic biological data from various sources, such as protein-protein interaction (PPI), genome sequences scaffolding, Mass spectrometry (MS) molecular networking and metabolic flux, demands an efficient way for better visualization and interpretation beyond the conventional, two-dimensional visualization tools. Results We developed a 3D Cytoscape Client/Server (3DScapeCS) plugin, which adopted Cytoscape in interpreting different types of data, and UbiGraph for three-dimensional visualization. The extra dimension is useful in accommodating, visualizing, and distinguishing large-scale networks with multiple crossed connections in five case studies. Conclusions Evaluation on several experimental data using 3DScapeCS and its special features, including multilevel graph layout, time-course data animation, and parallel visualization has proven its usefulness in visualizing complex data and help to make insightful conclusions. PMID:24225050

Linking pain and the body: neural correlates of visually induced analgesia.

PubMed

Longo, Matthew R; Iannetti, Gian Domenico; Mancini, Flavia; Driver, Jon; Haggard, Patrick

2012-02-22

The visual context of seeing the body can reduce the experience of acute pain, producing a multisensory analgesia. Here we investigated the neural correlates of this "visually induced analgesia" using fMRI. We induced acute pain with an infrared laser while human participants looked either at their stimulated right hand or at another object. Behavioral results confirmed the expected analgesic effect of seeing the body, while fMRI results revealed an associated reduction of laser-induced activity in ipsilateral primary somatosensory cortex (SI) and contralateral operculoinsular cortex during the visual context of seeing the body. We further identified two known cortical networks activated by sensory stimulation: (1) a set of brain areas consistently activated by painful stimuli (the so-called "pain matrix"), and (2) an extensive set of posterior brain areas activated by the visual perception of the body ("visual body network"). Connectivity analyses via psychophysiological interactions revealed that the visual context of seeing the body increased effective connectivity (i.e., functional coupling) between posterior parietal nodes of the visual body network and the purported pain matrix. Increased connectivity with these posterior parietal nodes was seen for several pain-related regions, including somatosensory area SII, anterior and posterior insula, and anterior cingulate cortex. These findings suggest that visually induced analgesia does not involve an overall reduction of the cortical response elicited by laser stimulation, but is consequent to the interplay between the brain's pain network and a posterior network for body perception, resulting in modulation of the experience of pain.

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

PubMed Central

Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann

2014-01-01

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders. PMID:24466019

Design and implementation of space physics multi-model application integration based on web

NASA Astrophysics Data System (ADS)

Jiang, Wenping; Zou, Ziming

With the development of research on space environment and space science, how to develop network online computing environment of space weather, space environment and space physics models for Chinese scientific community is becoming more and more important in recent years. Currently, There are two software modes on space physics multi-model application integrated system (SPMAIS) such as C/S and B/S. the C/S mode which is traditional and stand-alone, demands a team or workshop from many disciplines and specialties to build their own multi-model application integrated system, that requires the client must be deployed in different physical regions when user visits the integrated system. Thus, this requirement brings two shortcomings: reducing the efficiency of researchers who use the models to compute; inconvenience of accessing the data. Therefore, it is necessary to create a shared network resource access environment which could help users to visit the computing resources of space physics models through the terminal quickly for conducting space science research and forecasting spatial environment. The SPMAIS develops high-performance, first-principles in B/S mode based on computational models of the space environment and uses these models to predict "Space Weather", to understand space mission data and to further our understanding of the solar system. the main goal of space physics multi-model application integration system (SPMAIS) is to provide an easily and convenient user-driven online models operating environment. up to now, the SPMAIS have contained dozens of space environment models , including international AP8/AE8 IGRF T96 models and solar proton prediction model geomagnetic transmission model etc. which are developed by Chinese scientists. another function of SPMAIS is to integrate space observation data sets which offers input data for models online high-speed computing. In this paper, service-oriented architecture (SOA) concept that divides system into independent modules according to different business needs is applied to solve the problem of the independence of the physical space between multiple models. The classic MVC(Model View Controller) software design pattern is concerned to build the architecture of space physics multi-model application integrated system. The JSP+servlet+javabean technology is used to integrate the web application programs of space physics multi-model. It solves the problem of multi-user requesting the same job of model computing and effectively balances each server computing tasks. In addition, we also complete follow tasks: establishing standard graphical user interface based on Java Applet application program; Designing the interface between model computing and model computing results visualization; Realizing three-dimensional network visualization without plug-ins; Using Java3D technology to achieve a three-dimensional network scene interaction; Improved ability to interact with web pages and dynamic execution capabilities, including rendering three-dimensional graphics, fonts and color control. Through the design and implementation of the SPMAIS based on Web, we provide an online computing and application runtime environment of space physics multi-model. The practical application improves that researchers could be benefit from our system in space physics research and engineering applications.

Novel integrative genomic tool for interrogating lithium response in bipolar disorder

PubMed Central

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-01-01

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery. PMID:25646593

Novel integrative genomic tool for interrogating lithium response in bipolar disorder.

PubMed

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-02-03

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.

From field notes to data portal - An operational QA/QC framework for tower networks

NASA Astrophysics Data System (ADS)

Sturtevant, C.; Hackley, S.; Meehan, T.; Roberti, J. A.; Holling, G.; Bonarrigo, S.

2016-12-01

Quality assurance and control (QA/QC) is one of the most important yet challenging aspects of producing research-quality data. This is especially so for environmental sensor networks collecting numerous high-frequency measurement streams at distributed sites. Here, the quality issues are multi-faceted, including sensor malfunctions, unmet theoretical assumptions, and measurement interference from the natural environment. To complicate matters, there are often multiple personnel managing different sites or different steps in the data flow. For large, centrally managed sensor networks such as NEON, the separation of field and processing duties is in the extreme. Tower networks such as Ameriflux, ICOS, and NEON continue to grow in size and sophistication, yet tools for robust, efficient, scalable QA/QC have lagged. Quality control remains a largely manual process relying on visual inspection of the data. In addition, notes of observed measurement interference or visible problems are often recorded on paper without an explicit pathway to data flagging during processing. As such, an increase in network size requires a near-proportional increase in personnel devoted to QA/QC, quickly stressing the human resources available. There is a need for a scalable, operational QA/QC framework that combines the efficiency and standardization of automated tests with the power and flexibility of visual checks, and includes an efficient communication pathway from field personnel to data processors to end users. Here we propose such a framework and an accompanying set of tools in development, including a mobile application template for recording tower maintenance and an R/shiny application for efficiently monitoring and synthesizing data quality issues. This framework seeks to incorporate lessons learned from the Ameriflux community and provide tools to aid continued network advancements.

A visualization environment for supercomputing-based applications in computational mechanics

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

Pavlakos, C.J.; Schoof, L.A.; Mareda, J.F.

1993-06-01

In this paper, we characterize a visualization environment that has been designed and prototyped for a large community of scientists and engineers, with an emphasis in superconducting-based computational mechanics. The proposed environment makes use of a visualization server concept to provide effective, interactive visualization to the user`s desktop. Benefits of using the visualization server approach are discussed. Some thoughts regarding desirable features for visualization server hardware architectures are also addressed. A brief discussion of the software environment is included. The paper concludes by summarizing certain observations which we have made regarding the implementation of such visualization environments.

Comparing the Consumption of CPU Hours with Scientific Output for the Extreme Science and Engineering Discovery Environment (XSEDE).

PubMed

Knepper, Richard; Börner, Katy

2016-01-01

This paper presents the results of a study that compares resource usage with publication output using data about the consumption of CPU cycles from the Extreme Science and Engineering Discovery Environment (XSEDE) and resulting scientific publications for 2,691 institutions/teams. Specifically, the datasets comprise a total of 5,374,032,696 central processing unit (CPU) hours run in XSEDE during July 1, 2011 to August 18, 2015 and 2,882 publications that cite the XSEDE resource. Three types of studies were conducted: a geospatial analysis of XSEDE providers and consumers, co-authorship network analysis of XSEDE publications, and bi-modal network analysis of how XSEDE resources are used by different research fields. Resulting visualizations show that a diverse set of consumers make use of XSEDE resources, that users of XSEDE publish together frequently, and that the users of XSEDE with the highest resource usage tend to be "traditional" high-performance computing (HPC) community members from astronomy, atmospheric science, physics, chemistry, and biology.

Emotions promote social interaction by synchronizing brain activity across individuals

PubMed Central

Nummenmaa, Lauri; Glerean, Enrico; Viinikainen, Mikko; Jääskeläinen, Iiro P.; Hari, Riitta; Sams, Mikko

2012-01-01

Sharing others’ emotional states may facilitate understanding their intentions and actions. Here we show that networks of brain areas “tick together” in participants who are viewing similar emotional events in a movie. Participants’ brain activity was measured with functional MRI while they watched movies depicting unpleasant, neutral, and pleasant emotions. After scanning, participants watched the movies again and continuously rated their experience of pleasantness–unpleasantness (i.e., valence) and of arousal–calmness. Pearson’s correlation coefficient was used to derive multisubject voxelwise similarity measures [intersubject correlations (ISCs)] of functional MRI data. Valence and arousal time series were used to predict the moment-to-moment ISCs computed using a 17-s moving average. During movie viewing, participants' brain activity was synchronized in lower- and higher-order sensory areas and in corticolimbic emotion circuits. Negative valence was associated with increased ISC in the emotion-processing network (thalamus, ventral striatum, insula) and in the default-mode network (precuneus, temporoparietal junction, medial prefrontal cortex, posterior superior temporal sulcus). High arousal was associated with increased ISC in the somatosensory cortices and visual and dorsal attention networks comprising the visual cortex, bilateral intraparietal sulci, and frontal eye fields. Seed-voxel–based correlation analysis confirmed that these sets of regions constitute dissociable, functional networks. We propose that negative valence synchronizes individuals’ brain areas supporting emotional sensations and understanding of another’s actions, whereas high arousal directs individuals’ attention to similar features of the environment. By enhancing the synchrony of brain activity across individuals, emotions may promote social interaction and facilitate interpersonal understanding. PMID:22623534

Top-down alpha oscillatory network interactions during visuospatial attention orienting.

PubMed

Doesburg, Sam M; Bedo, Nicolas; Ward, Lawrence M

2016-05-15

Neuroimaging and lesion studies indicate that visual attention is controlled by a distributed network of brain areas. The covert control of visuospatial attention has also been associated with retinotopic modulation of alpha-band oscillations within early visual cortex, which are thought to underlie inhibition of ignored areas of visual space. The relation between distributed networks mediating attention control and more focal oscillatory mechanisms, however, remains unclear. The present study evaluated the hypothesis that alpha-band, directed, network interactions within the attention control network are systematically modulated by the locus of visuospatial attention. We localized brain areas involved in visuospatial attention orienting using magnetoencephalographic (MEG) imaging and investigated alpha-band Granger-causal interactions among activated regions using narrow-band transfer entropy. The deployment of attention to one side of visual space was indexed by lateralization of alpha power changes between about 400ms and 700ms post-cue onset. The changes in alpha power were associated, in the same time period, with lateralization of anterior-to-posterior information flow in the alpha-band from various brain areas involved in attention control, including the anterior cingulate cortex, left middle and inferior frontal gyri, left superior temporal gyrus, and right insula, and inferior parietal lobule, to early visual areas. We interpreted these results to indicate that distributed network interactions mediated by alpha oscillations exert top-down influences on early visual cortex to modulate inhibition of processing for ignored areas of visual space. Copyright © 2016. Published by Elsevier Inc.

Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program-Awareness

DTIC Science & Technology

2012-04-30

individualistic , Western view of human behavior. That is, it assumes that people adhere to the individualistic principles of Western culture . What happens to...people with networks rich in structural holes that live/work in environments that adhere to other principles, such as those of a collectivistic ... culture ? •http://orgtheory.wordpress.com/2007/06/19/structural-holes-in-context/ Preferential Attachment (PA) [Barabási & Albert, 1999] • The most

An insect-inspired model for visual binding II: functional analysis and visual attention.

PubMed

Northcutt, Brandon D; Higgins, Charles M

2017-04-01

We have developed a neural network model capable of performing visual binding inspired by neuronal circuitry in the optic glomeruli of flies: a brain area that lies just downstream of the optic lobes where early visual processing is performed. This visual binding model is able to detect objects in dynamic image sequences and bind together their respective characteristic visual features-such as color, motion, and orientation-by taking advantage of their common temporal fluctuations. Visual binding is represented in the form of an inhibitory weight matrix which learns over time which features originate from a given visual object. In the present work, we show that information represented implicitly in this weight matrix can be used to explicitly count the number of objects present in the visual image, to enumerate their specific visual characteristics, and even to create an enhanced image in which one particular object is emphasized over others, thus implementing a simple form of visual attention. Further, we present a detailed analysis which reveals the function and theoretical limitations of the visual binding network and in this context describe a novel network learning rule which is optimized for visual binding.

Energy optimization in mobile sensor networks

NASA Astrophysics Data System (ADS)

Yu, Shengwei

Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.

Smart unattended sensor networks with scene understanding capabilities

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2006-05-01

Unattended sensor systems are new technologies that are supposed to provide enhanced situation awareness to military and law enforcement agencies. A network of such sensors cannot be very effective in field conditions only if it can transmit visual information to human operators or alert them on motion. In the real field conditions, events may happen in many nodes of a network simultaneously. But the real number of control personnel is always limited, and attention of human operators can be simply attracted to particular network nodes, while more dangerous threat may be unnoticed at the same time in the other nodes. Sensor networks would be more effective if equipped with a system that is similar to human vision in its abilities to understand visual information. Human vision uses for that a rough but wide peripheral system that tracks motions and regions of interests, narrow but precise foveal vision that analyzes and recognizes objects in the center of selected region of interest, and visual intelligence that provides scene and object contexts and resolves ambiguity and uncertainty in the visual information. Biologically-inspired Network-Symbolic models convert image information into an 'understandable' Network-Symbolic format, which is similar to relational knowledge models. The equivalent of interaction between peripheral and foveal systems in the network-symbolic system is achieved via interaction between Visual and Object Buffers and the top-level knowledge system.

FloVis: Leveraging Visualization to Protect Sensitive Network Infrastructure

DTIC Science & Technology

2010-11-01

words, we are clustering the hourly web surfing patterns of users on a small private network. The data in this case is filtered NetFlow records...Entity-based NetFlow Visualization Utility for Identifying Intrusive Behavior. In Goodall et al. (eds.), Mathematics and Visualization (Proceedings

Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.

PubMed

Mustafin, Zakhar Sergeevich; Lashin, Sergey Alexandrovich; Matushkin, Yury Georgievich; Gunbin, Konstantin Vladimirovich; Afonnikov, Dmitry Arkadievich

2017-01-27

There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/ ) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged 'network evolution' found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results. Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface. Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

What is the optimal architecture for visual information routing?

PubMed

Wolfrum, Philipp; von der Malsburg, Christoph

2007-12-01

Analyzing the design of networks for visual information routing is an underconstrained problem due to insufficient anatomical and physiological data. We propose here optimality criteria for the design of routing networks. For a very general architecture, we derive the number of routing layers and the fanout that minimize the required neural circuitry. The optimal fanout l is independent of network size, while the number k of layers scales logarithmically (with a prefactor below 1), with the number n of visual resolution units to be routed independently. The results are found to agree with data of the primate visual system.

Alerts Analysis and Visualization in Network-based Intrusion Detection Systems

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

Yang, Dr. Li

2010-08-01

The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of reviewing and responding to intrusion attempts. The project presented in this work consists of three primary components. The first component provides a visual mapping of the network topology that allows the end-user to easily browse clustered alerts. Themore » second component is based on the flocking behavior of birds such that birds tend to follow other birds with similar behaviors. This component allows the end-user to see the clustering process and provides an efficient means for reviewing alert data. The third component discovers and visualizes patterns of multistage attacks by profiling the attacker s behaviors.« less

Report on the Dagstuhl Seminar on Visualization and Monitoring of Network Traffic

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

Keim, Daniel; Pras, Aiko; Schonwalder, Jurgen

2011-01-26

The Dagstuhl Seminar on Visualization and Monitoring of Network Traffic [1] took place May 17-20, 2009 in Dagstuhl, Germany. Dagstuhl seminars promote personal interaction and open discussion of results as well as new ideas. Unlike at most conferences, the focus is not solely on the presentation of established results but to equal parts on results, ideas, sketches, and open problems. The aim of this particular seminar was to bring together experts from the information visualization community and the networking community in order to discuss the state of the art of monitoring and visualization of network traffic. People from the differentmore » research communities involved jointly organized the seminar. The co-chairs of the seminar from the networking community were Aiko Pras (University of Twente) and Jürgen Schönwälder (Jacobs University Bremen). The co-chairs from the visualization community were Daniel A. Keim (University of Konstanz) and Pak Chung Wong (Pacific Northwest National Lab). Florian Mansmann (University of Konstanz) helped with producing this report. The seminar was organized and supported by Schloss Dagstuhl and the EC IST-EMANICS Network of Excellence [1].« less

Enabling Comprehension of Patient Subgroups and Characteristics in Large Bipartite Networks: Implications for Precision Medicine

PubMed Central

Bhavnani, Suresh K.; Chen, Tianlong; Ayyaswamy, Archana; Visweswaran, Shyam; Bellala, Gowtham; Rohit, Divekar; Kevin E., Bassler

2017-01-01

A primary goal of precision medicine is to identify patient subgroups based on their characteristics (e.g., comorbidities or genes) with the goal of designing more targeted interventions. While network visualization methods such as Fruchterman-Reingold have been used to successfully identify such patient subgroups in small to medium sized data sets, they often fail to reveal comprehensible visual patterns in large and dense networks despite having significant clustering. We therefore developed an algorithm called ExplodeLayout, which exploits the existence of significant clusters in bipartite networks to automatically “explode” a traditional network layout with the goal of separating overlapping clusters, while at the same time preserving key network topological properties that are critical for the comprehension of patient subgroups. We demonstrate the utility of ExplodeLayout by visualizing a large dataset extracted from Medicare consisting of readmitted hip-fracture patients and their comorbidities, demonstrate its statistically significant improvement over a traditional layout algorithm, and discuss how the resulting network visualization enabled clinicians to infer mechanisms precipitating hospital readmission in specific patient subgroups. PMID:28815099

Neural network modelling of the influence of channelopathies on reflex visual attention.

PubMed

Gravier, Alexandre; Quek, Chai; Duch, Włodzisław; Wahab, Abdul; Gravier-Rymaszewska, Joanna

2016-02-01

This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network's rate of failure to shift attention is lower than the control network's, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.

GRAPEVINE: Grids about anything by Poisson's equation in a visually interactive networking environment

NASA Technical Reports Server (NTRS)

Sorenson, Reese L.; Mccann, Karen

1992-01-01

A proven 3-D multiple-block elliptic grid generator, designed to run in 'batch mode' on a supercomputer, is improved by the creation of a modern graphical user interface (GUI) running on a workstation. The two parts are connected in real time by a network. The resultant system offers a significant speedup in the process of preparing and formatting input data and the ability to watch the grid solution converge by replotting the grid at each iteration step. The result is a reduction in user time and CPU time required to generate the grid and an enhanced understanding of the elliptic solution process. This software system, called GRAPEVINE, is described, and certain observations are made concerning the creation of such software.

Hierarchical organization of brain functional networks during visual tasks.

PubMed

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

A client–server framework for 3D remote visualization of radiotherapy treatment space

PubMed Central

Santhanam, Anand P.; Min, Yugang; Dou, Tai H.; Kupelian, Patrick; Low, Daniel A.

2013-01-01

Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi-3D camera framework was used for acquiring the 3D treatment space. A client–server framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the server side to enable hardware flexibility on the client side. On the server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi graphics processing units (GPU) system. The rendered 3D images were then encoded using a GPU-based H.264 encoding for streaming. Results showed that for a stereo image size of 1280 × 960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mbps network, the treatment space visualization occurred at 8–40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments. PMID:23440605

Deep imitation learning for 3D navigation tasks.

PubMed

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Visualizing deep neural network by alternately image blurring and deblurring.

PubMed

Wang, Feng; Liu, Haijun; Cheng, Jian

2018-01-01

Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

NASA Technical Reports Server (NTRS)

Alena, Richard I.; Lee, Charles

2004-01-01

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the shortest route updates the edges in its path.

Modulation for emergent networks: serotonin and dopamine.

PubMed

Weng, Juyang; Paslaski, Stephen; Daly, James; VanDam, Courtland; Brown, Jacob

2013-05-01

In autonomous learning, value-sensitive experiences can improve the efficiency of learning. A learning network needs be motivated so that the limited computational resources and the limited lifetime are devoted to events that are of high value for the agent to compete in its environment. The neuromodulatory system of the brain is mainly responsible for developing such a motivation system. Although reinforcement learning has been extensively studied, many existing models are symbolic whose internal nodes or modules have preset meanings. Neural networks have been used to automatically generate internal emergent representations. However, modeling an emergent motivational system for neural networks is still a great challenge. By emergent, we mean that the internal representations emerge autonomously through interactions with the external environments. This work proposes a generic emergent modulatory system for emergent networks, which includes two subsystems - the serotonin system and the dopamine system. The former signals a large class of stimuli that are intrinsically aversive (e.g., stress or pain). The latter signals a large class of stimuli that are intrinsically appetitive (e.g., pleasure or sweet). We experimented with this motivational system for two settings. The first is a visual recognition setting to investigate how such a system can learn through interactions with a teacher, who does not directly give answers, but only punishments and rewards. The second is a setting for wandering in the presence of a friend and a foe. Copyright © 2012 Elsevier Ltd. All rights reserved.

Distributed Estimation, Coding, and Scheduling in Wireless Visual Sensor Networks

ERIC Educational Resources Information Center

Yu, Chao

2013-01-01

In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN)…

Considerations for the future development of virtual technology as a rehabilitation tool

PubMed Central

Kenyon, Robert V; Leigh, Jason; Keshner, Emily A

2004-01-01

Background Virtual environments (VE) are a powerful tool for various forms of rehabilitation. Coupling VE with high-speed networking [Tele-Immersion] that approaches speeds of 100 Gb/sec can greatly expand its influence in rehabilitation. Accordingly, these new networks will permit various peripherals attached to computers on this network to be connected and to act as fast as if connected to a local PC. This innovation may soon allow the development of previously unheard of networked rehabilitation systems. Rapid advances in this technology need to be coupled with an understanding of how human behavior is affected when immersed in the VE. Methods This paper will discuss various forms of VE that are currently available for rehabilitation. The characteristic of these new networks and examine how such networks might be used for extending the rehabilitation clinic to remote areas will be explained. In addition, we will present data from an immersive dynamic virtual environment united with motion of a posture platform to record biomechanical and physiological responses to combined visual, vestibular, and proprioceptive inputs. A 6 degree-of-freedom force plate provides measurements of moments exerted on the base of support. Kinematic data from the head, trunk, and lower limb was collected using 3-D video motion analysis. Results Our data suggest that when there is a confluence of meaningful inputs, neither vision, vestibular, or proprioceptive inputs are suppressed in healthy adults; the postural response is modulated by all existing sensory signals in a non-additive fashion. Individual perception of the sensory structure appears to be a significant component of the response to these protocols and underlies much of the observed response variability. Conclusion The ability to provide new technology for rehabilitation services is emerging as an important option for clinicians and patients. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies. Quantification of individual perceptual styles in the VE will support development of individualized treatment programs. The virtual environment can be a valuable tool for therapeutic interventions that require adaptation to complex, multimodal environments. PMID:15679951

CyberPetri at CDX 2016: Real-time Network Situation Awareness

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

Arendt, Dustin L.; Best, Daniel M.; Burtner, Edwin R.

CyberPetri is a novel visualization technique that provides a flexible map of the network based on available characteristics, such as IP address, operating system, or service. Previous work introduced CyberPetri as a visualization feature in Ocelot, a network defense tool that helped security analysts understand and respond to an active defense scenario. In this paper we present a case study in which we use the CyberPetri visualization technique to support real-time situation awareness during the 2016 Cyber Defense Exercise.

The ‘when’ parietal pathway explored by lesion studies

PubMed Central

Battelli, Lorella; Walsh, Vincent; Pascual-Leone, Alvaro; Cavanagh, Patrick

2016-01-01

Summary The perception of events in space and time is at the root of our interactions with the environment. The precision with which we perceive visual events in time enables us to act upon objects with great accuracy and the loss of such functions, due to brain lesions can be catastrophic. We outline a visual timing mechanism that deals with the trajectory of an object’s existence across time, a critical function when keeping track of multiple objects that temporally overlap or occur sequentially. Recent evidence suggests these functions are served by an extended network of areas which we call the ‘when’ pathway. Here we show that the when pathway is distinct from and interacts with, the well established ‘where’ and ‘what’ pathways. PMID:18708141

Spectral properties of the temporal evolution of brain network structure.

PubMed

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Spectral properties of the temporal evolution of brain network structure

NASA Astrophysics Data System (ADS)

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Retinotopic patterns of functional connectivity between V1 and large-scale brain networks during resting fixation

PubMed Central

Griffis, Joseph C.; Elkhetali, Abdurahman S.; Burge, Wesley K.; Chen, Richard H.; Bowman, Anthony D.; Szaflarski, Jerzy P.; Visscher, Kristina M.

2016-01-01

Psychophysical and neurobiological evidence suggests that central and peripheral vision are specialized for different functions. This specialization of function might be expected to lead to differences in the large-scale functional interactions of early cortical areas that represent central and peripheral visual space. Here, we characterize differences in whole-brain functional connectivity among sectors in primary visual cortex (V1) corresponding to central, near-peripheral, and far-peripheral vision during resting fixation. Importantly, our analyses reveal that eccentricity sectors in V1 have different functional connectivity with non-visual areas associated with large-scale brain networks. Regions associated with the fronto-parietal control network are most strongly connected with central sectors of V1, regions associated with the cingulo-opercular control network are most strongly connected with near-peripheral sectors of V1, and regions associated with the default mode and auditory networks are most strongly connected with far-peripheral sectors of V1. Additional analyses suggest that similar patterns are present during eyes-closed rest. These results suggest that different types of visual information may be prioritized by large-scale brain networks with distinct functional profiles, and provide insights into how the small-scale functional specialization within early visual regions such as V1 relates to the large-scale organization of functionally distinct whole-brain networks. PMID:27554527

SAVS: A Space and Atmospheric Visualization Science system

NASA Technical Reports Server (NTRS)

Szuszczewicz, E. P.; Mankofsky, A.; Blanchard, P.; Goodrich, C.; McNabb, D.; Kamins, D.

1995-01-01

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Virtual reality: a reality for future military pilotage?

NASA Astrophysics Data System (ADS)

McIntire, John P.; Martinsen, Gary L.; Marasco, Peter L.; Havig, Paul R.

2009-05-01

Virtual reality (VR) systems provide exciting new ways to interact with information and with the world. The visual VR environment can be synthetic (computer generated) or be an indirect view of the real world using sensors and displays. With the potential opportunities of a VR system, the question arises about what benefits or detriments a military pilot might incur by operating in such an environment. Immersive and compelling VR displays could be accomplished with an HMD (e.g., imagery on the visor), large area collimated displays, or by putting the imagery on an opaque canopy. But what issues arise when, instead of viewing the world directly, a pilot views a "virtual" image of the world? Is 20/20 visual acuity in a VR system good enough? To deliver this acuity over the entire visual field would require over 43 megapixels (MP) of display surface for an HMD or about 150 MP for an immersive CAVE system, either of which presents a serious challenge with current technology. Additionally, the same number of sensor pixels would be required to drive the displays to this resolution (and formidable network architectures required to relay this information), or massive computer clusters are necessary to create an entirely computer-generated virtual reality with this resolution. Can we presently implement such a system? What other visual requirements or engineering issues should be considered? With the evolving technology, there are many technological issues and human factors considerations that need to be addressed before a pilot is placed within a virtual cockpit.

Toward a Scalable Visualization System for Network Traffic Monitoring

NASA Astrophysics Data System (ADS)

Malécot, Erwan Le; Kohara, Masayoshi; Hori, Yoshiaki; Sakurai, Kouichi

With the multiplication of attacks against computer networks, system administrators are required to monitor carefully the traffic exchanged by the networks they manage. However, that monitoring task is increasingly laborious because of the augmentation of the amount of data to analyze. And that trend is going to intensify with the explosion of the number of devices connected to computer networks along with the global rise of the available network bandwidth. So system administrators now heavily rely on automated tools to assist them and simplify the analysis of the data. Yet, these tools provide limited support and, most of the time, require highly skilled operators. Recently, some research teams have started to study the application of visualization techniques to the analysis of network traffic data. We believe that this original approach can also allow system administrators to deal with the large amount of data they have to process. In this paper, we introduce a tool for network traffic monitoring using visualization techniques that we developed in order to assist the system administrators of our corporate network. We explain how we designed the tool and some of the choices we made regarding the visualization techniques to use. The resulting tool proposes two linked representations of the network traffic and activity, one in 2D and the other in 3D. As 2D and 3D visualization techniques have different assets, we resulted in combining them in our tool to take advantage of their complementarity. We finally tested our tool in order to evaluate the accuracy of our approach.

Supporting tactical intelligence using collaborative environments and social networking

NASA Astrophysics Data System (ADS)

Wollocko, Arthur B.; Farry, Michael P.; Stark, Robert F.

2013-05-01

Modern military environments place an increased emphasis on the collection and analysis of intelligence at the tactical level. The deployment of analytical tools at the tactical level helps support the Warfighter's need for rapid collection, analysis, and dissemination of intelligence. However, given the lack of experience and staffing at the tactical level, most of the available intelligence is not exploited. Tactical environments are staffed by a new generation of intelligence analysts who are well-versed in modern collaboration environments and social networking. An opportunity exists to enhance tactical intelligence analysis by exploiting these personnel strengths, but is dependent on appropriately designed information sharing technologies. Existing social information sharing technologies enable users to publish information quickly, but do not unite or organize information in a manner that effectively supports intelligence analysis. In this paper, we present an alternative approach to structuring and supporting tactical intelligence analysis that combines the benefits of existing concepts, and provide detail on a prototype system embodying that approach. Since this approach employs familiar collaboration support concepts from social media, it enables new-generation analysts to identify the decision-relevant data scattered among databases and the mental models of other personnel, increasing the timeliness of collaborative analysis. Also, the approach enables analysts to collaborate visually to associate heterogeneous and uncertain data within the intelligence analysis process, increasing the robustness of collaborative analyses. Utilizing this familiar dynamic collaboration environment, we hope to achieve a significant reduction of time and skill required to glean actionable intelligence in these challenging operational environments.

A design for living technology: experiments with the mind time machine.

PubMed

Ikegami, Takashi

2013-01-01

Living technology aims to help people expand their experiences in everyday life. The environment offers people ways to interact with it, which we call affordances. Living technology is a design for new affordances. When we experience something new, we remember it by the way we perceive and interact with it. Recent studies in neuroscience have led to the idea of a default mode network, which is a baseline activity of a brain system. The autonomy of artificial life must be understood as a sort of default mode that self-organizes its baseline activity, preparing for its external inputs and its interaction with humans. I thus propose a method for creating a suitable default mode as a design principle for living technology. I built a machine called the mind time machine (MTM), which runs continuously for 10 h per day and receives visual data from its environment using 15 video cameras. The MTM receives and edits the video inputs while it self-organizes the momentary now. Its base program is a neural network that includes chaotic dynamics inside the system and a meta-network that consists of video feedback systems. Using this system as the hardware and a default mode network as a conceptual framework, I describe the system's autonomous behavior. Using the MTM as a testing ground, I propose a design principle for living technology.

Software For Graphical Representation Of A Network

NASA Technical Reports Server (NTRS)

Mcallister, R. William; Mclellan, James P.

1993-01-01

System Visualization Tool (SVT) computer program developed to provide systems engineers with means of graphically representing networks. Generates diagrams illustrating structures and states of networks defined by users. Provides systems engineers powerful tool simplifing analysis of requirements and testing and maintenance of complex software-controlled systems. Employs visual models supporting analysis of chronological sequences of requirements, simulation data, and related software functions. Applied to pneumatic, hydraulic, and propellant-distribution networks. Used to define and view arbitrary configurations of such major hardware components of system as propellant tanks, valves, propellant lines, and engines. Also graphically displays status of each component. Advantage of SVT: utilizes visual cues to represent configuration of each component within network. Written in Turbo Pascal(R), version 5.0.

Consistent visualizations of changing knowledge

PubMed Central

Tipney, Hannah J.; Schuyler, Ronald P.; Hunter, Lawrence

2009-01-01

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change. PMID:21347184

Communicating Ocean Acidification and Climate Change to Public Audiences Using Scientific Data, Interactive Exploration Tools, and Visual Narratives

NASA Astrophysics Data System (ADS)

Miller, M. K.; Rossiter, A.; Spitzer, W.

2016-12-01

The Exploratorium, a hands-on science museum, explores local environmental conditions of San Francisco Bay to connect audiences to the larger global implications of ocean acidification and climate change. The work is centered in the Fisher Bay Observatory at Pier 15, a glass-walled gallery sited for explorations of urban San Francisco and the Bay. Interactive exhibits, high-resolution data visualizations, and mediated activities and conversations communicate to public audiences the impacts of excess carbon dioxide in the atmosphere and ocean. Through a 10-year education partnership with NOAA and two environmental literacy grants funded by its Office of Education, the Exploratorium has been part of two distinct but complementary strategies to increase climate literacy beyond traditional classroom settings. We will discuss two projects that address the ways complex scientific information can be transformed into learning opportunities for the public, providing information citizens can use for decision-making in their personal lives and their communities. The Visualizing Change project developed "visual narratives" that combine scientific visualizations and other images with story telling about the science and potential solutions of climate impacts on the ocean. The narratives were designed to engage curiosity and provide the public with hopeful and useful information to stimulate solutions-oriented behavior rather than to communicate despair about climate change. Training workshops for aquarium and museum docents prepare informal educators to use the narratives and help them frame productive conversations with the pubic. The Carbon Networks project, led by the Exploratorium, uses local and Pacific Rim data to explore the current state of climate change and ocean acidification. The Exploratorium collects and displays local ocean and atmosphere data as a member of the Central and Northern California Ocean Observing System and as an observing station for NOAA's Pacific Marine Environment Lab's carbon buoy network. Other Carbon Network partners, the Pacific Science Center and Waikiki Aquarium, also have access to local carbon data from NOAA. The project collectively explores the development of hands-on activities, teaching resources, and workshops for museum educators and classroom teachers.

A functional hierarchy within the parietofrontal network in stimulus selection and attention control.

PubMed

Ibos, Guilhem; Duhamel, Jean-René; Ben Hamed, Suliann

2013-05-08

Although we are confronted with an ever-changing environment, we do not have the capacity to analyze all incoming sensory information. Perception is selective and is guided both by salient events occurring in our visual field and by cognitive premises about what needs our attention. Although the lateral intraparietal area (LIP) and frontal eye field (FEF) are known to represent the position of visual attention, their respective contributions to its control are still unclear. Here, we report LIP and FEF neuronal activities recorded while monkeys performed a voluntary attention-orientation target-detection task. We show that both encode behaviorally significant events, but that the FEF plays a specific role in mapping abstract cue instructions onto a spatial priority map to voluntarily guide attention. On the basis of a latency analysis, we show that the coding of stimulus identity and position precedes the emergence of an explicit attentional signal within the FEF. We also describe dynamic temporal hierarchies between LIP and FEF: stimuli carrying the highest intrinsic saliency are signaled by LIP before FEF, whereas stimuli carrying the highest extrinsic saliency are signaled in FEF before LIP. This suggests that whereas the parietofrontal attentional network most probably processes visual information in a recurrent way, exogenous processing predominates in the parietal cortex and the endogenous control of attention takes place in the FEF.

Control system of hexacopter using color histogram footprint and convolutional neural network

NASA Astrophysics Data System (ADS)

Ruliputra, R. N.; Darma, S.

2017-07-01

The development of unmanned aerial vehicles (UAV) has been growing rapidly in recent years. The use of logic thinking which is implemented into the program algorithms is needed to make a smart system. By using visual input from a camera, UAV is able to fly autonomously by detecting a target. However, some weaknesses arose as usage in the outdoor environment might change the target's color intensity. Color histogram footprint overcomes the problem because it divides color intensity into separate bins that make the detection tolerant to the slight change of color intensity. Template matching compare its detection result with a template of the reference image to determine the target position and use it to position the vehicle in the middle of the target with visual feedback control based on Proportional-Integral-Derivative (PID) controller. Color histogram footprint method localizes the target by calculating the back projection of its histogram. It has an average success rate of 77 % from a distance of 1 meter. It can position itself in the middle of the target by using visual feedback control with an average positioning time of 73 seconds. After the hexacopter is in the middle of the target, Convolutional Neural Networks (CNN) classifies a number contained in the target image to determine a task depending on the classified number, either landing, yawing, or return to launch. The recognition result shows an optimum success rate of 99.2 %.

Lightweight UDP Pervasive Protocol in Smart Home Environment Based on Labview

NASA Astrophysics Data System (ADS)

Kurniawan, Wijaya; Hannats Hanafi Ichsan, Mochammad; Rizqika Akbar, Sabriansyah; Arwani, Issa

2017-04-01

TCP (Transmission Control Protocol) technology in a reliable environment was not a problem, but not in an environment where the entire Smart Home network connected locally. Currently employing pervasive protocols using TCP technology, when data transmission is sent, it would be slower because they have to perform handshaking process in advance and could not broadcast the data. On smart home environment, it does not need large size and complex data transmission between monitoring site and monitoring center required in Smart home strain monitoring system. UDP (User Datagram Protocol) technology is quick and simple on data transmission process. UDP can broadcast messages because the UDP did not require handshaking and with more efficient memory usage. LabVIEW is a programming language software for processing and visualization of data in the field of data acquisition. This paper proposes to examine Pervasive UDP protocol implementations in smart home environment based on LabVIEW. UDP coded in LabVIEW and experiments were performed on a PC and can work properly.

BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

PubMed

Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

2009-02-15

BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

Self-organizing neural integration of pose-motion features for human action recognition

PubMed Central

Parisi, German I.; Weber, Cornelius; Wermter, Stefan

2015-01-01

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions. PMID:26106323

Shedding light on emotional perception: Interaction of brightness and semantic content in extrastriate visual cortex.

PubMed

Schettino, Antonio; Keil, Andreas; Porcu, Emanuele; Müller, Matthias M

2016-06-01

The rapid extraction of affective cues from the visual environment is crucial for flexible behavior. Previous studies have reported emotion-dependent amplitude modulations of two event-related potential (ERP) components - the N1 and EPN - reflecting sensory gain control mechanisms in extrastriate visual areas. However, it is unclear whether both components are selective electrophysiological markers of attentional orienting toward emotional material or are also influenced by physical features of the visual stimuli. To address this question, electrical brain activity was recorded from seventeen male participants while viewing original and bright versions of neutral and erotic pictures. Bright neutral scenes were rated as more pleasant compared to their original counterpart, whereas erotic scenes were judged more positively when presented in their original version. Classical and mass univariate ERP analysis showed larger N1 amplitude for original relative to bright erotic pictures, with no differences for original and bright neutral scenes. Conversely, the EPN was only modulated by picture content and not by brightness, substantiating the idea that this component is a unique electrophysiological marker of attention allocation toward emotional material. Complementary topographic analysis revealed the early selective expression of a centro-parietal positivity following the presentation of original erotic scenes only, reflecting the recruitment of neural networks associated with sustained attention and facilitated memory encoding for motivationally relevant material. Overall, these results indicate that neural networks subtending the extraction of emotional information are differentially recruited depending on low-level perceptual features, which ultimately influence affective evaluations. Copyright © 2016 Elsevier Inc. All rights reserved.

CI-KNOW: Cyberinfrastructure Knowledge Networks on the Web. A Social Network Enabled Recommender System for Locating Resources in Cyberinfrastructures

NASA Astrophysics Data System (ADS)

Green, H. D.; Contractor, N. S.; Yao, Y.

2006-12-01

A knowledge network is a multi-dimensional network created from the interactions and interconnections among the scientists, documents, data, analytic tools, and interactive collaboration spaces (like forums and wikis) associated with a collaborative environment. CI-KNOW is a suite of software tools that leverages automated data collection, social network theories, analysis techniques and algorithms to infer an individual's interests and expertise based on their interactions and activities within a knowledge network. The CI-KNOW recommender system mines the knowledge network associated with a scientific community's use of cyberinfrastructure tools and uses relational metadata to record connections among entities in the knowledge network. Recent developments in social network theories and methods provide the backbone for a modular system that creates recommendations from relational metadata. A network navigation portlet allows users to locate colleagues, documents, data or analytic tools in the knowledge network and to explore their networks through a visual, step-wise process. An internal auditing portlet offers administrators diagnostics to assess the growth and health of the entire knowledge network. The first instantiation of the prototype CI-KNOW system is part of the Environmental Cyberinfrastructure Demonstration project at the National Center for Supercomputing Applications, which supports the activities of hydrologic and environmental science communities (CLEANER and CUAHSI) under the umbrella of the WATERS network environmental observatory planning activities (http://cleaner.ncsa.uiuc.edu). This poster summarizes the key aspects of the CI-KNOW system, highlighting the key inputs, calculation mechanisms, and output modalities.

Netgram: Visualizing Communities in Evolving Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2015-01-01

Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems. PMID:26356538

Disentangling the intragroup HI in Compact Groups of galaxies by means of X3D visualization

NASA Astrophysics Data System (ADS)

Verdes-Montenegro, Lourdes; Vogt, Frederic; Aubery, Claire; Duret, Laetitie; Garrido, Julián; Sánchez, Susana; Yun, Min S.; Borthakur, Sanchayeeta; Hess, Kelley; Cluver, Michelle; Del Olmo, Ascensión; Perea, Jaime

2017-03-01

As an extreme kind of environment, Hickson Compact groups (HCGs) have shown to be very complex systems. HI-VLA observations revealed an intrincated network of HI tails and bridges, tracing pre-processing through extreme tidal interactions. We found HCGs to show a large HI deficiency supporting an evolutionary sequence where gas-rich groups transform via tidal interactions and ISM (interstellar medium) stripping into gas-poor systems. We detected as well a diffuse HI component in the groups, increasing with evolutionary phase, although with uncertain distribution. The complex net of detected HI as observed with the VLA seems hence so puzzling as the missing one. In this talk we revisit the existing VLA information on the HI distribution and kinematics of HCGs by means of X3D visualization. X3D constitutes a powerful tool to extract the most from HI data cubes and a mean of simplifying and easing the access to data visualization and publication via three-dimensional (3-D) diagrams.

NASA Webworldwind: Multidimensional Virtual Globe for Geo Big Data Visualization

NASA Astrophysics Data System (ADS)

Brovelli, M. A.; Hogan, P.; Prestifilippo, G.; Zamboni, G.

2016-06-01

In this paper, we presented a web application created using the NASA WebWorldWind framework. The application is capable of visualizing n-dimensional data using a Voxel model. In this case study, we handled social media data and Call Detailed Records (CDR) of telecommunication networks. These were retrieved from the "BigData Challenge 2015" of Telecom Italia. We focused on the visualization process for a suitable way to show this geo-data in a 3D environment, incorporating more than three dimensions. This engenders an interactive way to browse the data in their real context and understand them quickly. Users will be able to handle several varieties of data, import their dataset using a particular data structure, and then mash them up in the WebWorldWind virtual globe. A broad range of public use this tool for diverse purposes is possible, without much experience in the field, thanks to the intuitive user-interface of this web app.

Distributed 3D Information Visualization - Towards Integration of the Dynamic 3D Graphics and Web Services

NASA Astrophysics Data System (ADS)

Vucinic, Dean; Deen, Danny; Oanta, Emil; Batarilo, Zvonimir; Lacor, Chris

This paper focuses on visualization and manipulation of graphical content in distributed network environments. The developed graphical middleware and 3D desktop prototypes were specialized for situational awareness. This research was done in the LArge Scale COllaborative decision support Technology (LASCOT) project, which explored and combined software technologies to support human-centred decision support system for crisis management (earthquake, tsunami, flooding, airplane or oil-tanker incidents, chemical, radio-active or other pollutants spreading, etc.). The performed state-of-the-art review did not identify any publicly available large scale distributed application of this kind. Existing proprietary solutions rely on the conventional technologies and 2D representations. Our challenge was to apply the "latest" available technologies, such Java3D, X3D and SOAP, compatible with average computer graphics hardware. The selected technologies are integrated and we demonstrate: the flow of data, which originates from heterogeneous data sources; interoperability across different operating systems and 3D visual representations to enhance the end-users interactions.

Oscillatory network with self-organized dynamical connections for synchronization-based image segmentation.

PubMed

Kuzmina, Margarita; Manykin, Eduard; Surina, Irina

2004-01-01

An oscillatory network of columnar architecture located in 3D spatial lattice was recently designed by the authors as oscillatory model of the brain visual cortex. Single network oscillator is a relaxational neural oscillator with internal dynamics tunable by visual image characteristics - local brightness and elementary bar orientation. It is able to demonstrate either activity state (stable undamped oscillations) or "silence" (quickly damped oscillations). Self-organized nonlocal dynamical connections of oscillators depend on oscillator activity levels and orientations of cortical receptive fields. Network performance consists in transfer into a state of clusterized synchronization. At current stage grey-level image segmentation tasks are carried out by 2D oscillatory network, obtained as a limit version of the source model. Due to supplemented network coupling strength control the 2D reduced network provides synchronization-based image segmentation. New results on segmentation of brightness and texture images presented in the paper demonstrate accurate network performance and informative visualization of segmentation results, inherent in the model.

A Collaborative Decision Environment for UAV Operations

NASA Technical Reports Server (NTRS)

D'Ortenzio, Matthew V.; Enomoto, Francis Y.; Johan, Sandra L.

2005-01-01

NASA is developing Intelligent Mission Management (IMM) technology for science missions employing long endurance unmanned aerial vehicles (UAV's). The IMM groundbased component is the Collaborative Decision Environment (CDE), a ground system that provides the Mission/Science team with situational awareness, collaboration, and decisionmaking tools. The CDE is used for pre-flight planning, mission monitoring, and visualization of acquired data. It integrates external data products used for planning and executing a mission, such as weather, satellite data products, and topographic maps by leveraging established and emerging Open Geospatial Consortium (OGC) standards to acquire external data products via the Internet, and an industry standard geographic information system (GIs) toolkit for visualization As a Science/Mission team may be geographically dispersed, the CDE is capable of providing access to remote users across wide area networks using Web Services technology. A prototype CDE is being developed for an instrument checkout flight on a manned aircraft in the fall of 2005, in preparation for a full deployment in support of the US Forest Service and NASA Ames Western States Fire Mission in 2006.

Real-time scalable visual analysis on mobile devices

NASA Astrophysics Data System (ADS)

Pattath, Avin; Ebert, David S.; May, Richard A.; Collins, Timothy F.; Pike, William

2008-02-01

Interactive visual presentation of information can help an analyst gain faster and better insight from data. When combined with situational or context information, visualization on mobile devices is invaluable to in-field responders and investigators. However, several challenges are posed by the form-factor of mobile devices in developing such systems. In this paper, we classify these challenges into two broad categories - issues in general mobile computing and issues specific to visual analysis on mobile devices. Using NetworkVis and Infostar as example systems, we illustrate some of the techniques that we employed to overcome many of the identified challenges. NetworkVis is an OpenVG-based real-time network monitoring and visualization system developed for Windows Mobile devices. Infostar is a flash-based interactive, real-time visualization application intended to provide attendees access to conference information. Linked time-synchronous visualization, stylus/button-based interactivity, vector graphics, overview-context techniques, details-on-demand and statistical information display are some of the highlights of these applications.

Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

PubMed Central

Ramsey, Lenny E.; Metcalf, Nicholas V.; Chacko, Ravi V.; Weinberger, Kilian; Baldassarre, Antonello; Hacker, Carl D.; Shulman, Gordon L.; Corbetta, Maurizio

2016-01-01

Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain–behavior relationships in stroke. PMID:27402738

VANLO - Interactive visual exploration of aligned biological networks

PubMed Central

Brasch, Steffen; Linsen, Lars; Fuellen, Georg

2009-01-01

Background Protein-protein interaction (PPI) is fundamental to many biological processes. In the course of evolution, biological networks such as protein-protein interaction networks have developed. Biological networks of different species can be aligned by finding instances (e.g. proteins) with the same common ancestor in the evolutionary process, so-called orthologs. For a better understanding of the evolution of biological networks, such aligned networks have to be explored. Visualization can play a key role in making the various relationships transparent. Results We present a novel visualization system for aligned biological networks in 3D space that naturally embeds existing 2D layouts. In addition to displaying the intra-network connectivities, we also provide insight into how the individual networks relate to each other by placing aligned entities on top of each other in separate layers. We optimize the layout of the entire alignment graph in a global fashion that takes into account inter- as well as intra-network relationships. The layout algorithm includes a step of merging aligned networks into one graph, laying out the graph with respect to application-specific requirements, splitting the merged graph again into individual networks, and displaying the network alignment in layers. In addition to representing the data in a static way, we also provide different interaction techniques to explore the data with respect to application-specific tasks. Conclusion Our system provides an intuitive global understanding of aligned PPI networks and it allows the investigation of key biological questions. We evaluate our system by applying it to real-world examples documenting how our system can be used to investigate the data with respect to these key questions. Our tool VANLO (Visualization of Aligned Networks with Layout Optimization) can be accessed at . PMID:19821976

Using network projections to explore co-incidence and context in large clinical datasets: Application to homelessness among U.S. Veterans.

PubMed

Pettey, Warren B P; Toth, Damon J A; Redd, Andrew; Carter, Marjorie E; Samore, Matthew H; Gundlapalli, Adi V

2016-06-01

Network projections of data can provide an efficient format for data exploration of co-incidence in large clinical datasets. We present and explore the utility of a network projection approach to finding patterns in health care data that could be exploited to prevent homelessness among U.S. Veterans. We divided Veteran ICD-9-CM (ICD9) data into two time periods (0-59 and 60-364days prior to the first evidence of homelessness) and then used Pajek social network analysis software to visualize these data as three different networks. A multi-relational network simultaneously displayed the magnitude of ties between the most frequent ICD9 pairings. A new association network visualized ICD9 pairings that greatly increased or decreased. A signed, subtraction network visualized the presence, absence, and magnitude difference between ICD9 associations by time period. A cohort of 9468 U.S. Veterans was identified as having administrative evidence of homelessness and visits in both time periods. They were seen in 222,599 outpatient visits that generated 484,339 ICD9 codes (average of 11.4 (range 1-23) visits and 2.2 (range 1-60) ICD9 codes per visit). Using the three network projection methods, we were able to show distinct differences in the pattern of co-morbidities in the two time periods. In the more distant time period preceding homelessness, the network was dominated by routine health maintenance visits and physical ailment diagnoses. In the 59days immediately prior to the homelessness identification, alcohol related diagnoses along with economic circumstances such as unemployment, legal circumstances, along with housing instability were noted. Network visualizations of large clinical datasets traditionally treated as tabular and difficult to manipulate reveal rich, previously hidden connections between data variables related to homelessness. A key feature is the ability to visualize changes in variables with temporality and in proximity to the event of interest. These visualizations lend support to cognitive tasks such as exploration of large clinical datasets as a prelude to hypothesis generation. Published by Elsevier Inc.

Transportation networks : data, analysis, methodology development and visualization.

DOT National Transportation Integrated Search

2007-12-29

This project provides data compilation, analysis methodology and visualization methodology for the current network : data assets of the Alabama Department of Transportation (ALDOT). This study finds that ALDOT is faced with a : considerable number of...

Exploring Transformations in Caribbean Indigenous Social Networks through Visibility Studies: the Case of Late Pre-Colonial Landscapes in East-Guadeloupe (French West Indies).

PubMed

Brughmans, Tom; de Waal, Maaike S; Hofman, Corinne L; Brandes, Ulrik

2018-01-01

This paper presents a study of the visual properties of natural and Amerindian cultural landscapes in late pre-colonial East-Guadeloupe and of how these visual properties affected social interactions. Through a review of descriptive and formal visibility studies in Caribbean archaeology, it reveals that the ability of visual properties to affect past human behaviour is frequently evoked but the more complex of these hypotheses are rarely studied formally. To explore such complex hypotheses, the current study applies a range of techniques: total viewsheds, cumulative viewsheds, visual neighbourhood configurations and visibility networks. Experiments were performed to explore the control of seascapes, the functioning of hypothetical smoke signalling networks, the correlation of these visual properties with stylistic similarities of material culture found at sites and the change of visual properties over time. The results of these experiments suggest that only few sites in Eastern Guadeloupe are located in areas that are particularly suitable to visually control possible sea routes for short- and long-distance exchange; that visual control over sea areas was not a factor of importance for the existence of micro-style areas; that during the early phase of the Late Ceramic Age networks per landmass are connected and dense and that they incorporate all sites, a structure that would allow hypothetical smoke signalling networks; and that the visual properties of locations of the late sites Morne Souffleur and Morne Cybèle-1 were not ideal for defensive purposes. These results led us to propose a multi-scalar hypothesis for how lines of sight between settlements in the Lesser Antilles could have structured past human behaviour: short-distance visibility networks represent the structuring of navigation and communication within landmasses, whereas the landmasses themselves served as focal points for regional navigation and interaction. We conclude by emphasising that since our archaeological theories about visual properties usually take a multi-scalar landscape perspective, there is a need for this perspective to be reflected in our formal visibility methods as is made possible by the methods used in this paper.

Design and implementation of temperature and humidity monitoring system for poultry farm

NASA Astrophysics Data System (ADS)

Purnomo, Hindriyanto Dwi; Somya, Ramos; Fibriani, Charitas; Purwoko, Angga; Sadiyah, Ulfa

2016-10-01

Automatic monitoring system gains significant interest in poultry industry due to the need of consistent environment condition. Appropriate environment increase the feed conversion ratio as well as birds productivity. This will increase the competitiveness of the poultry industry. In this research, a temperature and humidity monitoring system is proposed to observer the temperature and relative humidity of a poultry house. The system is intended to be applied in the poultry industry with partnership schema. The proposed system is equipped with CCTV for visual monitoring. The measured temperature and humidity implement wireless sensor network technology. The experiment results reveals that proposed system have the potential to increase the effectiveness of monitoring of poultry house in poultry industry with partnership schema.

Dynamic power scheduling system for JPEG2000 delivery over wireless networks

NASA Astrophysics Data System (ADS)

Martina, Maurizio; Vacca, Fabrizio

2003-06-01

Third generation mobile terminals diffusion is encouraging the development of new multimedia based applications. The reliable transmission of audiovisual content will gain major interest being one of the most valuable services. Nevertheless, mobile scenario is severely power constrained: high compression ratios and refined energy management strategies are highly advisable. JPEG2000 as the source encoding stage assures excellent performance with extremely good visual quality. However the limited power budged imposes to limit the computational effort in order to save as much power as possible. Starting from an error prone environment, as the wireless one, high error-resilience features need to be employed. This paper tries to investigate the trade-off between quality and power in such a challenging environment.

An Integrated Hydrologic Monitoring Network

NASA Astrophysics Data System (ADS)

Tedesco, L. P.; Baker, M. P.; Hall, B. E.

2004-12-01

Ecological studies depend on the ability to monitor an environment, collect data at appropriate spatial and temporal scales, and analyze that data from the diverse viewpoints of many relevant disciplines. Historically, environmental studies have been conducted by small teams of researchers, usually collecting data by hand at some set but low frequency, and organizing it according to ad hoc, project-specific goals. Recent years have seen dramatic advancement in the ability to gather environmental data remotely and therefore at much higher frequency. We are working to create a dynamic and integrated network of environmental sensors in natural environments to acquire real time data and create tools for visualization appropriate for different audiences to promote scientific exploration. Instrumentation includes an array of water quality and water level sondes and probes distributed throughout three Central Indiana counties. Instrument platforms currently include five river monitoring platforms utilizing YSI water quality and level probes; a lake buoy array that includes three YSI sonde packages monitoring physical, chemical and biological parameters; and over fifteen YSI and Solinist groundwater probes recording both level and water quality. Many sites are providing real-time data and several additional sites are scheduled to be online in the coming months. Visualization of this real time data from remote sensors distributed throughout Central Indiana provides numerous challenges. The benefits of successfully integrating remotely deployed environmental sensors in a post 9-11 world is obvious. We are working to bridge both the extremes associated with the frequency of data collection and the lack of data coordination by creating techniques for data networking and retrieval, and data management, analysis, and visualization capabilities that operate across a range of computing platforms to make this data immediately accessible and useful to a range of interested parties, across multiple disciplines. We are working to integrate multiple data streams into a coherent data base and create applications that allow users to view data from multiple instruments at different sites. Creating visualizations of real time, dynamic data from the everyday world and delivering it via web applications as well as through innovative display spaces will be a key outcome of this program. On-line tools for QA/QC, data queries, graphing, and sensitivity analysis are under development. Our goal is to use the instrumented sites to create analysis and presentation applications to foster a community of learners interested in understanding these ecosystems, and the larger environmental issues that they represent. This broad-based community will include environmental researchers, university faculty in lecture halls, math and science teachers, university and K-12 students, civic leaders, and educators at informal learning centers.

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

A Visually Attractive "Interconnected Network of Ideas" for Organizing the Teaching and Learning of Descriptive Inorganic Chemistry

ERIC Educational Resources Information Center

Rodgers, Glen E.

2014-01-01

A visually attractive interconnected network of ideas that helps general and second-year inorganic chemistry students make sense of the descriptive inorganic chemistry of the main-group elements is presented. The eight network components include the periodic law, the uniqueness principle, the diagonal effect, the inert-pair effect, the…

Distributed Cognition on the road: Using EAST to explore future road transportation systems.

PubMed

Banks, Victoria A; Stanton, Neville A; Burnett, Gary; Hermawati, Setia

2018-04-01

Connected and Autonomous Vehicles (CAV) are set to revolutionise the way in which we use our transportation system. However, we do not fully understand how the integration of wireless and autonomous technology into the road transportation network affects overall network dynamism. This paper uses the theoretical principles underlying Distributed Cognition to explore the dependencies and interdependencies that exist between system agents located within the road environment, traffic management centres and other external agencies in both non-connected and connected transportation systems. This represents a significant step forward in modelling complex sociotechnical systems as it shows that the principles underlying Distributed Cognition can be applied to macro-level systems using the visual representations afforded by the Event Analysis of Systemic Teamwork (EAST) method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Specialized Computer Systems for Environment Visualization

NASA Astrophysics Data System (ADS)

Al-Oraiqat, Anas M.; Bashkov, Evgeniy A.; Zori, Sergii A.

2018-06-01

The need for real time image generation of landscapes arises in various fields as part of tasks solved by virtual and augmented reality systems, as well as geographic information systems. Such systems provide opportunities for collecting, storing, analyzing and graphically visualizing geographic data. Algorithmic and hardware software tools for increasing the realism and efficiency of the environment visualization in 3D visualization systems are proposed. This paper discusses a modified path tracing algorithm with a two-level hierarchy of bounding volumes and finding intersections with Axis-Aligned Bounding Box. The proposed algorithm eliminates the branching and hence makes the algorithm more suitable to be implemented on the multi-threaded CPU and GPU. A modified ROAM algorithm is used to solve the qualitative visualization of reliefs' problems and landscapes. The algorithm is implemented on parallel systems—cluster and Compute Unified Device Architecture-networks. Results show that the implementation on MPI clusters is more efficient than Graphics Processing Unit/Graphics Processing Clusters and allows real-time synthesis. The organization and algorithms of the parallel GPU system for the 3D pseudo stereo image/video synthesis are proposed. With realizing possibility analysis on a parallel GPU-architecture of each stage, 3D pseudo stereo synthesis is performed. An experimental prototype of a specialized hardware-software system 3D pseudo stereo imaging and video was developed on the CPU/GPU. The experimental results show that the proposed adaptation of 3D pseudo stereo imaging to the architecture of GPU-systems is efficient. Also it accelerates the computational procedures of 3D pseudo-stereo synthesis for the anaglyph and anamorphic formats of the 3D stereo frame without performing optimization procedures. The acceleration is on average 11 and 54 times for test GPUs.

Neural Networks for Computer Vision: A Framework for Specifications of a General Purpose Vision System

NASA Astrophysics Data System (ADS)

Skrzypek, Josef; Mesrobian, Edmond; Gungner, David J.

1989-03-01

The development of autonomous land vehicles (ALV) capable of operating in an unconstrained environment has proven to be a formidable research effort. The unpredictability of events in such an environment calls for the design of a robust perceptual system, an impossible task requiring the programming of a system bases on the expectation of future, unconstrained events. Hence, the need for a "general purpose" machine vision system that is capable of perceiving and understanding images in an unconstrained environment in real-time. The research undertaken at the UCLA Machine Perception Laboratory addresses this need by focusing on two specific issues: 1) the long term goals for machine vision research as a joint effort between the neurosciences and computer science; and 2) a framework for evaluating progress in machine vision. In the past, vision research has been carried out independently within different fields including neurosciences, psychology, computer science, and electrical engineering. Our interdisciplinary approach to vision research is based on the rigorous combination of computational neuroscience, as derived from neurophysiology and neuropsychology, with computer science and electrical engineering. The primary motivation behind our approach is that the human visual system is the only existing example of a "general purpose" vision system and using a neurally based computing substrate, it can complete all necessary visual tasks in real-time.

PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways.

PubMed

Demir, E; Babur, O; Dogrusoz, U; Gursoy, A; Nisanci, G; Cetin-Atalay, R; Ozturk, M

2002-07-01

Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. A prototype of Patika is available upon request from the authors.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex.

PubMed

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo; Vanni, Simo

2015-01-01

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

New Abstraction Networks and a New Visualization Tool in Support of Auditing the SNOMED CT Content

PubMed Central

Geller, James; Ochs, Christopher; Perl, Yehoshua; Xu, Junchuan

2012-01-01

Medical terminologies are large and complex. Frequently, errors are hidden in this complexity. Our objective is to find such errors, which can be aided by deriving abstraction networks from a large terminology. Abstraction networks preserve important features but eliminate many minor details, which are often not useful for identifying errors. Providing visualizations for such abstraction networks aids auditors by allowing them to quickly focus on elements of interest within a terminology. Previously we introduced area taxonomies and partial area taxonomies for SNOMED CT. In this paper, two advanced, novel kinds of abstraction networks, the relationship-constrained partial area subtaxonomy and the root-constrained partial area subtaxonomy are defined and their benefits are demonstrated. We also describe BLUSNO, an innovative software tool for quickly generating and visualizing these SNOMED CT abstraction networks. BLUSNO is a dynamic, interactive system that provides quick access to well organized information about SNOMED CT. PMID:23304293

New abstraction networks and a new visualization tool in support of auditing the SNOMED CT content.

PubMed

Geller, James; Ochs, Christopher; Perl, Yehoshua; Xu, Junchuan

2012-01-01

Medical terminologies are large and complex. Frequently, errors are hidden in this complexity. Our objective is to find such errors, which can be aided by deriving abstraction networks from a large terminology. Abstraction networks preserve important features but eliminate many minor details, which are often not useful for identifying errors. Providing visualizations for such abstraction networks aids auditors by allowing them to quickly focus on elements of interest within a terminology. Previously we introduced area taxonomies and partial area taxonomies for SNOMED CT. In this paper, two advanced, novel kinds of abstraction networks, the relationship-constrained partial area subtaxonomy and the root-constrained partial area subtaxonomy are defined and their benefits are demonstrated. We also describe BLUSNO, an innovative software tool for quickly generating and visualizing these SNOMED CT abstraction networks. BLUSNO is a dynamic, interactive system that provides quick access to well organized information about SNOMED CT.

Example of a Bayes network of relations among visual features

NASA Astrophysics Data System (ADS)

Agosta, John M.

1991-10-01

Bayes probability networks, also termed `influence diagrams,' promise to be a versatile, rigorous, and expressive uncertainty reasoning tool. This paper presents an example of how a Bayes network can express constraints among visual hypotheses. An example is presented of a model composed of cylindric primitives, inferred from a line drawing of a plumbing fixture. Conflict between interpretations of candidate cylinders is expressed by two parameters, one for the presence and one for the absence of visual evidence of their intersection. It is shown how `partial exclusion' relations are so generated and how they determine the degree of competition among the set of hypotheses. Solving this network obtains the assemblies of cylinders most likely to form an object.

Entropy-based heavy tailed distribution transformation and visual analytics for monitoring massive network traffic

NASA Astrophysics Data System (ADS)

Han, Keesook J.; Hodge, Matthew; Ross, Virginia W.

2011-06-01

For monitoring network traffic, there is an enormous cost in collecting, storing, and analyzing network traffic datasets. Data mining based network traffic analysis has a growing interest in the cyber security community, but is computationally expensive for finding correlations between attributes in massive network traffic datasets. To lower the cost and reduce computational complexity, it is desirable to perform feasible statistical processing on effective reduced datasets instead of on the original full datasets. Because of the dynamic behavior of network traffic, traffic traces exhibit mixtures of heavy tailed statistical distributions or overdispersion. Heavy tailed network traffic characterization and visualization are important and essential tasks to measure network performance for the Quality of Services. However, heavy tailed distributions are limited in their ability to characterize real-time network traffic due to the difficulty of parameter estimation. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) was developed to convert the heavy tailed distribution into a transformed distribution to find the linear approximation. The EHTDT linearization has the advantage of being amenable to characterize and aggregate overdispersion of network traffic in realtime. Results of applying the EHTDT for innovative visual analytics to real network traffic data are presented.

Video transmission on ATM networks. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chen, Yun-Chung

1993-01-01

The broadband integrated services digital network (B-ISDN) is expected to provide high-speed and flexible multimedia applications. Multimedia includes data, graphics, image, voice, and video. Asynchronous transfer mode (ATM) is the adopted transport techniques for B-ISDN and has the potential for providing a more efficient and integrated environment for multimedia. It is believed that most broadband applications will make heavy use of visual information. The prospect of wide spread use of image and video communication has led to interest in coding algorithms for reducing bandwidth requirements and improving image quality. The major results of a study on the bridging of network transmission performance and video coding are: Using two representative video sequences, several video source models are developed. The fitness of these models are validated through the use of statistical tests and network queuing performance. A dual leaky bucket algorithm is proposed as an effective network policing function. The concept of the dual leaky bucket algorithm can be applied to a prioritized coding approach to achieve transmission efficiency. A mapping of the performance/control parameters at the network level into equivalent parameters at the video coding level is developed. Based on that, a complete set of principles for the design of video codecs for network transmission is proposed.

Reward processing in the value-driven attention network: reward signals tracking cue identity and location.

PubMed

Anderson, Brian A

2017-03-01

Through associative reward learning, arbitrary cues acquire the ability to automatically capture visual attention. Previous studies have examined the neural correlates of value-driven attentional orienting, revealing elevated activity within a network of brain regions encompassing the visual corticostriatal loop [caudate tail, lateral occipital complex (LOC) and early visual cortex] and intraparietal sulcus (IPS). Such attentional priority signals raise a broader question concerning how visual signals are combined with reward signals during learning to create a representation that is sensitive to the confluence of the two. This study examines reward signals during the cued reward training phase commonly used to generate value-driven attentional biases. High, compared with low, reward feedback preferentially activated the value-driven attention network, in addition to regions typically implicated in reward processing. Further examination of these reward signals within the visual system revealed information about the identity of the preceding cue in the caudate tail and LOC, and information about the location of the preceding cue in IPS, while early visual cortex represented both location and identity. The results reveal teaching signals within the value-driven attention network during associative reward learning, and further suggest functional specialization within different regions of this network during the acquisition of an integrated representation of stimulus value. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

The Study of Learners' Preference for Visual Complexity on Small Screens of Mobile Computers Using Neural Networks

ERIC Educational Resources Information Center

Wang, Lan-Ting; Lee, Kun-Chou

2014-01-01

The vision plays an important role in educational technologies because it can produce and communicate quite important functions in teaching and learning. In this paper, learners' preference for the visual complexity on small screens of mobile computers is studied by neural networks. The visual complexity in this study is divided into five…

Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.

PubMed

Aslam, Tariq M; Zaki, Haider R; Mahmood, Sajjad; Ali, Zaria C; Ahmad, Nur A; Thorell, Mariana R; Balaskas, Konstantinos

2018-01-01

To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific controlled OCT changes on vision. Artificial intelligence (neural network) study. We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity. A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact. The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies. Copyright © 2017 Elsevier Inc. All rights reserved.

Bandwidth Optimization On Design Of Visual Display Information System Based Networking At Politeknik Negeri Bali

NASA Astrophysics Data System (ADS)

Sudiartha, IKG; Catur Bawa, IGNB

2018-01-01

Information can not be separated from the social life of the community, especially in the world of education. One of the information fields is academic calendar information, activity agenda, announcement and campus activity news. In line with technological developments, text-based information is becoming obsolete. For that need creativity to present information more quickly, accurately and interesting by exploiting the development of digital technology and internet. In this paper will be developed applications for the provision of information in the form of visual display, applied to computer network system with multimedia applications. Network-based applications provide ease in updating data through internet services, attractive presentations with multimedia support. The application “Networking Visual Display Information Unit” can be used as a medium that provides information services for students and academic employee more interesting and ease in updating information than the bulletin board. The information presented in the form of Running Text, Latest Information, Agenda, Academic Calendar and Video provide an interesting presentation and in line with technological developments at the Politeknik Negeri Bali. Through this research is expected to create software “Networking Visual Display Information Unit” with optimal bandwidth usage by combining local data sources and data through the network. This research produces visual display design with optimal bandwidth usage and application in the form of supporting software.

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

PubMed Central

Hurley, Daniel; Araki, Hiromitsu; Tamada, Yoshinori; Dunmore, Ben; Sanders, Deborah; Humphreys, Sally; Affara, Muna; Imoto, Seiya; Yasuda, Kaori; Tomiyasu, Yuki; Tashiro, Kosuke; Savoie, Christopher; Cho, Vicky; Smith, Stephen; Kuhara, Satoru; Miyano, Satoru; Charnock-Jones, D. Stephen; Crampin, Edmund J.; Print, Cristin G.

2012-01-01

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions. PMID:22121215

Off-the-shelf Control of Data Analysis Software

NASA Astrophysics Data System (ADS)

Wampler, S.

The Gemini Project must provide convenient access to data analysis facilities to a wide user community. The international nature of this community makes the selection of data analysis software particularly interesting, with staunch advocates of systems such as ADAM and IRAF among the users. Additionally, the continuing trends towards increased use of networked systems and distributed processing impose additional complexity. To meet these needs, the Gemini Project is proposing the novel approach of using low-cost, off-the-shelf software to abstract out both the control and distribution of data analysis from the functionality of the data analysis software. For example, the orthogonal nature of control versus function means that users might select analysis routines from both ADAM and IRAF as appropriate, distributing these routines across a network of machines. It is the belief of the Gemini Project that this approach results in a system that is highly flexible, maintainable, and inexpensive to develop. The Khoros visualization system is presented as an example of control software that is currently available for providing the control and distribution within a data analysis system. The visual programming environment provided with Khoros is also discussed as a means to providing convenient access to this control.

Using Social Network Graphs as Visualization Tools to Influence Peer Selection Decision-Making Strategies to Access Information about Complex Socioscientific Issues

ERIC Educational Resources Information Center

Yoon, Susan A.

2011-01-01

This study extends previous research that explores how visualization affordances that computational tools provide and social network analyses that account for individual- and group-level dynamic processes can work in conjunction to improve learning outcomes. The study's main hypothesis is that when social network graphs are used in instruction,…

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

PubMed Central

Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

2014-01-01

We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user's N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user's N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues. PMID:24982999

Comparing the Consumption of CPU Hours with Scientific Output for the Extreme Science and Engineering Discovery Environment (XSEDE)

PubMed Central

Börner, Katy

2016-01-01

This paper presents the results of a study that compares resource usage with publication output using data about the consumption of CPU cycles from the Extreme Science and Engineering Discovery Environment (XSEDE) and resulting scientific publications for 2,691 institutions/teams. Specifically, the datasets comprise a total of 5,374,032,696 central processing unit (CPU) hours run in XSEDE during July 1, 2011 to August 18, 2015 and 2,882 publications that cite the XSEDE resource. Three types of studies were conducted: a geospatial analysis of XSEDE providers and consumers, co-authorship network analysis of XSEDE publications, and bi-modal network analysis of how XSEDE resources are used by different research fields. Resulting visualizations show that a diverse set of consumers make use of XSEDE resources, that users of XSEDE publish together frequently, and that the users of XSEDE with the highest resource usage tend to be “traditional” high-performance computing (HPC) community members from astronomy, atmospheric science, physics, chemistry, and biology. PMID:27310174

Using EMG to anticipate head motion for virtual-environment applications

NASA Technical Reports Server (NTRS)

Barniv, Yair; Aguilar, Mario; Hasanbelliu, Erion

2005-01-01

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Using EMG to anticipate head motion for virtual-environment applications.

PubMed

Barniv, Yair; Aguilar, Mario; Hasanbelliu, Erion

2005-06-01

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

Mapping Gnss Restricted Environments with a Drone Tandem and Indirect Position Control

NASA Astrophysics Data System (ADS)

Cledat, E.; Cucci, D. A.

2017-08-01

The problem of autonomously mapping highly cluttered environments, such as urban and natural canyons, is intractable with the current UAV technology. The reason lies in the absence or unreliability of GNSS signals due to partial sky occlusion or multi-path effects. High quality carrier-phase observations are also required in efficient mapping paradigms, such as Assisted Aerial Triangulation, to achieve high ground accuracy without the need of dense networks of ground control points. In this work we consider a drone tandem in which the first drone flies outside the canyon, where GNSS constellation is ideal, visually tracks the second drone and provides an indirect position control for it. This enables both autonomous guidance and accurate mapping of GNSS restricted environments without the need of ground control points. We address the technical feasibility of this concept considering preliminary real-world experiments in comparable conditions and we perform a mapping accuracy prediction based on a simulation scenario.

Mediator infrastructure for information integration and semantic data integration environment for biomedical research.

PubMed

Grethe, Jeffrey S; Ross, Edward; Little, David; Sanders, Brian; Gupta, Amarnath; Astakhov, Vadim

2009-01-01

This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.

Brain networks for visual creativity: a functional connectivity study of planning a visual artwork.

PubMed

De Pisapia, Nicola; Bacci, Francesca; Parrott, Danielle; Melcher, David

2016-12-19

Throughout recorded history, and across cultures, humans have made visual art. In recent years, the neural bases of creativity, including artistic creativity, have become a topic of interest. In this study we investigated the neural bases of the visual creative process with both professional artists and a group of control participants. We tested the idea that creativity (planning an artwork) would influence the functional connectivity between regions involved in the default mode network (DMN), implicated in divergent thinking and generating novel ideas, and the executive control network (EN), implicated in evaluating and selecting ideas. We measured functional connectivity with functional Magnetic Resonance Imaging (fMRI) during three different conditions: rest, visual imagery of the alphabet and planning an artwork to be executed immediately after the scanning session. Consistent with our hypothesis, we found stronger connectivity between areas of the DMN and EN during the creative task, and this difference was enhanced in professional artists. These findings suggest that creativity involves an expert balance of two brain networks typically viewed as being in opposition.

Brain networks for visual creativity: a functional connectivity study of planning a visual artwork

PubMed Central

De Pisapia, Nicola; Bacci, Francesca; Parrott, Danielle; Melcher, David

2016-01-01

Throughout recorded history, and across cultures, humans have made visual art. In recent years, the neural bases of creativity, including artistic creativity, have become a topic of interest. In this study we investigated the neural bases of the visual creative process with both professional artists and a group of control participants. We tested the idea that creativity (planning an artwork) would influence the functional connectivity between regions involved in the default mode network (DMN), implicated in divergent thinking and generating novel ideas, and the executive control network (EN), implicated in evaluating and selecting ideas. We measured functional connectivity with functional Magnetic Resonance Imaging (fMRI) during three different conditions: rest, visual imagery of the alphabet and planning an artwork to be executed immediately after the scanning session. Consistent with our hypothesis, we found stronger connectivity between areas of the DMN and EN during the creative task, and this difference was enhanced in professional artists. These findings suggest that creativity involves an expert balance of two brain networks typically viewed as being in opposition. PMID:27991592

Visualization techniques for computer network defense

NASA Astrophysics Data System (ADS)

Beaver, Justin M.; Steed, Chad A.; Patton, Robert M.; Cui, Xiaohui; Schultz, Matthew

2011-06-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operator to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

PubMed Central

Teng, Santani

2017-01-01

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research. This article is part of the themed issue ‘Auditory and visual scene analysis’. PMID:28044019

The role of temporo-parietal junction (TPJ) in global Gestalt perception.

PubMed

Huberle, Elisabeth; Karnath, Hans-Otto

2012-07-01

Grouping processes enable the coherent perception of our environment. A number of brain areas has been suggested to be involved in the integration of elements into objects including early and higher visual areas along the ventral visual pathway as well as motion-processing areas of the dorsal visual pathway. However, integration not only is required for the cortical representation of individual objects, but is also essential for the perception of more complex visual scenes consisting of several different objects and/or shapes. The present fMRI experiments aimed to address such integration processes. We investigated the neural correlates underlying the global Gestalt perception of hierarchically organized stimuli that allowed parametrical degrading of the object at the global level. The comparison of intact versus disturbed perception of the global Gestalt revealed a network of cortical areas including the temporo-parietal junction (TPJ), anterior cingulate cortex and the precuneus. The TPJ location corresponds well with the areas known to be typically lesioned in stroke patients with simultanagnosia following bilateral brain damage. These patients typically show a deficit in identifying the global Gestalt of a visual scene. Further, we found the closest relation between behavioral performance and fMRI activation for the TPJ. Our data thus argue for a significant role of the TPJ in human global Gestalt perception.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.

PubMed

Cichy, Radoslaw Martin; Teng, Santani

2017-02-19

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.This article is part of the themed issue 'Auditory and visual scene analysis'. © 2017 The Authors.

Design of a Threat-Based Gunnery Performance Test: Issues and Procedures for Crew and Platoon Tank Gunnery

DTIC Science & Technology

1990-06-01

Uses visual communication . _._Changes direction/formation __Crews transmit timely, accurate quickly. messages. NOTES. Figure 22. Sample engagement...and concise. The network control station (NCS) effectively maintains network discipline. Radio security equipment, visual communication , wire...net discipline, (c) clarity and brevity of radio messages, (d) use of transmission security equipment, (e) use of visual communication , (f) use of wire

Protein-Protein Interaction Network and Gene Ontology

NASA Astrophysics Data System (ADS)

Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation

PubMed Central

Haider, Bilal; Krause, Matthew R.; Duque, Alvaro; Yu, Yuguo; Touryan, Jonathan; Mazer, James A.; McCormick, David A.

2011-01-01

SUMMARY During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RSC) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RSC neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses. PMID:20152117

Contact Trees: Network Visualization beyond Nodes and Edges

PubMed Central

Sallaberry, Arnaud; Fu, Yang-chih; Ho, Hwai-Chung; Ma, Kwan-Liu

2016-01-01

Node-Link diagrams make it possible to take a quick glance at how nodes (or actors) in a network are connected by edges (or ties). A conventional network diagram of a “contact tree” maps out a root and branches that represent the structure of nodes and edges, often without further specifying leaves or fruits that would have grown from small branches. By furnishing such a network structure with leaves and fruits, we reveal details about “contacts” in our ContactTrees upon which ties and relationships are constructed. Our elegant design employs a bottom-up approach that resembles a recent attempt to understand subjective well-being by means of a series of emotions. Such a bottom-up approach to social-network studies decomposes each tie into a series of interactions or contacts, which can help deepen our understanding of the complexity embedded in a network structure. Unlike previous network visualizations, ContactTrees highlight how relationships form and change based upon interactions among actors, as well as how relationships and networks vary by contact attributes. Based on a botanical tree metaphor, the design is easy to construct and the resulting tree-like visualization can display many properties at both tie and contact levels, thus recapturing a key ingredient missing from conventional techniques of network visualization. We demonstrate ContactTrees using data sets consisting of up to three waves of 3-month contact diaries over the 2004-2012 period, and discuss how this design can be applied to other types of datasets. PMID:26784350

Functional segregation of the human cingulate cortex is confirmed by functional connectivity based neuroanatomical parcellation.

PubMed

Yu, Chunshui; Zhou, Yuan; Liu, Yong; Jiang, Tianzi; Dong, Haiwei; Zhang, Yunting; Walter, Martin

2011-02-14

The four-region model with 7 specified subregions represents a theoretical construct of functionally segregated divisions of the cingulate cortex based on integrated neurobiological assessments. Under this framework, we aimed to investigate the functional specialization of the human cingulate cortex by analyzing the resting-state functional connectivity (FC) of each subregion from a network perspective. In 20 healthy subjects we systematically investigated the FC patterns of the bilateral subgenual (sACC) and pregenual (pACC) anterior cingulate cortices, anterior (aMCC) and posterior (pMCC) midcingulate cortices, dorsal (dPCC) and ventral (vPCC) posterior cingulate cortices and retrosplenial cortices (RSC). We found that each cingulate subregion was specifically integrated in the predescribed functional networks and showed anti-correlated resting-state fluctuations. The sACC and pACC were involved in an affective network and anti-correlated with the sensorimotor and cognitive networks, while the pACC also correlated with the default-mode network and anti-correlated with the visual network. In the midcingulate cortex, however, the aMCC was correlated with the cognitive and sensorimotor networks and anti-correlated with the visual, affective and default-mode networks, whereas the pMCC only correlated with the sensorimotor network and anti-correlated with the cognitive and visual networks. The dPCC and vPCC involved in the default-mode network and anti-correlated with the sensorimotor, cognitive and visual networks, in contrast, the RSC was mainly correlated with the PCC and thalamus. Based on a strong hypothesis driven approach of anatomical partitions of the cingulate cortex, we could confirm their segregation in terms of functional neuroanatomy, as suggested earlier by task studies or exploratory multi-seed investigations. Copyright © 2010 Elsevier Inc. All rights reserved.

Real-time optimizations for integrated smart network camera

NASA Astrophysics Data System (ADS)

Desurmont, Xavier; Lienard, Bruno; Meessen, Jerome; Delaigle, Jean-Francois

2005-02-01

We present an integrated real-time smart network camera. This system is composed of an image sensor, an embedded PC based electronic card for image processing and some network capabilities. The application detects events of interest in visual scenes, highlights alarms and computes statistics. The system also produces meta-data information that could be shared between other cameras in a network. We describe the requirements of such a system and then show how the design of the system is optimized to process and compress video in real-time. Indeed, typical video-surveillance algorithms as background differencing, tracking and event detection should be highly optimized and simplified to be used in this hardware. To have a good adequation between hardware and software in this light embedded system, the software management is written on top of the java based middle-ware specification established by the OSGi alliance. We can integrate easily software and hardware in complex environments thanks to the Java Real-Time specification for the virtual machine and some network and service oriented java specifications (like RMI and Jini). Finally, we will report some outcomes and typical case studies of such a camera like counter-flow detection.

Neuronify: An Educational Simulator for Neural Circuits.

PubMed

Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

2017-01-01

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

Neuronify: An Educational Simulator for Neural Circuits

PubMed Central

Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

2017-01-01

Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

An insect-inspired model for visual binding I: learning objects and their characteristics.

PubMed

Northcutt, Brandon D; Dyhr, Jonathan P; Higgins, Charles M

2017-04-01

Visual binding is the process of associating the responses of visual interneurons in different visual submodalities all of which are responding to the same object in the visual field. Recently identified neuropils in the insect brain termed optic glomeruli reside just downstream of the optic lobes and have an internal organization that could support visual binding. Working from anatomical similarities between optic and olfactory glomeruli, we have developed a model of visual binding based on common temporal fluctuations among signals of independent visual submodalities. Here we describe and demonstrate a neural network model capable both of refining selectivity of visual information in a given visual submodality, and of associating visual signals produced by different objects in the visual field by developing inhibitory neural synaptic weights representing the visual scene. We also show that this model is consistent with initial physiological data from optic glomeruli. Further, we discuss how this neural network model may be implemented in optic glomeruli at a neuronal level.

A low complexity visualization tool that helps to perform complex systems analysis

NASA Astrophysics Data System (ADS)

Beiró, M. G.; Alvarez-Hamelin, J. I.; Busch, J. R.

2008-12-01

In this paper, we present an extension of large network visualization (LaNet-vi), a tool to visualize large scale networks using the k-core decomposition. One of the new features is how vertices compute their angular position. While in the later version it is done using shell clusters, in this version we use the angular coordinate of vertices in higher k-shells, and arrange the highest shell according to a cliques decomposition. The time complexity goes from O(n\\sqrt n) to O(n) upon bounds on a heavy-tailed degree distribution. The tool also performs a k-core-connectivity analysis, highlighting vertices that are not k-connected; e.g. this property is useful to measure robustness or quality of service (QoS) capabilities in communication networks. Finally, the actual version of LaNet-vi can draw labels and all the edges using transparencies, yielding an accurate visualization. Based on the obtained figure, it is possible to distinguish different sources and types of complex networks at a glance, in a sort of 'network iris-print'.

SocialMood: an information visualization tool to measure the mood of the people in social networks

NASA Astrophysics Data System (ADS)

Amorim, Guilherme; Franco, Roberto; Moraes, Rodolfo; Figueiredo, Bruno; Miranda, João.; Dobrões, José; Afonso, Ricardo; Meiguins, Bianchi

2013-12-01

Based on the arena of social networks, the tool developed in this study aims to identify trends mood among undergraduate students. Combining the methodology Self-Assessment Manikin (SAM), which originated in the field of Psychology, the system filters the content provided on the Web and isolates certain words, establishing a range of values as perceived positive, negative or neutral. A Big Data summarizing the results, assisting in the construction and visualization of behavioral profiles generic, so we have a guideline for the development of information visualization tools for social networks.

Network analysis of corticocortical connections reveals ventral and dorsal processing streams in mouse visual cortex

PubMed Central

Wang, Quanxin; Sporns, Olaf; Burkhalter, Andreas

2012-01-01

Much of the information used for visual perception and visually guided actions is processed in complex networks of connections within the cortex. To understand how this works in the normal brain and to determine the impact of disease, mice are promising models. In primate visual cortex, information is processed in a dorsal stream specialized for visuospatial processing and guided action and a ventral stream for object recognition. Here, we traced the outputs of 10 visual areas and used quantitative graph analytic tools of modern network science to determine, from the projection strengths in 39 cortical targets, the community structure of the network. We found a high density of the cortical graph that exceeded that previously shown in monkey. Each source area showed a unique distribution of projection weights across its targets (i.e. connectivity profile) that was well-fit by a lognormal function. Importantly, the community structure was strongly dependent on the location of the source area: outputs from medial/anterior extrastriate areas were more strongly linked to parietal, motor and limbic cortex, whereas lateral extrastriate areas were preferentially connected to temporal and parahippocampal cortex. These two subnetworks resemble dorsal and ventral cortical streams in primates, demonstrating that the basic layout of cortical networks is conserved across species. PMID:22457489

Absence of visual experience modifies the neural basis of numerical thinking.

PubMed

Kanjlia, Shipra; Lane, Connor; Feigenson, Lisa; Bedny, Marina

2016-10-04

In humans, the ability to reason about mathematical quantities depends on a frontoparietal network that includes the intraparietal sulcus (IPS). How do nature and nurture give rise to the neurobiology of numerical cognition? We asked how visual experience shapes the neural basis of numerical thinking by studying numerical cognition in congenitally blind individuals. Blind (n = 17) and blindfolded sighted (n = 19) participants solved math equations that varied in difficulty (e.g., 27 - 12 = x vs. 7 - 2 = x), and performed a control sentence comprehension task while undergoing fMRI. Whole-cortex analyses revealed that in both blind and sighted participants, the IPS and dorsolateral prefrontal cortices were more active during the math task than the language task, and activity in the IPS increased parametrically with equation difficulty. Thus, the classic frontoparietal number network is preserved in the total absence of visual experience. However, surprisingly, blind but not sighted individuals additionally recruited a subset of early visual areas during symbolic math calculation. The functional profile of these "visual" regions was identical to that of the IPS in blind but not sighted individuals. Furthermore, in blindness, number-responsive visual cortices exhibited increased functional connectivity with prefrontal and IPS regions that process numbers. We conclude that the frontoparietal number network develops independently of visual experience. In blindness, this number network colonizes parts of deafferented visual cortex. These results suggest that human cortex is highly functionally flexible early in life, and point to frontoparietal input as a mechanism of cross-modal plasticity in blindness.

Low-cost telepresence for collaborative virtual environments.

PubMed

Rhee, Seon-Min; Ziegler, Remo; Park, Jiyoung; Naef, Martin; Gross, Markus; Kim, Myoung-Hee

2007-01-01

We present a novel low-cost method for visual communication and telepresence in a CAVE -like environment, relying on 2D stereo-based video avatars. The system combines a selection of proven efficient algorithms and approximations in a unique way, resulting in a convincing stereoscopic real-time representation of a remote user acquired in a spatially immersive display. The system was designed to extend existing projection systems with acquisition capabilities requiring minimal hardware modifications and cost. The system uses infrared-based image segmentation to enable concurrent acquisition and projection in an immersive environment without a static background. The system consists of two color cameras and two additional b/w cameras used for segmentation in the near-IR spectrum. There is no need for special optics as the mask and color image are merged using image-warping based on a depth estimation. The resulting stereo image stream is compressed, streamed across a network, and displayed as a frame-sequential stereo texture on a billboard in the remote virtual environment.

Near-instant automatic access to visually presented words in the human neocortex: neuromagnetic evidence.

PubMed

Shtyrov, Yury; MacGregor, Lucy J

2016-05-24

Rapid and efficient processing of external information by the brain is vital to survival in a highly dynamic environment. The key channel humans use to exchange information is language, but the neural underpinnings of its processing are still not fully understood. We investigated the spatio-temporal dynamics of neural access to word representations in the brain by scrutinising the brain's activity elicited in response to psycholinguistically, visually and phonologically matched groups of familiar words and meaningless pseudowords. Stimuli were briefly presented on the visual-field periphery to experimental participants whose attention was occupied with a non-linguistic visual feature-detection task. The neural activation elicited by these unattended orthographic stimuli was recorded using multi-channel whole-head magnetoencephalography, and the timecourse of lexically-specific neuromagnetic responses was assessed in sensor space as well as at the level of cortical sources, estimated using individual MR-based distributed source reconstruction. Our results demonstrate a neocortical signature of automatic near-instant access to word representations in the brain: activity in the perisylvian language network characterised by specific activation enhancement for familiar words, starting as early as ~70 ms after the onset of unattended word stimuli and underpinned by temporal and inferior-frontal cortices.

An integrated network visualization framework towards metabolic engineering applications.

PubMed

Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

2014-12-30

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Static and dynamic views of visual cortical organization.

PubMed

Casagrande, Vivien A; Xu, Xiangmin; Sáry, Gyula

2002-01-01

Without the aid of modern techniques Cajal speculated that cells in the visual cortex were connected in circuits. From Cajal's time until fairly recently, the flow of information within the cells and circuits of visual cortex has been described as progressing from input to output, from sensation to action. In this chapter we argue that a paradigm shift in our concept of the visual cortical neuron is under way. The most important change in our view concerns the neuron's functional role. Visual cortical neurons do not have static functional signatures but instead function dynamically depending on the ongoing activity of the networks to which they belong. These networks are not merely top-down or bottom-up unidirectional transmission lines, but rather represent machinery that uses recurrent information and is dynamic and highly adaptable. With the advancement of technology for analyzing the conversations of multiple neurons at many levels in the visual system and higher resolution imaging, we predict that the paradigm shift will progress to the point where neurons are no longer viewed as independent processing units but as members of subsets of networks where their role is mapped in space-time coordinates in relationship to the other neuronal members. This view moves us far from Cajal's original views of the neuron. Nevertheless, we believe that understanding the basic morphology and wiring of networks will continue to contribute to our overall understanding of the visual cortex.

pV3-Gold Visualization Environment for Computer Simulations

NASA Technical Reports Server (NTRS)

Babrauckas, Theresa L.

1997-01-01

A new visualization environment, pV3-Gold, can be used during and after a computer simulation to extract and visualize the physical features in the results. This environment, which is an extension of the pV3 visualization environment developed at the Massachusetts Institute of Technology with guidance and support by researchers at the NASA Lewis Research Center, features many tools that allow users to display data in various ways.

A workflow for the 3D visualization of meteorological data

NASA Astrophysics Data System (ADS)

Helbig, Carolin; Rink, Karsten

2014-05-01

In the future, climate change will strongly influence our environment and living conditions. To predict possible changes, climate models that include basic and process conditions have been developed and big data sets are produced as a result of simulations. The combination of various variables of climate models with spatial data from different sources helps to identify correlations and to study key processes. For our case study we use results of the weather research and forecasting (WRF) model of two regions at different scales that include various landscapes in Northern Central Europe and Baden-Württemberg. We visualize these simulation results in combination with observation data and geographic data, such as river networks, to evaluate processes and analyze if the model represents the atmospheric system sufficiently. For this purpose, a continuous workflow that leads from the integration of heterogeneous raw data to visualization using open source software (e.g. OpenGeoSys Data Explorer, ParaView) is developed. These visualizations can be displayed on a desktop computer or in an interactive virtual reality environment. We established a concept that includes recommended 3D representations and a color scheme for the variables of the data based on existing guidelines and established traditions in the specific domain. To examine changes over time in observation and simulation data, we added the temporal dimension to the visualization. In a first step of the analysis, the visualizations are used to get an overview of the data and detect areas of interest such as regions of convection or wind turbulences. Then, subsets of data sets are extracted and the included variables can be examined in detail. An evaluation by experts from the domains of visualization and atmospheric sciences establish if they are self-explanatory and clearly arranged. These easy-to-understand visualizations of complex data sets are the basis for scientific communication. In addition, they have become an essential medium for the evaluation and verification of models. Particularly in interdisciplinary research projects, they support the scientists in discussions and help to set a general level of knowledge.

Bayesian estimation inherent in a Mexican-hat-type neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

Neural networks involved in learning lexical-semantic and syntactic information in a second language.

PubMed

Mueller, Jutta L; Rueschemeyer, Shirley-Ann; Ono, Kentaro; Sugiura, Motoaki; Sadato, Norihiro; Nakamura, Akinori

2014-01-01

The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient.

Surgical model-view-controller simulation software framework for local and collaborative applications

PubMed Central

Sankaranarayanan, Ganesh; Halic, Tansel; Arikatla, Venkata Sreekanth; Lu, Zhonghua; De, Suvranu

2010-01-01

Purpose Surgical simulations require haptic interactions and collaboration in a shared virtual environment. A software framework for decoupled surgical simulation based on a multi-controller and multi-viewer model-view-controller (MVC) pattern was developed and tested. Methods A software framework for multimodal virtual environments was designed, supporting both visual interactions and haptic feedback while providing developers with an integration tool for heterogeneous architectures maintaining high performance, simplicity of implementation, and straightforward extension. The framework uses decoupled simulation with updates of over 1,000 Hz for haptics and accommodates networked simulation with delays of over 1,000 ms without performance penalty. Results The simulation software framework was implemented and was used to support the design of virtual reality-based surgery simulation systems. The framework supports the high level of complexity of such applications and the fast response required for interaction with haptics. The efficacy of the framework was tested by implementation of a minimally invasive surgery simulator. Conclusion A decoupled simulation approach can be implemented as a framework to handle simultaneous processes of the system at the various frame rates each process requires. The framework was successfully used to develop collaborative virtual environments (VEs) involving geographically distributed users connected through a network, with the results comparable to VEs for local users. PMID:20714933

Surgical model-view-controller simulation software framework for local and collaborative applications.

PubMed

Maciel, Anderson; Sankaranarayanan, Ganesh; Halic, Tansel; Arikatla, Venkata Sreekanth; Lu, Zhonghua; De, Suvranu

2011-07-01

Surgical simulations require haptic interactions and collaboration in a shared virtual environment. A software framework for decoupled surgical simulation based on a multi-controller and multi-viewer model-view-controller (MVC) pattern was developed and tested. A software framework for multimodal virtual environments was designed, supporting both visual interactions and haptic feedback while providing developers with an integration tool for heterogeneous architectures maintaining high performance, simplicity of implementation, and straightforward extension. The framework uses decoupled simulation with updates of over 1,000 Hz for haptics and accommodates networked simulation with delays of over 1,000 ms without performance penalty. The simulation software framework was implemented and was used to support the design of virtual reality-based surgery simulation systems. The framework supports the high level of complexity of such applications and the fast response required for interaction with haptics. The efficacy of the framework was tested by implementation of a minimally invasive surgery simulator. A decoupled simulation approach can be implemented as a framework to handle simultaneous processes of the system at the various frame rates each process requires. The framework was successfully used to develop collaborative virtual environments (VEs) involving geographically distributed users connected through a network, with the results comparable to VEs for local users.

Development of climate data storage and processing model

NASA Astrophysics Data System (ADS)

Okladnikov, I. G.; Gordov, E. P.; Titov, A. G.

2016-11-01

We present a storage and processing model for climate datasets elaborated in the framework of a virtual research environment (VRE) for climate and environmental monitoring and analysis of the impact of climate change on the socio-economic processes on local and regional scales. The model is based on a «shared nothings» distributed computing architecture and assumes using a computing network where each computing node is independent and selfsufficient. Each node holds a dedicated software for the processing and visualization of geospatial data providing programming interfaces to communicate with the other nodes. The nodes are interconnected by a local network or the Internet and exchange data and control instructions via SSH connections and web services. Geospatial data is represented by collections of netCDF files stored in a hierarchy of directories in the framework of a file system. To speed up data reading and processing, three approaches are proposed: a precalculation of intermediate products, a distribution of data across multiple storage systems (with or without redundancy), and caching and reuse of the previously obtained products. For a fast search and retrieval of the required data, according to the data storage and processing model, a metadata database is developed. It contains descriptions of the space-time features of the datasets available for processing, their locations, as well as descriptions and run options of the software components for data analysis and visualization. The model and the metadata database together will provide a reliable technological basis for development of a high- performance virtual research environment for climatic and environmental monitoring.

Visual Representations of Microcosm in Textbooks of Chemistry: Constructing a Systemic Network for Their Main Conceptual Framework

ERIC Educational Resources Information Center

Papageorgiou, George; Amariotakis, Vasilios; Spiliotopoulou, Vasiliki

2017-01-01

The main objective of this work is to analyse the visual representations (VRs) of the microcosm depicted in nine Greek secondary chemistry school textbooks of the last three decades in order to construct a systemic network for their main conceptual framework and to evaluate the contribution of each one of the resulting categories to the network.…

Visual attention in preterm born adults: specifically impaired attentional sub-mechanisms that link with altered intrinsic brain networks in a compensation-like mode.

PubMed

Finke, Kathrin; Neitzel, Julia; Bäuml, Josef G; Redel, Petra; Müller, Hermann J; Meng, Chun; Jaekel, Julia; Daamen, Marcel; Scheef, Lukas; Busch, Barbara; Baumann, Nicole; Boecker, Henning; Bartmann, Peter; Habekost, Thomas; Wolke, Dieter; Wohlschläger, Afra; Sorg, Christian

2015-02-15

Although pronounced and lasting deficits in selective attention have been observed for preterm born individuals it is unknown which specific attentional sub-mechanisms are affected and how they relate to brain networks. We used the computationally specified 'Theory of Visual Attention' together with whole- and partial-report paradigms to compare attentional sub-mechanisms of pre- (n=33) and full-term (n=32) born adults. Resting-state fMRI was used to evaluate both between-group differences and inter-individual variance in changed functional connectivity of intrinsic brain networks relevant for visual attention. In preterm born adults, we found specific impairments of visual short-term memory (vSTM) storage capacity while other sub-mechanisms such as processing speed or attentional weighting were unchanged. Furthermore, changed functional connectivity was found in unimodal visual and supramodal attention-related intrinsic networks. Among preterm born adults, the individual pattern of changed connectivity in occipital and parietal cortices was systematically associated with vSTM in such a way that the more distinct the connectivity differences, the better the preterm adults' storage capacity. These findings provide first evidence for selectively changed attentional sub-mechanisms in preterm born adults and their relation to altered intrinsic brain networks. In particular, data suggest that cortical changes in intrinsic functional connectivity may compensate adverse developmental consequences of prematurity on visual short-term storage capacity. Copyright © 2014 Elsevier Inc. All rights reserved.

Visualizing Transmedia Networks: Links, Paths and Peripheries

ERIC Educational Resources Information Center

Ruppel, Marc Nathaniel

2012-01-01

'Visualizing Transmedia Networks: Links, Paths and Peripheries' examines the increasingly complex rhetorical intersections between narrative and media ("old" and "new") in the creation of transmedia fictions, loosely defined as multisensory and multimodal stories told extensively across a diverse media set. In order…

Intrusion Detection System Visualization of Network Alerts

DTIC Science & Technology

2010-07-01

Intrusion Detection System Visualization of Network Alerts Dolores M. Zage and Wayne M. Zage Ball State University Final Report July 2010...contracts. Staff Wayne Zage, Director of the S2ERC and Professor, Department of Computer Science, Ball State University Dolores Zage, Research

Attention supports verbal short-term memory via competition between dorsal and ventral attention networks.

PubMed

Majerus, Steve; Attout, Lucie; D'Argembeau, Arnaud; Degueldre, Christian; Fias, Wim; Maquet, Pierre; Martinez Perez, Trecy; Stawarczyk, David; Salmon, Eric; Van der Linden, Martial; Phillips, Christophe; Balteau, Evelyne

2012-05-01

Interactions between the neural correlates of short-term memory (STM) and attention have been actively studied in the visual STM domain but much less in the verbal STM domain. Here we show that the same attention mechanisms that have been shown to shape the neural networks of visual STM also shape those of verbal STM. Based on previous research in visual STM, we contrasted the involvement of a dorsal attention network centered on the intraparietal sulcus supporting task-related attention and a ventral attention network centered on the temporoparietal junction supporting stimulus-related attention. We observed that, with increasing STM load, the dorsal attention network was activated while the ventral attention network was deactivated, especially during early maintenance. Importantly, activation in the ventral attention network increased in response to task-irrelevant stimuli briefly presented during the maintenance phase of the STM trials but only during low-load STM conditions, which were associated with the lowest levels of activity in the dorsal attention network during encoding and early maintenance. By demonstrating a trade-off between task-related and stimulus-related attention networks during verbal STM, this study highlights the dynamics of attentional processes involved in verbal STM.

Network Monitoring Traffic Compression Using Singular Value Decomposition

DTIC Science & Technology

2014-03-27

Shootouts." Workshop on Intrusion Detection and Network Monitoring. 1999. [12] Goodall , John R. "Visualization is better! a comparative evaluation...34 Visualization for Cyber Security, 2009. VizSec 2009. 6th International Workshop on IEEE, 2009. [13] Goodall , John R., and Mark Sowul. "VIAssist...Viruses and Log Visualization.” In Australian Digital Forensics Conference. Paper 54, 2008. [30] Tesone, Daniel R., and John R. Goodall . "Balancing

Lateralization of Resting State Networks and Relationship to Age and Gender

PubMed Central

Agcaoglu, O.; Miller, R.; Mayer, A.R.; Hugdahl, K.; Calhoun, V.D.

2014-01-01

Brain lateralization is a widely studied topic, however there has been little work focused on lateralization of intrinsic networks (regions showing similar patterns of covariation among voxels) in the resting brain. In this study, we evaluate resting state network lateralization in an age and gender-balanced functional magnetic resonance imaging (fMRI) dataset comprising over 600 healthy subjects ranging in age from 12 to 71. After establishing sample-wide network lateralization properties, we continue with an investigation of age and gender effects on network lateralization. All data was gathered on the same scanner and preprocessed using an automated pipeline (Scott et al., 2011). Networks were extracted via group independent component analysis (gICA) (Calhoun, Adali, Pearlson, & Pekar, 2001). Twenty-eight resting state networks discussed in previous (Allen et al., 2011) work were re-analyzed with a focus on lateralization. We calculated homotopic voxelwise measures of laterality in addition to a global lateralization measure, called the laterality cofactor, for each network. As expected, many of the intrinsic brain networks were lateralized. For example, the visual network was strongly right lateralized, auditory network and default mode networks were mostly left lateralized. Attentional and frontal networks included nodes that were left lateralized and other nodes that were right lateralized. Age was strongly related to lateralization in multiple regions including sensorimotor network regions precentral gyrus, postcentral gyrus and supramarginal gyrus; and visual network regions lingual gyrus; attentional network regions inferior parietal lobule, superior parietal lobule and middle temporal gyrus; and frontal network regions including the inferior frontal gyrus. Gender showed significant effects mainly in two regions, including visual and frontal networks. For example, the inferior frontal gyrus was more right lateralized in males. Significant effects of age were found in sensorimotor and visual networks on the global measure. In summary, we report a large-sample of lateralization study that finds intrinsic functional brain networks to be highly lateralized, with regions that are strongly related to gender and age locally, and with age a strong factor in lateralization, and gender exhibiting a trend-level effect on global measures of laterality. PMID:25241084

Lateralization of resting state networks and relationship to age and gender.

PubMed

Agcaoglu, O; Miller, R; Mayer, A R; Hugdahl, K; Calhoun, V D

2015-01-01

Brain lateralization is a widely studied topic, however there has been little work focused on lateralization of intrinsic networks (regions showing similar patterns of covariation among voxels) in the resting brain. In this study, we evaluate resting state network lateralization in an age and gender-balanced functional magnetic resonance imaging (fMRI) dataset comprising over 600 healthy subjects ranging in age from 12 to 71. After establishing sample-wide network lateralization properties, we continue with an investigation of age and gender effects on network lateralization. All data was gathered on the same scanner and preprocessed using an automated pipeline (Scott et al., 2011). Networks were extracted via group independent component analysis (gICA) (Calhoun et al., 2001). Twenty-eight resting state networks discussed in previous (Allen et al., 2011) work were re-analyzed with a focus on lateralization. We calculated homotopic voxelwise measures of laterality in addition to a global lateralization measure, called the laterality cofactor, for each network. As expected, many of the intrinsic brain networks were lateralized. For example, the visual network was strongly right lateralized, auditory network and default mode networks were mostly left lateralized. Attentional and frontal networks included nodes that were left lateralized and other nodes that were right lateralized. Age was strongly related to lateralization in multiple regions including sensorimotor network regions precentral gyrus, postcentral gyrus and supramarginal gyrus; and visual network regions lingual gyrus; attentional network regions inferior parietal lobule, superior parietal lobule and middle temporal gyrus; and frontal network regions including the inferior frontal gyrus. Gender showed significant effects mainly in two regions, including visual and frontal networks. For example, the inferior frontal gyrus was more right lateralized in males. Significant effects of age were found in sensorimotor and visual networks on the global measure. In summary, we report a large-sample of lateralization study that finds intrinsic functional brain networks to be highly lateralized, with regions that are strongly related to gender and age locally, and with age a strong factor in lateralization, and gender exhibiting a trend-level effect on global measures of laterality. Copyright © 2014 Elsevier Inc. All rights reserved.

Age-dependent modulation of the somatosensory network upon eye closure.

PubMed

Brodoehl, Stefan; Klingner, Carsten; Witte, Otto W

2016-02-01

Eye closure even in complete darkness can improve somatosensory perception by switching the brain to a uni-sensory processing mode. This causes an increased information flow between the thalamus and the somatosensory cortex while decreasing modulation by the visual cortex. Previous work suggests that these modulations are age-dependent and that the benefit in somatosensory performance due to eye closing diminishes with age. The cause of this age-dependency and to what extent somatosensory processing is involved remains unclear. Therefore, we intended to characterize the underlying age-dependent modifications in the interaction and connectivity of different sensory networks caused by eye closure. We performed functional MR-imaging with tactile stimulation of the right hand under the conditions of opened and closed eyes in healthy young and elderly participants. Conditional Granger causality analysis was performed to assess the somatosensory and visual networks, including the thalamus. Independent of age, eye closure improved the information transfer from the thalamus to and within the somatosensory cortex. However, beyond that, we found an age-dependent recruitment strategy. Whereas young participants were characterized by an optimized information flow within the relays of the somatosensory network, elderly participants revealed a stronger modulatory influence of the visual network upon the somatosensory cortex. Our results demonstrate that the modulation of the somatosensory and visual networks by eye closure diminishes with age and that the dominance of the visual system is more pronounced in the aging brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Mobile device geo-localization and object visualization in sensor networks

NASA Astrophysics Data System (ADS)

Lemaire, Simon; Bodensteiner, Christoph; Arens, Michael

2014-10-01

In this paper we present a method to visualize geo-referenced objects on modern smartphones using a multi- functional application design. The application applies different localization and visualization methods including the smartphone camera image. The presented application copes well with different scenarios. A generic application work flow and augmented reality visualization techniques are described. The feasibility of the approach is experimentally validated using an online desktop selection application in a network with a modern of-the-shelf smartphone. Applications are widespread and include for instance crisis and disaster management or military applications.

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-01-01

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

PubMed

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-06-10

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.

Analysis and Visualization of Internet QA Bulletin Boards Represented as Heterogeneous Networks

NASA Astrophysics Data System (ADS)

Murata, Tsuyoshi; Ikeya, Tomoyuki

Visualizing and analyzing social interactions of CGM (Consumer Generated Media) are important for understanding overall activities on the internet. Social interactions are often represented as simple networks that are composed of homogeneous nodes and edges between them. However, related entities in real world are often not homogeneous. Such relations are naturally represented as heterogeneous networks composed of more than one kind of nodes and edges connecting them. In the case of CGM, for example, users and their contents constitute nodes of heterogeneous networks. There are related users (user communities) and related contents (contents communities) in the heterogeneous networks. Discovering both communities and finding correspondence among them will clarify the characteristics of the communites. This paper describes an attempt for visualizing and analyzing social interactions of Yahoo! Chiebukuro (Japanese Yahoo! Answers). New criteria for measuring correspondence between user communities and board communites are defined, and characteristics of both communities are analyzed using the criteria.

SOVEREIGN: An autonomous neural system for incrementally learning planned action sequences to navigate towards a rewarded goal.

PubMed

Gnadt, William; Grossberg, Stephen

2008-06-01

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and size-invariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Advanced helmet mounted display (AHMD)

NASA Astrophysics Data System (ADS)

Sisodia, Ashok; Bayer, Michael; Townley-Smith, Paul; Nash, Brian; Little, Jay; Cassarly, William; Gupta, Anurag

2007-04-01

Due to significantly increased U.S. military involvement in deterrent, observer, security, peacekeeping and combat roles around the world, the military expects significant future growth in the demand for deployable virtual reality trainers with networked simulation capability of the battle space visualization process. The use of HMD technology in simulated virtual environments has been initiated by the demand for more effective training tools. The AHMD overlays computer-generated data (symbology, synthetic imagery, enhanced imagery) augmented with actual and simulated visible environment. The AHMD can be used to support deployable reconfigurable training solutions as well as traditional simulation requirements, UAV augmented reality, air traffic control and Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications. This paper will describe the design improvements implemented for production of the AHMD System.

New software for 3D fracture network analysis and visualization

NASA Astrophysics Data System (ADS)

Song, J.; Noh, Y.; Choi, Y.; Um, J.; Hwang, S.

2013-12-01

This study presents new software to perform analysis and visualization of the fracture network system in 3D. The developed software modules for the analysis and visualization, such as BOUNDARY, DISK3D, FNTWK3D, CSECT and BDM, have been developed using Microsoft Visual Basic.NET and Visualization TookKit (VTK) open-source library. Two case studies revealed that each module plays a role in construction of analysis domain, visualization of fracture geometry in 3D, calculation of equivalent pipes, production of cross-section map and management of borehole data, respectively. The developed software for analysis and visualization of the 3D fractured rock mass can be used to tackle the geomechanical problems related to strength, deformability and hydraulic behaviors of the fractured rock masses.

Comparing Eyewitness-Derived Trajectories of Bright Meteors to Ground Truth Data

NASA Technical Reports Server (NTRS)

Moser, D. E.

2016-01-01

The NASA Meteoroid Environment Office is a US government agency tasked with analyzing meteors of public interest. When queried about a meteor observed over the United States, the MEO must respond with a characterization of the trajectory, orbit, and size within a few hours. If the event is outside meteor network coverage and there is no imagery recorded by the public, a timely assessment can be difficult if not impossible. In this situation, visual reports made by eyewitnesses may be the only resource available. This has led to the development of a tool to quickly calculate crude meteor trajectories from eyewitness reports made to the American Meteor Society. A description of the tool, example case studies, and a comparison to ground truth data observed by the NASA All Sky Fireball Network are presented.

Dynamic reorganization of human resting-state networks during visuospatial attention.

PubMed

Spadone, Sara; Della Penna, Stefania; Sestieri, Carlo; Betti, Viviana; Tosoni, Annalisa; Perrucci, Mauro Gianni; Romani, Gian Luca; Corbetta, Maurizio

2015-06-30

Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rerouting of visual information across hemispheres, changed with respect to rest. During the attention task, the two networks remained relatively segregated, and their general pattern of within-network correlation was maintained. However, attention induced a decrease of correlation in the VIS network and an increase of the DAN→VIS IFC and DFC, especially in a top-down direction. In contrast, within the DAN, IFC was not modified by attention, whereas DFC was enhanced. Importantly, IFC modulations were behaviorally relevant. We conclude that a stable backbone of within-network functional connectivity topography remains in place when transitioning between resting wakefulness and attention selection. However, relative decrease of correlation of ongoing "idling" activity in visual cortex and synchronization between frontoparietal and visual cortex were behaviorally relevant, indicating that modulations of resting activity patterns are important for task performance. Higher order resting connectivity in the DAN was relatively unaffected during attention, potentially indicating a role for simultaneous ongoing activity as a "prior" for attention selection.

A case for spiking neural network simulation based on configurable multiple-FPGA systems.

PubMed

Yang, Shufan; Wu, Qiang; Li, Renfa

2011-09-01

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

How mutation alters the evolutionary dynamics of cooperation on networks

NASA Astrophysics Data System (ADS)

Ichinose, Genki; Satotani, Yoshiki; Sayama, Hiroki

2018-05-01

Cooperation is ubiquitous at every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. A recently proposed numerical metric, average gradient of selection (AGoS), a useful tool for interpreting and visualizing evolutionary dynamics on networks, allows simulation results to be visualized on a one-dimensional phase space. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we extend AGoS so that it can analyze the evolution of cooperation where mutation may alter strategies of individuals on networks. We show that our extended AGoS correctly visualizes the final states of cooperation with mutation in the individual-based simulations. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the payoff functions, fraction of cooperators, and network structures. Moreover, we found that scale-free networks are the most vulnerable to mutation and thus the dynamics of cooperation are altered from bistability to coexistence on those networks, undergoing an imperfect pitchfork bifurcation.

A Markov chain model for image ranking system in social networks

NASA Astrophysics Data System (ADS)

Zin, Thi Thi; Tin, Pyke; Toriu, Takashi; Hama, Hiromitsu

2014-03-01

In today world, different kinds of networks such as social, technological, business and etc. exist. All of the networks are similar in terms of distributions, continuously growing and expanding in large scale. Among them, many social networks such as Facebook, Twitter, Flickr and many others provides a powerful abstraction of the structure and dynamics of diverse kinds of inter personal connection and interaction. Generally, the social network contents are created and consumed by the influences of all different social navigation paths that lead to the contents. Therefore, identifying important and user relevant refined structures such as visual information or communities become major factors in modern decision making world. Moreover, the traditional method of information ranking systems cannot be successful due to their lack of taking into account the properties of navigation paths driven by social connections. In this paper, we propose a novel image ranking system in social networks by using the social data relational graphs from social media platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social relevance). Specifically, we propose a Markov chain based Social-Visual Ranking algorithm by taking social relevance into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed social-visual ranking method.

Functional Connectivity Between Superior Parietal Lobule and Primary Visual Cortex "at Rest" Predicts Visual Search Efficiency.

PubMed

Bueichekú, Elisenda; Ventura-Campos, Noelia; Palomar-García, María-Ángeles; Miró-Padilla, Anna; Parcet, María-Antonia; Ávila, César

2015-10-01

Spatiotemporal activity that emerges spontaneously "at rest" has been proposed to reflect individual a priori biases in cognitive processing. This research focused on testing neurocognitive models of visual attention by studying the functional connectivity (FC) of the superior parietal lobule (SPL), given its central role in establishing priority maps during visual search tasks. Twenty-three human participants completed a functional magnetic resonance imaging session that featured a resting-state scan, followed by a visual search task based on the alphanumeric category effect. As expected, the behavioral results showed longer reaction times and more errors for the within-category (i.e., searching a target letter among letters) than the between-category search (i.e., searching a target letter among numbers). The within-category condition was related to greater activation of the superior and inferior parietal lobules, occipital cortex, inferior frontal cortex, dorsal anterior cingulate cortex, and the superior colliculus than the between-category search. The resting-state FC analysis of the SPL revealed a broad network that included connections with the inferotemporal cortex, dorsolateral prefrontal cortex, and dorsal frontal areas like the supplementary motor area and frontal eye field. Noteworthy, the regression analysis revealed that the more efficient participants in the visual search showed stronger FC between the SPL and areas of primary visual cortex (V1) related to the search task. We shed some light on how the SPL establishes a priority map of the environment during visual attention tasks and how FC is a valuable tool for assessing individual differences while performing cognitive tasks.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Characterising intra- and inter-intrinsic network synchrony in combat-related post-traumatic stress disorder.

PubMed

Dunkley, Benjamin T; Doesburg, Sam M; Jetly, Rakesh; Sedge, Paul A; Pang, Elizabeth W; Taylor, Margot J

2015-11-30

Soldiers with post-traumatic stress disorder (PTSD) exhibit elevated gamma-band synchrony in left fronto-temporal cortex, and connectivity measures in these regions correlate with comorbidities and PTSD severity, which suggests increased gamma synchrony is related to symptomology. However, little is known about the role of intrinsic, phase-synchronised networks in the disorder. Using magnetoencephalography (MEG), we characterised spectral connectivity in the default-mode, salience, visual, and attention networks during resting-state in a PTSD population and a trauma-exposed control group. Intrinsic network connectivity was examined in canonical frequency bands. We observed increased inter-network synchronisation in the PTSD group compared with controls in the gamma (30-80 Hz) and high-gamma range (80-150 Hz). Analyses of connectivity and symptomology revealed that PTSD severity was positively associated with beta synchrony in the ventral-attention-to-salience networks, and gamma synchrony within the salience network, but also negatively correlated with beta synchrony within the visual network. These novel results show that frequency-specific, network-level atypicalities may reflect trauma-related alterations of ongoing functional connectivity, and correlations of beta synchrony in attentional-to-salience and visual networks with PTSD severity suggest complicated network interactions mediate symptoms. These results contribute to accumulating evidence that PTSD is a complicated network-based disorder expressed as altered neural interactions. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

Viewfinders: A Visual Environmental Literacy Curriculum. Elementary Unit: Exploring Community Appearance and the Environment.

ERIC Educational Resources Information Center

Dunn Foundation, Warwick, RI.

Recognizing that community growth and change are inevitable, Viewfinders' goals are as follows: to introduce students and teachers to the concept of the visual environment; enhance an understanding of the interrelationship between the built and natural environment; create an awareness that the visual environment affects the economy and quality of…

A neural network model of ventriloquism effect and aftereffect.

PubMed

Magosso, Elisa; Cuppini, Cristiano; Ursino, Mauro

2012-01-01

Presenting simultaneous but spatially discrepant visual and auditory stimuli induces a perceptual translocation of the sound towards the visual input, the ventriloquism effect. General explanation is that vision tends to dominate over audition because of its higher spatial reliability. The underlying neural mechanisms remain unclear. We address this question via a biologically inspired neural network. The model contains two layers of unimodal visual and auditory neurons, with visual neurons having higher spatial resolution than auditory ones. Neurons within each layer communicate via lateral intra-layer synapses; neurons across layers are connected via inter-layer connections. The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity at the position of the visual stimulus. Main results are: i) the less localized stimulus is strongly biased toward the most localized stimulus and not vice versa; ii) amount of the ventriloquism effect changes with visual-auditory spatial disparity; iii) ventriloquism is a robust behavior of the network with respect to parameter value changes. Moreover, the model implements Hebbian rules for potentiation and depression of lateral synapses, to explain ventriloquism aftereffect (that is, the enduring sound shift after exposure to spatially disparate audio-visual stimuli). By adaptively changing the weights of lateral synapses during cross-modal stimulation, the model produces post-adaptive shifts of auditory localization that agree with in-vivo observations. The model demonstrates that two unimodal layers reciprocally interconnected may explain ventriloquism effect and aftereffect, even without the presence of any convergent multimodal area. The proposed study may provide advancement in understanding neural architecture and mechanisms at the basis of visual-auditory integration in the spatial realm.

Exploratory Visual Analytics of a Dynamically Built Network of Nodes in a WebGL-Enabled Browser

DTIC Science & Technology

2014-01-01

dimensionality reduction, feature extraction, high-dimensional data, t-distributed stochastic neighbor embedding, neighbor retrieval visualizer, visual...WebGL-enabled rendering is supported natively by browsers such as the latest Mozilla Firefox , Google Chrome, and Microsoft Internet Explorer 11. At the...appropriate names. The resultant 26-node network is displayed in a Mozilla Firefox browser in figure 2 (also see appendix B). 3 Figure 1. The

Action Prediction in Younger versus Older Adults: Neural Correlates of Motor Familiarity

PubMed Central

Diersch, Nadine; Mueller, Karsten; Cross, Emily S.; Stadler, Waltraud; Rieger, Martina; Schütz-Bosbach, Simone

2013-01-01

Generating predictions during action observation is essential for efficient navigation through our social environment. With age, the sensitivity in action prediction declines. In younger adults, the action observation network (AON), consisting of premotor, parietal and occipitotemporal cortices, has been implicated in transforming executed and observed actions into a common code. Much less is known about age-related changes in the neural representation of observed actions. Using fMRI, the present study measured brain activity in younger and older adults during the prediction of temporarily occluded actions (figure skating elements and simple movement exercises). All participants were highly familiar with the movement exercises whereas only some participants were experienced figure skaters. With respect to the AON, the results confirm that this network was preferentially engaged for the more familiar movement exercises. Compared to younger adults, older adults recruited visual regions to perform the task and, additionally, the hippocampus and caudate when the observed actions were familiar to them. Thus, instead of effectively exploiting the sensorimotor matching properties of the AON, older adults seemed to rely predominantly on the visual dynamics of the observed actions to perform the task. Our data further suggest that the caudate played an important role during the prediction of the less familiar figure skating elements in better-performing groups. Together, these findings show that action prediction engages a distributed network in the brain, which is modulated by the content of the observed actions and the age and experience of the observer. PMID:23704980

Action prediction in younger versus older adults: neural correlates of motor familiarity.

PubMed

Diersch, Nadine; Mueller, Karsten; Cross, Emily S; Stadler, Waltraud; Rieger, Martina; Schütz-Bosbach, Simone

2013-01-01

Generating predictions during action observation is essential for efficient navigation through our social environment. With age, the sensitivity in action prediction declines. In younger adults, the action observation network (AON), consisting of premotor, parietal and occipitotemporal cortices, has been implicated in transforming executed and observed actions into a common code. Much less is known about age-related changes in the neural representation of observed actions. Using fMRI, the present study measured brain activity in younger and older adults during the prediction of temporarily occluded actions (figure skating elements and simple movement exercises). All participants were highly familiar with the movement exercises whereas only some participants were experienced figure skaters. With respect to the AON, the results confirm that this network was preferentially engaged for the more familiar movement exercises. Compared to younger adults, older adults recruited visual regions to perform the task and, additionally, the hippocampus and caudate when the observed actions were familiar to them. Thus, instead of effectively exploiting the sensorimotor matching properties of the AON, older adults seemed to rely predominantly on the visual dynamics of the observed actions to perform the task. Our data further suggest that the caudate played an important role during the prediction of the less familiar figure skating elements in better-performing groups. Together, these findings show that action prediction engages a distributed network in the brain, which is modulated by the content of the observed actions and the age and experience of the observer.

Visualization Techniques for Computer Network Defense

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

Beaver, Justin M; Steed, Chad A; Patton, Robert M

2011-01-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operatormore » to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.« less

VISUALIZATION AND SIMULATION OF NON-AQUEOUS PHASE LIQUIDS SOLUBILIZATION IN PORE NETWORKS

EPA Science Inventory

The design of in-situ remediation of contaminated soils is mostly based on a description at the macroscopic scale using a averaged quantities. These cannot address issues at the pore and pore network scales. In this paper, visualization experiments and numerical simulations in ...

Idiosyncratic characteristics of saccadic eye movements when viewing different visual environments.

PubMed

Andrews, T J; Coppola, D M

1999-08-01

Eye position was recorded in different viewing conditions to assess whether the temporal and spatial characteristics of saccadic eye movements in different individuals are idiosyncratic. Our aim was to determine the degree to which oculomotor control is based on endogenous factors. A total of 15 naive subjects viewed five visual environments: (1) The absence of visual stimulation (i.e. a dark room); (2) a repetitive visual environment (i.e. simple textured patterns); (3) a complex natural scene; (4) a visual search task; and (5) reading text. Although differences in visual environment had significant effects on eye movements, idiosyncrasies were also apparent. For example, the mean fixation duration and size of an individual's saccadic eye movements when passively viewing a complex natural scene covaried significantly with those same parameters in the absence of visual stimulation and in a repetitive visual environment. In contrast, an individual's spatio-temporal characteristics of eye movements during active tasks such as reading text or visual search covaried together, but did not correlate with the pattern of eye movements detected when viewing a natural scene, simple patterns or in the dark. These idiosyncratic patterns of eye movements in normal viewing reveal an endogenous influence on oculomotor control. The independent covariance of eye movements during different visual tasks shows that saccadic eye movements during active tasks like reading or visual search differ from those engaged during the passive inspection of visual scenes.

Genonets server-a web server for the construction, analysis and visualization of genotype networks.

PubMed

Khalid, Fahad; Aguilar-Rodríguez, José; Wagner, Andreas; Payne, Joshua L

2016-07-08

A genotype network is a graph in which vertices represent genotypes that have the same phenotype. Edges connect vertices if their corresponding genotypes differ in a single small mutation. Genotype networks are used to study the organization of genotype spaces. They have shed light on the relationship between robustness and evolvability in biological systems as different as RNA macromolecules and transcriptional regulatory circuits. Despite the importance of genotype networks, no tool exists for their automatic construction, analysis and visualization. Here we fill this gap by presenting the Genonets Server, a tool that provides the following features: (i) the construction of genotype networks for categorical and univariate phenotypes from DNA, RNA, amino acid or binary sequences; (ii) analyses of genotype network topology and how it relates to robustness and evolvability, as well as analyses of genotype network topography and how it relates to the navigability of a genotype network via mutation and natural selection; (iii) multiple interactive visualizations that facilitate exploratory research and education. The Genonets Server is freely available at http://ieu-genonets.uzh.ch. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Ocean Networks Canada's "Big Data" Initiative

NASA Astrophysics Data System (ADS)

Dewey, R. K.; Hoeberechts, M.; Moran, K.; Pirenne, B.; Owens, D.

2013-12-01

Ocean Networks Canada operates two large undersea observatories that collect, archive, and deliver data in real time over the Internet. These data contribute to our understanding of the complex changes taking place on our ocean planet. Ocean Networks Canada's VENUS was the world's first cabled seafloor observatory to enable researchers anywhere to connect in real time to undersea experiments and observations. Its NEPTUNE observatory is the largest cabled ocean observatory, spanning a wide range of ocean environments. Most recently, we installed a new small observatory in the Arctic. Together, these observatories deliver "Big Data" across many disciplines in a cohesive manner using the Oceans 2.0 data management and archiving system that provides national and international users with open access to real-time and archived data while also supporting a collaborative work environment. Ocean Networks Canada operates these observatories to support science, innovation, and learning in four priority areas: study of the impact of climate change on the ocean; the exploration and understanding the unique life forms in the extreme environments of the deep ocean and below the seafloor; the exchange of heat, fluids, and gases that move throughout the ocean and atmosphere; and the dynamics of earthquakes, tsunamis, and undersea landslides. To date, the Ocean Networks Canada archive contains over 130 TB (collected over 7 years) and the current rate of data acquisition is ~50 TB per year. This data set is complex and diverse. Making these "Big Data" accessible and attractive to users is our priority. In this presentation, we share our experience as a "Big Data" institution where we deliver simple and multi-dimensional calibrated data cubes to a diverse pool of users. Ocean Networks Canada also conducts extensive user testing. Test results guide future tool design and development of "Big Data" products. We strive to bridge the gap between the raw, archived data and the needs and experience of a diverse user community, each requiring tailored data visualization and integrated products. By doing this we aim to design tools that maximize exploitation of the data.

Seeing More by Showing Less: Orientation-Dependent Transparency Rendering for Fiber Tractography Visualization

PubMed Central

Tax, Chantal M. W.; Chamberland, Maxime; van Stralen, Marijn; Viergever, Max A.; Whittingstall, Kevin; Fortin, David; Descoteaux, Maxime; Leemans, Alexander

2015-01-01

Fiber tractography plays an important role in exploring the architectural organization of fiber trajectories, both in fundamental neuroscience and in clinical applications. With the advent of diffusion MRI (dMRI) approaches that can also model “crossing fibers”, the complexity of the fiber network as reconstructed with tractography has increased tremendously. Many pathways interdigitate and overlap, which hampers an unequivocal 3D visualization of the network and impedes an efficient study of its organization. We propose a novel fiber tractography visualization approach that interactively and selectively adapts the transparency rendering of fiber trajectories as a function of their orientation to enhance the visibility of the spatial context. More specifically, pathways that are oriented (locally or globally) along a user-specified opacity axis can be made more transparent or opaque. This substantially improves the 3D visualization of the fiber network and the exploration of tissue configurations that would otherwise be largely covered by other pathways. We present examples of fiber bundle extraction and neurosurgical planning cases where the added benefit of our new visualization scheme is demonstrated over conventional fiber visualization approaches. PMID:26444010

MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

PubMed

Kelley, James J; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S

2017-08-15

Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. dslun@rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Seeing More by Showing Less: Orientation-Dependent Transparency Rendering for Fiber Tractography Visualization.

PubMed

Tax, Chantal M W; Chamberland, Maxime; van Stralen, Marijn; Viergever, Max A; Whittingstall, Kevin; Fortin, David; Descoteaux, Maxime; Leemans, Alexander

2015-01-01

Fiber tractography plays an important role in exploring the architectural organization of fiber trajectories, both in fundamental neuroscience and in clinical applications. With the advent of diffusion MRI (dMRI) approaches that can also model "crossing fibers", the complexity of the fiber network as reconstructed with tractography has increased tremendously. Many pathways interdigitate and overlap, which hampers an unequivocal 3D visualization of the network and impedes an efficient study of its organization. We propose a novel fiber tractography visualization approach that interactively and selectively adapts the transparency rendering of fiber trajectories as a function of their orientation to enhance the visibility of the spatial context. More specifically, pathways that are oriented (locally or globally) along a user-specified opacity axis can be made more transparent or opaque. This substantially improves the 3D visualization of the fiber network and the exploration of tissue configurations that would otherwise be largely covered by other pathways. We present examples of fiber bundle extraction and neurosurgical planning cases where the added benefit of our new visualization scheme is demonstrated over conventional fiber visualization approaches.

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

Scholtz, Jean

A new field of research, visual analytics, has recently been introduced. This has been defined as “the science of analytical reasoning facilitated by visual interfaces." Visual analytic environments, therefore, support analytical reasoning using visual representations and interactions, with data representations and transformation capabilities, to support production, presentation and dissemination. As researchers begin to develop visual analytic environments, it will be advantageous to develop metrics and methodologies to help researchers measure the progress of their work and understand the impact their work will have on the users who will work in such environments. This paper presents five areas or aspects ofmore » visual analytic environments that should be considered as metrics and methodologies for evaluation are developed. Evaluation aspects need to include usability, but it is necessary to go beyond basic usability. The areas of situation awareness, collaboration, interaction, creativity, and utility are proposed as areas for initial consideration. The steps that need to be undertaken to develop systematic evaluation methodologies and metrics for visual analytic environments are outlined.« less

From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets

NASA Astrophysics Data System (ADS)

Kayastha, Shilva; Kunimoto, Ryo; Horvath, Dragos; Varnek, Alexandre; Bajorath, Jürgen

2017-11-01

The analysis of structure-activity relationships (SARs) becomes rather challenging when large and heterogeneous compound data sets are studied. In such cases, many different compounds and their activities need to be compared, which quickly goes beyond the capacity of subjective assessments. For a comprehensive large-scale exploration of SARs, computational analysis and visualization methods are required. Herein, we introduce a two-layered SAR visualization scheme specifically designed for increasingly large compound data sets. The approach combines a new compound pair-based variant of generative topographic mapping (GTM), a machine learning approach for nonlinear mapping, with chemical space networks (CSNs). The GTM component provides a global view of the activity landscapes of large compound data sets, in which informative local SAR environments are identified, augmented by a numerical SAR scoring scheme. Prioritized local SAR regions are then projected into CSNs that resolve these regions at the level of individual compounds and their relationships. Analysis of CSNs makes it possible to distinguish between regions having different SAR characteristics and select compound subsets that are rich in SAR information.

Real-Time 3D Visualization

NASA Technical Reports Server (NTRS)

1997-01-01

Butler Hine, former director of the Intelligent Mechanism Group (IMG) at Ames Research Center, and five others partnered to start Fourth Planet, Inc., a visualization company that specializes in the intuitive visual representation of dynamic, real-time data over the Internet and Intranet. Over a five-year period, the then NASA researchers performed ten robotic field missions in harsh climes to mimic the end- to-end operations of automated vehicles trekking across another world under control from Earth. The core software technology for these missions was the Virtual Environment Vehicle Interface (VEVI). Fourth Planet has released VEVI4, the fourth generation of the VEVI software, and NetVision. VEVI4 is a cutting-edge computer graphics simulation and remote control applications tool. The NetVision package allows large companies to view and analyze in virtual 3D space such things as the health or performance of their computer network or locate a trouble spot on an electric power grid. Other products are forthcoming. Fourth Planet is currently part of the NASA/Ames Technology Commercialization Center, a business incubator for start-up companies.

A virtual reality-based method of decreasing transmission time of visual feedback for a tele-operative robotic catheter operating system.

PubMed

Guo, Jin; Guo, Shuxiang; Tamiya, Takashi; Hirata, Hideyuki; Ishihara, Hidenori

2016-03-01

An Internet-based tele-operative robotic catheter operating system was designed for vascular interventional surgery, to afford unskilled surgeons the opportunity to learn basic catheter/guidewire skills, while allowing experienced physicians to perform surgeries cooperatively. Remote surgical procedures, limited by variable transmission times for visual feedback, have been associated with deterioration in operability and vascular wall damage during surgery. At the patient's location, the catheter shape/position was detected in real time and converted into three-dimensional coordinates in a world coordinate system. At the operation location, the catheter shape was reconstructed in a virtual-reality environment, based on the coordinates received. The data volume reduction significantly reduced visual feedback transmission times. Remote transmission experiments, conducted over inter-country distances, demonstrated the improved performance of the proposed prototype. The maximum error for the catheter shape reconstruction was 0.93 mm and the transmission time was reduced considerably. The results were positive and demonstrate the feasibility of remote surgery using conventional network infrastructures. Copyright © 2015 John Wiley & Sons, Ltd.

The dark side of photovoltaic — 3D simulation of glare assessing risk and discomfort

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

Rose, Thomas; Wollert, Alexander

2015-04-15

Photovoltaic (PV) systems form an important force in the implementation of renewable energies, but as we all know, the force has always its dark side. Besides efficiency considerations and discussions about architectures of power distribution networks, the increasing numbers of installations of PV systems for implementing renewable energies have secondary effects. PV systems can generate glare due to optical reflections and hence might be a serious concern. On the one hand, glare could affect safety, e.g. regarding traffic. On the other hand, glare is a constant source of discomfort in vicinities of PV systems. Hence, assessment of glare is decisivemore » for the success of renewable energies near municipalities and traffic zones for the success of solar power. Several courts decided on the change of PV systems and even on their de-installation because of glare effects. Thus, location-based assessments are required to limit potential reflections and to avoid risks for public infrastructure or discomfort of residents. The question arises on how to calculate reflections accurately according to the environment's topography. Our approach is founded in a 3D-based simulation methodology to calculate and visualize reflections based on the geometry of the environment of PV systems. This computational model is implemented by an interactive tool for simulation and visualization. Hence, project planners receive flexible assistance for adjusting the parameters of solar panels amid the planning process and in particular before the installation of a PV system. - Highlights: • Solar panels cause glare that impacts neighborhoods and traffic infrastructures. • Glare might cause disability and discomfort. • 3D environment for the calculation of glare • Interactive tool to simulate and visualize reflections • Impact assessment of solar power plant farms.« less

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG.

PubMed

Rojas, Gonzalo M; Fuentes, Jorge A; Gálvez, Marcelo

2016-01-01

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10-20 EEG electrodes with Yeo's seven functional connectivity networks.

A visual-environment simulator with variable contrast

NASA Astrophysics Data System (ADS)

Gusarova, N. F.; Demin, A. V.; Polshchikov, G. V.

1987-01-01

A visual-environment simulator is proposed in which the image contrast can be varied continuously up to the reversal of the image. Contrast variability can be achieved by using two independently adjustable light sources to simultaneously illuminate the carrier of visual information (e.g., a slide or a cinematographic film). It is shown that such a scheme makes it possible to adequately model a complex visual environment.

Ondex Web: web-based visualization and exploration of heterogeneous biological networks.

PubMed

Taubert, Jan; Hassani-Pak, Keywan; Castells-Brooke, Nathalie; Rawlings, Christopher J

2014-04-01

Ondex Web is a new web-based implementation of the network visualization and exploration tools from the Ondex data integration platform. New features such as context-sensitive menus and annotation tools provide users with intuitive ways to explore and manipulate the appearance of heterogeneous biological networks. Ondex Web is open source, written in Java and can be easily embedded into Web sites as an applet. Ondex Web supports loading data from a variety of network formats, such as XGMML, NWB, Pajek and OXL. http://ondex.rothamsted.ac.uk/OndexWeb.

PubNet: a flexible system for visualizing literature derived networks

PubMed Central

Douglas, Shawn M; Montelione, Gaetano T; Gerstein, Mark

2005-01-01

We have developed PubNet, a web-based tool that extracts several types of relationships returned by PubMed queries and maps them into networks, allowing for graphical visualization, textual navigation, and topological analysis. PubNet supports the creation of complex networks derived from the contents of individual citations, such as genes, proteins, Protein Data Bank (PDB) IDs, Medical Subject Headings (MeSH) terms, and authors. This feature allows one to, for example, examine a literature derived network of genes based on functional similarity. PMID:16168087

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

PubMed

Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

2015-04-01

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

A graph-theoretical analysis algorithm for quantifying the transition from sensory input to motor output by an emotional stimulus.

PubMed

Karmonik, Christof; Fung, Steve H; Dulay, M; Verma, A; Grossman, Robert G

2013-01-01

Graph-theoretical analysis algorithms have been used for identifying subnetworks in the human brain during the Default Mode State. Here, these methods are expanded to determine the interaction of the sensory and the motor subnetworks during the performance of an approach-avoidance paradigm utilizing the correlation strength between the signal intensity time courses as measure of synchrony. From functional magnetic resonance imaging (fMRI) data of 9 healthy volunteers, two signal time courses, one from the primary visual cortex (sensory input) and one from the motor cortex (motor output) were identified and a correlation difference map was calculated. Graph networks were created from this map and visualized with spring-embedded layouts and 3D layouts in the original anatomical space. Functional clusters in these networks were identified with the MCODE clustering algorithm. Interactions between the sensory sub-network and the motor sub-network were quantified through the interaction strengths of these clusters. The percentages of interactions involving the visual cortex ranged from 85 % to 18 % and the motor cortex ranged from 40 % to 9 %. Other regions with high interactions were: frontal cortex (19 ± 18 %), insula (17 ± 22 %), cuneus (16 ± 15 %), supplementary motor area (SMA, 11 ± 18 %) and subcortical regions (11 ± 10 %). Interactions between motor cortex, SMA and visual cortex accounted for 12 %, between visual cortex and cuneus for 8 % and between motor cortex, SMA and cuneus for 6 % of all interactions. These quantitative findings are supported by the visual impressions from the 2D and 3D network layouts.

Pedestrian simulation and distribution in urban space based on visibility analysis and agent simulation

NASA Astrophysics Data System (ADS)

Ying, Shen; Li, Lin; Gao, Yurong

2009-10-01

Spatial visibility analysis is the important direction of pedestrian behaviors because our visual conception in space is the straight method to get environment information and navigate your actions. Based on the agent modeling and up-tobottom method, the paper develop the framework about the analysis of the pedestrian flow depended on visibility. We use viewshed in visibility analysis and impose the parameters on agent simulation to direct their motion in urban space. We analyze the pedestrian behaviors in micro-scale and macro-scale of urban open space. The individual agent use visual affordance to determine his direction of motion in micro-scale urban street on district. And we compare the distribution of pedestrian flow with configuration in macro-scale urban environment, and mine the relationship between the pedestrian flow and distribution of urban facilities and urban function. The paper first computes the visibility situations at the vantage point in urban open space, such as street network, quantify the visibility parameters. The multiple agents use visibility parameters to decide their direction of motion, and finally pedestrian flow reach to a stable state in urban environment through the simulation of multiple agent system. The paper compare the morphology of visibility parameters and pedestrian distribution with urban function and facilities layout to confirm the consistence between them, which can be used to make decision support in urban design.

NV: Nessus Vulnerability Visualization for the Web

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

Harrison, Lane; Spahn, Riley B; Iannacone, Michael D

2012-01-01

Network vulnerability is a critical component of network se- curity. Yet vulnerability analysis has received relatively lit- tle attention from the security visualization community. In this paper we describe nv, a web-based Nessus vulnerability visualization. Nv utilizes treemaps and linked histograms to allow system administrators to discover, analyze, and man- age vulnerabilities on their networks. In addition to visual- izing single Nessus scans, nv supports the analysis of sequen- tial scans by showing which vulnerabilities have been fixed, remain open, or are newly discovered. Nv was also designed to operate completely in-browser, to avoid sending sensitive data to outside servers.more » We discuss the design of nv, as well as provide case studies demonstrating vulnerability analysis workflows which include a multiple-node testbed and data from the 2011 VAST Challenge.« less

Basic Lessons in ORA and AutoMap 2011

DTIC Science & Technology

2011-06-13

A small legend also appears. Below is a screen capture showing the visualization of the agent x event graph from the Stargate Summit Meta-Network...4 The visualization displays the connections between all items in the Stargate Summit Meta-Network. The red circles represent the agents, the...It takes the examples I used for the Stargate dataset. 5 lessons - 201-207 A step by step run through of creating the Meta-Network from

ORA User’s Guide 2012

DTIC Science & Technology

2012-06-11

places, resources, knowledge sets or other common Node Classes*. 285 This example will use the Stargate dataset (SG-1). This dataset is included...create a new Meta-Network. Below is the NodeSet for Stargate with the original 16 node NodeSet. 376 From the main menu select, Actions > Add...measures by simply gauging their size visually and intuitively. First, visualize one of your networks. Below is the Stargate agent x event network to

Basic Lessons in *ORA and Automap 2009

DTIC Science & Technology

2009-06-01

screen capture showing the visualization of the agent x event graph from the Stargate Summit Meta-Network. The visualization displays the connections...for the Stargate dataset. 25.2 lessons - 201-207 A step by step run through of creating the Meta-Network from working with Excel, exporting data to...For the purpose of creating the Stargate MetaNetwork the two-episode story arc (Summit / Last Stand) was choosen as the basis for all the nodes

Game theoretic approach for cooperative feature extraction in camera networks

NASA Astrophysics Data System (ADS)

Redondi, Alessandro E. C.; Baroffio, Luca; Cesana, Matteo; Tagliasacchi, Marco

2016-07-01

Visual sensor networks (VSNs) consist of several camera nodes with wireless communication capabilities that can perform visual analysis tasks such as object identification, recognition, and tracking. Often, VSN deployments result in many camera nodes with overlapping fields of view. In the past, such redundancy has been exploited in two different ways: (1) to improve the accuracy/quality of the visual analysis task by exploiting multiview information or (2) to reduce the energy consumed for performing the visual task, by applying temporal scheduling techniques among the cameras. We propose a game theoretic framework based on the Nash bargaining solution to bridge the gap between the two aforementioned approaches. The key tenet of the proposed framework is for cameras to reduce the consumed energy in the analysis process by exploiting the redundancy in the reciprocal fields of view. Experimental results in both simulated and real-life scenarios confirm that the proposed scheme is able to increase the network lifetime, with a negligible loss in terms of visual analysis accuracy.

Cyber Friendly Fire

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

Greitzer, Frank L.; Carroll, Thomas E.; Roberts, Adam D.

Cyber friendly fire (FF) is a new concept that has been brought to the attention of Department of Defense (DoD) stakeholders through two workshops that were planned and conducted by the Air Force Research Laboratory (AFRL) and research conducted for AFRL by the Pacific Northwest National Laboratory. With this previous work in mind, we offer a definition of cyber FF as intentional offensive or defensive cyber/electronic actions intended to protect cyber systems against enemy forces or to attack enemy cyber systems, which unintentionally harms the mission effectiveness of friendly or neutral forces. Just as with combat friendly fire, a fundamentalmore » need in avoiding cyber FF is to maintain situation awareness (SA). We suggest that cyber SA concerns knowledge of a system's topology (connectedness and relationships of the nodes in a system), and critical knowledge elements such as the characteristics and vulnerabilities of the components that comprise the system (and that populate the nodes), the nature of the activities or work performed, and the available defensive (and offensive) countermeasures that may be applied to thwart network attacks. A training implication is to raise awareness and understanding of these critical knowledge units; an approach to decision aids and/or visualizations is to focus on supporting these critical knowledge units. To study cyber FF, we developed an unclassified security test range comprising a combination of virtual and physical devices that present a closed network for testing, simulation, and evaluation. This network offers services found on a production network without the associated costs of a real production network. Containing enough detail to appear realistic, this virtual and physical environment can be customized to represent different configurations. For our purposes, the test range was configured to appear as an Internet-connected Managed Service Provider (MSP) offering specialized web applications to the general public. The network is essentially divided into a production component that hosts the web and network services, and a user component that hosts thirty employee workstations and other end devices. The organization's network is separated from the Internet by a Cisco ASA network security device that both firewalls and detects intrusions. Business sensitive information is stored in various servers. This includes data comprising thousands of internal documents, such as finance and technical designs, email messages for the organization's employees including the CEO, CFO, and CIO, the organization's source code, and Personally Identifiable client data. Release of any of this information to unauthorized parties would have a significant, detrimental impact on the organization's reputation, which would harm earnings. The valuable information stored in these servers pose obvious points of interest for an adversary. We constructed several scenarios around this environment to support studies in cyber SA and cyber FF that may be run in the test range. We describe mitigation strategies to combat cyber FF including both training concepts and suggestions for decision aids and visualization approaches. Finally, we discuss possible future research directions.« less

Spectrum-Based and Collaborative Network Topology Analysis and Visualization

ERIC Educational Resources Information Center

Hu, Xianlin

2013-01-01

Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…

Network portal: a database for storage, analysis and visualization of biological networks

PubMed Central

Turkarslan, Serdar; Wurtmann, Elisabeth J.; Wu, Wei-Ju; Jiang, Ning; Bare, J. Christopher; Foley, Karen; Reiss, David J.; Novichkov, Pavel; Baliga, Nitin S.

2014-01-01

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal. PMID:24271392

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

PubMed

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Modality-specificity of Selective Attention Networks.

PubMed

Stewart, Hannah J; Amitay, Sygal

2015-01-01

To establish the modality specificity and generality of selective attention networks. Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled "general attention." The third component was labeled "auditory attention," as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as "spatial orienting" and "spatial conflict," respectively-they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task-all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.

High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention.

PubMed

Lobier, Muriel; Palva, J Matias; Palva, Satu

2018-01-15

Visuospatial attention prioritizes processing of attended visual stimuli. It is characterized by lateralized alpha-band (8-14 Hz) amplitude suppression in visual cortex and increased neuronal activity in a network of frontal and parietal areas. It has remained unknown what mechanisms coordinate neuronal processing among frontoparietal network and visual cortices and implement the attention-related modulations of alpha-band amplitudes and behavior. We investigated whether large-scale network synchronization could be such a mechanism. We recorded human cortical activity with magnetoencephalography (MEG) during a visuospatial attention task. We then identified the frequencies and anatomical networks of inter-areal phase synchronization from source localized MEG data. We found that visuospatial attention is associated with robust and sustained long-range synchronization of cortical oscillations exclusively in the high-alpha (10-14 Hz) frequency band. This synchronization connected frontal, parietal and visual regions and was observed concurrently with amplitude suppression of low-alpha (6-9 Hz) band oscillations in visual cortex. Furthermore, stronger high-alpha phase synchronization was associated with decreased reaction times to attended stimuli and larger suppression of alpha-band amplitudes. These results thus show that high-alpha band phase synchronization is functionally significant and could coordinate the neuronal communication underlying the implementation of visuospatial attention. Copyright © 2017 Elsevier Inc. All rights reserved.

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

PubMed

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2017-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

PubMed Central

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2018-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them. PMID:27896980

Frontal networks associated with command following after hemorrhagic stroke.

PubMed

Mikell, Charles B; Banks, Garrett P; Frey, Hans-Peter; Youngerman, Brett E; Nelp, Taylor B; Karas, Patrick J; Chan, Andrew K; Voss, Henning U; Connolly, E Sander; Claassen, Jan

2015-01-01

Level of consciousness is frequently assessed by command-following ability in the clinical setting. However, it is unclear what brain circuits are needed to follow commands. We sought to determine what networks differentiate command following from noncommand following patients after hemorrhagic stroke. Structural MRI, resting-state functional MRI, and electroencephalography were performed on 25 awake and unresponsive patients with acute intracerebral and subarachnoid hemorrhage. Structural injury was assessed via volumetric T1-weighted MRI analysis. Functional connectivity differences were analyzed against a template of standard resting-state networks. The default mode network (DMN) and the task-positive network were investigated using seed-based functional connectivity. Networks were interrogated by pairwise coherence of electroencephalograph leads in regions of interest defined by functional MRI. Functional imaging of unresponsive patients identified significant differences in 6 of 16 standard resting-state networks. Significant voxels were found in premotor cortex, dorsal anterior cingulate gyrus, and supplementary motor area. Direct interrogation of the DMN and task-positive network revealed loss of connectivity between the DMN and the orbitofrontal cortex and new connections between the task-positive network and DMN. Coherence between electrodes corresponding to right executive network and visual networks was also decreased in unresponsive patients. Resting-state functional MRI and electroencephalography coherence data support a model in which multiple, chiefly frontal networks are required for command following. Loss of DMN anticorrelation with task-positive network may reflect a loss of inhibitory control of the DMN by motor-executive regions. Frontal networks should thus be a target for future investigations into the mechanism of responsiveness in the intensive care unit environment. © 2014 American Heart Association, Inc.

Network visualization of conformational sampling during molecular dynamics simulation.

PubMed

Ahlstrom, Logan S; Baker, Joseph Lee; Ehrlich, Kent; Campbell, Zachary T; Patel, Sunita; Vorontsov, Ivan I; Tama, Florence; Miyashita, Osamu

2013-11-01

Effective data reduction methods are necessary for uncovering the inherent conformational relationships present in large molecular dynamics (MD) trajectories. Clustering algorithms provide a means to interpret the conformational sampling of molecules during simulation by grouping trajectory snapshots into a few subgroups, or clusters, but the relationships between the individual clusters may not be readily understood. Here we show that network analysis can be used to visualize the dominant conformational states explored during simulation as well as the connectivity between them, providing a more coherent description of conformational space than traditional clustering techniques alone. We compare the results of network visualization against 11 clustering algorithms and principal component conformer plots. Several MD simulations of proteins undergoing different conformational changes demonstrate the effectiveness of networks in reaching functional conclusions. Copyright © 2013 Elsevier Inc. All rights reserved.

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

PubMed Central

2010-01-01

Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks. PMID:21070623

Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy.

PubMed

Widjaja, E; Zamyadi, M; Raybaud, C; Snead, O C; Smith, M L

2013-12-01

Epilepsy is considered a disorder of neural networks. The aims of this study were to assess functional connectivity within resting-state networks and functional network connectivity across resting-state networks by use of resting-state fMRI in children with frontal lobe epilepsy and to relate changes in resting-state networks with neuropsychological function. Fifteen patients with frontal lobe epilepsy and normal MR imaging and 14 healthy control subjects were recruited. Spatial independent component analysis was used to identify the resting-state networks, including frontal, attention, default mode network, sensorimotor, visual, and auditory networks. The Z-maps of resting-state networks were compared between patients and control subjects. The relation between abnormal connectivity and neuropsychological function was assessed. Correlations from all pair-wise combinations of independent components were performed for each group and compared between groups. The frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-state networks in patients. There was a weak association between connectivity in frontal network and executive function (P = .029) and a significant association between sensorimotor network and fine motor function (P = .004). Control subjects had 79 pair-wise independent components that showed significant temporal coherence across all resting-state networks except for default mode network-auditory network. Patients had 66 pairs of independent components that showed significant temporal coherence across all resting-state networks. Group comparison showed reduced functional network connectivity between default mode network-attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network-sensorimotor, and frontal-visual networks in patients relative to control subjects. We found abnormal functional connectivity within and across resting-state networks in children with frontal lobe epilepsy. Impairment in functional connectivity was associated with impaired neuropsychological function.

Dynamic functional connectivity shapes individual differences in associative learning.

PubMed

Fatima, Zainab; Kovacevic, Natasha; Misic, Bratislav; McIntosh, Anthony Randal

2016-11-01

Current neuroscientific research has shown that the brain reconfigures its functional interactions at multiple timescales. Here, we sought to link transient changes in functional brain networks to individual differences in behavioral and cognitive performance by using an active learning paradigm. Participants learned associations between pairs of unrelated visual stimuli by using feedback. Interindividual behavioral variability was quantified with a learning rate measure. By using a multivariate statistical framework (partial least squares), we identified patterns of network organization across multiple temporal scales (within a trial, millisecond; across a learning session, minute) and linked these to the rate of change in behavioral performance (fast and slow). Results indicated that posterior network connectivity was present early in the trial for fast, and later in the trial for slow performers. In contrast, connectivity in an associative memory network (frontal, striatal, and medial temporal regions) occurred later in the trial for fast, and earlier for slow performers. Time-dependent changes in the posterior network were correlated with visual/spatial scores obtained from independent neuropsychological assessments, with fast learners performing better on visual/spatial subtests. No relationship was found between functional connectivity dynamics in the memory network and visual/spatial test scores indicative of cognitive skill. By using a comprehensive set of measures (behavioral, cognitive, and neurophysiological), we report that individual variations in learning-related performance change are supported by differences in cognitive ability and time-sensitive connectivity in functional neural networks. Hum Brain Mapp 37:3911-3928, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

PubMed

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-06-04

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-03-01

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

An extreme events laboratory to provide network centric collaborative situation assessment and decision making

NASA Astrophysics Data System (ADS)

Panulla, Brian J.; More, Loretta D.; Shumaker, Wade R.; Jones, Michael D.; Hooper, Robert; Vernon, Jeffrey M.; Aungst, Stanley G.

2009-05-01

Rapid improvements in communications infrastructure and sophistication of commercial hand-held devices provide a major new source of information for assessing extreme situations such as environmental crises. In particular, ad hoc collections of humans can act as "soft sensors" to augment data collected by traditional sensors in a net-centric environment (in effect, "crowd-sourcing" observational data). A need exists to understand how to task such soft sensors, characterize their performance and fuse the data with traditional data sources. In order to quantitatively study such situations, as well as study distributed decision-making, we have developed an Extreme Events Laboratory (EEL) at The Pennsylvania State University. This facility provides a network-centric, collaborative situation assessment and decision-making capability by supporting experiments involving human observers, distributed decision making and cognition, and crisis management. The EEL spans the information chain from energy detection via sensors, human observations, signal and image processing, pattern recognition, statistical estimation, multi-sensor data fusion, visualization and analytics, and modeling and simulation. The EEL command center combines COTS and custom collaboration tools in innovative ways, providing capabilities such as geo-spatial visualization and dynamic mash-ups of multiple data sources. This paper describes the EEL and several on-going human-in-the-loop experiments aimed at understanding the new collective observation and analysis landscape.

Distributed Coding/Decoding Complexity in Video Sensor Networks

PubMed Central

Cordeiro, Paulo J.; Assunção, Pedro

2012-01-01

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality. PMID:22736972

Distributed coding/decoding complexity in video sensor networks.

PubMed

Cordeiro, Paulo J; Assunção, Pedro

2012-01-01

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG

PubMed Central

Rojas, Gonzalo M.; Fuentes, Jorge A.; Gálvez, Marcelo

2016-01-01

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10–20 EEG electrodes with Yeo’s seven functional connectivity networks. PMID:27807416

Frequency-band signatures of visual responses to naturalistic input in ferret primary visual cortex during free viewing.

PubMed

Sellers, Kristin K; Bennett, Davis V; Fröhlich, Flavio

2015-02-19

Neuronal firing responses in visual cortex reflect the statistics of visual input and emerge from the interaction with endogenous network dynamics. Artificial visual stimuli presented to animals in which the network dynamics were constrained by anesthetic agents or trained behavioral tasks have provided fundamental understanding of how individual neurons in primary visual cortex respond to input. In contrast, very little is known about the mesoscale network dynamics and their relationship to microscopic spiking activity in the awake animal during free viewing of naturalistic visual input. To address this gap in knowledge, we recorded local field potential (LFP) and multiunit activity (MUA) simultaneously in all layers of primary visual cortex (V1) of awake, freely viewing ferrets presented with naturalistic visual input (nature movie clips). We found that naturalistic visual stimuli modulated the entire oscillation spectrum; low frequency oscillations were mostly suppressed whereas higher frequency oscillations were enhanced. In average across all cortical layers, stimulus-induced change in delta and alpha power negatively correlated with the MUA responses, whereas sensory-evoked increases in gamma power positively correlated with MUA responses. The time-course of the band-limited power in these frequency bands provided evidence for a model in which naturalistic visual input switched V1 between two distinct, endogenously present activity states defined by the power of low (delta, alpha) and high (gamma) frequency oscillatory activity. Therefore, the two mesoscale activity states delineated in this study may define the degree of engagement of the circuit with the processing of sensory input. Copyright © 2014 Elsevier B.V. All rights reserved.

White matter tract integrity predicts visual search performance in young and older adults.

PubMed

Bennett, Ilana J; Motes, Michael A; Rao, Neena K; Rypma, Bart

2012-02-01

Functional imaging research has identified frontoparietal attention networks involved in visual search, with mixed evidence regarding whether different networks are engaged when the search target differs from distracters by a single (elementary) versus multiple (conjunction) features. Neural correlates of visual search, and their potential dissociation, were examined here using integrity of white matter connecting the frontoparietal networks. The effect of aging on these brain-behavior relationships was also of interest. Younger and older adults performed a visual search task and underwent diffusion tensor imaging (DTI) to reconstruct 2 frontoparietal (superior and inferior longitudinal fasciculus; SLF and ILF) and 2 midline (genu, splenium) white matter tracts. As expected, results revealed age-related declines in conjunction, but not elementary, search performance; and in ILF and genu tract integrity. Importantly, integrity of the superior longitudinal fasciculus, ILF, and genu tracts predicted search performance (conjunction and elementary), with no significant age group differences in these relationships. Thus, integrity of white matter tracts connecting frontoparietal attention networks contributes to search performance in younger and older adults. Copyright © 2012 Elsevier Inc. All rights reserved.

White Matter Tract Integrity Predicts Visual Search Performance in Young and Older Adults

PubMed Central

Bennett, Ilana J.; Motes, Michael A.; Rao, Neena K.; Rypma, Bart

2011-01-01

Functional imaging research has identified fronto-parietal attention networks involved in visual search, with mixed evidence regarding whether different networks are engaged when the search target differs from distracters by a single (elementary) versus multiple (conjunction) features. Neural correlates of visual search, and their potential dissociation, were examined here using integrity of white matter connecting the fronto-parietal networks. The effect of aging on these brain-behavior relationships was also of interest. Younger and older adults performed a visual search task and underwent diffusion tensor imaging (DTI) to reconstruct two fronto-parietal (superior and inferior longitudinal fasciculus, SLF and ILF) and two midline (genu, splenium) white matter tracts. As expected, results revealed age-related declines in conjunction, but not elementary, search performance; and in ILF and genu tract integrity. Importantly, integrity of the SLF, ILF, and genu tracts predicted search performance (conjunction and elementary), with no significant age group differences in these relationships. Thus, integrity of white matter tracts connecting fronto-parietal attention networks contributes to search performance in younger and older adults. PMID:21402431

Short-Term Memory for Space and Time Flexibly Recruit Complementary Sensory-Biased Frontal Lobe Attention Networks.

PubMed

Michalka, Samantha W; Kong, Lingqiang; Rosen, Maya L; Shinn-Cunningham, Barbara G; Somers, David C

2015-08-19

The frontal lobes control wide-ranging cognitive functions; however, functional subdivisions of human frontal cortex are only coarsely mapped. Here, functional magnetic resonance imaging reveals two distinct visual-biased attention regions in lateral frontal cortex, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), anatomically interdigitated with two auditory-biased attention regions, transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic functional connectivity analysis demonstrates that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Interestingly, we observe that spatial and temporal short-term memory (STM), respectively, recruit visual and auditory attention networks in the frontal lobe, independent of sensory modality. These findings not only demonstrate that both sensory modality and information domain influence frontal lobe functional organization, they also demonstrate that spatial processing co-localizes with visual processing and that temporal processing co-localizes with auditory processing in lateral frontal cortex. Copyright © 2015 Elsevier Inc. All rights reserved.

Applying social network analysis to understand the knowledge sharing behaviour of practitioners in a clinical online discussion forum.

PubMed

Stewart, Samuel Alan; Abidi, Syed Sibte Raza

2012-12-04

Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment--an online discussion forum--for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional communication, but a dearth of non-nurse participants has been identified as a shortcoming. The results of the analysis suggest that the discussion forum is active and healthy, and that, though few, the interprofessional and interinstitutional ties are strong.

Texture and art with deep neural networks.

PubMed

Gatys, Leon A; Ecker, Alexander S; Bethge, Matthias

2017-10-01

Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they have a long history of informing each other. Recent advances in texture synthesis that were motivated by visual neuroscience have led to a substantial advance in image synthesis and manipulation in computer vision using convolutional neural networks (CNNs). Here, we review these recent advances and discuss how they can in turn inspire new research in visual perception and computational neuroscience. Copyright © 2017. Published by Elsevier Ltd.

A Visual Evaluation Study of Graph Sampling Techniques

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

Zhang, Fangyan; Zhang, Song; Wong, Pak C.

2017-01-29

We evaluate a dozen prevailing graph-sampling techniques with an ultimate goal to better visualize and understand big and complex graphs that exhibit different properties and structures. The evaluation uses eight benchmark datasets with four different graph types collected from Stanford Network Analysis Platform and NetworkX to give a comprehensive comparison of various types of graphs. The study provides a practical guideline for visualizing big graphs of different sizes and structures. The paper discusses results and important observations from the study.

Postural and Spatial Orientation Driven by Virtual Reality

PubMed Central

Keshner, Emily A.; Kenyon, Robert V.

2009-01-01

Orientation in space is a perceptual variable intimately related to postural orientation that relies on visual and vestibular signals to correctly identify our position relative to vertical. We have combined a virtual environment with motion of a posture platform to produce visual-vestibular conditions that allow us to explore how motion of the visual environment may affect perception of vertical and, consequently, affect postural stabilizing responses. In order to involve a higher level perceptual process, we needed to create a visual environment that was immersive. We did this by developing visual scenes that possess contextual information using color, texture, and 3-dimensional structures. Update latency of the visual scene was close to physiological latencies of the vestibulo-ocular reflex. Using this system we found that even when healthy young adults stand and walk on a stable support surface, they are unable to ignore wide field of view visual motion and they adapt their postural orientation to the parameters of the visual motion. Balance training within our environment elicited measurable rehabilitation outcomes. Thus we believe that virtual environments can serve as a clinical tool for evaluation and training of movement in situations that closely reflect conditions found in the physical world. PMID:19592796

GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration

PubMed Central

Stolper, Charles D.; Kahng, Minsuk; Lin, Zhiyuan; Foerster, Florian; Goel, Aakash; Stasko, John; Chau, Duen Horng

2015-01-01

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs. PMID:26005315

Cognitive Control Network Contributions to Memory-Guided Visual Attention

PubMed Central

Rosen, Maya L.; Stern, Chantal E.; Michalka, Samantha W.; Devaney, Kathryn J.; Somers, David C.

2016-01-01

Visual attentional capacity is severely limited, but humans excel in familiar visual contexts, in part because long-term memories guide efficient deployment of attention. To investigate the neural substrates that support memory-guided visual attention, we performed a set of functional MRI experiments that contrast long-term, memory-guided visuospatial attention with stimulus-guided visuospatial attention in a change detection task. Whereas the dorsal attention network was activated for both forms of attention, the cognitive control network (CCN) was preferentially activated during memory-guided attention. Three posterior nodes in the CCN, posterior precuneus, posterior callosal sulcus/mid-cingulate, and lateral intraparietal sulcus exhibited the greatest specificity for memory-guided attention. These 3 regions exhibit functional connectivity at rest, and we propose that they form a subnetwork within the broader CCN. Based on the task activation patterns, we conclude that the nodes of this subnetwork are preferentially recruited for long-term memory guidance of visuospatial attention. PMID:25750253

Influence of moving visual environment on sit-to-stand kinematics in children and adults.

PubMed

Slaboda, Jill C; Barton, Joseph E; Keshner, Emily A

2009-08-01

The effect of visual field motion on the sit-to-stand kinematics of adults and children was investigated. Children (8 to12 years of age) and adults (21 to 49 years of age) were seated in a virtual environment that rotated in the pitch and roll directions. Participants stood up either (1) concurrent with onset of visual motion or (2) after an immersion period in the moving visual environment, and (3) without visual input. Angular velocities of the head with respect to the trunk, and trunk with respect to the environment, w ere calculated as was head andtrunk center of mass. Both adults and children reduced head and trunk angular velocity after immersion in the moving visual environment. Unlike adults, children demonstrated significant differences in displacement of the head center of mass during the immersion and concurrent trials when compared to trials without visual input. Results suggest a time-dependent effect of vision on sit-to-stand kinematics in adults, whereas children are influenced by the immediate presence or absence of vision.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

PubMed Central

Xia, Kai; Dong, Dong; Han, Jing-Dong J

2006-01-01

Background Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. Results By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. Conclusion IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. PMID:17112386

Absence of visual experience modifies the neural basis of numerical thinking

PubMed Central

Kanjlia, Shipra; Lane, Connor; Feigenson, Lisa; Bedny, Marina

2016-01-01

In humans, the ability to reason about mathematical quantities depends on a frontoparietal network that includes the intraparietal sulcus (IPS). How do nature and nurture give rise to the neurobiology of numerical cognition? We asked how visual experience shapes the neural basis of numerical thinking by studying numerical cognition in congenitally blind individuals. Blind (n = 17) and blindfolded sighted (n = 19) participants solved math equations that varied in difficulty (e.g., 27 − 12 = x vs. 7 − 2 = x), and performed a control sentence comprehension task while undergoing fMRI. Whole-cortex analyses revealed that in both blind and sighted participants, the IPS and dorsolateral prefrontal cortices were more active during the math task than the language task, and activity in the IPS increased parametrically with equation difficulty. Thus, the classic frontoparietal number network is preserved in the total absence of visual experience. However, surprisingly, blind but not sighted individuals additionally recruited a subset of early visual areas during symbolic math calculation. The functional profile of these “visual” regions was identical to that of the IPS in blind but not sighted individuals. Furthermore, in blindness, number-responsive visual cortices exhibited increased functional connectivity with prefrontal and IPS regions that process numbers. We conclude that the frontoparietal number network develops independently of visual experience. In blindness, this number network colonizes parts of deafferented visual cortex. These results suggest that human cortex is highly functionally flexible early in life, and point to frontoparietal input as a mechanism of cross-modal plasticity in blindness. PMID:27638209

Abnormal brain activation in neurofibromatosis type 1: a link between visual processing and the default mode network.

PubMed

Violante, Inês R; Ribeiro, Maria J; Cunha, Gil; Bernardino, Inês; Duarte, João V; Ramos, Fabiana; Saraiva, Jorge; Silva, Eduardo; Castelo-Branco, Miguel

2012-01-01

Neurofibromatosis type 1 (NF1) is one of the most common single gene disorders affecting the human nervous system with a high incidence of cognitive deficits, particularly visuospatial. Nevertheless, neurophysiological alterations in low-level visual processing that could be relevant to explain the cognitive phenotype are poorly understood. Here we used functional magnetic resonance imaging (fMRI) to study early cortical visual pathways in children and adults with NF1. We employed two distinct stimulus types differing in contrast and spatial and temporal frequencies to evoke relatively different activation of the magnocellular (M) and parvocellular (P) pathways. Hemodynamic responses were investigated in retinotopically-defined regions V1, V2 and V3 and then over the acquired cortical volume. Relative to matched control subjects, patients with NF1 showed deficient activation of the low-level visual cortex to both stimulus types. Importantly, this finding was observed for children and adults with NF1, indicating that low-level visual processing deficits do not ameliorate with age. Moreover, only during M-biased stimulation patients with NF1 failed to deactivate or even activated anterior and posterior midline regions of the default mode network. The observation that the magnocellular visual pathway is impaired in NF1 in early visual processing and is specifically associated with a deficient deactivation of the default mode network may provide a neural explanation for high-order cognitive deficits present in NF1, particularly visuospatial and attentional. A link between magnocellular and default mode network processing may generalize to neuropsychiatric disorders where such deficits have been separately identified.

Measurement and Control System Based on Wireless Senor Network for Granary

NASA Astrophysics Data System (ADS)

Song, Jian

A wireless measurement and control system for granary is developed for the sake of overcoming the shortcoming of the wired measurement and control system such as complex wiring and low anti-interference capacity. In this system, Zigbee technology is applied with Zigbee protocol stack development platform by TI, and wireless senor network is used to collect and control the temperature and the humidity. It is composed of the upper PC, central control node based on CC2530, sensor nodes, sensor modules and the executive device. The wireless sensor node is programmed by C language in IAR Embedded Workbench for MCS-51 Evaluation environment. The upper PC control system software is developed based on Visual C++ 6.0 platform. It is shown by experiments that data transmission in the system is accurate and reliable and the error of the temperature and humidity is below 2%, meeting the functional requirements for the granary measurement and control system.

Remote network control plasma diagnostic system for Tokamak T-10

NASA Astrophysics Data System (ADS)

Troynov, V. I.; Zimin, A. M.; Krupin, V. A.; Notkin, G. E.; Nurgaliev, M. R.

2016-09-01

The parameters of molecular plasma in closed magnetic trap is studied in this paper. Using the system of molecular diagnostics, which was designed by the authors on the «Tokamak T-10» facility, the radiation of hydrogen isotopes at the plasma edge is investigated. The scheme of optical radiation registration within visible spectrum is described. For visualization, identification and processing of registered molecular spectra a new software is developed using MatLab environment. The software also includes electronic atlas of electronic-vibrational-rotational transitions for molecules of protium and deuterium. To register radiation from limiter cross-section a network control system is designed using the means of the Internet/Intranet. Remote control system diagram and methods are given. The examples of web-interfaces for working out equipment control scenarios and viewing of results are provided. After test run in Intranet, the remote diagnostic system will be accessible through Internet.

The Pathway Tools software.

PubMed

Karp, Peter D; Paley, Suzanne; Romero, Pedro

2002-01-01

Bioinformatics requires reusable software tools for creating model-organism databases (MODs). The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users.

Low-contrast underwater living fish recognition using PCANet

NASA Astrophysics Data System (ADS)

Sun, Xin; Yang, Jianping; Wang, Changgang; Dong, Junyu; Wang, Xinhua

2018-04-01

Quantitative and statistical analysis of ocean creatures is critical to ecological and environmental studies. And living fish recognition is one of the most essential requirements for fishery industry. However, light attenuation and scattering phenomenon are present in the underwater environment, which makes underwater images low-contrast and blurry. This paper tries to design a robust framework for accurate fish recognition. The framework introduces a two stage PCA Network to extract abstract features from fish images. On a real-world fish recognition dataset, we use a linear SVM classifier and set penalty coefficients to conquer data unbalanced issue. Feature visualization results show that our method can avoid the feature distortion in boundary regions of underwater image. Experiments results show that the PCA Network can extract discriminate features and achieve promising recognition accuracy. The framework improves the recognition accuracy of underwater living fishes and can be easily applied to marine fishery industry.

The Bilingual Language Interaction Network for Comprehension of Speech*

PubMed Central

Marian, Viorica

2013-01-01

During speech comprehension, bilinguals co-activate both of their languages, resulting in cross-linguistic interaction at various levels of processing. This interaction has important consequences for both the structure of the language system and the mechanisms by which the system processes spoken language. Using computational modeling, we can examine how cross-linguistic interaction affects language processing in a controlled, simulated environment. Here we present a connectionist model of bilingual language processing, the Bilingual Language Interaction Network for Comprehension of Speech (BLINCS), wherein interconnected levels of processing are created using dynamic, self-organizing maps. BLINCS can account for a variety of psycholinguistic phenomena, including cross-linguistic interaction at and across multiple levels of processing, cognate facilitation effects, and audio-visual integration during speech comprehension. The model also provides a way to separate two languages without requiring a global language-identification system. We conclude that BLINCS serves as a promising new model of bilingual spoken language comprehension. PMID:24363602

Left-Lateralized Contributions of Saccades to Cortical Activity During a One-Back Word Recognition Task.

PubMed

Chang, Yu-Cherng C; Khan, Sheraz; Taulu, Samu; Kuperberg, Gina; Brown, Emery N; Hämäläinen, Matti S; Temereanca, Simona

2018-01-01

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures was employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades vs. similar external visual stimulation emerged during 150-350 ms in a left-lateralized cortical network. This network included: (i) left lateral occipitotemporal (LOT) and nearby inferotemporal (IT) cortex; (ii) left posterior Sylvian fissure (PSF) and nearby multimodal cortex; and (iii) medial parietooccipital (PO), posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition.

Left-Lateralized Contributions of Saccades to Cortical Activity During a One-Back Word Recognition Task

PubMed Central

Chang, Yu-Cherng C.; Khan, Sheraz; Taulu, Samu; Kuperberg, Gina; Brown, Emery N.; Hämäläinen, Matti S.; Temereanca, Simona

2018-01-01

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures was employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades vs. similar external visual stimulation emerged during 150–350 ms in a left-lateralized cortical network. This network included: (i) left lateral occipitotemporal (LOT) and nearby inferotemporal (IT) cortex; (ii) left posterior Sylvian fissure (PSF) and nearby multimodal cortex; and (iii) medial parietooccipital (PO), posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition. PMID:29867372

A framework for visualization of battlefield network behavior

NASA Astrophysics Data System (ADS)

Perzov, Yury; Yurcik, William

2006-05-01

An extensible network simulation application was developed to study wireless battlefield communications. The application monitors node mobility and depicts broadcast and unicast traffic as expanding rings and directed links. The network simulation was specially designed to support fault injection to show the impact of air strikes on disabling nodes. The application takes standard ns-2 trace files as an input and provides for performance data output in different graphical forms (histograms and x/y plots). Network visualization via animation of simulation output can be saved in AVI format that may serve as a basis for a real-time battlefield awareness system.

802.11s Wireless Mesh Network Visualization Application

NASA Technical Reports Server (NTRS)

Mauldin, James Alexander

2014-01-01

Results of past experimentation at NASA Johnson Space Center showed that the IEEE 802.11s standard has better performance than the widely implemented alternative protocol B.A.T.M.A.N (Better Approach to Mobile Ad hoc Networking). 802.11s is now formally incorporated into the Wi- Fi 802.11-2012 standard, which specifies a hybrid wireless mesh networking protocol (HWMP). In order to quickly analyze changes to the routing algorithm and to support optimizing the mesh network behavior for our intended application a visualization tool was developed by modifying and integrating open source tools.

Disturbed temporal dynamics of brain synchronization in vision loss.

PubMed

Bola, Michał; Gall, Carolin; Sabel, Bernhard A

2015-06-01

Damage along the visual pathway prevents bottom-up visual input from reaching further processing stages and consequently leads to loss of vision. But perception is not a simple bottom-up process - rather it emerges from activity of widespread cortical networks which coordinate visual processing in space and time. Here we set out to study how vision loss affects activity of brain visual networks and how networks' activity is related to perception. Specifically, we focused on studying temporal patterns of brain activity. To this end, resting-state eyes-closed EEG was recorded from partially blind patients suffering from chronic retina and/or optic-nerve damage (n = 19) and healthy controls (n = 13). Amplitude (power) of oscillatory activity and phase locking value (PLV) were used as measures of local and distant synchronization, respectively. Synchronization time series were created for the low- (7-9 Hz) and high-alpha band (11-13 Hz) and analyzed with three measures of temporal patterns: (i) length of synchronized-/desynchronized-periods, (ii) Higuchi Fractal Dimension (HFD), and (iii) Detrended Fluctuation Analysis (DFA). We revealed that patients exhibit less complex, more random and noise-like temporal dynamics of high-alpha band activity. More random temporal patterns were associated with worse performance in static (r = -.54, p = .017) and kinetic perimetry (r = .47, p = .041). We conclude that disturbed temporal patterns of neural synchronization in vision loss patients indicate disrupted communication within brain visual networks caused by prolonged deafferentation. We propose that because the state of brain networks is essential for normal perception, impaired brain synchronization in patients with vision loss might aggravate the functional consequences of reduced visual input. Copyright © 2015 Elsevier Ltd. All rights reserved.

Efficient encoding of motion is mediated by gap junctions in the fly visual system.

PubMed

Wang, Siwei; Borst, Alexander; Zaslavsky, Noga; Tishby, Naftali; Segev, Idan

2017-12-01

Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly's ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system's readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.

A Rotational Motion Perception Neural Network Based on Asymmetric Spatiotemporal Visual Information Processing.

PubMed

Hu, Bin; Yue, Shigang; Zhang, Zhuhong

All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.

Level-2 Milestone 4797: Early Users on Max, Sequoia Visualization Cluster

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

Cupps, Kim C.

This report documents the fact that an early user has run successfully on Max, the Sequoia visualization cluster, ASC L2 milestone 4797: Early Users on Sequoia Visualization System (Max), due December 31, 2013. The Max visualization and data analysis cluster will provide Sequoia users with compute cycles and an interactive option for data exploration and analysis. The system will be integrated in the first quarter of FY14 and the system is expected to be moved to the classified network by the second quarter of FY14. The goal of this milestone is to have early users running their visualization and datamore » analysis work on the Max cluster on the classified network.« less

Distinct and overlapping fMRI activation networks for processing of novel identities and locations of objects.

PubMed

Pihlajamäki, Maija; Tanila, Heikki; Könönen, Mervi; Hänninen, Tuomo; Aronen, Hannu J; Soininen, Hilkka

2005-10-01

The ventral visual stream processes information about the identity of objects ('what'), whereas the dorsal stream processes the spatial locations of objects ('where'). There is a corresponding, although disputed, distinction for the ventrolateral and dorsolateral prefrontal areas. Furthermore, there seems to be a distinction between the anterior and posterior medial temporal lobe (MTL) structures in the processing of novel items and new spatial arrangements, respectively. Functional differentiation of the intermediary mid-line cortical and temporal neocortical structures that communicate with the occipitotemporal, occipitoparietal, prefrontal, and MTL structures, however, is unclear. Therefore, in the present functional magnetic resonance imaging (fMRI) study, we examined whether the distinction among the MTL structures extends to these closely connected cortical areas. The most striking difference in the fMRI responses during visual presentation of changes in either items or their locations was the bilateral activation of the temporal lobe and ventrolateral prefrontal cortical areas for novel object identification in contrast to wide parietal and dorsolateral prefrontal activation for the novel locations of objects. An anterior-posterior distinction of fMRI responses similar to the MTL was observed in the cingulate/retrosplenial, and superior and middle temporal cortices. In addition to the distinct areas of activation, certain frontal, parietal, and temporo-occipital areas responded to both object and spatial novelty, suggesting a common attentional network for both types of changes in the visual environment. These findings offer new insights to the functional roles and intrinsic specialization of the cingulate/retrosplenial, and lateral temporal cortical areas in visuospatial cognition.

Headphone and Head-Mounted Visual Displays for Virtual Environments

NASA Technical Reports Server (NTRS)

Begault, Duran R.; Ellis, Stephen R.; Wenzel, Elizabeth M.; Trejo, Leonard J. (Technical Monitor)

1998-01-01

A realistic auditory environment can contribute to both the overall subjective sense of presence in a virtual display, and to a quantitative metric predicting human performance. Here, the role of audio in a virtual display and the importance of auditory-visual interaction are examined. Conjectures are proposed regarding the effectiveness of audio compared to visual information for creating a sensation of immersion, the frame of reference within a virtual display, and the compensation of visual fidelity by supplying auditory information. Future areas of research are outlined for improving simulations of virtual visual and acoustic spaces. This paper will describe some of the intersensory phenomena that arise during operator interaction within combined visual and auditory virtual environments. Conjectures regarding audio-visual interaction will be proposed.

Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks.

PubMed

Guo, Xiaojuan; Wang, Yan; Chen, Kewei; Wu, Xia; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2014-01-01

Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.

Information Virtulization in Virtual Environments

NASA Technical Reports Server (NTRS)

Bryson, Steve; Kwak, Dochan (Technical Monitor)

2001-01-01

Virtual Environments provide a natural setting for a wide range of information visualization applications, particularly wlieit the information to be visualized is defined on a three-dimensional domain (Bryson, 1996). This chapter provides an overview of the issues that arise when designing and implementing an information visualization application in a virtual environment. Many design issues that arise, such as, e.g., issues of display, user tracking are common to any application of virtual environments. In this chapter we focus on those issues that are special to information visualization applications, as issues of wider concern are addressed elsewhere in this book.

CellMap visualizes protein-protein interactions and subcellular localization

PubMed Central

Dallago, Christian; Goldberg, Tatyana; Andrade-Navarro, Miguel Angel; Alanis-Lobato, Gregorio; Rost, Burkhard

2018-01-01

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers. PMID:29497493

NaviCell Web Service for network-based data visualization.

PubMed

Bonnet, Eric; Viara, Eric; Kuperstein, Inna; Calzone, Laurence; Cohen, David P A; Barillot, Emmanuel; Zinovyev, Andrei

2015-07-01

Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of 'omics' data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

NaviCell Web Service for network-based data visualization

PubMed Central

Bonnet, Eric; Viara, Eric; Kuperstein, Inna; Calzone, Laurence; Cohen, David P. A.; Barillot, Emmanuel; Zinovyev, Andrei

2015-01-01

Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of ‘omics’ data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases. PMID:25958393

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

PubMed

Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

2016-06-01

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Deep learning of orthographic representations in baboons.

PubMed

Hannagan, Thomas; Ziegler, Johannes C; Dufau, Stéphane; Fagot, Joël; Grainger, Jonathan

2014-01-01

What is the origin of our ability to learn orthographic knowledge? We use deep convolutional networks to emulate the primate's ventral visual stream and explore the recent finding that baboons can be trained to discriminate English words from nonwords. The networks were exposed to the exact same sequence of stimuli and reinforcement signals as the baboons in the experiment, and learned to map real visual inputs (pixels) of letter strings onto binary word/nonword responses. We show that the networks' highest levels of representations were indeed sensitive to letter combinations as postulated in our previous research. The model also captured the key empirical findings, such as generalization to novel words, along with some intriguing inter-individual differences. The present work shows the merits of deep learning networks that can simulate the whole processing chain all the way from the visual input to the response while allowing researchers to analyze the complex representations that emerge during the learning process.

Target recognition and scene interpretation in image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-08-01

Vision is only a part of a system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. These mechanisms provide a reliable recognition if the object is occluded or cannot be recognized as a whole. It is hard to split the entire system apart, and reliable solutions to the target recognition problems are possible only within the solution of a more generic Image Understanding Problem. Brain reduces informational and computational complexities, using implicit symbolic coding of features, hierarchical compression, and selective processing of visual information. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, is the most feasible for such models. It converts visual information into relational Network-Symbolic structures, avoiding artificial precise computations of 3-dimensional models. Network-Symbolic Transformations derive abstract structures, which allows for invariant recognition of an object as exemplar of a class. Active vision helps creating consistent models. Attention, separation of figure from ground and perceptual grouping are special kinds of network-symbolic transformations. Such Image/Video Understanding Systems will be reliably recognizing targets.

fMRI characterisation of widespread brain networks relevant for behavioural variability in fine hand motor control with and without visual feedback.

PubMed

Mayhew, Stephen D; Porcaro, Camillo; Tecchio, Franca; Bagshaw, Andrew P

2017-03-01

A bilateral visuo-parietal-motor network is responsible for fine control of hand movements. However, the sub-regions which are devoted to maintenance of contraction stability and how these processes fluctuate with trial-quality of task execution and in the presence/absence of visual feedback remains unclear. We addressed this by integrating behavioural and fMRI measurements during right-hand isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback of the applied force. We quantified single-trial behavioural performance during 1) the whole task period and 2) stable contraction maintenance, and regressed these metrics against the fMRI data to identify the brain activity most relevant to trial-by-trial fluctuations in performance during specific task phases. fMRI-behaviour correlations in a bilateral network of visual, premotor, primary motor, parietal and inferior frontal cortical regions emerged during performance of the entire feedback task, but only in premotor, parietal cortex and thalamus during the stable contraction period. The trials with the best task performance showed increased bilaterality and amplitude of fMRI responses. With feedback, stronger BOLD-behaviour coupling was found during 10% compared to 30% contractions. Only a small subset of regions in this network were weakly correlated with behaviour without feedback, despite wider network activated during this task than in the presence of feedback. These findings reflect a more focused network strongly coupled to behavioural fluctuations when providing visual feedback, whereas without it the task recruited widespread brain activity almost uncoupled from behavioural performance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Bridging the Host-Network Divide: Survey, Taxonomy, and Solution

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

Fink, Glenn A.; Duggirala, Vedavyas; Correa, Ricardo

2007-04-17

Abstract: "This paper presents a new direction in security awareness tools for system administration--the Host-Network (HoNe) Visualizer. Our requirements for the HoNe Visualizer come from needs system administrators expressed in interviews, from reviewing the literature, and from conducting usability studies with prototypes. We present a tool taxonomy that serves as a framework for our literature review, and we use the taxonomy to show what is missing in the administrator's arsenal. Then we unveil our tool and its supporting infrastructure that we believe will fill the empty niche. We found that most security tools provide either an internal view of amore » host or an external view of traffic on a network. Our interviewees revealed how they must construct a mental end-to-end view from separate tools that individually give an incomplete view, expending valuable time and mental effort. Because of limitations designed into TCP/IP [RFC-791, RFC-793], no tool can effectively correlate host and network data into an end-to-end view without kernel modifications. Currently, no other visualization exists to support end-to-end analysis. But HoNe's infrastructure overcomes TCP/IP's limitations bridging the network and transport layers in the network stack and making end-to-end correlation possible. The capstone is the HoNe Visualizer that amplifies the users' cognitive power and reduces their mental workload by illustrating the correlated data graphically. Users said HoNe would be particularly good for discovering day-zero exploits. Our usability study revealed that users performed better on intrusion detection tasks using our visualization than with tools they were accustomed to using regardless of their experience level."« less

Modality-specificity of Selective Attention Networks

PubMed Central

Stewart, Hannah J.; Amitay, Sygal

2015-01-01

Objective: To establish the modality specificity and generality of selective attention networks. Method: Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. Results: The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled “general attention.” The third component was labeled “auditory attention,” as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as “spatial orienting” and “spatial conflict,” respectively—they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task—all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). Conclusions: These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific. PMID:26635709

Computational model for perception of objects and motions.

PubMed

Yang, WenLu; Zhang, LiQing; Ma, LiBo

2008-06-01

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.